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step-67.h
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1
898 * stage_5_order_4, /* Kennedy, Carpenter, Lewis, 2000 */
899 * stage_7_order_4, /* Tselios, Simos, 2007 */
900 * stage_9_order_5, /* Kennedy, Carpenter, Lewis, 2000 */
901 * };
902 * constexpr LowStorageRungeKuttaScheme lsrk_scheme = stage_5_order_4;
903 *
904 * @endcode
905 *
906 * Eventually, we select a detail of the spatial discretization, namely the
907 * numerical flux (Riemann solver) at the faces between cells. For this
908 * program, we have implemented a modified variant of the Lax--Friedrichs
909 * flux and the Harten--Lax--van Leer (HLL) flux.
910 *
911 * @code
912 * enum EulerNumericalFlux
913 * {
914 * lax_friedrichs_modified,
915 * harten_lax_vanleer,
916 * };
917 * constexpr EulerNumericalFlux numerical_flux_type = lax_friedrichs_modified;
918 *
919 *
920 *
921 * @endcode
922 *
923 *
924 * <a name="Equationdata"></a>
925 * <h3>Equation data</h3>
926 *
927
928 *
929 * We now define a class with the exact solution for the test case 0 and one
930 * with a background flow field for test case 1 of the channel. Given that
931 * the Euler equations are a problem with @f$d+2@f$ equations in @f$d@f$ dimensions,
932 * we need to tell the Function base class about the correct number of
933 * components.
934 *
935 * @code
936 * template <int dim>
937 * class ExactSolution : public Function<dim>
938 * {
939 * public:
940 * ExactSolution(const double time)
941 * : Function<dim>(dim + 2, time)
942 * {}
943 *
944 * virtual double value(const Point<dim> & p,
945 * const unsigned int component = 0) const override;
946 * };
947 *
948 *
949 *
950 * @endcode
951 *
952 * As far as the actual function implemented is concerned, the analytical
953 * test case is an isentropic vortex case (see e.g. the book by Hesthaven
954 * and Warburton, Example 6.1 in Section 6.6 on page 209) which fulfills the
955 * Euler equations with zero force term on the right hand side. Given that
956 * definition, we return either the density, the momentum, or the energy
957 * depending on which component is requested. Note that the original
958 * definition of the density involves the @f$\frac{1}{\gamma -1}@f$-th power of
959 * some expression. Since `std::pow()` has pretty slow implementations on
960 * some systems, we replace it by logarithm followed by exponentiation (of
961 * base 2), which is mathematically equivalent but usually much better
962 * optimized. This formula might lose accuracy in the last digits
963 * for very small numbers compared to `std::pow()`, but we are happy with
964 * it anyway, since small numbers map to data close to 1.
965 *
966
967 *
968 * For the channel test case, we simply select a density of 1, a velocity of
969 * 0.4 in @f$x@f$ direction and zero in the other directions, and an energy that
970 * corresponds to a speed of sound of 1.3 measured against the background
971 * velocity field, computed from the relation @f$E = \frac{c^2}{\gamma (\gamma
972 * -1)} + \frac 12 \rho \|u\|^2@f$.
973 *
974 * @code
975 * template <int dim>
976 * double ExactSolution<dim>::value(const Point<dim> & x,
977 * const unsigned int component) const
978 * {
979 * const double t = this->get_time();
980 *
981 * switch (testcase)
982 * {
983 * case 0:
984 * {
985 * Assert(dim == 2, ExcNotImplemented());
986 * const double beta = 5;
987 *
988 * Point<dim> x0;
989 * x0[0] = 5.;
990 * const double radius_sqr =
991 * (x - x0).norm_square() - 2. * (x[0] - x0[0]) * t + t * t;
992 * const double factor =
993 * beta / (numbers::PI * 2) * std::exp(1. - radius_sqr);
994 * const double density_log = std::log2(
995 * std::abs(1. - (gamma - 1.) / gamma * 0.25 * factor * factor));
996 * const double density = std::exp2(density_log * (1. / (gamma - 1.)));
997 * const double u = 1. - factor * (x[1] - x0[1]);
998 * const double v = factor * (x[0] - t - x0[0]);
999 *
1000 * if (component == 0)
1001 * return density;
1002 * else if (component == 1)
1003 * return density * u;
1004 * else if (component == 2)
1005 * return density * v;
1006 * else
1007 * {
1008 * const double pressure =
1009 * std::exp2(density_log * (gamma / (gamma - 1.)));
1010 * return pressure / (gamma - 1.) +
1011 * 0.5 * (density * u * u + density * v * v);
1012 * }
1013 * }
1014 *
1015 * case 1:
1016 * {
1017 * if (component == 0)
1018 * return 1.;
1019 * else if (component == 1)
1020 * return 0.4;
1021 * else if (component == dim + 1)
1022 * return 3.097857142857143;
1023 * else
1024 * return 0.;
1025 * }
1026 *
1027 * default:
1028 * Assert(false, ExcNotImplemented());
1029 * return 0.;
1030 * }
1031 * }
1032 *
1033 *
1034 *
1035 * @endcode
1036 *
1037 *
1038 * <a name="LowstorageexplicitRungeKuttatimeintegrators"></a>
1039 * <h3>Low-storage explicit Runge--Kutta time integrators</h3>
1040 *
1041
1042 *
1043 * The next few lines implement a few low-storage variants of Runge--Kutta
1044 * methods. These methods have specific Butcher tableaux with coefficients
1045 * @f$b_i@f$ and @f$a_i@f$ as shown in the introduction. As usual in Runge--Kutta
1046 * method, we can deduce time steps, @f$c_i = \sum_{j=1}^{i-2} b_i + a_{i-1}@f$
1047 * from those coefficients. The main advantage of this kind of scheme is the
1048 * fact that only two vectors are needed per stage, namely the accumulated
1049 * part of the solution @f$\mathbf{w}@f$ (that will hold the solution
1050 * @f$\mathbf{w}^{n+1}@f$ at the new time @f$t^{n+1}@f$ after the last stage), the
1051 * update vector @f$\mathbf{r}_i@f$ that gets evaluated during the stages, plus
1052 * one vector @f$\mathbf{k}_i@f$ to hold the evaluation of the operator. Such a
1053 * Runge--Kutta setup reduces the memory storage and memory access. As the
1054 * memory bandwidth is often the performance-limiting factor on modern
1055 * hardware when the evaluation of the differential operator is
1056 * well-optimized, performance can be improved over standard time
1057 * integrators. This is true also when taking into account that a
1058 * conventional Runge--Kutta scheme might allow for slightly larger time
1059 * steps as more free parameters allow for better stability properties.
1060 *
1061
1062 *
1063 * In this tutorial programs, we concentrate on a few variants of
1064 * low-storage schemes defined in the article by Kennedy, Carpenter, and
1065 * Lewis (2000), as well as one variant described by Tselios and Simos
1066 * (2007). There is a large series of other schemes available, which could
1067 * be addressed by additional sets of coefficients or slightly different
1068 * update formulas.
1069 *
1070
1071 *
1072 * We define a single class for the four integrators, distinguished by the
1073 * enum described above. To each scheme, we then fill the vectors for the
1074 * @f$b_i@f$ and @f$a_i@f$ to the given variables in the class.
1075 *
1076 * @code
1077 * class LowStorageRungeKuttaIntegrator
1078 * {
1079 * public:
1080 * LowStorageRungeKuttaIntegrator(const LowStorageRungeKuttaScheme scheme)
1081 * {
1083 * @endcode
1084 *
1085 * First comes the three-stage scheme of order three by Kennedy et al.
1086 * (2000). While its stability region is significantly smaller than for
1087 * the other schemes, it only involves three stages, so it is very
1088 * competitive in terms of the work per stage.
1089 *
1090 * @code
1091 * switch (scheme)
1092 * {
1093 * case stage_3_order_3:
1094 * {
1096 * break;
1097 * }
1098 *
1099 * @endcode
1100 *
1101 * The next scheme is a five-stage scheme of order four, again
1102 * defined in the paper by Kennedy et al. (2000).
1103 *
1104 * @code
1105 * case stage_5_order_4:
1106 * {
1108 * break;
1109 * }
1110 *
1111 * @endcode
1112 *
1113 * The following scheme of seven stages and order four has been
1114 * explicitly derived for acoustics problems. It is a balance of
1115 * accuracy for imaginary eigenvalues among fourth order schemes,
1116 * combined with a large stability region. Since DG schemes are
1117 * dissipative among the highest frequencies, this does not
1118 * necessarily translate to the highest possible time step per
1119 * stage. In the context of the present tutorial program, the
1120 * numerical flux plays a crucial role in the dissipation and thus
1121 * also the maximal stable time step size. For the modified
1122 * Lax--Friedrichs flux, this scheme is similar to the
1123 * `stage_5_order_4` scheme in terms of step size per stage if only
1124 * stability is considered, but somewhat less efficient for the HLL
1125 * flux.
1126 *
1127 * @code
1128 * case stage_7_order_4:
1129 * {
1131 * break;
1132 * }
1133 *
1134 * @endcode
1135 *
1136 * The last scheme included here is the nine-stage scheme of order
1137 * five from Kennedy et al. (2000). It is the most accurate among
1138 * the schemes used here, but the higher order of accuracy
1139 * sacrifices some stability, so the step length normalized per
1140 * stage is less than for the fourth order schemes.
1141 *
1142 * @code
1143 * case stage_9_order_5:
1144 * {
1146 * break;
1147 * }
1148 *
1149 * default:
1150 * AssertThrow(false, ExcNotImplemented());
1151 * }
1154 * rk_integrator(lsrk);
1155 * rk_integrator.get_coefficients(ai, bi, ci);
1156 * }
1157 *
1158 * unsigned int n_stages() const
1159 * {
1160 * return bi.size();
1161 * }
1162 *
1163 * @endcode
1164 *
1165 * The main function of the time integrator is to go through the stages,
1166 * evaluate the operator, prepare the @f$\mathbf{r}_i@f$ vector for the next
1167 * evaluation, and update the solution vector @f$\mathbf{w}@f$. We hand off
1168 * the work to the `pde_operator` involved in order to be able to merge
1169 * the vector operations of the Runge--Kutta setup with the evaluation of
1170 * the differential operator for better performance, so all we do here is
1171 * to delegate the vectors and coefficients.
1172 *
1173
1174 *
1175 * We separately call the operator for the first stage because we need
1176 * slightly modified arguments there: We evaluate the solution from
1177 * the old solution @f$\mathbf{w}^n@f$ rather than a @f$\mathbf r_i@f$ vector, so
1178 * the first argument is `solution`. We here let the stage vector
1179 * @f$\mathbf{r}_i@f$ also hold the temporary result of the evaluation, as it
1180 * is not used otherwise. For all subsequent stages, we use the vector
1181 * `vec_ki` as the second vector argument to store the result of the
1182 * operator evaluation. Finally, when we are at the last stage, we must
1183 * skip the computation of the vector @f$\mathbf{r}_{s+1}@f$ as there is no
1184 * coefficient @f$a_s@f$ available (nor will it be used).
1185 *
1186 * @code
1187 * template <typename VectorType, typename Operator>
1188 * void perform_time_step(const Operator &pde_operator,
1189 * const double current_time,
1190 * const double time_step,
1191 * VectorType & solution,
1192 * VectorType & vec_ri,
1193 * VectorType & vec_ki) const
1194 * {
1195 * AssertDimension(ai.size() + 1, bi.size());
1196 *
1197 * pde_operator.perform_stage(current_time,
1198 * bi[0] * time_step,
1199 * ai[0] * time_step,
1200 * solution,
1201 * vec_ri,
1202 * solution,
1203 * vec_ri);
1204 *
1205 * for (unsigned int stage = 1; stage < bi.size(); ++stage)
1206 * {
1207 * const double c_i = ci[stage];
1208 * pde_operator.perform_stage(current_time + c_i * time_step,
1209 * bi[stage] * time_step,
1210 * (stage == bi.size() - 1 ?
1211 * 0 :
1212 * ai[stage] * time_step),
1213 * vec_ri,
1214 * vec_ki,
1215 * solution,
1216 * vec_ri);
1217 * }
1218 * }
1219 *
1220 * private:
1221 * std::vector<double> bi;
1222 * std::vector<double> ai;
1223 * std::vector<double> ci;
1224 * };
1225 *
1226 *
1227 *
1228 * @endcode
1229 *
1230 *
1231 * <a name="ImplementationofpointwiseoperationsoftheEulerequations"></a>
1232 * <h3>Implementation of point-wise operations of the Euler equations</h3>
1233 *
1234
1235 *
1236 * In the following functions, we implement the various problem-specific
1237 * operators pertaining to the Euler equations. Each function acts on the
1238 * vector of conserved variables @f$[\rho, \rho\mathbf{u}, E]@f$ that we hold in
1239 * the solution vectors, and computes various derived quantities.
1240 *
1241
1242 *
1243 * First out is the computation of the velocity, that we derive from the
1244 * momentum variable @f$\rho \mathbf{u}@f$ by division by @f$\rho@f$. One thing to
1245 * note here is that we decorate all those functions with the keyword
1246 * `DEAL_II_ALWAYS_INLINE`. This is a special macro that maps to a
1247 * compiler-specific keyword that tells the compiler to never create a
1248 * function call for any of those functions, and instead move the
1249 * implementation <a
1250 * href="https://en.wikipedia.org/wiki/Inline_function">inline</a> to where
1251 * they are called. This is critical for performance because we call into some
1252 * of those functions millions or billions of times: For example, we both use
1253 * the velocity for the computation of the flux further down, but also for the
1254 * computation of the pressure, and both of these places are evaluated at
1255 * every quadrature point of every cell. Making sure these functions are
1256 * inlined ensures not only that the processor does not have to execute a jump
1257 * instruction into the function (and the corresponding return jump), but also
1258 * that the compiler can re-use intermediate information from one function's
1259 * context in code that comes after the place where the function was called.
1260 * (We note that compilers are generally quite good at figuring out which
1261 * functions to inline by themselves. Here is a place where compilers may or
1262 * may not have figured it out by themselves but where we know for sure that
1263 * inlining is a win.)
1264 *
1265
1266 *
1267 * Another trick we apply is a separate variable for the inverse density
1268 * @f$\frac{1}{\rho}@f$. This enables the compiler to only perform a single
1269 * division for the flux, despite the division being used at several
1270 * places. As divisions are around ten to twenty times as expensive as
1271 * multiplications or additions, avoiding redundant divisions is crucial for
1272 * performance. We note that taking the inverse first and later multiplying
1273 * with it is not equivalent to a division in floating point arithmetic due
1274 * to roundoff effects, so the compiler is not allowed to exchange one way by
1275 * the other with standard optimization flags. However, it is also not
1276 * particularly difficult to write the code in the right way.
1277 *
1278
1279 *
1280 * To summarize, the chosen strategy of always inlining and careful
1281 * definition of expensive arithmetic operations allows us to write compact
1282 * code without passing all intermediate results around, despite making sure
1283 * that the code maps to excellent machine code.
1284 *
1285 * @code
1286 * template <int dim, typename Number>
1287 * inline DEAL_II_ALWAYS_INLINE
1288 * Tensor<1, dim, Number>
1289 * euler_velocity(const Tensor<1, dim + 2, Number> &conserved_variables)
1290 * {
1291 * const Number inverse_density = Number(1.) / conserved_variables[0];
1292 *
1293 * Tensor<1, dim, Number> velocity;
1294 * for (unsigned int d = 0; d < dim; ++d)
1295 * velocity[d] = conserved_variables[1 + d] * inverse_density;
1296 *
1297 * return velocity;
1298 * }
1299 *
1300 * @endcode
1301 *
1302 * The next function computes the pressure from the vector of conserved
1303 * variables, using the formula @f$p = (\gamma - 1) \left(E - \frac 12 \rho
1304 * \mathbf{u}\cdot \mathbf{u}\right)@f$. As explained above, we use the
1305 * velocity from the `euler_velocity()` function. Note that we need to
1306 * specify the first template argument `dim` here because the compiler is
1307 * not able to deduce it from the arguments of the tensor, whereas the
1308 * second argument (number type) can be automatically deduced.
1309 *
1310 * @code
1311 * template <int dim, typename Number>
1312 * inline DEAL_II_ALWAYS_INLINE
1313 * Number
1314 * euler_pressure(const Tensor<1, dim + 2, Number> &conserved_variables)
1315 * {
1316 * const Tensor<1, dim, Number> velocity =
1317 * euler_velocity<dim>(conserved_variables);
1318 *
1319 * Number rho_u_dot_u = conserved_variables[1] * velocity[0];
1320 * for (unsigned int d = 1; d < dim; ++d)
1321 * rho_u_dot_u += conserved_variables[1 + d] * velocity[d];
1322 *
1323 * return (gamma - 1.) * (conserved_variables[dim + 1] - 0.5 * rho_u_dot_u);
1324 * }
1325 *
1326 * @endcode
1327 *
1328 * Here is the definition of the Euler flux function, i.e., the definition
1329 * of the actual equation. Given the velocity and pressure (that the
1330 * compiler optimization will make sure are done only once), this is
1331 * straight-forward given the equation stated in the introduction.
1332 *
1333 * @code
1334 * template <int dim, typename Number>
1335 * inline DEAL_II_ALWAYS_INLINE
1336 * Tensor<1, dim + 2, Tensor<1, dim, Number>>
1337 * euler_flux(const Tensor<1, dim + 2, Number> &conserved_variables)
1338 * {
1339 * const Tensor<1, dim, Number> velocity =
1340 * euler_velocity<dim>(conserved_variables);
1341 * const Number pressure = euler_pressure<dim>(conserved_variables);
1342 *
1343 * Tensor<1, dim + 2, Tensor<1, dim, Number>> flux;
1344 * for (unsigned int d = 0; d < dim; ++d)
1345 * {
1346 * flux[0][d] = conserved_variables[1 + d];
1347 * for (unsigned int e = 0; e < dim; ++e)
1348 * flux[e + 1][d] = conserved_variables[e + 1] * velocity[d];
1349 * flux[d + 1][d] += pressure;
1350 * flux[dim + 1][d] =
1351 * velocity[d] * (conserved_variables[dim + 1] + pressure);
1352 * }
1353 *
1354 * return flux;
1355 * }
1356 *
1357 * @endcode
1358 *
1359 * This next function is a helper to simplify the implementation of the
1360 * numerical flux, implementing the action of a tensor of tensors (with
1361 * non-standard outer dimension of size `dim + 2`, so the standard overloads
1362 * provided by deal.II's tensor classes do not apply here) with another
1363 * tensor of the same inner dimension, i.e., a matrix-vector product.
1364 *
1365 * @code
1366 * template <int n_components, int dim, typename Number>
1367 * inline DEAL_II_ALWAYS_INLINE
1369 * operator*(const Tensor<1, n_components, Tensor<1, dim, Number>> &matrix,
1370 * const Tensor<1, dim, Number> & vector)
1371 * {
1373 * for (unsigned int d = 0; d < n_components; ++d)
1374 * result[d] = matrix[d] * vector;
1375 * return result;
1376 * }
1377 *
1378 * @endcode
1379 *
1380 * This function implements the numerical flux (Riemann solver). It gets the
1381 * state from the two sides of an interface and the normal vector, oriented
1382 * from the side of the solution @f$\mathbf{w}^-@f$ towards the solution
1383 * @f$\mathbf{w}^+@f$. In finite volume methods which rely on piece-wise
1384 * constant data, the numerical flux is the central ingredient as it is the
1385 * only place where the physical information is entered. In DG methods, the
1386 * numerical flux is less central due to the polynomials within the elements
1387 * and the physical flux used there. As a result of higher-degree
1388 * interpolation with consistent values from both sides in the limit of a
1389 * continuous solution, the numerical flux can be seen as a control of the
1390 * jump of the solution from both sides to weakly impose continuity. It is
1391 * important to realize that a numerical flux alone cannot stabilize a
1392 * high-order DG method in the presence of shocks, and thus any DG method
1393 * must be combined with further shock-capturing techniques to handle those
1394 * cases. In this tutorial, we focus on wave-like solutions of the Euler
1395 * equations in the subsonic regime without strong discontinuities where our
1396 * basic scheme is sufficient.
1397 *
1398
1399 *
1400 * Nonetheless, the numerical flux is decisive in terms of the numerical
1401 * dissipation of the overall scheme and influences the admissible time step
1402 * size with explicit Runge--Kutta methods. We consider two choices, a
1403 * modified Lax--Friedrichs scheme and the widely used Harten--Lax--van Leer
1404 * (HLL) flux. For both variants, we first need to get the velocities and
1405 * pressures from both sides of the interface and evaluate the physical
1406 * Euler flux.
1407 *
1408
1409 *
1410 * For the local Lax--Friedrichs flux, the definition is @f$\hat{\mathbf{F}}
1411 * =\frac{\mathbf{F}(\mathbf{w}^-)+\mathbf{F}(\mathbf{w}^+)}{2} +
1412 * \frac{\lambda}{2}\left[\mathbf{w}^--\mathbf{w}^+\right]\otimes
1413 * \mathbf{n^-}@f$, where the factor @f$\lambda =
1414 * \max\left(\|\mathbf{u}^-\|+c^-, \|\mathbf{u}^+\|+c^+\right)@f$ gives the
1415 * maximal wave speed and @f$c = \sqrt{\gamma p / \rho}@f$ is the speed of
1416 * sound. Here, we choose two modifications of that expression for reasons
1417 * of computational efficiency, given the small impact of the flux on the
1418 * solution. For the above definition of the factor @f$\lambda@f$, we would need
1419 * to take four square roots, two for the two velocity norms and two for the
1420 * speed of sound on either side. The first modification is hence to rather
1421 * use @f$\sqrt{\|\mathbf{u}\|^2+c^2}@f$ as an estimate of the maximal speed
1422 * (which is at most a factor of 2 away from the actual maximum, as shown in
1423 * the introduction). This allows us to pull the square root out of the
1424 * maximum and get away with a single square root computation. The second
1425 * modification is to further relax on the parameter @f$\lambda@f$---the smaller
1426 * it is, the smaller the dissipation factor (which is multiplied by the
1427 * jump in @f$\mathbf{w}@f$, which might result in a smaller or bigger
1428 * dissipation in the end). This allows us to fit the spectrum into the
1429 * stability region of the explicit Runge--Kutta integrator with bigger time
1430 * steps. However, we cannot make dissipation too small because otherwise
1431 * imaginary eigenvalues grow larger. Finally, the current conservative
1432 * formulation is not energy-stable in the limit of @f$\lambda\to 0@f$ as it is
1433 * not skew-symmetric, and would need additional measures such as split-form
1434 * DG schemes in that case.
1435 *
1436
1437 *
1438 * For the HLL flux, we follow the formula from literature, introducing an
1439 * additional weighting of the two states from Lax--Friedrichs by a
1440 * parameter @f$s@f$. It is derived from the physical transport directions of
1441 * the Euler equations in terms of the current direction of velocity and
1442 * sound speed. For the velocity, we here choose a simple arithmetic average
1443 * which is sufficient for DG scenarios and moderate jumps in material
1444 * parameters.
1445 *
1446
1447 *
1448 * Since the numerical flux is multiplied by the normal vector in the weak
1449 * form, we multiply by the result by the normal vector for all terms in the
1450 * equation. In these multiplications, the `operator*` defined above enables
1451 * a compact notation similar to the mathematical definition.
1452 *
1453
1454 *
1455 * In this and the following functions, we use variable suffixes `_m` and
1456 * `_p` to indicate quantities derived from @f$\mathbf{w}^-@f$ and @f$\mathbf{w}^+@f$,
1457 * i.e., values "here" and "there" relative to the current cell when looking
1458 * at a neighbor cell.
1459 *
1460 * @code
1461 * template <int dim, typename Number>
1462 * inline DEAL_II_ALWAYS_INLINE
1464 * euler_numerical_flux(const Tensor<1, dim + 2, Number> &u_m,
1465 * const Tensor<1, dim + 2, Number> &u_p,
1466 * const Tensor<1, dim, Number> & normal)
1467 * {
1468 * const auto velocity_m = euler_velocity<dim>(u_m);
1469 * const auto velocity_p = euler_velocity<dim>(u_p);
1470 *
1471 * const auto pressure_m = euler_pressure<dim>(u_m);
1472 * const auto pressure_p = euler_pressure<dim>(u_p);
1473 *
1474 * const auto flux_m = euler_flux<dim>(u_m);
1475 * const auto flux_p = euler_flux<dim>(u_p);
1476 *
1477 * switch (numerical_flux_type)
1478 * {
1479 * case lax_friedrichs_modified:
1480 * {
1481 * const auto lambda =
1482 * 0.5 * std::sqrt(std::max(velocity_p.norm_square() +
1483 * gamma * pressure_p * (1. / u_p[0]),
1484 * velocity_m.norm_square() +
1485 * gamma * pressure_m * (1. / u_m[0])));
1486 *
1487 * return 0.5 * (flux_m * normal + flux_p * normal) +
1488 * 0.5 * lambda * (u_m - u_p);
1489 * }
1490 *
1491 * case harten_lax_vanleer:
1492 * {
1493 * const auto avg_velocity_normal =
1494 * 0.5 * ((velocity_m + velocity_p) * normal);
1495 * const auto avg_c = std::sqrt(std::abs(
1496 * 0.5 * gamma *
1497 * (pressure_p * (1. / u_p[0]) + pressure_m * (1. / u_m[0]))));
1498 * const Number s_pos =
1499 * std::max(Number(), avg_velocity_normal + avg_c);
1500 * const Number s_neg =
1501 * std::min(Number(), avg_velocity_normal - avg_c);
1502 * const Number inverse_s = Number(1.) / (s_pos - s_neg);
1503 *
1504 * return inverse_s *
1505 * ((s_pos * (flux_m * normal) - s_neg * (flux_p * normal)) -
1506 * s_pos * s_neg * (u_m - u_p));
1507 * }
1508 *
1509 * default:
1510 * {
1511 * Assert(false, ExcNotImplemented());
1512 * return {};
1513 * }
1514 * }
1515 * }
1516 *
1517 *
1518 *
1519 * @endcode
1520 *
1521 * This and the next function are helper functions to provide compact
1522 * evaluation calls as multiple points get batched together via a
1523 * VectorizedArray argument (see the @ref step_37 "step-37" tutorial for details). This
1524 * function is used for the subsonic outflow boundary conditions where we
1525 * need to set the energy component to a prescribed value. The next one
1526 * requests the solution on all components and is used for inflow boundaries
1527 * where all components of the solution are set.
1528 *
1529 * @code
1530 * template <int dim, typename Number>
1532 * evaluate_function(const Function<dim> & function,
1533 * const Point<dim, VectorizedArray<Number>> &p_vectorized,
1534 * const unsigned int component)
1535 * {
1536 * VectorizedArray<Number> result;
1537 * for (unsigned int v = 0; v < VectorizedArray<Number>::size(); ++v)
1538 * {
1539 * Point<dim> p;
1540 * for (unsigned int d = 0; d < dim; ++d)
1541 * p[d] = p_vectorized[d][v];
1542 * result[v] = function.value(p, component);
1543 * }
1544 * return result;
1545 * }
1546 *
1547 *
1548 * template <int dim, typename Number, int n_components = dim + 2>
1550 * evaluate_function(const Function<dim> & function,
1551 * const Point<dim, VectorizedArray<Number>> &p_vectorized)
1552 * {
1553 * AssertDimension(function.n_components, n_components);
1555 * for (unsigned int v = 0; v < VectorizedArray<Number>::size(); ++v)
1556 * {
1557 * Point<dim> p;
1558 * for (unsigned int d = 0; d < dim; ++d)
1559 * p[d] = p_vectorized[d][v];
1560 * for (unsigned int d = 0; d < n_components; ++d)
1561 * result[d][v] = function.value(p, d);
1562 * }
1563 * return result;
1564 * }
1565 *
1566 *
1567 *
1568 * @endcode
1569 *
1570 *
1571 * <a name="TheEulerOperationclass"></a>
1572 * <h3>The EulerOperation class</h3>
1573 *
1574
1575 *
1576 * This class implements the evaluators for the Euler problem, in analogy to
1577 * the `LaplaceOperator` class of @ref step_37 "step-37" or @ref step_59 "step-59". Since the present
1578 * operator is non-linear and does not require a matrix interface (to be
1579 * handed over to preconditioners), we skip the various `vmult` functions
1580 * otherwise present in matrix-free operators and only implement an `apply`
1581 * function as well as the combination of `apply` with the required vector
1582 * updates for the low-storage Runge--Kutta time integrator mentioned above
1583 * (called `perform_stage`). Furthermore, we have added three additional
1584 * functions involving matrix-free routines, namely one to compute an
1585 * estimate of the time step scaling (that is combined with the Courant
1586 * number for the actual time step size) based on the velocity and speed of
1587 * sound in the elements, one for the projection of solutions (specializing
1588 * VectorTools::project() for the DG case), and one to compute the errors
1589 * against a possible analytical solution or norms against some background
1590 * state.
1591 *
1592
1593 *
1594 * The rest of the class is similar to other matrix-free tutorials. As
1595 * discussed in the introduction, we provide a few functions to allow a user
1596 * to pass in various forms of boundary conditions on different parts of the
1597 * domain boundary marked by types::boundary_id variables, as well as
1598 * possible body forces.
1599 *
1600 * @code
1601 * template <int dim, int degree, int n_points_1d>
1602 * class EulerOperator
1603 * {
1604 * public:
1605 * static constexpr unsigned int n_quadrature_points_1d = n_points_1d;
1606 *
1607 * EulerOperator(TimerOutput &timer_output);
1608 *
1609 * void reinit(const Mapping<dim> & mapping,
1610 * const DoFHandler<dim> &dof_handler);
1611 *
1612 * void set_inflow_boundary(const types::boundary_id boundary_id,
1613 * std::unique_ptr<Function<dim>> inflow_function);
1614 *
1615 * void set_subsonic_outflow_boundary(
1616 * const types::boundary_id boundary_id,
1617 * std::unique_ptr<Function<dim>> outflow_energy);
1618 *
1619 * void set_wall_boundary(const types::boundary_id boundary_id);
1620 *
1621 * void set_body_force(std::unique_ptr<Function<dim>> body_force);
1622 *
1623 * void apply(const double current_time,
1626 *
1627 * void
1628 * perform_stage(const Number cur_time,
1629 * const Number factor_solution,
1630 * const Number factor_ai,
1635 *
1636 * void project(const Function<dim> & function,
1638 *
1639 * std::array<double, 3> compute_errors(
1640 * const Function<dim> & function,
1641 * const LinearAlgebra::distributed::Vector<Number> &solution) const;
1642 *
1643 * double compute_cell_transport_speed(
1644 * const LinearAlgebra::distributed::Vector<Number> &solution) const;
1645 *
1646 * void
1647 * initialize_vector(LinearAlgebra::distributed::Vector<Number> &vector) const;
1648 *
1649 * private:
1651 *
1652 * TimerOutput &timer;
1653 *
1654 * std::map<types::boundary_id, std::unique_ptr<Function<dim>>>
1655 * inflow_boundaries;
1656 * std::map<types::boundary_id, std::unique_ptr<Function<dim>>>
1657 * subsonic_outflow_boundaries;
1658 * std::set<types::boundary_id> wall_boundaries;
1659 * std::unique_ptr<Function<dim>> body_force;
1660 *
1661 * void local_apply_inverse_mass_matrix(
1662 * const MatrixFree<dim, Number> & data,
1665 * const std::pair<unsigned int, unsigned int> & cell_range) const;
1666 *
1667 * void local_apply_cell(
1668 * const MatrixFree<dim, Number> & data,
1671 * const std::pair<unsigned int, unsigned int> & cell_range) const;
1672 *
1673 * void local_apply_face(
1674 * const MatrixFree<dim, Number> & data,
1677 * const std::pair<unsigned int, unsigned int> & face_range) const;
1678 *
1679 * void local_apply_boundary_face(
1680 * const MatrixFree<dim, Number> & data,
1683 * const std::pair<unsigned int, unsigned int> & face_range) const;
1684 * };
1685 *
1686 *
1687 *
1688 * template <int dim, int degree, int n_points_1d>
1689 * EulerOperator<dim, degree, n_points_1d>::EulerOperator(TimerOutput &timer)
1690 * : timer(timer)
1691 * {}
1692 *
1693 *
1694 *
1695 * @endcode
1696 *
1697 * For the initialization of the Euler operator, we set up the MatrixFree
1698 * variable contained in the class. This can be done given a mapping to
1699 * describe possible curved boundaries as well as a DoFHandler object
1700 * describing the degrees of freedom. Since we use a discontinuous Galerkin
1701 * discretization in this tutorial program where no constraints are imposed
1702 * strongly on the solution field, we do not need to pass in an
1703 * AffineConstraints object and rather use a dummy for the
1704 * construction. With respect to quadrature, we want to select two different
1705 * ways of computing the underlying integrals: The first is a flexible one,
1706 * based on a template parameter `n_points_1d` (that will be assigned the
1707 * `n_q_points_1d` value specified at the top of this file). More accurate
1708 * integration is necessary to avoid the aliasing problem due to the
1709 * variable coefficients in the Euler operator. The second less accurate
1710 * quadrature formula is a tight one based on `fe_degree+1` and needed for
1711 * the inverse mass matrix. While that formula provides an exact inverse
1712 * only on affine element shapes and not on deformed elements, it enables
1713 * the fast inversion of the mass matrix by tensor product techniques,
1714 * necessary to ensure optimal computational efficiency overall.
1715 *
1716 * @code
1717 * template <int dim, int degree, int n_points_1d>
1718 * void EulerOperator<dim, degree, n_points_1d>::reinit(
1719 * const Mapping<dim> & mapping,
1720 * const DoFHandler<dim> &dof_handler)
1721 * {
1722 * const std::vector<const DoFHandler<dim> *> dof_handlers = {&dof_handler};
1723 * const AffineConstraints<double> dummy;
1724 * const std::vector<const AffineConstraints<double> *> constraints = {&dummy};
1725 * const std::vector<Quadrature<1>> quadratures = {QGauss<1>(n_q_points_1d),
1726 * QGauss<1>(fe_degree + 1)};
1727 *
1728 * typename MatrixFree<dim, Number>::AdditionalData additional_data;
1729 * additional_data.mapping_update_flags =
1731 * update_values);
1732 * additional_data.mapping_update_flags_inner_faces =
1734 * update_values);
1735 * additional_data.mapping_update_flags_boundary_faces =
1737 * update_values);
1738 * additional_data.tasks_parallel_scheme =
1740 *
1741 * data.reinit(
1742 * mapping, dof_handlers, constraints, quadratures, additional_data);
1743 * }
1744 *
1745 *
1746 *
1747 * template <int dim, int degree, int n_points_1d>
1748 * void EulerOperator<dim, degree, n_points_1d>::initialize_vector(
1750 * {
1751 * data.initialize_dof_vector(vector);
1752 * }
1753 *
1754 *
1755 *
1756 * @endcode
1757 *
1758 * The subsequent four member functions are the ones that must be called from
1759 * outside to specify the various types of boundaries. For an inflow boundary,
1760 * we must specify all components in terms of density @f$\rho@f$, momentum @f$\rho
1761 * \mathbf{u}@f$ and energy @f$E@f$. Given this information, we then store the
1762 * function alongside the respective boundary id in a map member variable of
1763 * this class. Likewise, we proceed for the subsonic outflow boundaries (where
1764 * we request a function as well, which we use to retrieve the energy) and for
1765 * wall (no-penetration) boundaries where we impose zero normal velocity (no
1766 * function necessary, so we only request the boundary id). For the present
1767 * DG code where boundary conditions are solely applied as part of the weak
1768 * form (during time integration), the call to set the boundary conditions
1769 * can appear both before or after the `reinit()` call to this class. This
1770 * is different from continuous finite element codes where the boundary
1771 * conditions determine the content of the AffineConstraints object that is
1772 * sent into MatrixFree for initialization, thus requiring to be set before
1773 * the initialization of the matrix-free data structures.
1774 *
1775
1776 *
1777 * The checks added in each of the four function are used to
1778 * ensure that boundary conditions are mutually exclusive on the various
1779 * parts of the boundary, i.e., that a user does not accidentally designate a
1780 * boundary as both an inflow and say a subsonic outflow boundary.
1781 *
1782 * @code
1783 * template <int dim, int degree, int n_points_1d>
1784 * void EulerOperator<dim, degree, n_points_1d>::set_inflow_boundary(
1785 * const types::boundary_id boundary_id,
1786 * std::unique_ptr<Function<dim>> inflow_function)
1787 * {
1788 * AssertThrow(subsonic_outflow_boundaries.find(boundary_id) ==
1789 * subsonic_outflow_boundaries.end() &&
1790 * wall_boundaries.find(boundary_id) == wall_boundaries.end(),
1791 * ExcMessage("You already set the boundary with id " +
1792 * std::to_string(static_cast<int>(boundary_id)) +
1793 * " to another type of boundary before now setting " +
1794 * "it as inflow"));
1795 * AssertThrow(inflow_function->n_components == dim + 2,
1796 * ExcMessage("Expected function with dim+2 components"));
1797 *
1798 * inflow_boundaries[boundary_id] = std::move(inflow_function);
1799 * }
1800 *
1801 *
1802 * template <int dim, int degree, int n_points_1d>
1803 * void EulerOperator<dim, degree, n_points_1d>::set_subsonic_outflow_boundary(
1804 * const types::boundary_id boundary_id,
1805 * std::unique_ptr<Function<dim>> outflow_function)
1806 * {
1807 * AssertThrow(inflow_boundaries.find(boundary_id) ==
1808 * inflow_boundaries.end() &&
1809 * wall_boundaries.find(boundary_id) == wall_boundaries.end(),
1810 * ExcMessage("You already set the boundary with id " +
1811 * std::to_string(static_cast<int>(boundary_id)) +
1812 * " to another type of boundary before now setting " +
1813 * "it as subsonic outflow"));
1814 * AssertThrow(outflow_function->n_components == dim + 2,
1815 * ExcMessage("Expected function with dim+2 components"));
1816 *
1817 * subsonic_outflow_boundaries[boundary_id] = std::move(outflow_function);
1818 * }
1819 *
1820 *
1821 * template <int dim, int degree, int n_points_1d>
1822 * void EulerOperator<dim, degree, n_points_1d>::set_wall_boundary(
1823 * const types::boundary_id boundary_id)
1824 * {
1825 * AssertThrow(inflow_boundaries.find(boundary_id) ==
1826 * inflow_boundaries.end() &&
1827 * subsonic_outflow_boundaries.find(boundary_id) ==
1828 * subsonic_outflow_boundaries.end(),
1829 * ExcMessage("You already set the boundary with id " +
1830 * std::to_string(static_cast<int>(boundary_id)) +
1831 * " to another type of boundary before now setting " +
1832 * "it as wall boundary"));
1833 *
1834 * wall_boundaries.insert(boundary_id);
1835 * }
1836 *
1837 *
1838 * template <int dim, int degree, int n_points_1d>
1839 * void EulerOperator<dim, degree, n_points_1d>::set_body_force(
1840 * std::unique_ptr<Function<dim>> body_force)
1841 * {
1842 * AssertDimension(body_force->n_components, dim);
1843 *
1844 * this->body_force = std::move(body_force);
1845 * }
1846 *
1847 *
1848 *
1849 * @endcode
1850 *
1851 *
1852 * <a name="Localevaluators"></a>
1853 * <h4>Local evaluators</h4>
1854 *
1855
1856 *
1857 * Now we proceed to the local evaluators for the Euler problem. The
1858 * evaluators are relatively simple and follow what has been presented in
1859 * @ref step_37 "step-37", @ref step_48 "step-48", or @ref step_59 "step-59". The first notable difference is the fact
1860 * that we use an FEEvaluation with a non-standard number of quadrature
1861 * points. Whereas we previously always set the number of quadrature points
1862 * to equal the polynomial degree plus one (ensuring exact integration on
1863 * affine element shapes), we now set the number quadrature points as a
1864 * separate variable (e.g. the polynomial degree plus two or three halves of
1865 * the polynomial degree) to more accurately handle nonlinear terms. Since
1866 * the evaluator is fed with the appropriate loop lengths via the template
1867 * argument and keeps the number of quadrature points in the whole cell in
1868 * the variable FEEvaluation::n_q_points, we now automatically operate on
1869 * the more accurate formula without further changes.
1870 *
1871
1872 *
1873 * The second difference is due to the fact that we are now evaluating a
1874 * multi-component system, as opposed to the scalar systems considered
1875 * previously. The matrix-free framework provides several ways to handle the
1876 * multi-component case. The variant shown here utilizes an FEEvaluation
1877 * object with multiple components embedded into it, specified by the fourth
1878 * template argument `dim + 2` for the components in the Euler system. As a
1879 * consequence, the return type of FEEvaluation::get_value() is not a scalar
1880 * any more (that would return a VectorizedArray type, collecting data from
1881 * several elements), but a Tensor of `dim+2` components. The functionality
1882 * is otherwise similar to the scalar case; it is handled by a template
1883 * specialization of a base class, called FEEvaluationAccess. An alternative
1884 * variant would have been to use several FEEvaluation objects, a scalar one
1885 * for the density, a vector-valued one with `dim` components for the
1886 * momentum, and another scalar evaluator for the energy. To ensure that
1887 * those components point to the correct part of the solution, the
1888 * constructor of FEEvaluation takes three optional integer arguments after
1889 * the required MatrixFree field, namely the number of the DoFHandler for
1890 * multi-DoFHandler systems (taking the first by default), the number of the
1891 * quadrature point in case there are multiple Quadrature objects (see more
1892 * below), and as a third argument the component within a vector system. As
1893 * we have a single vector for all components, we would go with the third
1894 * argument, and set it to `0` for the density, `1` for the vector-valued
1895 * momentum, and `dim+1` for the energy slot. FEEvaluation then picks the
1896 * appropriate subrange of the solution vector during
1897 * FEEvaluationBase::read_dof_values() and
1898 * FEEvaluation::distributed_local_to_global() or the more compact
1899 * FEEvaluation::gather_evaluate() and FEEvaluation::integrate_scatter()
1900 * calls.
1901 *
1902
1903 *
1904 * When it comes to the evaluation of the body force vector, we distinguish
1905 * between two cases for efficiency reasons: In case we have a constant
1906 * function (derived from Functions::ConstantFunction), we can precompute
1907 * the value outside the loop over quadrature points and simply use the
1908 * value everywhere. For a more general function, we instead need to call
1909 * the `evaluate_function()` method we provided above; this path is more
1910 * expensive because we need to access the memory associated with the
1911 * quadrature point data.
1912 *
1913
1914 *
1915 * The rest follows the other tutorial programs. Since we have implemented
1916 * all physics for the Euler equations in the separate `euler_flux()`
1917 * function, all we have to do here is to call this function
1918 * given the current solution evaluated at quadrature points, returned by
1919 * `phi.get_value(q)`, and tell the FEEvaluation object to queue the flux
1920 * for testing it by the gradients of the shape functions (which is a Tensor
1921 * of outer `dim+2` components, each holding a tensor of `dim` components
1922 * for the @f$x,y,z@f$ component of the Euler flux). One final thing worth
1923 * mentioning is the order in which we queue the data for testing by the
1924 * value of the test function, `phi.submit_value()`, in case we are given an
1925 * external function: We must do this after calling `phi.get_value(q)`,
1926 * because `get_value()` (reading the solution) and `submit_value()`
1927 * (queuing the value for multiplication by the test function and summation
1928 * over quadrature points) access the same underlying data field. Here it
1929 * would be easy to achieve also without temporary variable `w_q` since
1930 * there is no mixing between values and gradients. For more complicated
1931 * setups, one has to first copy out e.g. both the value and gradient at a
1932 * quadrature point and then queue results again by
1933 * FEEvaluationBase::submit_value() and FEEvaluationBase::submit_gradient().
1934 *
1935
1936 *
1937 * As a final note, we mention that we do not use the first MatrixFree
1938 * argument of this function, which is a call-back from MatrixFree::loop().
1939 * The interfaces imposes the present list of arguments, but since we are in
1940 * a member function where the MatrixFree object is already available as the
1941 * `data` variable, we stick with that to avoid confusion.
1942 *
1943 * @code
1944 * template <int dim, int degree, int n_points_1d>
1945 * void EulerOperator<dim, degree, n_points_1d>::local_apply_cell(
1946 * const MatrixFree<dim, Number> &,
1947 * LinearAlgebra::distributed::Vector<Number> & dst,
1948 * const LinearAlgebra::distributed::Vector<Number> &src,
1949 * const std::pair<unsigned int, unsigned int> & cell_range) const
1950 * {
1952 *
1953 * Tensor<1, dim, VectorizedArray<Number>> constant_body_force;
1954 * const Functions::ConstantFunction<dim> *constant_function =
1955 * dynamic_cast<Functions::ConstantFunction<dim> *>(body_force.get());
1956 *
1957 * if (constant_function)
1958 * constant_body_force = evaluate_function<dim, Number, dim>(
1959 * *constant_function, Point<dim, VectorizedArray<Number>>());
1960 *
1961 * for (unsigned int cell = cell_range.first; cell < cell_range.second; ++cell)
1962 * {
1963 * phi.reinit(cell);
1964 * phi.gather_evaluate(src, EvaluationFlags::values);
1965 *
1966 * for (unsigned int q = 0; q < phi.n_q_points; ++q)
1967 * {
1968 * const auto w_q = phi.get_value(q);
1969 * phi.submit_gradient(euler_flux<dim>(w_q), q);
1970 * if (body_force.get() != nullptr)
1971 * {
1973 * constant_function ? constant_body_force :
1974 * evaluate_function<dim, Number, dim>(
1975 * *body_force, phi.quadrature_point(q));
1976 *
1978 * for (unsigned int d = 0; d < dim; ++d)
1979 * forcing[d + 1] = w_q[0] * force[d];
1980 * for (unsigned int d = 0; d < dim; ++d)
1981 * forcing[dim + 1] += force[d] * w_q[d + 1];
1982 *
1983 * phi.submit_value(forcing, q);
1984 * }
1985 * }
1986 *
1987 * phi.integrate_scatter(((body_force.get() != nullptr) ?
1991 * dst);
1992 * }
1993 * }
1994 *
1995 *
1996 *
1997 * @endcode
1998 *
1999 * The next function concerns the computation of integrals on interior
2000 * faces, where we need evaluators from both cells adjacent to the face. We
2001 * associate the variable `phi_m` with the solution component @f$\mathbf{w}^-@f$
2002 * and the variable `phi_p` with the solution component @f$\mathbf{w}^+@f$. We
2003 * distinguish the two sides in the constructor of FEFaceEvaluation by the
2004 * second argument, with `true` for the interior side and `false` for the
2005 * exterior side, with interior and exterior denoting the orientation with
2006 * respect to the normal vector.
2007 *
2008
2009 *
2010 * Note that the calls FEFaceEvaluation::gather_evaluate() and
2011 * FEFaceEvaluation::integrate_scatter() combine the access to the vectors
2012 * and the sum factorization parts. This combined operation not only saves a
2013 * line of code, but also contains an important optimization: Given that we
2014 * use a nodal basis in terms of the Lagrange polynomials in the points of
2015 * the Gauss-Lobatto quadrature formula, only @f$(p+1)^{d-1}@f$ out of the
2016 * @f$(p+1)^d@f$ basis functions evaluate to non-zero on each face. Thus, the
2017 * evaluator only accesses the necessary data in the vector and skips the
2018 * parts which are multiplied by zero. If we had first read the vector, we
2019 * would have needed to load all data from the vector, as the call in
2020 * isolation would not know what data is required in subsequent
2021 * operations. If the subsequent FEFaceEvaluation::evaluate() call requests
2022 * values and derivatives, indeed all @f$(p+1)^d@f$ vector entries for each
2023 * component are needed, as the normal derivative is nonzero for all basis
2024 * functions.
2025 *
2026
2027 *
2028 * The arguments to the evaluators as well as the procedure is similar to
2029 * the cell evaluation. We again use the more accurate (over-)integration
2030 * scheme due to the nonlinear terms, specified as the third template
2031 * argument in the list. At the quadrature points, we then go to our
2032 * free-standing function for the numerical flux. It receives the solution
2033 * evaluated at quadrature points from both sides (i.e., @f$\mathbf{w}^-@f$ and
2034 * @f$\mathbf{w}^+@f$), as well as the normal vector onto the minus side. As
2035 * explained above, the numerical flux is already multiplied by the normal
2036 * vector from the minus side. We need to switch the sign because the
2037 * boundary term comes with a minus sign in the weak form derived in the
2038 * introduction. The flux is then queued for testing both on the minus sign
2039 * and on the plus sign, with switched sign as the normal vector from the
2040 * plus side is exactly opposed to the one from the minus side.
2041 *
2042 * @code
2043 * template <int dim, int degree, int n_points_1d>
2044 * void EulerOperator<dim, degree, n_points_1d>::local_apply_face(
2045 * const MatrixFree<dim, Number> &,
2048 * const std::pair<unsigned int, unsigned int> & face_range) const
2049 * {
2051 * true);
2053 * false);
2054 *
2055 * for (unsigned int face = face_range.first; face < face_range.second; ++face)
2056 * {
2057 * phi_p.reinit(face);
2058 * phi_p.gather_evaluate(src, EvaluationFlags::values);
2059 *
2060 * phi_m.reinit(face);
2061 * phi_m.gather_evaluate(src, EvaluationFlags::values);
2062 *
2063 * for (unsigned int q = 0; q < phi_m.n_q_points; ++q)
2064 * {
2065 * const auto numerical_flux =
2066 * euler_numerical_flux<dim>(phi_m.get_value(q),
2067 * phi_p.get_value(q),
2068 * phi_m.get_normal_vector(q));
2069 * phi_m.submit_value(-numerical_flux, q);
2070 * phi_p.submit_value(numerical_flux, q);
2071 * }
2072 *
2073 * phi_p.integrate_scatter(EvaluationFlags::values, dst);
2074 * phi_m.integrate_scatter(EvaluationFlags::values, dst);
2075 * }
2076 * }
2077 *
2078 *
2079 *
2080 * @endcode
2081 *
2082 * For faces located at the boundary, we need to impose the appropriate
2083 * boundary conditions. In this tutorial program, we implement four cases as
2084 * mentioned above. (A fifth case, for supersonic outflow conditions is
2085 * discussed in the "Results" section below.) The discontinuous Galerkin
2086 * method imposes boundary conditions not as constraints, but only
2087 * weakly. Thus, the various conditions are imposed by finding an appropriate
2088 * <i>exterior</i> quantity @f$\mathbf{w}^+@f$ that is then handed to the
2089 * numerical flux function also used for the interior faces. In essence,
2090 * we "pretend" a state on the outside of the domain in such a way that
2091 * if that were reality, the solution of the PDE would satisfy the boundary
2092 * conditions we want.
2093 *
2094
2095 *
2096 * For wall boundaries, we need to impose a no-normal-flux condition on the
2097 * momentum variable, whereas we use a Neumann condition for the density and
2098 * energy with @f$\rho^+ = \rho^-@f$ and @f$E^+ = E^-@f$. To achieve the no-normal
2099 * flux condition, we set the exterior values to the interior values and
2100 * subtract two times the velocity in wall-normal direction, i.e., in the
2101 * direction of the normal vector.
2102 *
2103
2104 *
2105 * For inflow boundaries, we simply set the given Dirichlet data
2106 * @f$\mathbf{w}_\mathrm{D}@f$ as a boundary value. An alternative would have been
2107 * to use @f$\mathbf{w}^+ = -\mathbf{w}^- + 2 \mathbf{w}_\mathrm{D}@f$, the
2108 * so-called mirror principle.
2109 *
2110
2111 *
2112 * The imposition of outflow is essentially a Neumann condition, i.e.,
2113 * setting @f$\mathbf{w}^+ = \mathbf{w}^-@f$. For the case of subsonic outflow,
2114 * we still need to impose a value for the energy, which we derive from the
2115 * respective function. A special step is needed for the case of
2116 * <i>backflow</i>, i.e., the case where there is a momentum flux into the
2117 * domain on the Neumann portion. According to the literature (a fact that can
2118 * be derived by appropriate energy arguments), we must switch to another
2119 * variant of the flux on inflow parts, see Gravemeier, Comerford,
2120 * Yoshihara, Ismail, Wall, "A novel formulation for Neumann inflow
2121 * conditions in biomechanics", Int. J. Numer. Meth. Biomed. Eng., vol. 28
2122 * (2012). Here, the momentum term needs to be added once again, which
2123 * corresponds to removing the flux contribution on the momentum
2124 * variables. We do this in a post-processing step, and only for the case
2125 * when we both are at an outflow boundary and the dot product between the
2126 * normal vector and the momentum (or, equivalently, velocity) is
2127 * negative. As we work on data of several quadrature points at once for
2128 * SIMD vectorizations, we here need to explicitly loop over the array
2129 * entries of the SIMD array.
2130 *
2131
2132 *
2133 * In the implementation below, we check for the various types
2134 * of boundaries at the level of quadrature points. Of course, we could also
2135 * have moved the decision out of the quadrature point loop and treat entire
2136 * faces as of the same kind, which avoids some map/set lookups in the inner
2137 * loop over quadrature points. However, the loss of efficiency is hardly
2138 * noticeable, so we opt for the simpler code here. Also note that the final
2139 * `else` clause will catch the case when some part of the boundary was not
2140 * assigned any boundary condition via `EulerOperator::set_..._boundary(...)`.
2141 *
2142 * @code
2143 * template <int dim, int degree, int n_points_1d>
2144 * void EulerOperator<dim, degree, n_points_1d>::local_apply_boundary_face(
2145 * const MatrixFree<dim, Number> &,
2148 * const std::pair<unsigned int, unsigned int> & face_range) const
2149 * {
2151 *
2152 * for (unsigned int face = face_range.first; face < face_range.second; ++face)
2153 * {
2154 * phi.reinit(face);
2155 * phi.gather_evaluate(src, EvaluationFlags::values);
2156 *
2157 * for (unsigned int q = 0; q < phi.n_q_points; ++q)
2158 * {
2159 * const auto w_m = phi.get_value(q);
2160 * const auto normal = phi.get_normal_vector(q);
2161 *
2162 * auto rho_u_dot_n = w_m[1] * normal[0];
2163 * for (unsigned int d = 1; d < dim; ++d)
2164 * rho_u_dot_n += w_m[1 + d] * normal[d];
2165 *
2166 * bool at_outflow = false;
2167 *
2169 * const auto boundary_id = data.get_boundary_id(face);
2170 * if (wall_boundaries.find(boundary_id) != wall_boundaries.end())
2171 * {
2172 * w_p[0] = w_m[0];
2173 * for (unsigned int d = 0; d < dim; ++d)
2174 * w_p[d + 1] = w_m[d + 1] - 2. * rho_u_dot_n * normal[d];
2175 * w_p[dim + 1] = w_m[dim + 1];
2176 * }
2177 * else if (inflow_boundaries.find(boundary_id) !=
2178 * inflow_boundaries.end())
2179 * w_p =
2180 * evaluate_function(*inflow_boundaries.find(boundary_id)->second,
2181 * phi.quadrature_point(q));
2182 * else if (subsonic_outflow_boundaries.find(boundary_id) !=
2183 * subsonic_outflow_boundaries.end())
2184 * {
2185 * w_p = w_m;
2186 * w_p[dim + 1] = evaluate_function(
2187 * *subsonic_outflow_boundaries.find(boundary_id)->second,
2188 * phi.quadrature_point(q),
2189 * dim + 1);
2190 * at_outflow = true;
2191 * }
2192 * else
2193 * AssertThrow(false,
2194 * ExcMessage("Unknown boundary id, did "
2195 * "you set a boundary condition for "
2196 * "this part of the domain boundary?"));
2197 *
2198 * auto flux = euler_numerical_flux<dim>(w_m, w_p, normal);
2199 *
2200 * if (at_outflow)
2201 * for (unsigned int v = 0; v < VectorizedArray<Number>::size(); ++v)
2202 * {
2203 * if (rho_u_dot_n[v] < -1e-12)
2204 * for (unsigned int d = 0; d < dim; ++d)
2205 * flux[d + 1][v] = 0.;
2206 * }
2207 *
2208 * phi.submit_value(-flux, q);
2209 * }
2210 *
2211 * phi.integrate_scatter(EvaluationFlags::values, dst);
2212 * }
2213 * }
2214 *
2215 *
2216 *
2217 * @endcode
2218 *
2219 * The next function implements the inverse mass matrix operation. The
2220 * algorithms and rationale have been discussed extensively in the
2221 * introduction, so we here limit ourselves to the technicalities of the
2222 * MatrixFreeOperators::CellwiseInverseMassMatrix class. It does similar
2223 * operations as the forward evaluation of the mass matrix, except with a
2224 * different interpolation matrix, representing the inverse @f$S^{-1}@f$
2225 * factors. These represent a change of basis from the specified basis (in
2226 * this case, the Lagrange basis in the points of the Gauss--Lobatto
2227 * quadrature formula) to the Lagrange basis in the points of the Gauss
2228 * quadrature formula. In the latter basis, we can apply the inverse of the
2229 * point-wise `JxW` factor, i.e., the quadrature weight times the
2230 * determinant of the Jacobian of the mapping from reference to real
2231 * coordinates. Once this is done, the basis is changed back to the nodal
2232 * Gauss-Lobatto basis again. All of these operations are done by the
2233 * `apply()` function below. What we need to provide is the local fields to
2234 * operate on (which we extract from the global vector by an FEEvaluation
2235 * object) and write the results back to the destination vector of the mass
2236 * matrix operation.
2237 *
2238
2239 *
2240 * One thing to note is that we added two integer arguments (that are
2241 * optional) to the constructor of FEEvaluation, the first being 0
2242 * (selecting among the DoFHandler in multi-DoFHandler systems; here, we
2243 * only have one) and the second being 1 to make the quadrature formula
2244 * selection. As we use the quadrature formula 0 for the over-integration of
2245 * nonlinear terms, we use the formula 1 with the default @f$p+1@f$ (or
2246 * `fe_degree+1` in terms of the variable name) points for the mass
2247 * matrix. This leads to square contributions to the mass matrix and ensures
2248 * exact integration, as explained in the introduction.
2249 *
2250 * @code
2251 * template <int dim, int degree, int n_points_1d>
2252 * void EulerOperator<dim, degree, n_points_1d>::local_apply_inverse_mass_matrix(
2253 * const MatrixFree<dim, Number> &,
2256 * const std::pair<unsigned int, unsigned int> & cell_range) const
2257 * {
2260 * inverse(phi);
2261 *
2262 * for (unsigned int cell = cell_range.first; cell < cell_range.second; ++cell)
2263 * {
2264 * phi.reinit(cell);
2265 * phi.read_dof_values(src);
2266 *
2267 * inverse.apply(phi.begin_dof_values(), phi.begin_dof_values());
2268 *
2269 * phi.set_dof_values(dst);
2270 * }
2271 * }
2272 *
2273 *
2274 *
2275 * @endcode
2276 *
2277 *
2278 * <a name="Theapplyandrelatedfunctions"></a>
2279 * <h4>The apply() and related functions</h4>
2280 *
2281
2282 *
2283 * We now come to the function which implements the evaluation of the Euler
2284 * operator as a whole, i.e., @f$\mathcal M^{-1} \mathcal L(t, \mathbf{w})@f$,
2285 * calling into the local evaluators presented above. The steps should be
2286 * clear from the previous code. One thing to note is that we need to adjust
2287 * the time in the functions we have associated with the various parts of
2288 * the boundary, in order to be consistent with the equation in case the
2289 * boundary data is time-dependent. Then, we call MatrixFree::loop() to
2290 * perform the cell and face integrals, including the necessary ghost data
2291 * exchange in the `src` vector. The seventh argument to the function,
2292 * `true`, specifies that we want to zero the `dst` vector as part of the
2293 * loop, before we start accumulating integrals into it. This variant is
2294 * preferred over explicitly calling `dst = 0.;` before the loop as the
2295 * zeroing operation is done on a subrange of the vector in parts that are
2296 * written by the integrals nearby. This enhances data locality and allows
2297 * for caching, saving one roundtrip of vector data to main memory and
2298 * enhancing performance. The last two arguments to the loop determine which
2299 * data is exchanged: Since we only access the values of the shape functions
2300 * one faces, typical of first-order hyperbolic problems, and since we have
2301 * a nodal basis with nodes at the reference element surface, we only need
2302 * to exchange those parts. This again saves precious memory bandwidth.
2303 *
2304
2305 *
2306 * Once the spatial operator @f$\mathcal L@f$ is applied, we need to make a
2307 * second round and apply the inverse mass matrix. Here, we call
2308 * MatrixFree::cell_loop() since only cell integrals appear. The cell loop
2309 * is cheaper than the full loop as access only goes to the degrees of
2310 * freedom associated with the locally owned cells, which is simply the
2311 * locally owned degrees of freedom for DG discretizations. Thus, no ghost
2312 * exchange is needed here.
2313 *
2314
2315 *
2316 * Around all these functions, we put timer scopes to record the
2317 * computational time for statistics about the contributions of the various
2318 * parts.
2319 *
2320 * @code
2321 * template <int dim, int degree, int n_points_1d>
2322 * void EulerOperator<dim, degree, n_points_1d>::apply(
2323 * const double current_time,
2324 * const LinearAlgebra::distributed::Vector<Number> &src,
2325 * LinearAlgebra::distributed::Vector<Number> & dst) const
2326 * {
2327 * {
2328 * TimerOutput::Scope t(timer, "apply - integrals");
2329 *
2330 * for (auto &i : inflow_boundaries)
2331 * i.second->set_time(current_time);
2332 * for (auto &i : subsonic_outflow_boundaries)
2333 * i.second->set_time(current_time);
2334 *
2335 * data.loop(&EulerOperator::local_apply_cell,
2336 * &EulerOperator::local_apply_face,
2337 * &EulerOperator::local_apply_boundary_face,
2338 * this,
2339 * dst,
2340 * src,
2341 * true,
2344 * }
2345 *
2346 * {
2347 * TimerOutput::Scope t(timer, "apply - inverse mass");
2348 *
2349 * data.cell_loop(&EulerOperator::local_apply_inverse_mass_matrix,
2350 * this,
2351 * dst,
2352 * dst);
2353 * }
2354 * }
2355 *
2356 *
2357 *
2358 * @endcode
2359 *
2360 * Let us move to the function that does an entire stage of a Runge--Kutta
2361 * update. It calls EulerOperator::apply() followed by some updates
2362 * to the vectors, namely `next_ri = solution + factor_ai * k_i` and
2363 * `solution += factor_solution * k_i`. Rather than performing these
2364 * steps through the vector interfaces, we here present an alternative
2365 * strategy that is faster on cache-based architectures. As the memory
2366 * consumed by the vectors is often much larger than what fits into caches,
2367 * the data has to effectively come from the slow RAM memory. The situation
2368 * can be improved by loop fusion, i.e., performing both the updates to
2369 * `next_ki` and `solution` within a single sweep. In that case, we would
2370 * read the two vectors `rhs` and `solution` and write into `next_ki` and
2371 * `solution`, compared to at least 4 reads and two writes in the baseline
2372 * case. Here, we go one step further and perform the loop immediately when
2373 * the mass matrix inversion has finished on a part of the
2374 * vector. MatrixFree::cell_loop() provides a mechanism to attach an
2375 * `std::function` both before the loop over cells first touches a vector
2376 * entry (which we do not use here, but is e.g. used for zeroing the vector)
2377 * and a second `std::function` to be called after the loop last touches
2378 * an entry. The callback is in form of a range over the given vector (in
2379 * terms of the local index numbering in the MPI universe) that can be
2380 * addressed by `local_element()` functions.
2381 *
2382
2383 *
2384 * For this second callback, we create a lambda that works on a range and
2385 * write the respective update on this range. Ideally, we would add the
2386 * `DEAL_II_OPENMP_SIMD_PRAGMA` before the local loop to suggest to the
2387 * compiler to SIMD parallelize this loop (which means in practice that we
2388 * ensure that there is no overlap, also called aliasing, between the index
2389 * ranges of the pointers we use inside the loops). It turns out that at the
2390 * time of this writing, GCC 7.2 fails to compile an OpenMP pragma inside a
2391 * lambda function, so we comment this pragma out below. If your compiler is
2392 * newer, you should be able to uncomment these lines again.
2393 *
2394
2395 *
2396 * Note that we select a different code path for the last
2397 * Runge--Kutta stage when we do not need to update the `next_ri`
2398 * vector. This strategy gives a considerable speedup. Whereas the inverse
2399 * mass matrix and vector updates take more than 60% of the computational
2400 * time with default vector updates on a 40-core machine, the percentage is
2401 * around 35% with the more optimized variant. In other words, this is a
2402 * speedup of around a third.
2403 *
2404 * @code
2405 * template <int dim, int degree, int n_points_1d>
2406 * void EulerOperator<dim, degree, n_points_1d>::perform_stage(
2407 * const Number current_time,
2408 * const Number factor_solution,
2409 * const Number factor_ai,
2410 * const LinearAlgebra::distributed::Vector<Number> &current_ri,
2411 * LinearAlgebra::distributed::Vector<Number> & vec_ki,
2412 * LinearAlgebra::distributed::Vector<Number> & solution,
2413 * LinearAlgebra::distributed::Vector<Number> & next_ri) const
2414 * {
2415 * {
2416 * TimerOutput::Scope t(timer, "rk_stage - integrals L_h");
2417 *
2418 * for (auto &i : inflow_boundaries)
2419 * i.second->set_time(current_time);
2420 * for (auto &i : subsonic_outflow_boundaries)
2421 * i.second->set_time(current_time);
2422 *
2423 * data.loop(&EulerOperator::local_apply_cell,
2424 * &EulerOperator::local_apply_face,
2425 * &EulerOperator::local_apply_boundary_face,
2426 * this,
2427 * vec_ki,
2428 * current_ri,
2429 * true,
2432 * }
2433 *
2434 *
2435 * {
2436 * TimerOutput::Scope t(timer, "rk_stage - inv mass + vec upd");
2437 * data.cell_loop(
2438 * &EulerOperator::local_apply_inverse_mass_matrix,
2439 * this,
2440 * next_ri,
2441 * vec_ki,
2442 * std::function<void(const unsigned int, const unsigned int)>(),
2443 * [&](const unsigned int start_range, const unsigned int end_range) {
2444 * const Number ai = factor_ai;
2445 * const Number bi = factor_solution;
2446 * if (ai == Number())
2447 * {
2448 * /* DEAL_II_OPENMP_SIMD_PRAGMA */
2449 * for (unsigned int i = start_range; i < end_range; ++i)
2450 * {
2451 * const Number k_i = next_ri.local_element(i);
2452 * const Number sol_i = solution.local_element(i);
2453 * solution.local_element(i) = sol_i + bi * k_i;
2454 * }
2455 * }
2456 * else
2457 * {
2458 * /* DEAL_II_OPENMP_SIMD_PRAGMA */
2459 * for (unsigned int i = start_range; i < end_range; ++i)
2460 * {
2461 * const Number k_i = next_ri.local_element(i);
2462 * const Number sol_i = solution.local_element(i);
2463 * solution.local_element(i) = sol_i + bi * k_i;
2464 * next_ri.local_element(i) = sol_i + ai * k_i;
2465 * }
2466 * }
2467 * });
2468 * }
2469 * }
2470 *
2471 *
2472 *
2473 * @endcode
2474 *
2475 * Having discussed the implementation of the functions that deal with
2476 * advancing the solution by one time step, let us now move to functions
2477 * that implement other, ancillary operations. Specifically, these are
2478 * functions that compute projections, evaluate errors, and compute the speed
2479 * of information transport on a cell.
2480 *
2481
2482 *
2483 * The first of these functions is essentially equivalent to
2484 * VectorTools::project(), just much faster because it is specialized for DG
2485 * elements where there is no need to set up and solve a linear system, as
2486 * each element has independent basis functions. The reason why we show the
2487 * code here, besides a small speedup of this non-critical operation, is that
2488 * it shows additional functionality provided by
2490 *
2491
2492 *
2493 * The projection operation works as follows: If we denote the matrix of
2494 * shape functions evaluated at quadrature points by @f$S@f$, the projection on
2495 * cell @f$K@f$ is an operation of the form @f$\underbrace{S J^K S^\mathrm
2496 * T}_{\mathcal M^K} \mathbf{w}^K = S J^K
2497 * \tilde{\mathbf{w}}(\mathbf{x}_q)_{q=1:n_q}@f$, where @f$J^K@f$ is the diagonal
2498 * matrix containing the determinant of the Jacobian times the quadrature
2499 * weight (JxW), @f$\mathcal M^K@f$ is the cell-wise mass matrix, and
2500 * @f$\tilde{\mathbf{w}}(\mathbf{x}_q)_{q=1:n_q}@f$ is the evaluation of the
2501 * field to be projected onto quadrature points. (In reality the matrix @f$S@f$
2502 * has additional structure through the tensor product, as explained in the
2503 * introduction.) This system can now equivalently be written as
2504 * @f$\mathbf{w}^K = \left(S J^K S^\mathrm T\right)^{-1} S J^K
2505 * \tilde{\mathbf{w}}(\mathbf{x}_q)_{q=1:n_q} = S^{-\mathrm T}
2506 * \left(J^K\right)^{-1} S^{-1} S J^K
2507 * \tilde{\mathbf{w}}(\mathbf{x}_q)_{q=1:n_q}@f$. Now, the term @f$S^{-1} S@f$ and
2508 * then @f$\left(J^K\right)^{-1} J^K@f$ cancel, resulting in the final
2509 * expression @f$\mathbf{w}^K = S^{-\mathrm T}
2510 * \tilde{\mathbf{w}}(\mathbf{x}_q)_{q=1:n_q}@f$. This operation is
2511 * implemented by
2513 * The name is derived from the fact that this projection is simply
2514 * the multiplication by @f$S^{-\mathrm T}@f$, a basis change from the
2515 * nodal basis in the points of the Gaussian quadrature to the given finite
2516 * element basis. Note that we call FEEvaluation::set_dof_values() to write
2517 * the result into the vector, overwriting previous content, rather than
2518 * accumulating the results as typical in integration tasks -- we can do
2519 * this because every vector entry has contributions from only a single
2520 * cell for discontinuous Galerkin discretizations.
2521 *
2522 * @code
2523 * template <int dim, int degree, int n_points_1d>
2524 * void EulerOperator<dim, degree, n_points_1d>::project(
2525 * const Function<dim> & function,
2526 * LinearAlgebra::distributed::Vector<Number> &solution) const
2527 * {
2530 * inverse(phi);
2531 * solution.zero_out_ghost_values();
2532 * for (unsigned int cell = 0; cell < data.n_cell_batches(); ++cell)
2533 * {
2534 * phi.reinit(cell);
2535 * for (unsigned int q = 0; q < phi.n_q_points; ++q)
2536 * phi.submit_dof_value(evaluate_function(function,
2537 * phi.quadrature_point(q)),
2538 * q);
2539 * inverse.transform_from_q_points_to_basis(dim + 2,
2540 * phi.begin_dof_values(),
2541 * phi.begin_dof_values());
2542 * phi.set_dof_values(solution);
2543 * }
2544 * }
2545 *
2546 *
2547 *
2548 * @endcode
2549 *
2550 * The next function again repeats functionality also provided by the
2551 * deal.II library, namely VectorTools::integrate_difference(). We here show
2552 * the explicit code to highlight how the vectorization across several cells
2553 * works and how to accumulate results via that interface: Recall that each
2554 * <i>lane</i> of the vectorized array holds data from a different cell. By
2555 * the loop over all cell batches that are owned by the current MPI process,
2556 * we could then fill a VectorizedArray of results; to obtain a global sum,
2557 * we would need to further go on and sum across the entries in the SIMD
2558 * array. However, such a procedure is not stable as the SIMD array could in
2559 * fact not hold valid data for all its lanes. This happens when the number
2560 * of locally owned cells is not a multiple of the SIMD width. To avoid
2561 * invalid data, we must explicitly skip those invalid lanes when accessing
2562 * the data. While one could imagine that we could make it work by simply
2563 * setting the empty lanes to zero (and thus, not contribute to a sum), the
2564 * situation is more complicated than that: What if we were to compute a
2565 * velocity out of the momentum? Then, we would need to divide by the
2566 * density, which is zero -- the result would consequently be NaN and
2567 * contaminate the result. This trap is avoided by accumulating the results
2568 * from the valid SIMD range as we loop through the cell batches, using the
2569 * function MatrixFree::n_active_entries_per_cell_batch() to give us the
2570 * number of lanes with valid data. It equals VectorizedArray::size() on
2571 * most cells, but can be less on the last cell batch if the number of cells
2572 * has a remainder compared to the SIMD width.
2573 *
2574 * @code
2575 * template <int dim, int degree, int n_points_1d>
2576 * std::array<double, 3> EulerOperator<dim, degree, n_points_1d>::compute_errors(
2577 * const Function<dim> & function,
2578 * const LinearAlgebra::distributed::Vector<Number> &solution) const
2579 * {
2580 * TimerOutput::Scope t(timer, "compute errors");
2581 * double errors_squared[3] = {};
2583 *
2584 * for (unsigned int cell = 0; cell < data.n_cell_batches(); ++cell)
2585 * {
2586 * phi.reinit(cell);
2587 * phi.gather_evaluate(solution, EvaluationFlags::values);
2588 * VectorizedArray<Number> local_errors_squared[3] = {};
2589 * for (unsigned int q = 0; q < phi.n_q_points; ++q)
2590 * {
2591 * const auto error =
2592 * evaluate_function(function, phi.quadrature_point(q)) -
2593 * phi.get_value(q);
2594 * const auto JxW = phi.JxW(q);
2595 *
2596 * local_errors_squared[0] += error[0] * error[0] * JxW;
2597 * for (unsigned int d = 0; d < dim; ++d)
2598 * local_errors_squared[1] += (error[d + 1] * error[d + 1]) * JxW;
2599 * local_errors_squared[2] += (error[dim + 1] * error[dim + 1]) * JxW;
2600 * }
2601 * for (unsigned int v = 0; v < data.n_active_entries_per_cell_batch(cell);
2602 * ++v)
2603 * for (unsigned int d = 0; d < 3; ++d)
2604 * errors_squared[d] += local_errors_squared[d][v];
2605 * }
2606 *
2607 * Utilities::MPI::sum(errors_squared, MPI_COMM_WORLD, errors_squared);
2608 *
2609 * std::array<double, 3> errors;
2610 * for (unsigned int d = 0; d < 3; ++d)
2611 * errors[d] = std::sqrt(errors_squared[d]);
2612 *
2613 * return errors;
2614 * }
2615 *
2616 *
2617 *
2618 * @endcode
2619 *
2620 * This final function of the EulerOperator class is used to estimate the
2621 * transport speed, scaled by the mesh size, that is relevant for setting
2622 * the time step size in the explicit time integrator. In the Euler
2623 * equations, there are two speeds of transport, namely the convective
2624 * velocity @f$\mathbf{u}@f$ and the propagation of sound waves with sound
2625 * speed @f$c = \sqrt{\gamma p/\rho}@f$ relative to the medium moving at
2626 * velocity @f$\mathbf u@f$.
2627 *
2628
2629 *
2630 * In the formula for the time step size, we are interested not by
2631 * these absolute speeds, but by the amount of time it takes for
2632 * information to cross a single cell. For information transported along with
2633 * the medium, @f$\mathbf u@f$ is scaled by the mesh size,
2634 * so an estimate of the maximal velocity can be obtained by computing
2635 * @f$\|J^{-\mathrm T} \mathbf{u}\|_\infty@f$, where @f$J@f$ is the Jacobian of the
2636 * transformation from real to the reference domain. Note that
2637 * FEEvaluationBase::inverse_jacobian() returns the inverse and transpose
2638 * Jacobian, representing the metric term from real to reference
2639 * coordinates, so we do not need to transpose it again. We store this limit
2640 * in the variable `convective_limit` in the code below.
2641 *
2642
2643 *
2644 * The sound propagation is isotropic, so we need to take mesh sizes in any
2645 * direction into account. The appropriate mesh size scaling is then given
2646 * by the minimal singular value of @f$J@f$ or, equivalently, the maximal
2647 * singular value of @f$J^{-1}@f$. Note that one could approximate this quantity
2648 * by the minimal distance between vertices of a cell when ignoring curved
2649 * cells. To get the maximal singular value of the Jacobian, the general
2650 * strategy would be some LAPACK function. Since all we need here is an
2651 * estimate, we can avoid the hassle of decomposing a tensor of
2652 * VectorizedArray numbers into several matrices and go into an (expensive)
2653 * eigenvalue function without vectorization, and instead use a few
2654 * iterations (five in the code below) of the power method applied to
2655 * @f$J^{-1}J^{-\mathrm T}@f$. The speed of convergence of this method depends
2656 * on the ratio of the largest to the next largest eigenvalue and the
2657 * initial guess, which is the vector of all ones. This might suggest that
2658 * we get slow convergence on cells close to a cube shape where all
2659 * lengths are almost the same. However, this slow convergence means that
2660 * the result will sit between the two largest singular values, which both
2661 * are close to the maximal value anyway. In all other cases, convergence
2662 * will be quick. Thus, we can merely hardcode 5 iterations here and be
2663 * confident that the result is good.
2664 *
2665 * @code
2666 * template <int dim, int degree, int n_points_1d>
2667 * double EulerOperator<dim, degree, n_points_1d>::compute_cell_transport_speed(
2668 * const LinearAlgebra::distributed::Vector<Number> &solution) const
2669 * {
2670 * TimerOutput::Scope t(timer, "compute transport speed");
2671 * Number max_transport = 0;
2673 *
2674 * for (unsigned int cell = 0; cell < data.n_cell_batches(); ++cell)
2675 * {
2676 * phi.reinit(cell);
2677 * phi.gather_evaluate(solution, EvaluationFlags::values);
2678 * VectorizedArray<Number> local_max = 0.;
2679 * for (unsigned int q = 0; q < phi.n_q_points; ++q)
2680 * {
2681 * const auto solution = phi.get_value(q);
2682 * const auto velocity = euler_velocity<dim>(solution);
2683 * const auto pressure = euler_pressure<dim>(solution);
2684 *
2685 * const auto inverse_jacobian = phi.inverse_jacobian(q);
2686 * const auto convective_speed = inverse_jacobian * velocity;
2687 * VectorizedArray<Number> convective_limit = 0.;
2688 * for (unsigned int d = 0; d < dim; ++d)
2689 * convective_limit =
2690 * std::max(convective_limit, std::abs(convective_speed[d]));
2691 *
2692 * const auto speed_of_sound =
2693 * std::sqrt(gamma * pressure * (1. / solution[0]));
2694 *
2696 * for (unsigned int d = 0; d < dim; ++d)
2697 * eigenvector[d] = 1.;
2698 * for (unsigned int i = 0; i < 5; ++i)
2699 * {
2700 * eigenvector = transpose(inverse_jacobian) *
2701 * (inverse_jacobian * eigenvector);
2702 * VectorizedArray<Number> eigenvector_norm = 0.;
2703 * for (unsigned int d = 0; d < dim; ++d)
2704 * eigenvector_norm =
2705 * std::max(eigenvector_norm, std::abs(eigenvector[d]));
2706 * eigenvector /= eigenvector_norm;
2707 * }
2708 * const auto jac_times_ev = inverse_jacobian * eigenvector;
2709 * const auto max_eigenvalue = std::sqrt(
2710 * (jac_times_ev * jac_times_ev) / (eigenvector * eigenvector));
2711 * local_max =
2712 * std::max(local_max,
2713 * max_eigenvalue * speed_of_sound + convective_limit);
2714 * }
2715 *
2716 * @endcode
2717 *
2718 * Similarly to the previous function, we must make sure to accumulate
2719 * speed only on the valid cells of a cell batch.
2720 *
2721 * @code
2722 * for (unsigned int v = 0; v < data.n_active_entries_per_cell_batch(cell);
2723 * ++v)
2724 * for (unsigned int d = 0; d < 3; ++d)
2725 * max_transport = std::max(max_transport, local_max[v]);
2726 * }
2727 *
2728 * max_transport = Utilities::MPI::max(max_transport, MPI_COMM_WORLD);
2729 *
2730 * return max_transport;
2731 * }
2732 *
2733 *
2734 *
2735 * @endcode
2736 *
2737 *
2738 * <a name="TheEulerProblemclass"></a>
2739 * <h3>The EulerProblem class</h3>
2740 *
2741
2742 *
2743 * This class combines the EulerOperator class with the time integrator and
2744 * the usual global data structures such as FiniteElement and DoFHandler, to
2745 * actually run the simulations of the Euler problem.
2746 *
2747
2748 *
2749 * The member variables are a triangulation, a finite element, a mapping (to
2750 * create high-order curved surfaces, see e.g. @ref step_10 "step-10"), and a DoFHandler to
2751 * describe the degrees of freedom. In addition, we keep an instance of the
2752 * EulerOperator described above around, which will do all heavy lifting in
2753 * terms of integrals, and some parameters for time integration like the
2754 * current time or the time step size.
2755 *
2756
2757 *
2758 * Furthermore, we use a PostProcessor instance to write some additional
2759 * information to the output file, in similarity to what was done in
2760 * @ref step_33 "step-33". The interface of the DataPostprocessor class is intuitive,
2761 * requiring us to provide information about what needs to be evaluated
2762 * (typically only the values of the solution, except for the Schlieren plot
2763 * that we only enable in 2D where it makes sense), and the names of what
2764 * gets evaluated. Note that it would also be possible to extract most
2765 * information by calculator tools within visualization programs such as
2766 * ParaView, but it is so much more convenient to do it already when writing
2767 * the output.
2768 *
2769 * @code
2770 * template <int dim>
2771 * class EulerProblem
2772 * {
2773 * public:
2774 * EulerProblem();
2775 *
2776 * void run();
2777 *
2778 * private:
2779 * void make_grid_and_dofs();
2780 *
2781 * void output_results(const unsigned int result_number);
2782 *
2784 *
2785 * ConditionalOStream pcout;
2786 *
2787 * #ifdef DEAL_II_WITH_P4EST
2789 * #else
2791 * #endif
2792 *
2793 * FESystem<dim> fe;
2794 * MappingQ<dim> mapping;
2795 * DoFHandler<dim> dof_handler;
2796 *
2797 * TimerOutput timer;
2798 *
2799 * EulerOperator<dim, fe_degree, n_q_points_1d> euler_operator;
2800 *
2801 * double time, time_step;
2802 *
2803 * class Postprocessor : public DataPostprocessor<dim>
2804 * {
2805 * public:
2806 * Postprocessor();
2807 *
2808 * virtual void evaluate_vector_field(
2810 * std::vector<Vector<double>> &computed_quantities) const override;
2811 *
2812 * virtual std::vector<std::string> get_names() const override;
2813 *
2814 * virtual std::vector<
2816 * get_data_component_interpretation() const override;
2817 *
2818 * virtual UpdateFlags get_needed_update_flags() const override;
2819 *
2820 * private:
2821 * const bool do_schlieren_plot;
2822 * };
2823 * };
2824 *
2825 *
2826 *
2827 * template <int dim>
2828 * EulerProblem<dim>::Postprocessor::Postprocessor()
2829 * : do_schlieren_plot(dim == 2)
2830 * {}
2831 *
2832 *
2833 *
2834 * @endcode
2835 *
2836 * For the main evaluation of the field variables, we first check that the
2837 * lengths of the arrays equal the expected values (the lengths `2*dim+4` or
2838 * `2*dim+5` are derived from the sizes of the names we specify in the
2839 * get_names() function below). Then we loop over all evaluation points and
2840 * fill the respective information: First we fill the primal solution
2841 * variables of density @f$\rho@f$, momentum @f$\rho \mathbf{u}@f$ and energy @f$E@f$,
2842 * then we compute the derived velocity @f$\mathbf u@f$, the pressure @f$p@f$, the
2843 * speed of sound @f$c=\sqrt{\gamma p / \rho}@f$, as well as the Schlieren plot
2844 * showing @f$s = |\nabla \rho|^2@f$ in case it is enabled. (See @ref step_69 "step-69" for
2845 * another example where we create a Schlieren plot.)
2846 *
2847 * @code
2848 * template <int dim>
2849 * void EulerProblem<dim>::Postprocessor::evaluate_vector_field(
2851 * std::vector<Vector<double>> & computed_quantities) const
2852 * {
2853 * const unsigned int n_evaluation_points = inputs.solution_values.size();
2854 *
2855 * if (do_schlieren_plot == true)
2856 * Assert(inputs.solution_gradients.size() == n_evaluation_points,
2857 * ExcInternalError());
2858 *
2859 * Assert(computed_quantities.size() == n_evaluation_points,
2860 * ExcInternalError());
2861 * Assert(inputs.solution_values[0].size() == dim + 2, ExcInternalError());
2862 * Assert(computed_quantities[0].size() ==
2863 * dim + 2 + (do_schlieren_plot == true ? 1 : 0),
2864 * ExcInternalError());
2865 *
2866 * for (unsigned int q = 0; q < n_evaluation_points; ++q)
2867 * {
2868 * Tensor<1, dim + 2> solution;
2869 * for (unsigned int d = 0; d < dim + 2; ++d)
2870 * solution[d] = inputs.solution_values[q](d);
2871 *
2872 * const double density = solution[0];
2873 * const Tensor<1, dim> velocity = euler_velocity<dim>(solution);
2874 * const double pressure = euler_pressure<dim>(solution);
2875 *
2876 * for (unsigned int d = 0; d < dim; ++d)
2877 * computed_quantities[q](d) = velocity[d];
2878 * computed_quantities[q](dim) = pressure;
2879 * computed_quantities[q](dim + 1) = std::sqrt(gamma * pressure / density);
2880 *
2881 * if (do_schlieren_plot == true)
2882 * computed_quantities[q](dim + 2) =
2883 * inputs.solution_gradients[q][0] * inputs.solution_gradients[q][0];
2884 * }
2885 * }
2886 *
2887 *
2888 *
2889 * template <int dim>
2890 * std::vector<std::string> EulerProblem<dim>::Postprocessor::get_names() const
2891 * {
2892 * std::vector<std::string> names;
2893 * for (unsigned int d = 0; d < dim; ++d)
2894 * names.emplace_back("velocity");
2895 * names.emplace_back("pressure");
2896 * names.emplace_back("speed_of_sound");
2897 *
2898 * if (do_schlieren_plot == true)
2899 * names.emplace_back("schlieren_plot");
2900 *
2901 * return names;
2902 * }
2903 *
2904 *
2905 *
2906 * @endcode
2907 *
2908 * For the interpretation of quantities, we have scalar density, energy,
2909 * pressure, speed of sound, and the Schlieren plot, and vectors for the
2910 * momentum and the velocity.
2911 *
2912 * @code
2913 * template <int dim>
2914 * std::vector<DataComponentInterpretation::DataComponentInterpretation>
2915 * EulerProblem<dim>::Postprocessor::get_data_component_interpretation() const
2916 * {
2917 * std::vector<DataComponentInterpretation::DataComponentInterpretation>
2918 * interpretation;
2919 * for (unsigned int d = 0; d < dim; ++d)
2920 * interpretation.push_back(
2922 * interpretation.push_back(DataComponentInterpretation::component_is_scalar);
2923 * interpretation.push_back(DataComponentInterpretation::component_is_scalar);
2924 *
2925 * if (do_schlieren_plot == true)
2926 * interpretation.push_back(
2928 *
2929 * return interpretation;
2930 * }
2931 *
2932 *
2933 *
2934 * @endcode
2935 *
2936 * With respect to the necessary update flags, we only need the values for
2937 * all quantities but the Schlieren plot, which is based on the density
2938 * gradient.
2939 *
2940 * @code
2941 * template <int dim>
2942 * UpdateFlags EulerProblem<dim>::Postprocessor::get_needed_update_flags() const
2943 * {
2944 * if (do_schlieren_plot == true)
2946 * else
2947 * return update_values;
2948 * }
2949 *
2950 *
2951 *
2952 * @endcode
2953 *
2954 * The constructor for this class is unsurprising: We set up a parallel
2955 * triangulation based on the `MPI_COMM_WORLD` communicator, a vector finite
2956 * element with `dim+2` components for density, momentum, and energy, a
2957 * high-order mapping of the same degree as the underlying finite element,
2958 * and initialize the time and time step to zero.
2959 *
2960 * @code
2961 * template <int dim>
2962 * EulerProblem<dim>::EulerProblem()
2963 * : pcout(std::cout, Utilities::MPI::this_mpi_process(MPI_COMM_WORLD) == 0)
2964 * #ifdef DEAL_II_WITH_P4EST
2965 * , triangulation(MPI_COMM_WORLD)
2966 * #endif
2967 * , fe(FE_DGQ<dim>(fe_degree), dim + 2)
2968 * , mapping(fe_degree)
2969 * , dof_handler(triangulation)
2970 * , timer(pcout, TimerOutput::never, TimerOutput::wall_times)
2971 * , euler_operator(timer)
2972 * , time(0)
2973 * , time_step(0)
2974 * {}
2975 *
2976 *
2977 *
2978 * @endcode
2979 *
2980 * As a mesh, this tutorial program implements two options, depending on the
2981 * global variable `testcase`: For the analytical variant (`testcase==0`),
2982 * the domain is @f$(0, 10) \times (-5, 5)@f$, with Dirichlet boundary
2983 * conditions (inflow) all around the domain. For `testcase==1`, we set the
2984 * domain to a cylinder in a rectangular box, derived from the flow past
2985 * cylinder testcase for incompressible viscous flow by Sch&auml;fer and
2986 * Turek (1996). Here, we have a larger variety of boundaries. The inflow
2987 * part at the left of the channel is given the inflow type, for which we
2988 * choose a constant inflow profile, whereas we set a subsonic outflow at
2989 * the right. For the boundary around the cylinder (boundary id equal to 2)
2990 * as well as the channel walls (boundary id equal to 3) we use the wall
2991 * boundary type, which is no-normal flow. Furthermore, for the 3D cylinder
2992 * we also add a gravity force in vertical direction. Having the base mesh
2993 * in place (including the manifolds set by
2994 * GridGenerator::channel_with_cylinder()), we can then perform the
2995 * specified number of global refinements, create the unknown numbering from
2996 * the DoFHandler, and hand the DoFHandler and Mapping objects to the
2997 * initialization of the EulerOperator.
2998 *
2999 * @code
3000 * template <int dim>
3001 * void EulerProblem<dim>::make_grid_and_dofs()
3002 * {
3003 * switch (testcase)
3004 * {
3005 * case 0:
3006 * {
3007 * Point<dim> lower_left;
3008 * for (unsigned int d = 1; d < dim; ++d)
3009 * lower_left[d] = -5;
3010 *
3011 * Point<dim> upper_right;
3012 * upper_right[0] = 10;
3013 * for (unsigned int d = 1; d < dim; ++d)
3014 * upper_right[d] = 5;
3015 *
3017 * lower_left,
3018 * upper_right);
3019 * triangulation.refine_global(2);
3020 *
3021 * euler_operator.set_inflow_boundary(
3022 * 0, std::make_unique<ExactSolution<dim>>(0));
3023 *
3024 * break;
3025 * }
3026 *
3027 * case 1:
3028 * {
3030 * triangulation, 0.03, 1, 0, true);
3031 *
3032 * euler_operator.set_inflow_boundary(
3033 * 0, std::make_unique<ExactSolution<dim>>(0));
3034 * euler_operator.set_subsonic_outflow_boundary(
3035 * 1, std::make_unique<ExactSolution<dim>>(0));
3036 *
3037 * euler_operator.set_wall_boundary(2);
3038 * euler_operator.set_wall_boundary(3);
3039 *
3040 * if (dim == 3)
3041 * euler_operator.set_body_force(
3042 * std::make_unique<Functions::ConstantFunction<dim>>(
3043 * std::vector<double>({0., 0., -0.2})));
3044 *
3045 * break;
3046 * }
3047 *
3048 * default:
3049 * Assert(false, ExcNotImplemented());
3050 * }
3051 *
3052 * triangulation.refine_global(n_global_refinements);
3053 *
3054 * dof_handler.distribute_dofs(fe);
3055 *
3056 * euler_operator.reinit(mapping, dof_handler);
3057 * euler_operator.initialize_vector(solution);
3058 *
3059 * @endcode
3060 *
3061 * In the following, we output some statistics about the problem. Because we
3062 * often end up with quite large numbers of cells or degrees of freedom, we
3063 * would like to print them with a comma to separate each set of three
3064 * digits. This can be done via "locales", although the way this works is
3065 * not particularly intuitive. @ref step_32 "step-32" explains this in slightly more
3066 * detail.
3067 *
3068 * @code
3069 * std::locale s = pcout.get_stream().getloc();
3070 * pcout.get_stream().imbue(std::locale(""));
3071 * pcout << "Number of degrees of freedom: " << dof_handler.n_dofs()
3072 * << " ( = " << (dim + 2) << " [vars] x "
3073 * << triangulation.n_global_active_cells() << " [cells] x "
3074 * << Utilities::pow(fe_degree + 1, dim) << " [dofs/cell/var] )"
3075 * << std::endl;
3076 * pcout.get_stream().imbue(s);
3077 * }
3078 *
3079 *
3080 *
3081 * @endcode
3082 *
3083 * For output, we first let the Euler operator compute the errors of the
3084 * numerical results. More precisely, we compute the error against the
3085 * analytical result for the analytical solution case, whereas we compute
3086 * the deviation against the background field with constant density and
3087 * energy and constant velocity in @f$x@f$ direction for the second test case.
3088 *
3089
3090 *
3091 * The next step is to create output. This is similar to what is done in
3092 * @ref step_33 "step-33": We let the postprocessor defined above control most of the
3093 * output, except for the primal field that we write directly. For the
3094 * analytical solution test case, we also perform another projection of the
3095 * analytical solution and print the difference between that field and the
3096 * numerical solution. Once we have defined all quantities to be written, we
3097 * build the patches for output. Similarly to @ref step_65 "step-65", we create a
3098 * high-order VTK output by setting the appropriate flag, which enables us
3099 * to visualize fields of high polynomial degrees. Finally, we call the
3100 * `DataOutInterface::write_vtu_in_parallel()` function to write the result
3101 * to the given file name. This function uses special MPI parallel write
3102 * facilities, which are typically more optimized for parallel file systems
3103 * than the standard library's `std::ofstream` variants used in most other
3104 * tutorial programs. A particularly nice feature of the
3105 * `write_vtu_in_parallel()` function is the fact that it can combine output
3106 * from all MPI ranks into a single file, making it unnecessary to have a
3107 * central record of all such files (namely, the "pvtu" file).
3108 *
3109
3110 *
3111 * For parallel programs, it is often instructive to look at the partitioning
3112 * of cells among processors. To this end, one can pass a vector of numbers
3113 * to DataOut::add_data_vector() that contains as many entries as the
3114 * current processor has active cells; these numbers should then be the
3115 * rank of the processor that owns each of these cells. Such a vector
3116 * could, for example, be obtained from
3117 * GridTools::get_subdomain_association(). On the other hand, on each MPI
3118 * process, DataOut will only read those entries that correspond to locally
3119 * owned cells, and these of course all have the same value: namely, the rank
3120 * of the current process. What is in the remaining entries of the vector
3121 * doesn't actually matter, and so we can just get away with a cheap trick: We
3122 * just fill *all* values of the vector we give to DataOut::add_data_vector()
3123 * with the rank of the current MPI process. The key is that on each process,
3124 * only the entries corresponding to the locally owned cells will be read,
3125 * ignoring the (wrong) values in other entries. The fact that every process
3126 * submits a vector in which the correct subset of entries is correct is all
3127 * that is necessary.
3128 *
3129 * @code
3130 * template <int dim>
3131 * void EulerProblem<dim>::output_results(const unsigned int result_number)
3132 * {
3133 * const std::array<double, 3> errors =
3134 * euler_operator.compute_errors(ExactSolution<dim>(time), solution);
3135 * const std::string quantity_name = testcase == 0 ? "error" : "norm";
3136 *
3137 * pcout << "Time:" << std::setw(8) << std::setprecision(3) << time
3138 * << ", dt: " << std::setw(8) << std::setprecision(2) << time_step
3139 * << ", " << quantity_name << " rho: " << std::setprecision(4)
3140 * << std::setw(10) << errors[0] << ", rho * u: " << std::setprecision(4)
3141 * << std::setw(10) << errors[1] << ", energy:" << std::setprecision(4)
3142 * << std::setw(10) << errors[2] << std::endl;
3143 *
3144 * {
3145 * TimerOutput::Scope t(timer, "output");
3146 *
3147 * Postprocessor postprocessor;
3148 * DataOut<dim> data_out;
3149 *
3150 * DataOutBase::VtkFlags flags;
3151 * flags.write_higher_order_cells = true;
3152 * data_out.set_flags(flags);
3153 *
3154 * data_out.attach_dof_handler(dof_handler);
3155 * {
3156 * std::vector<std::string> names;
3157 * names.emplace_back("density");
3158 * for (unsigned int d = 0; d < dim; ++d)
3159 * names.emplace_back("momentum");
3160 * names.emplace_back("energy");
3161 *
3162 * std::vector<DataComponentInterpretation::DataComponentInterpretation>
3163 * interpretation;
3164 * interpretation.push_back(
3166 * for (unsigned int d = 0; d < dim; ++d)
3167 * interpretation.push_back(
3169 * interpretation.push_back(
3171 *
3172 * data_out.add_data_vector(dof_handler, solution, names, interpretation);
3173 * }
3174 * data_out.add_data_vector(solution, postprocessor);
3175 *
3177 * if (testcase == 0 && dim == 2)
3178 * {
3179 * reference.reinit(solution);
3180 * euler_operator.project(ExactSolution<dim>(time), reference);
3181 * reference.sadd(-1., 1, solution);
3182 * std::vector<std::string> names;
3183 * names.emplace_back("error_density");
3184 * for (unsigned int d = 0; d < dim; ++d)
3185 * names.emplace_back("error_momentum");
3186 * names.emplace_back("error_energy");
3187 *
3188 * std::vector<DataComponentInterpretation::DataComponentInterpretation>
3189 * interpretation;
3190 * interpretation.push_back(
3192 * for (unsigned int d = 0; d < dim; ++d)
3193 * interpretation.push_back(
3195 * interpretation.push_back(
3197 *
3198 * data_out.add_data_vector(dof_handler,
3199 * reference,
3200 * names,
3201 * interpretation);
3202 * }
3203 *
3204 * Vector<double> mpi_owner(triangulation.n_active_cells());
3205 * mpi_owner = Utilities::MPI::this_mpi_process(MPI_COMM_WORLD);
3206 * data_out.add_data_vector(mpi_owner, "owner");
3207 *
3208 * data_out.build_patches(mapping,
3209 * fe.degree,
3211 *
3212 * const std::string filename =
3213 * "solution_" + Utilities::int_to_string(result_number, 3) + ".vtu";
3214 * data_out.write_vtu_in_parallel(filename, MPI_COMM_WORLD);
3215 * }
3216 * }
3217 *
3218 *
3219 *
3220 * @endcode
3221 *
3222 * The EulerProblem::run() function puts all pieces together. It starts off
3223 * by calling the function that creates the mesh and sets up data structures,
3224 * and then initializing the time integrator and the two temporary vectors of
3225 * the low-storage integrator. We call these vectors `rk_register_1` and
3226 * `rk_register_2`, and use the first vector to represent the quantity
3227 * @f$\mathbf{r}_i@f$ and the second one for @f$\mathbf{k}_i@f$ in the formulas for
3228 * the Runge--Kutta scheme outlined in the introduction. Before we start the
3229 * time loop, we compute the time step size by the
3230 * `EulerOperator::compute_cell_transport_speed()` function. For reasons of
3231 * comparison, we compare the result obtained there with the minimal mesh
3232 * size and print them to screen. For velocities and speeds of sound close
3233 * to unity as in this tutorial program, the predicted effective mesh size
3234 * will be close, but they could vary if scaling were different.
3235 *
3236 * @code
3237 * template <int dim>
3238 * void EulerProblem<dim>::run()
3239 * {
3240 * {
3241 * const unsigned int n_vect_number = VectorizedArray<Number>::size();
3242 * const unsigned int n_vect_bits = 8 * sizeof(Number) * n_vect_number;
3243 *
3244 * pcout << "Running with "
3245 * << Utilities::MPI::n_mpi_processes(MPI_COMM_WORLD)
3246 * << " MPI processes" << std::endl;
3247 * pcout << "Vectorization over " << n_vect_number << ' '
3248 * << (std::is_same<Number, double>::value ? "doubles" : "floats")
3249 * << " = " << n_vect_bits << " bits ("
3251 * << std::endl;
3252 * }
3253 *
3254 * make_grid_and_dofs();
3255 *
3256 * const LowStorageRungeKuttaIntegrator integrator(lsrk_scheme);
3257 *
3260 * rk_register_1.reinit(solution);
3261 * rk_register_2.reinit(solution);
3262 *
3263 * euler_operator.project(ExactSolution<dim>(time), solution);
3264 *
3265 * double min_vertex_distance = std::numeric_limits<double>::max();
3266 * for (const auto &cell : triangulation.active_cell_iterators())
3267 * if (cell->is_locally_owned())
3268 * min_vertex_distance =
3269 * std::min(min_vertex_distance, cell->minimum_vertex_distance());
3270 * min_vertex_distance =
3271 * Utilities::MPI::min(min_vertex_distance, MPI_COMM_WORLD);
3272 *
3273 * time_step = courant_number * integrator.n_stages() /
3274 * euler_operator.compute_cell_transport_speed(solution);
3275 * pcout << "Time step size: " << time_step
3276 * << ", minimal h: " << min_vertex_distance
3277 * << ", initial transport scaling: "
3278 * << 1. / euler_operator.compute_cell_transport_speed(solution)
3279 * << std::endl
3280 * << std::endl;
3281 *
3282 * output_results(0);
3283 *
3284 * @endcode
3285 *
3286 * Now we are ready to start the time loop, which we run until the time
3287 * has reached the desired end time. Every 5 time steps, we compute a new
3288 * estimate for the time step -- since the solution is nonlinear, it is
3289 * most effective to adapt the value during the course of the
3290 * simulation. In case the Courant number was chosen too aggressively, the
3291 * simulation will typically blow up with time step NaN, so that is easy
3292 * to detect here. One thing to note is that roundoff errors might
3293 * propagate to the leading digits due to an interaction of slightly
3294 * different time step selections that in turn lead to slightly different
3295 * solutions. To decrease this sensitivity, it is common practice to round
3296 * or truncate the time step size to a few digits, e.g. 3 in this case. In
3297 * case the current time is near the prescribed 'tick' value for output
3298 * (e.g. 0.02), we also write the output. After the end of the time loop,
3299 * we summarize the computation by printing some statistics, which is
3300 * mostly done by the TimerOutput::print_wall_time_statistics() function.
3301 *
3302 * @code
3303 * unsigned int timestep_number = 0;
3304 *
3305 * while (time < final_time - 1e-12)
3306 * {
3307 * ++timestep_number;
3308 * if (timestep_number % 5 == 0)
3309 * time_step =
3310 * courant_number * integrator.n_stages() /
3312 * euler_operator.compute_cell_transport_speed(solution), 3);
3313 *
3314 * {
3315 * TimerOutput::Scope t(timer, "rk time stepping total");
3316 * integrator.perform_time_step(euler_operator,
3317 * time,
3318 * time_step,
3319 * solution,
3320 * rk_register_1,
3321 * rk_register_2);
3322 * }
3323 *
3324 * time += time_step;
3325 *
3326 * if (static_cast<int>(time / output_tick) !=
3327 * static_cast<int>((time - time_step) / output_tick) ||
3328 * time >= final_time - 1e-12)
3329 * output_results(
3330 * static_cast<unsigned int>(std::round(time / output_tick)));
3331 * }
3332 *
3333 * timer.print_wall_time_statistics(MPI_COMM_WORLD);
3334 * pcout << std::endl;
3335 * }
3336 *
3337 * } // namespace Euler_DG
3338 *
3339 *
3340 *
3341 * @endcode
3342 *
3343 * The main() function is not surprising and follows what was done in all
3344 * previous MPI programs: As we run an MPI program, we need to call `MPI_Init()`
3345 * and `MPI_Finalize()`, which we do through the
3346 * Utilities::MPI::MPI_InitFinalize data structure. Note that we run the program
3347 * only with MPI, and set the thread count to 1.
3348 *
3349 * @code
3350 * int main(int argc, char **argv)
3351 * {
3352 * using namespace Euler_DG;
3353 * using namespace dealii;
3354 *
3355 * Utilities::MPI::MPI_InitFinalize mpi_initialization(argc, argv, 1);
3356 *
3357 * try
3358 * {
3360 *
3361 * EulerProblem<dimension> euler_problem;
3362 * euler_problem.run();
3363 * }
3364 * catch (std::exception &exc)
3365 * {
3366 * std::cerr << std::endl
3367 * << std::endl
3368 * << "----------------------------------------------------"
3369 * << std::endl;
3370 * std::cerr << "Exception on processing: " << std::endl
3371 * << exc.what() << std::endl
3372 * << "Aborting!" << std::endl
3373 * << "----------------------------------------------------"
3374 * << std::endl;
3375 *
3376 * return 1;
3377 * }
3378 * catch (...)
3379 * {
3380 * std::cerr << std::endl
3381 * << std::endl
3382 * << "----------------------------------------------------"
3383 * << std::endl;
3384 * std::cerr << "Unknown exception!" << std::endl
3385 * << "Aborting!" << std::endl
3386 * << "----------------------------------------------------"
3387 * << std::endl;
3388 * return 1;
3389 * }
3390 *
3391 * return 0;
3392 * }
3393 * @endcode
3394<a name="Results"></a><h1>Results</h1>
3395
3396
3397<a name="Programoutput"></a><h3>Program output</h3>
3398
3399
3400Running the program with the default settings on a machine with 40 processes
3401produces the following output:
3402@code
3403Running with 40 MPI processes
3404Vectorization over 8 doubles = 512 bits (AVX512)
3405Number of degrees of freedom: 147,456 ( = 4 [vars] x 1,024 [cells] x 36 [dofs/cell/var] )
3406Time step size: 0.00689325, minimal h: 0.3125, initial transport scaling: 0.102759
3407
3408Time: 0, dt: 0.0069, error rho: 2.76e-07, rho * u: 1.259e-06, energy: 2.987e-06
3409Time: 1.01, dt: 0.0069, error rho: 1.37e-06, rho * u: 2.252e-06, energy: 4.153e-06
3410Time: 2.01, dt: 0.0069, error rho: 1.561e-06, rho * u: 2.43e-06, energy: 4.493e-06
3411Time: 3.01, dt: 0.0069, error rho: 1.714e-06, rho * u: 2.591e-06, energy: 4.762e-06
3412Time: 4.01, dt: 0.0069, error rho: 1.843e-06, rho * u: 2.625e-06, energy: 4.985e-06
3413Time: 5.01, dt: 0.0069, error rho: 1.496e-06, rho * u: 1.961e-06, energy: 4.142e-06
3414Time: 6, dt: 0.0083, error rho: 1.007e-06, rho * u: 7.119e-07, energy: 2.972e-06
3415Time: 7, dt: 0.0095, error rho: 9.096e-07, rho * u: 3.786e-07, energy: 2.626e-06
3416Time: 8, dt: 0.0096, error rho: 8.439e-07, rho * u: 3.338e-07, energy: 2.43e-06
3417Time: 9, dt: 0.0096, error rho: 7.822e-07, rho * u: 2.984e-07, energy: 2.248e-06
3418Time: 10, dt: 0.0096, error rho: 7.231e-07, rho * u: 2.666e-07, energy: 2.074e-06
3419
3420+-------------------------------------------+------------------+------------+------------------+
3421| Total wallclock time elapsed | 2.249s 30 | 2.249s | 2.249s 8 |
3422| | | |
3423| Section | no. calls | min time rank | avg time | max time rank |
3424+-------------------------------------------+------------------+------------+------------------+
3425| compute errors | 11 | 0.008066s 13 | 0.00952s | 0.01041s 20 |
3426| compute transport speed | 258 | 0.01012s 13 | 0.05392s | 0.08574s 25 |
3427| output | 11 | 0.9597s 13 | 0.9613s | 0.9623s 6 |
3428| rk time stepping total | 1283 | 0.9827s 25 | 1.015s | 1.06s 13 |
3429| rk_stage - integrals L_h | 6415 | 0.8803s 26 | 0.9198s | 0.9619s 14 |
3430| rk_stage - inv mass + vec upd | 6415 | 0.05677s 15 | 0.06487s | 0.07597s 13 |
3431+-------------------------------------------+------------------+------------+------------------+
3432@endcode
3433
3434The program output shows that all errors are small. This is due to the fact
3435that we use a relatively fine mesh of @f$32^2@f$ cells with polynomials of degree
34365 for a solution that is smooth. An interesting pattern shows for the time
3437step size: whereas it is 0.0069 up to time 5, it increases to 0.0096 for later
3438times. The step size increases once the vortex with some motion on top of the
3439speed of sound (and thus faster propagation) leaves the computational domain
3440between times 5 and 6.5. After that point, the flow is simply uniform
3441in the same direction, and the maximum velocity of the gas is reduced
3442compared to the previous state where the uniform velocity was overlaid
3443by the vortex. Our time step formula recognizes this effect.
3444
3445The final block of output shows detailed information about the timing
3446of individual parts of the programs; it breaks this down by showing
3447the time taken by the fastest and the slowest processor, and the
3448average time -- this is often useful in very large computations to
3449find whether there are processors that are consistently overheated
3450(and consequently are throttling their clock speed) or consistently
3451slow for other reasons.
3452The summary shows that 1283 time steps have been performed
3453in 1.02 seconds (looking at the average time among all MPI processes), while
3454the output of 11 files has taken additional 0.96 seconds. Broken down per time
3455step and into the five Runge--Kutta stages, the compute time per evaluation is
34560.16 milliseconds. This high performance is typical of matrix-free evaluators
3457and a reason why explicit time integration is very competitive against
3458implicit solvers, especially for large-scale simulations. The breakdown of
3459computational times at the end of the program run shows that the evaluation of
3460integrals in @f$\mathcal L_h@f$ contributes with around 0.92 seconds and the
3461application of the inverse mass matrix with 0.06 seconds. Furthermore, the
3462estimation of the transport speed for the time step size computation
3463contributes with another 0.05 seconds of compute time.
3464
3465If we use three more levels of global refinement and 9.4 million DoFs in total,
3466the final statistics are as follows (for the modified Lax--Friedrichs flux,
3467@f$p=5@f$, and the same system of 40 cores of dual-socket Intel Xeon Gold 6230):
3468@code
3469+-------------------------------------------+------------------+------------+------------------+
3470| Total wallclock time elapsed | 244.9s 12 | 244.9s | 244.9s 34 |
3471| | | |
3472| Section | no. calls | min time rank | avg time | max time rank |
3473+-------------------------------------------+------------------+------------+------------------+
3474| compute errors | 11 | 0.4239s 12 | 0.4318s | 0.4408s 9 |
3475| compute transport speed | 2053 | 3.962s 12 | 6.727s | 10.12s 7 |
3476| output | 11 | 30.35s 12 | 30.36s | 30.37s 9 |
3477| rk time stepping total | 10258 | 201.7s 7 | 205.1s | 207.8s 12 |
3478| rk_stage - integrals L_h | 51290 | 121.3s 6 | 126.6s | 136.3s 16 |
3479| rk_stage - inv mass + vec upd | 51290 | 66.19s 16 | 77.52s | 81.84s 10 |
3480+-------------------------------------------+------------------+------------+------------------+
3481@endcode
3482
3483Per time step, the solver now takes 0.02 seconds, about 25 times as long as
3484for the small problem with 147k unknowns. Given that the problem involves 64
3485times as many unknowns, the increase in computing time is not
3486surprising. Since we also do 8 times as many time steps, the compute time
3487should in theory increase by a factor of 512. The actual increase is 205 s /
34881.02 s = 202. This is because the small problem size cannot fully utilize the
348940 cores due to communication overhead. This becomes clear if we look into the
3490details of the operations done per time step. The evaluation of the
3491differential operator @f$\mathcal L_h@f$ with nearest neighbor communication goes
3492from 0.92 seconds to 127 seconds, i.e., it increases with a factor of 138. On
3493the other hand, the cost for application of the inverse mass matrix and the
3494vector updates, which do not need to communicate between the MPI processes at
3495all, has increased by a factor of 1195. The increase is more than the
3496theoretical factor of 512 because the operation is limited by the bandwidth
3497from RAM memory for the larger size while for the smaller size, all vectors
3498fit into the caches of the CPU. The numbers show that the mass matrix
3499evaluation and vector update part consume almost 40% of the time spent by the
3500Runge--Kutta stages -- despite using a low-storage Runge--Kutta integrator and
3501merging of vector operations! And despite using over-integration for the
3502@f$\mathcal L_h@f$ operator. For simpler differential operators and more expensive
3503time integrators, the proportion spent in the mass matrix and vector update
3504part can also reach 70%. If we compute a throughput number in terms of DoFs
3505processed per second and Runge--Kutta stage, we obtain @f[ \text{throughput} =
3506\frac{n_\mathrm{time steps} n_\mathrm{stages}
3507n_\mathrm{dofs}}{t_\mathrm{compute}} = \frac{10258 \cdot 5 \cdot
35089.4\,\text{MDoFs}}{205s} = 2360\, \text{MDoFs/s} @f] This throughput number is
3509very high, given that simply copying one vector to another one runs at
3510only around 10,000 MDoFs/s.
3511
3512If we go to the next-larger size with 37.7 million DoFs, the overall
3513simulation time is 2196 seconds, with 1978 seconds spent in the time
3514stepping. The increase in run time is a factor of 9.3 for the L_h operator
3515(1179 versus 127 seconds) and a factor of 10.3 for the inverse mass matrix and
3516vector updates (797 vs 77.5 seconds). The reason for this non-optimal increase
3517in run time can be traced back to cache effects on the given hardware (with 40
3518MB of L2 cache and 55 MB of L3 cache): While not all of the relevant data fits
3519into caches for 9.4 million DoFs (one vector takes 75 MB and we have three
3520vectors plus some additional data in MatrixFree), there is capacity for one and
3521a half vector nonetheless. Given that modern caches are more sophisticated than
3522the naive least-recently-used strategy (where we would have little re-use as
3523the data is used in a streaming-like fashion), we can assume that a sizeable
3524fraction of data can indeed be delivered from caches for the 9.4 million DoFs
3525case. For the larger case, even with optimal caching less than 10 percent of
3526data would fit into caches, with an associated loss in performance.
3527
3528
3529<a name="Convergenceratesfortheanalyticaltestcase"></a><h3>Convergence rates for the analytical test case</h3>
3530
3531
3532For the modified Lax--Friedrichs flux and measuring the error in the momentum
3533variable, we obtain the following convergence table (the rates are very
3534similar for the density and energy variables):
3535
3536<table align="center" class="doxtable">
3537 <tr>
3538 <th>&nbsp;</th>
3539 <th colspan="3"><i>p</i>=2</th>
3540 <th colspan="3"><i>p</i>=3</th>
3541 <th colspan="3"><i>p</i>=5</th>
3542 </tr>
3543 <tr>
3544 <th>n_cells</th>
3545 <th>n_dofs</th>
3546 <th>error mom</th>
3547 <th>rate</th>
3548 <th>n_dofs</th>
3549 <th>error mom</th>
3550 <th>rate</th>
3551 <th>n_dofs</th>
3552 <th>error mom</th>
3553 <th>rate</th>
3554 </tr>
3555 <tr>
3556 <td align="right">16</td>
3557 <td>&nbsp;</td>
3558 <td>&nbsp;</td>
3559 <td>&nbsp;</td>
3560 <td>&nbsp;</td>
3561 <td>&nbsp;</td>
3562 <td>&nbsp;</td>
3563 <td align="right">2,304</td>
3564 <td align="center">1.373e-01</td>
3565 <td>&nbsp;</td>
3566 </tr>
3567 <tr>
3568 <td align="right">64</td>
3569 <td>&nbsp;</td>
3570 <td>&nbsp;</td>
3571 <td>&nbsp;</td>
3572 <td align="right">4,096</td>
3573 <td align="center">9.130e-02</td>
3574 <td>&nbsp;</td>
3575 <td align="right">9,216</td>
3576 <td align="center">8.899e-03</td>
3577 <td>3.94</td>
3578 </tr>
3579 <tr>
3580 <td align="right">256</td>
3581 <td align="right">9,216</td>
3582 <td align="center">5.577e-02</td>
3583 <td>&nbsp;</td>
3584 <td align="right">16,384</td>
3585 <td align="center">7.381e-03</td>
3586 <td>3.64</td>
3587 <td align="right">36,864</td>
3588 <td align="center">2.082e-04</td>
3589 <td>5.42</td>
3590 </tr>
3591 <tr>
3592 <td align="right">1024</td>
3593 <td align="right">36,864</td>
3594 <td align="center">4.724e-03</td>
3595 <td>3.56</td>
3596 <td align="right">65,536</td>
3597 <td align="center">3.072e-04</td>
3598 <td>4.59</td>
3599 <td align="right">147,456</td>
3600 <td align="center">2.625e-06</td>
3601 <td>6.31</td>
3602 </tr>
3603 <tr>
3604 <td align="right">4096</td>
3605 <td align="right">147,456</td>
3606 <td align="center">6.205e-04</td>
3607 <td>2.92</td>
3608 <td align="right">262,144</td>
3609 <td align="center">1.880e-05</td>
3610 <td>4.03</td>
3611 <td align="right">589,824</td>
3612 <td align="center">3.268e-08</td>
3613 <td>6.33</td>
3614 </tr>
3615 <tr>
3616 <td align="right">16,384</td>
3617 <td align="right">589,824</td>
3618 <td align="center">8.279e-05</td>
3619 <td>2.91</td>
3620 <td align="right">1,048,576</td>
3621 <td align="center">1.224e-06</td>
3622 <td>3.94</td>
3623 <td align="right">2,359,296</td>
3624 <td align="center">9.252e-10</td>
3625 <td>5.14</td>
3626 </tr>
3627 <tr>
3628 <td align="right">65,536</td>
3629 <td align="right">2,359,296</td>
3630 <td align="center">1.105e-05</td>
3631 <td>2.91</td>
3632 <td align="right">4,194,304</td>
3633 <td align="center">7.871e-08</td>
3634 <td>3.96</td>
3635 <td align="right">9,437,184</td>
3636 <td align="center">1.369e-10</td>
3637 <td>2.77</td>
3638 </tr>
3639 <tr>
3640 <td align="right">262,144</td>
3641 <td align="right">9,437,184</td>
3642 <td align="center">1.615e-06</td>
3643 <td>2.77</td>
3644 <td align="right">16,777,216</td>
3645 <td align="center">4.961e-09</td>
3646 <td>3.99</td>
3647 <td align="right">37,748,736</td>
3648 <td align="center">7.091e-11</td>
3649 <td>0.95</td>
3650 </tr>
3651</table>
3652
3653If we switch to the Harten-Lax-van Leer flux, the results are as follows:
3654<table align="center" class="doxtable">
3655 <tr>
3656 <th>&nbsp;</th>
3657 <th colspan="3"><i>p</i>=2</th>
3658 <th colspan="3"><i>p</i>=3</th>
3659 <th colspan="3"><i>p</i>=5</th>
3660 </tr>
3661 <tr>
3662 <th>n_cells</th>
3663 <th>n_dofs</th>
3664 <th>error mom</th>
3665 <th>rate</th>
3666 <th>n_dofs</th>
3667 <th>error mom</th>
3668 <th>rate</th>
3669 <th>n_dofs</th>
3670 <th>error mom</th>
3671 <th>rate</th>
3672 </tr>
3673 <tr>
3674 <td align="right">16</td>
3675 <td>&nbsp;</td>
3676 <td>&nbsp;</td>
3677 <td>&nbsp;</td>
3678 <td>&nbsp;</td>
3679 <td>&nbsp;</td>
3680 <td>&nbsp;</td>
3681 <td align="right">2,304</td>
3682 <td align="center">1.339e-01</td>
3683 <td>&nbsp;</td>
3684 </tr>
3685 <tr>
3686 <td align="right">64</td>
3687 <td>&nbsp;</td>
3688 <td>&nbsp;</td>
3689 <td>&nbsp;</td>
3690 <td align="right">4,096</td>
3691 <td align="center">9.037e-02</td>
3692 <td>&nbsp;</td>
3693 <td align="right">9,216</td>
3694 <td align="center">8.849e-03</td>
3695 <td>3.92</td>
3696 </tr>
3697 <tr>
3698 <td align="right">256</td>
3699 <td align="right">9,216</td>
3700 <td align="center">4.204e-02</td>
3701 <td>&nbsp;</td>
3702 <td align="right">16,384</td>
3703 <td align="center">9.143e-03</td>
3704 <td>3.31</td>
3705 <td align="right">36,864</td>
3706 <td align="center">2.501e-04</td>
3707 <td>5.14</td>
3708 </tr>
3709 <tr>
3710 <td align="right">1024</td>
3711 <td align="right">36,864</td>
3712 <td align="center">4.913e-03</td>
3713 <td>3.09</td>
3714 <td align="right">65,536</td>
3715 <td align="center">3.257e-04</td>
3716 <td>4.81</td>
3717 <td align="right">147,456</td>
3718 <td align="center">3.260e-06</td>
3719 <td>6.26</td>
3720 </tr>
3721 <tr>
3722 <td align="right">4096</td>
3723 <td align="right">147,456</td>
3724 <td align="center">7.862e-04</td>
3725 <td>2.64</td>
3726 <td align="right">262,144</td>
3727 <td align="center">1.588e-05</td>
3728 <td>4.36</td>
3729 <td align="right">589,824</td>
3730 <td align="center">2.953e-08</td>
3731 <td>6.79</td>
3732 </tr>
3733 <tr>
3734 <td align="right">16,384</td>
3735 <td align="right">589,824</td>
3736 <td align="center">1.137e-04</td>
3737 <td>2.79</td>
3738 <td align="right">1,048,576</td>
3739 <td align="center">9.400e-07</td>
3740 <td>4.08</td>
3741 <td align="right">2,359,296</td>
3742 <td align="center">4.286e-10</td>
3743 <td>6.11</td>
3744 </tr>
3745 <tr>
3746 <td align="right">65,536</td>
3747 <td align="right">2,359,296</td>
3748 <td align="center">1.476e-05</td>
3749 <td>2.95</td>
3750 <td align="right">4,194,304</td>
3751 <td align="center">5.799e-08</td>
3752 <td>4.02</td>
3753 <td align="right">9,437,184</td>
3754 <td align="center">2.789e-11</td>
3755 <td>3.94</td>
3756 </tr>
3757 <tr>
3758 <td align="right">262,144</td>
3759 <td align="right">9,437,184</td>
3760 <td align="center">2.038e-06</td>
3761 <td>2.86</td>
3762 <td align="right">16,777,216</td>
3763 <td align="center">3.609e-09</td>
3764 <td>4.01</td>
3765 <td align="right">37,748,736</td>
3766 <td align="center">5.730e-11</td>
3767 <td>-1.04</td>
3768 </tr>
3769</table>
3770
3771The tables show that we get optimal @f$\mathcal O\left(h^{p+1}\right)@f$
3772convergence rates for both numerical fluxes. The errors are slightly smaller
3773for the Lax--Friedrichs flux for @f$p=2@f$, but the picture is reversed for
3774@f$p=3@f$; in any case, the differences on this testcase are relatively
3775small.
3776
3777For @f$p=5@f$, we reach the roundoff accuracy of @f$10^{-11}@f$ with both
3778fluxes on the finest grids. Also note that the errors are absolute with a
3779domain length of @f$10^2@f$, so relative errors are below @f$10^{-12}@f$. The HLL flux
3780is somewhat better for the highest degree, which is due to a slight inaccuracy
3781of the Lax--Friedrichs flux: The Lax--Friedrichs flux sets a Dirichlet
3782condition on the solution that leaves the domain, which results in a small
3783artificial reflection, which is accentuated for the Lax--Friedrichs
3784flux. Apart from that, we see that the influence of the numerical flux is
3785minor, as the polynomial part inside elements is the main driver of the
3786accucary. The limited influence of the flux also has consequences when trying
3787to approach more challenging setups with the higher-order DG setup: Taking for
3788example the parameters and grid of @ref step_33 "step-33", we get oscillations (which in turn
3789make density negative and make the solution explode) with both fluxes once the
3790high-mass part comes near the boundary, as opposed to the low-order finite
3791volume case (@f$p=0@f$). Thus, any case that leads to shocks in the solution
3792necessitates some form of limiting or artificial dissipation. For another
3793alternative, see the @ref step_69 "step-69" tutorial program.
3794
3795
3796<a name="Resultsforflowinchannelaroundcylinderin2D"></a><h3>Results for flow in channel around cylinder in 2D</h3>
3797
3798
3799For the test case of the flow around a cylinder in a channel, we need to
3800change the first code line to
3801@code
3802 constexpr unsigned int testcase = 1;
3803@endcode
3804This test case starts with a background field of a constant velocity
3805of Mach number 0.31 and a constant initial density; the flow will have
3806to go around an obstacle in the form of a cylinder. Since we impose a
3807no-penetration condition on the cylinder walls, the flow that
3808initially impinges head-on onto to cylinder has to rearrange,
3809which creates a big sound wave. The following pictures show the pressure at
3810times 0.1, 0.25, 0.5, and 1.0 (top left to bottom right) for the 2D case with
38115 levels of global refinement, using 102,400 cells with polynomial degree of
38125 and 14.7 million degrees of freedom over all 4 solution variables.
3813We clearly see the discontinuity that
3814propagates slowly in the upstream direction and more quickly in downstream
3815direction in the first snapshot at time 0.1. At time 0.25, the sound wave has
3816reached the top and bottom walls and reflected back to the interior. From the
3817different distances of the reflected waves from lower and upper walls we can
3818see the slight asymmetry of the Sch&auml;fer-Turek test case represented by
3819GridGenerator::channel_with_cylinder() with somewhat more space above the
3820cylinder compared to below. At later times, the picture is more chaotic with
3821many sound waves all over the place.
3822
3823<table align="center" class="doxtable" style="width:85%">
3824 <tr>
3825 <td>
3826 <img src="https://www.dealii.org/images/steps/developer/step-67.pressure_010.png" alt="" width="100%">
3827 </td>
3828 <td>
3829 <img src="https://www.dealii.org/images/steps/developer/step-67.pressure_025.png" alt="" width="100%">
3830 </td>
3831 </tr>
3832 <tr>
3833 <td>
3834 <img src="https://www.dealii.org/images/steps/developer/step-67.pressure_050.png" alt="" width="100%">
3835 </td>
3836 <td>
3837 <img src="https://www.dealii.org/images/steps/developer/step-67.pressure_100.png" alt="" width="100%">
3838 </td>
3839 </tr>
3840</table>
3841
3842The next picture shows an elevation plot of the pressure at time 1.0 looking
3843from the channel inlet towards the outlet at the same resolution -- here,
3844we can see the large number
3845of reflections. In the figure, two types of waves are visible. The
3846larger-amplitude waves correspond to various reflections that happened as the
3847initial discontinuity hit the walls, whereas the small-amplitude waves of
3848size similar to the elements correspond to numerical artifacts. They have their
3849origin in the finite resolution of the scheme and appear as the discontinuity
3850travels through elements with high-order polynomials. This effect can be cured
3851by increasing resolution. Apart from this effect, the rich wave structure is
3852the result of the transport accuracy of the high-order DG method.
3853
3854<img src="https://www.dealii.org/images/steps/developer/step-67.pressure_elevated.jpg" alt="" width="40%">
3855
3856If we run the program with degree 2 and 6 levels of global refinements (410k
3857cells, 14.7M unknowns), we get the following evolution of the pressure
3858(elevation plot, colored by the value of the density):
3859
3860@htmlonly
3861<p align="center">
3862 <iframe width="560" height="315" src="https://www.youtube.com/embed/ivxCLiSdQpc"
3863 frameborder="0"
3864 allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture"
3865 allowfullscreen></iframe>
3866 </p>
3867@endhtmlonly
3868
3869
3870With 2 levels of global refinement with 1,600 cells, the mesh and its
3871partitioning on 40 MPI processes looks as follows:
3872
3873<img src="https://www.dealii.org/images/steps/developer/step-67.grid-owner.png" alt="" width="70%">
3874
3875When we run the code with 4 levels of global refinements on 40 cores, we get
3876the following output:
3877@code
3878Running with 40 MPI processes
3879Vectorization over 8 doubles = 512 bits (AVX512)
3880Number of degrees of freedom: 3,686,400 ( = 4 [vars] x 25,600 [cells] x 36 [dofs/cell/var] )
3881Time step size: 7.39876e-05, minimal h: 0.001875, initial transport scaling: 0.00110294
3882
3883Time: 0, dt: 7.4e-05, norm rho: 4.17e-16, rho * u: 1.629e-16, energy: 1.381e-15
3884Time: 0.05, dt: 6.3e-05, norm rho: 0.02075, rho * u: 0.03801, energy: 0.08772
3885Time: 0.1, dt: 5.9e-05, norm rho: 0.02211, rho * u: 0.04515, energy: 0.08953
3886Time: 0.15, dt: 5.7e-05, norm rho: 0.02261, rho * u: 0.04592, energy: 0.08967
3887Time: 0.2, dt: 5.8e-05, norm rho: 0.02058, rho * u: 0.04361, energy: 0.08222
3888Time: 0.25, dt: 5.9e-05, norm rho: 0.01695, rho * u: 0.04203, energy: 0.06873
3889Time: 0.3, dt: 5.9e-05, norm rho: 0.01653, rho * u: 0.0401, energy: 0.06604
3890Time: 0.35, dt: 5.7e-05, norm rho: 0.01774, rho * u: 0.04264, energy: 0.0706
3891
3892...
3893
3894Time: 1.95, dt: 5.8e-05, norm rho: 0.01488, rho * u: 0.03923, energy: 0.05185
3895Time: 2, dt: 5.7e-05, norm rho: 0.01432, rho * u: 0.03969, energy: 0.04889
3896
3897+-------------------------------------------+------------------+------------+------------------+
3898| Total wallclock time elapsed | 273.6s 13 | 273.6s | 273.6s 0 |
3899| | | |
3900| Section | no. calls | min time rank | avg time | max time rank |
3901+-------------------------------------------+------------------+------------+------------------+
3902| compute errors | 41 | 0.01112s 35 | 0.0672s | 0.1337s 0 |
3903| compute transport speed | 6914 | 5.422s 35 | 15.96s | 29.99s 1 |
3904| output | 41 | 37.24s 35 | 37.3s | 37.37s 0 |
3905| rk time stepping total | 34564 | 205.4s 1 | 219.5s | 230.1s 35 |
3906| rk_stage - integrals L_h | 172820 | 153.6s 1 | 164.9s | 175.6s 27 |
3907| rk_stage - inv mass + vec upd | 172820 | 47.13s 13 | 53.09s | 64.05s 33 |
3908+-------------------------------------------+------------------+------------+------------------+
3909@endcode
3910
3911The norms shown here for the various quantities are the deviations
3912@f$\rho'@f$, @f$(\rho u)'@f$, and @f$E'@f$ against the background field (namely, the
3913initial condition). The distribution of run time is overall similar as in the
3914previous test case. The only slight difference is the larger proportion of
3915time spent in @f$\mathcal L_h@f$ as compared to the inverse mass matrix and vector
3916updates. This is because the geometry is deformed and the matrix-free
3917framework needs to load additional arrays for the geometry from memory that
3918are compressed in the affine mesh case.
3919
3920Increasing the number of global refinements to 5, the output becomes:
3921@code
3922Running with 40 MPI processes
3923Vectorization over 8 doubles = 512 bits (AVX512)
3924Number of degrees of freedom: 14,745,600 ( = 4 [vars] x 102,400 [cells] x 36 [dofs/cell/var] )
3925
3926...
3927
3928+-------------------------------------------+------------------+------------+------------------+
3929| Total wallclock time elapsed | 2693s 32 | 2693s | 2693s 23 |
3930| | | |
3931| Section | no. calls | min time rank | avg time | max time rank |
3932+-------------------------------------------+------------------+------------+------------------+
3933| compute errors | 41 | 0.04537s 32 | 0.173s | 0.3489s 0 |
3934| compute transport speed | 13858 | 40.75s 32 | 85.99s | 149.8s 0 |
3935| output | 41 | 153.8s 32 | 153.9s | 154.1s 0 |
3936| rk time stepping total | 69284 | 2386s 0 | 2450s | 2496s 32 |
3937| rk_stage - integrals L_h | 346420 | 1365s 32 | 1574s | 1718s 19 |
3938| rk_stage - inv mass + vec upd | 346420 | 722.5s 10 | 870.7s | 1125s 32 |
3939+-------------------------------------------+------------------+------------+------------------+
3940@endcode
3941
3942The effect on performance is similar to the analytical test case -- in
3943theory, computation times should increase by a factor of 8, but we actually
3944see an increase by a factor of 11 for the time steps (219.5 seconds versus
39452450 seconds). This can be traced back to caches, with the small case mostly
3946fitting in caches. An interesting effect, typical of programs with a mix of
3947local communication (integrals @f$\mathcal L_h@f$) and global communication (computation of
3948transport speed) with some load imbalance, can be observed by looking at the
3949MPI ranks that encounter the minimal and maximal time of different phases,
3950respectively. Rank 0 reports the fastest throughput for the "rk time stepping
3951total" part. At the same time, it appears to be slowest for the "compute
3952transport speed" part, almost a factor of 2 slower than the
3953average and almost a factor of 4 compared to the faster rank.
3954Since the latter involves global communication, we can attribute the
3955slowness in this part to the fact that the local Runge--Kutta stages have
3956advanced more quickly on this rank and need to wait until the other processors
3957catch up. At this point, one can wonder about the reason for this imbalance:
3958The number of cells is almost the same on all MPI processes.
3959However, the matrix-free framework is faster on affine and Cartesian
3960cells located towards the outlet of the channel, to which the lower MPI ranks
3961are assigned. On the other hand, rank 32, which reports the highest run time
3962for the Runga--Kutta stages, owns the curved cells near the cylinder, for
3963which no data compression is possible. To improve throughput, we could assign
3964different weights to different cell types when partitioning the
3965parallel::distributed::Triangulation object, or even measure the run time for a
3966few time steps and try to rebalance then.
3967
3968The throughput per Runge--Kutta stage can be computed to 2085 MDoFs/s for the
396914.7 million DoFs test case over the 346,000 Runge--Kutta stages, slightly slower
3970than the Cartesian mesh throughput of 2360 MDoFs/s reported above.
3971
3972Finally, if we add one additional refinement, we record the following output:
3973@code
3974Running with 40 MPI processes
3975Vectorization over 8 doubles = 512 bits (AVX512)
3976Number of degrees of freedom: 58,982,400 ( = 4 [vars] x 409,600 [cells] x 36 [dofs/cell/var] )
3977
3978...
3979
3980Time: 1.95, dt: 1.4e-05, norm rho: 0.01488, rho * u: 0.03923, energy: 0.05183
3981Time: 2, dt: 1.4e-05, norm rho: 0.01431, rho * u: 0.03969, energy: 0.04887
3982
3983+-------------------------------------------+------------------+------------+------------------+
3984| Total wallclock time elapsed | 2.166e+04s 26 | 2.166e+04s | 2.166e+04s 24 |
3985| | | |
3986| Section | no. calls | min time rank | avg time | max time rank |
3987+-------------------------------------------+------------------+------------+------------------+
3988| compute errors | 41 | 0.1758s 30 | 0.672s | 1.376s 1 |
3989| compute transport speed | 27748 | 321.3s 34 | 678.8s | 1202s 1 |
3990| output | 41 | 616.3s 32 | 616.4s | 616.4s 34 |
3991| rk time stepping total | 138733 | 1.983e+04s 1 | 2.036e+04s | 2.072e+04s 34 |
3992| rk_stage - integrals L_h | 693665 | 1.052e+04s 32 | 1.248e+04s | 1.387e+04s 19 |
3993| rk_stage - inv mass + vec upd | 693665 | 6404s 10 | 7868s | 1.018e+04s 32 |
3994+-------------------------------------------+------------------+------------+------------------+
3995@endcode
3996
3997The "rk time stepping total" part corresponds to a throughput of 2010 MDoFs/s. The
3998overall run time to perform 139k time steps is 20k seconds (5.7 hours) or 7
3999time steps per second -- not so bad for having nearly 60 million
4000unknowns. More throughput can be achieved by adding more cores to
4001the computation.
4002
4003
4004<a name="Resultsforflowinchannelaroundcylinderin3D"></a><h3>Results for flow in channel around cylinder in 3D</h3>
4005
4006
4007Switching the channel test case to 3D with 3 global refinements, the output is
4008@code
4009Running with 40 MPI processes
4010Vectorization over 8 doubles = 512 bits (AVX512)
4011Number of degrees of freedom: 221,184,000 ( = 5 [vars] x 204,800 [cells] x 216 [dofs/cell/var] )
4012
4013...
4014
4015Time: 1.95, dt: 0.00011, norm rho: 0.01131, rho * u: 0.03056, energy: 0.04091
4016Time: 2, dt: 0.00011, norm rho: 0.0119, rho * u: 0.03142, energy: 0.04425
4017
4018+-------------------------------------------+------------------+------------+------------------+
4019| Total wallclock time elapsed | 1.734e+04s 4 | 1.734e+04s | 1.734e+04s 38 |
4020| | | |
4021| Section | no. calls | min time rank | avg time | max time rank |
4022+-------------------------------------------+------------------+------------+------------------+
4023| compute errors | 41 | 0.6551s 34 | 3.216s | 7.281s 0 |
4024| compute transport speed | 3546 | 160s 34 | 393.2s | 776.9s 0 |
4025| output | 41 | 1350s 34 | 1353s | 1357s 0 |
4026| rk time stepping total | 17723 | 1.519e+04s 0 | 1.558e+04s | 1.582e+04s 34 |
4027| rk_stage - integrals L_h | 88615 | 1.005e+04s 32 | 1.126e+04s | 1.23e+04s 11 |
4028| rk_stage - inv mass + vec upd | 88615 | 3056s 11 | 4322s | 5759s 32 |
4029+-------------------------------------------+------------------+------------+------------------+
4030@endcode
4031
4032The physics are similar to the 2D case, with a slight motion in the z
4033direction due to the gravitational force. The throughput per Runge--Kutta
4034stage in this case is
4035@f[
4036\text{throughput} = \frac{n_\mathrm{time steps} n_\mathrm{stages}
4037n_\mathrm{dofs}}{t_\mathrm{compute}} =
4038\frac{17723 \cdot 5 \cdot 221.2\,\text{M}}{15580s} = 1258\, \text{MDoFs/s}.
4039@f]
4040
4041The throughput is lower than in 2D because the computation of the @f$\mathcal L_h@f$ term
4042is more expensive. This is due to over-integration with `degree+2` points and
4043the larger fraction of face integrals (worse volume-to-surface ratio) with
4044more expensive flux computations. If we only consider the inverse mass matrix
4045and vector update part, we record a throughput of 4857 MDoFs/s for the 2D case
4046of the isentropic vortex with 37.7 million unknowns, whereas the 3D case
4047runs with 4535 MDoFs/s. The performance is similar because both cases are in
4048fact limited by the memory bandwidth.
4049
4050If we go to four levels of global refinement, we need to increase the number
4051of processes to fit everything in memory -- the computation needs around 350
4052GB of RAM memory in this case. Also, the time it takes to complete 35k time
4053steps becomes more tolerable by adding additional resources. We therefore use
40546 nodes with 40 cores each, resulting in a computation with 240 MPI processes:
4055@code
4056Running with 240 MPI processes
4057Vectorization over 8 doubles = 512 bits (AVX512)
4058Number of degrees of freedom: 1,769,472,000 ( = 5 [vars] x 1,638,400 [cells] x 216 [dofs/cell/var] )
4059
4060...
4061
4062Time: 1.95, dt: 5.6e-05, norm rho: 0.01129, rho * u: 0.0306, energy: 0.04086
4063Time: 2, dt: 5.6e-05, norm rho: 0.01189, rho * u: 0.03145, energy: 0.04417
4064
4065+-------------------------------------------+------------------+------------+------------------+
4066| Total wallclock time elapsed | 5.396e+04s 151 | 5.396e+04s | 5.396e+04s 0 |
4067| | | |
4068| Section | no. calls | min time rank | avg time | max time rank |
4069+-------------------------------------------+------------------+------------+------------------+
4070| compute errors | 41 | 2.632s 178 | 7.221s | 16.56s 0 |
4071| compute transport speed | 7072 | 714s 193 | 1553s | 3351s 0 |
4072| output | 41 | 8065s 176 | 8070s | 8079s 0 |
4073| rk time stepping total | 35350 | 4.25e+04s 0 | 4.43e+04s | 4.515e+04s 193 |
4074| rk_stage - integrals L_h | 176750 | 2.936e+04s 134 | 3.222e+04s | 3.67e+04s 99 |
4075| rk_stage - inv mass + vec upd | 176750 | 7004s 99 | 1.207e+04s | 1.55e+04s 132 |
4076+-------------------------------------------+------------------+------------+------------------+
4077@endcode
4078This simulation had nearly 2 billion unknowns -- quite a large
4079computation indeed, and still only needed around 1.5 seconds per time
4080step.
4081
4082
4083<a name="Possibilitiesforextensions"></a><h3>Possibilities for extensions</h3>
4084
4085
4086The code presented here straight-forwardly extends to adaptive meshes, given
4087appropriate indicators for setting the refinement flags. Large-scale
4088adaptivity of a similar solver in the context of the acoustic wave equation
4089has been achieved by the <a href="https://github.com/kronbichler/exwave">exwave
4090project</a>. However, in the present context, the benefits of adaptivity are often
4091limited to early times and effects close to the origin of sound waves, as the
4092waves eventually reflect and diffract. This leads to steep gradients all over
4093the place, similar to turbulent flow, and a more or less globally
4094refined mesh.
4095
4096Another topic that we did not discuss in the results section is a comparison
4097of different time integration schemes. The program provides four variants of
4098low-storage Runga--Kutta integrators that each have slightly different
4099accuracy and stability behavior. Among the schemes implemented here, the
4100higher-order ones provide additional accuracy but come with slightly lower
4101efficiency in terms of step size per stage before they violate the CFL
4102condition. An interesting extension would be to compare the low-storage
4103variants proposed here with standard Runge--Kutta integrators or to use vector
4104operations that are run separate from the mass matrix operation and compare
4105performance.
4106
4107
4108<a name="Moreadvancednumericalfluxfunctionsandskewsymmetricformulations"></a><h4>More advanced numerical flux functions and skew-symmetric formulations</h4>
4109
4110
4111As mentioned in the introduction, the modified Lax--Friedrichs flux and the
4112HLL flux employed in this program are only two variants of a large body of
4113numerical fluxes available in the literature on the Euler equations. One
4114example is the HLLC flux (Harten-Lax-van Leer-Contact) flux which adds the
4115effect of rarefaction waves missing in the HLL flux, or the Roe flux. As
4116mentioned in the introduction, the effect of numerical fluxes on high-order DG
4117schemes is debatable (unlike for the case of low-order discretizations).
4118
4119A related improvement to increase the stability of the solver is to also
4120consider the spatial integral terms. A shortcoming in the rather naive
4121implementation used above is the fact that the energy conservation of the
4122original Euler equations (in the absence of shocks) only holds up to a
4123discretization error. If the solution is under-resolved, the discretization
4124error can give rise to an increase in the numerical energy and eventually
4125render the discretization unstable. This is because of the inexact numerical
4126integration of the terms in the Euler equations, which both contain rational
4127nonlinearities and higher-degree content from curved cells. A way out of this
4128dilemma are so-called skew-symmetric formulations, see @cite Gassner2013 for a
4129simple variant. Skew symmetry means that switching the role of the solution
4130@f$\mathbf{w}@f$ and test functions @f$\mathbf{v}@f$ in the weak form produces the
4131exact negative of the original quantity, apart from some boundary terms. In
4132the discrete setting, the challenge is to keep this skew symmetry also when
4133the integrals are only computed approximately (in the continuous case,
4134skew-symmetry is a consequence of integration by parts). Skew-symmetric
4135numerical schemes balance spatial derivatives in the conservative form
4136@f$(\nabla \mathbf v, \mathbf{F}(\mathbf w))_{K}@f$ with contributions in the
4137convective form @f$(\mathbf v, \tilde{\mathbf{F}}(\mathbf w)\nabla
4138\mathbf{w})_{K}@f$ for some @f$\tilde{\mathbf{F}}@f$. The precise terms depend on
4139the equation and the integration formula, and can in some cases by understood
4140by special skew-symmetric finite difference schemes.
4141
4142To get started, interested readers could take a look at
4143https://github.com/kronbichler/advection_miniapp, where a
4144skew-symmetric DG formulation is implemented with deal.II for a simple advection
4145equation.
4146
4147<a name="Equippingthecodeforsupersoniccalculations"></a><h4>Equipping the code for supersonic calculations</h4>
4148
4149
4150As mentioned in the introduction, the solution to the Euler equations develops
4151shocks as the Mach number increases, which require additional mechanisms to
4152stabilize the scheme, e.g. in the form of limiters. The main challenge besides
4153actually implementing the limiter or artificial viscosity approach would be to
4154load-balance the computations, as the additional computations involved for
4155limiting the oscillations in troubled cells would make them more expensive than the
4156plain DG cells without limiting. Furthermore, additional numerical fluxes that
4157better cope with the discontinuities would also be an option.
4158
4159One ingredient also necessary for supersonic flows are appropriate boundary
4160conditions. As opposed to the subsonic outflow boundaries discussed in the
4161introduction and implemented in the program, all characteristics are outgoing
4162for supersonic outflow boundaries, so we do not want to prescribe any external
4163data,
4164@f[
4165\mathbf{w}^+ = \mathbf{w}^- = \begin{pmatrix} \rho^-\\
4166(\rho \mathbf u)^- \\ E^-\end{pmatrix} \quad
4167 \text{(Neumann)}.
4168@f]
4169
4170In the code, we would simply add the additional statement
4171@code
4172 else if (supersonic_outflow_boundaries.find(boundary_id) !=
4173 supersonic_outflow_boundaries.end())
4174 {
4175 w_p = w_m;
4176 at_outflow = true;
4177 }
4178@endcode
4179in the `local_apply_boundary_face()` function.
4180
4181<a name="ExtensiontothelinearizedEulerequations"></a><h4>Extension to the linearized Euler equations</h4>
4182
4183
4184When the interest with an Euler solution is mostly in the propagation of sound
4185waves, it often makes sense to linearize the Euler equations around a
4186background state, i.e., a given density, velocity and energy (or pressure)
4187field, and only compute the change against these fields. This is the setting
4188of the wide field of aeroacoustics. Even though the resolution requirements
4189are sometimes considerably reduced, implementation gets somewhat more
4190complicated as the linearization gives rise to additional terms. From a code
4191perspective, in the operator evaluation we also need to equip the code with
4192the state to linearize against. This information can be provided either by
4193analytical functions (that are evaluated in terms of the position of the
4194quadrature points) or by a vector similar to the solution. Based on that
4195vector, we would create an additional FEEvaluation object to read from it and
4196provide the values of the field at quadrature points. If the background
4197velocity is zero and the density is constant, the linearized Euler equations
4198further simplify and can equivalently be written in the form of the
4199acoustic wave equation.
4200
4201A challenge in the context of sound propagation is often the definition of
4202boundary conditions, as the computational domain needs to be of finite size,
4203whereas the actual simulation often spans an infinite (or at least much
4204larger) physical domain. Conventional Dirichlet or Neumann boundary conditions
4205give rise to reflections of the sound waves that eventually propagate back to
4206the region of interest and spoil the solution. Therefore, various variants of
4207non-reflecting boundary conditions or sponge layers, often in the form of
4208<a
4209href="https://en.wikipedia.org/wiki/Perfectly_matched_layer">perfectly
4210matched layers</a> -- where the solution is damped without reflection
4211-- are common.
4212
4213
4214<a name="ExtensiontothecompressibleNavierStokesequations"></a><h4>Extension to the compressible Navier-Stokes equations</h4>
4215
4216
4217The solver presented in this tutorial program can also be extended to the
4218compressible Navier--Stokes equations by adding viscous terms, as described in
4219@cite FehnWallKronbichler2019. To keep as much of the performance obtained
4220here despite the additional cost of elliptic terms, e.g. via an interior
4221penalty method, one can switch the basis from FE_DGQ to FE_DGQHermite like in
4222the @ref step_59 "step-59" tutorial program.
4223
4224
4225<a name="Usingcellcentricloopsandsharedmemory"></a><h4>Using cell-centric loops and shared memory</h4>
4226
4227
4228In this tutorial, we used face-centric loops. Here, cell and face integrals
4229are treated in separate loops, resulting in multiple writing accesses into the
4230result vector, which is relatively expensive on modern hardware since writing
4231operations generally result also in an implicit read operation. Element-centric
4232loops, on the other hand, are processing a cell and in direct succession
4233processing all its 2d faces. Although this kind of loop implies that fluxes have
4234to be computed twice (for each side of an interior face), the fact that the
4235result vector has to accessed only once might - and the fact that the resulting
4236algorithm is free of race-conditions and as such perfectly suitable for
4237shared memory - already give a performance boost. If you are interested in these
4238advanced topics, you can take a look at @ref step_76 "step-76" where we take the present
4239tutorial and modify it so that we can use these features.
4240 *
4241 *
4242<a name="PlainProg"></a>
4243<h1> The plain program</h1>
4244@include "step-67.cc"
4245*/
void attach_dof_handler(const DoFHandler< dim, spacedim > &)
void add_data_vector(const VectorType &data, const std::vector< std::string > &names, const DataVectorType type=type_automatic, const std::vector< DataComponentInterpretation::DataComponentInterpretation > &data_component_interpretation={})
virtual void build_patches(const unsigned int n_subdivisions=0)
Definition: data_out.cc:1064
virtual UpdateFlags get_needed_update_flags() const =0
virtual void evaluate_vector_field(const DataPostprocessorInputs::Vector< dim > &input_data, std::vector< Vector< double > > &computed_quantities) const
virtual std::vector< std::string > get_names() const =0
virtual std::vector< DataComponentInterpretation::DataComponentInterpretation > get_data_component_interpretation() const
void distribute_dofs(const FiniteElement< dim, spacedim > &fe)
types::global_dof_index n_dofs() const
void set_dof_values(VectorType &dst, const unsigned int first_index=0, const std::bitset< VectorizedArrayType::size()> &mask=std::bitset< VectorizedArrayType::size()>().flip()) const
value_type get_value(const unsigned int q_point) const
Tensor< 2, dim, VectorizedArrayType > inverse_jacobian(const unsigned int q_point) const
const unsigned int n_q_points
void gather_evaluate(const VectorType &input_vector, const EvaluationFlags::EvaluationFlags evaluation_flag)
void evaluate(const EvaluationFlags::EvaluationFlags evaluation_flag)
virtual RangeNumberType value(const Point< dim > &p, const unsigned int component=0) const
unsigned int depth_console(const unsigned int n)
Definition: logstream.cc:350
Abstract base class for mapping classes.
Definition: mapping.h:311
void transform_from_q_points_to_basis(const unsigned int n_actual_components, const VectorizedArrayType *in_array, VectorizedArrayType *out_array) const
Definition: operators.h:1125
void loop(const std::function< void(const MatrixFree< dim, Number, VectorizedArrayType > &, OutVector &, const InVector &, const std::pair< unsigned int, unsigned int > &)> &cell_operation, const std::function< void(const MatrixFree< dim, Number, VectorizedArrayType > &, OutVector &, const InVector &, const std::pair< unsigned int, unsigned int > &)> &face_operation, const std::function< void(const MatrixFree< dim, Number, VectorizedArrayType > &, OutVector &, const InVector &, const std::pair< unsigned int, unsigned int > &)> &boundary_operation, OutVector &dst, const InVector &src, const bool zero_dst_vector=false, const DataAccessOnFaces dst_vector_face_access=DataAccessOnFaces::unspecified, const DataAccessOnFaces src_vector_face_access=DataAccessOnFaces::unspecified) const
unsigned int n_active_entries_per_cell_batch(const unsigned int cell_batch_index) const
void cell_loop(const std::function< void(const MatrixFree< dim, Number, VectorizedArrayType > &, OutVector &, const InVector &, const std::pair< unsigned int, unsigned int > &)> &cell_operation, OutVector &dst, const InVector &src, const bool zero_dst_vector=false) const
void reinit(const MappingType &mapping, const DoFHandler< dim > &dof_handler, const AffineConstraints< number2 > &constraint, const QuadratureType &quad, const AdditionalData &additional_data=AdditionalData())
Definition: point.h:111
Definition: tensor.h:503
void print_wall_time_statistics(const MPI_Comm &mpi_comm, const double print_quantile=0.) const
Definition: timer.cc:842
Definition: vector.h:109
#define DEAL_II_ALWAYS_INLINE
Definition: config.h:102
#define DEAL_II_OPENMP_SIMD_PRAGMA
Definition: config.h:142
Point< 3 > vertices[4]
DerivativeForm< 1, spacedim, dim, Number > transpose(const DerivativeForm< 1, dim, spacedim, Number > &DF)
UpdateFlags
@ update_values
Shape function values.
@ update_normal_vectors
Normal vectors.
@ update_JxW_values
Transformed quadrature weights.
@ update_gradients
Shape function gradients.
@ update_quadrature_points
Transformed quadrature points.
Point< 2 > second
Definition: grid_out.cc:4604
Point< 2 > first
Definition: grid_out.cc:4603
unsigned int level
Definition: grid_out.cc:4606
__global__ void set(Number *val, const Number s, const size_type N)
void write_vtu_in_parallel(const std::string &filename, const MPI_Comm &comm) const
#define Assert(cond, exc)
Definition: exceptions.h:1473
#define AssertDimension(dim1, dim2)
Definition: exceptions.h:1667
void set_flags(const FlagType &flags)
#define AssertThrow(cond, exc)
Definition: exceptions.h:1583
void loop(ITERATOR begin, typename identity< ITERATOR >::type end, DOFINFO &dinfo, INFOBOX &info, const std::function< void(DOFINFO &, typename INFOBOX::CellInfo &)> &cell_worker, const std::function< void(DOFINFO &, typename INFOBOX::CellInfo &)> &boundary_worker, const std::function< void(DOFINFO &, DOFINFO &, typename INFOBOX::CellInfo &, typename INFOBOX::CellInfo &)> &face_worker, ASSEMBLER &assembler, const LoopControl &lctrl=LoopControl())
Definition: loop.h:439
virtual void sadd(const Number s, const Number a, const VectorSpaceVector< Number > &V) override
void reinit(const size_type size, const bool omit_zeroing_entries=false)
LogStream deallog
Definition: logstream.cc:37
const Event initial
Definition: event.cc:65
void approximate(SynchronousIterators< std::tuple< typename DoFHandler< dim, spacedim >::active_cell_iterator, Vector< float >::iterator > > const &cell, const Mapping< dim, spacedim > &mapping, const DoFHandler< dim, spacedim > &dof_handler, const InputVector &solution, const unsigned int component)
void hyper_rectangle(Triangulation< dim, spacedim > &tria, const Point< dim > &p1, const Point< dim > &p2, const bool colorize=false)
void cylinder(Triangulation< dim > &tria, const double radius=1., const double half_length=1.)
void channel_with_cylinder(Triangulation< dim > &tria, const double shell_region_width=0.03, const unsigned int n_shells=2, const double skewness=2.0, const bool colorize=false)
void scale(const double scaling_factor, Triangulation< dim, spacedim > &triangulation)
Definition: grid_tools.cc:2084
double volume(const Triangulation< dim, spacedim > &tria, const Mapping< dim, spacedim > &mapping=(ReferenceCells::get_hypercube< dim >() .template get_default_linear_mapping< dim, spacedim >()))
Definition: grid_tools.cc:139
@ valid
Iterator points to a valid object.
@ matrix
Contents is actually a matrix.
@ diagonal
Matrix is diagonal.
@ general
No special properties.
double norm(const FEValuesBase< dim > &fe, const ArrayView< const std::vector< Tensor< 1, dim > > > &Du)
Definition: divergence.h:472
Point< spacedim > point(const gp_Pnt &p, const double tolerance=1e-10)
Definition: utilities.cc:190
SymmetricTensor< 2, dim, Number > E(const Tensor< 2, dim, Number > &F)
Tensor< 2, dim, Number > w(const Tensor< 2, dim, Number > &F, const Tensor< 2, dim, Number > &dF_dt)
SymmetricTensor< 2, dim, Number > e(const Tensor< 2, dim, Number > &F)
SymmetricTensor< 2, dim, Number > d(const Tensor< 2, dim, Number > &F, const Tensor< 2, dim, Number > &dF_dt)
Tensor< 2, dim, Number > F(const Tensor< 2, dim, Number > &Grad_u)
void call(const std::function< RT()> &function, internal::return_value< RT > &ret_val)
@ LOW_STORAGE_RK_STAGE9_ORDER5
@ LOW_STORAGE_RK_STAGE3_ORDER3
Definition: time_stepping.h:86
@ LOW_STORAGE_RK_STAGE7_ORDER4
Definition: time_stepping.h:96
@ LOW_STORAGE_RK_STAGE5_ORDER4
Definition: time_stepping.h:91
VectorType::value_type * end(VectorType &V)
void free(T *&pointer)
Definition: cuda.h:97
unsigned int this_mpi_process(const MPI_Comm &mpi_communicator)
Definition: mpi.cc:151
T min(const T &t, const MPI_Comm &mpi_communicator)
T sum(const T &t, const MPI_Comm &mpi_communicator)
unsigned int n_mpi_processes(const MPI_Comm &mpi_communicator)
Definition: mpi.cc:140
T max(const T &t, const MPI_Comm &mpi_communicator)
std::string get_time()
Definition: utilities.cc:1016
const std::string get_current_vectorization_level()
Definition: utilities.cc:941
constexpr T pow(const T base, const int iexp)
Definition: utilities.h:462
Number truncate_to_n_digits(const Number number, const unsigned int n_digits)
Definition: utilities.cc:581
std::string int_to_string(const unsigned int value, const unsigned int digits=numbers::invalid_unsigned_int)
Definition: utilities.cc:473
void integrate_difference(const Mapping< dim, spacedim > &mapping, const DoFHandler< dim, spacedim > &dof, const InVector &fe_function, const Function< spacedim, typename InVector::value_type > &exact_solution, OutVector &difference, const Quadrature< dim > &q, const NormType &norm, const Function< spacedim, double > *weight=nullptr, const double exponent=2.)
void project(const Mapping< dim, spacedim > &mapping, const DoFHandler< dim, spacedim > &dof, const AffineConstraints< typename VectorType::value_type > &constraints, const Quadrature< dim > &quadrature, const Function< spacedim, typename VectorType::value_type > &function, VectorType &vec, const bool enforce_zero_boundary=false, const Quadrature< dim - 1 > &q_boundary=(dim > 1 ? QGauss< dim - 1 >(2) :Quadrature< dim - 1 >(0)), const bool project_to_boundary_first=false)
void run(const Iterator &begin, const typename identity< Iterator >::type &end, Worker worker, Copier copier, const ScratchData &sample_scratch_data, const CopyData &sample_copy_data, const unsigned int queue_length, const unsigned int chunk_size)
Definition: work_stream.h:474
bool check(const ConstraintKinds kind_in, const unsigned int dim)
long double gamma(const unsigned int n)
constexpr TableIndices< 2 > merge(const TableIndices< 2 > &previous_indices, const unsigned int new_index, const unsigned int position)
unsigned int n_cells(const internal::TriangulationImplementation::NumberCache< 1 > &c)
Definition: tria.cc:13734
int(&) functions(const void *v1, const void *v2)
void reinit(MatrixBlock< MatrixType > &v, const BlockSparsityPattern &p)
Definition: matrix_block.h:618
static constexpr double PI
Definition: numbers.h:233
STL namespace.
::VectorizedArray< Number, width > exp(const ::VectorizedArray< Number, width > &)
::VectorizedArray< Number, width > min(const ::VectorizedArray< Number, width > &, const ::VectorizedArray< Number, width > &)
::VectorizedArray< Number, width > max(const ::VectorizedArray< Number, width > &, const ::VectorizedArray< Number, width > &)
::VectorizedArray< Number, width > sqrt(const ::VectorizedArray< Number, width > &)
::VectorizedArray< Number, width > pow(const ::VectorizedArray< Number, width > &, const Number p)
::VectorizedArray< Number, width > abs(const ::VectorizedArray< Number, width > &)
Definition: types.h:32
unsigned int boundary_id
Definition: types.h:129
const ::parallel::distributed::Triangulation< dim, spacedim > * triangulation
****code ** MPI_Finalize()
TasksParallelScheme tasks_parallel_scheme
Definition: matrix_free.h:343
UpdateFlags mapping_update_flags_inner_faces
Definition: matrix_free.h:407
UpdateFlags mapping_update_flags_boundary_faces
Definition: matrix_free.h:387
UpdateFlags mapping_update_flags
Definition: matrix_free.h:367
constexpr Number determinant(const SymmetricTensor< 2, dim, Number > &)
std::array< Number, 1 > eigenvalues(const SymmetricTensor< 2, 1, Number > &T)
const TriangulationDescription::Settings settings