// SPDX-FileCopyrightText: Copyright (c) 2017-2024, HONEE contributors. // SPDX-License-Identifier: Apache-2.0 OR BSD-2-Clause /// @file /// Structs and helper functions for data-driven subgrid-stress modeling /// See 'Invariant data-driven subgrid stress modeling in the strain-rate eigenframe for large eddy simulation' 2022 and 'S-frame discrepancy /// correction models for data-informed Reynolds stress closure' 2022 #pragma once #include #include "newtonian_state.h" #include "newtonian_types.h" #include "utils.h" #include "utils_eigensolver_jacobi.h" // @brief Calculate Frobenius norm of velocity gradient from eigenframe quantities CEED_QFUNCTION_HELPER CeedScalar VelocityGradientMagnitude(const CeedScalar strain_sframe[3], const CeedScalar vorticity_sframe[3]) { return sqrt(Dot3(strain_sframe, strain_sframe) + 0.5 * Dot3(vorticity_sframe, vorticity_sframe)); }; // @brief Change the order of basis vectors so that they align with vector and obey right-hand rule // @details The e_1 and e_3 basis vectors are the closest aligned to the vector. The e_2 is set via e_3 x e_1 // The basis vectors are assumed to form the rows of the basis matrix. CEED_QFUNCTION_HELPER void OrientBasisWithVector(CeedScalar basis[3][3], const CeedScalar vector[3]) { CeedScalar alignment[3] = {0.}, cross[3]; MatVec3(basis, vector, CEED_NOTRANSPOSE, alignment); if (alignment[0] < 0) ScaleN(basis[0], -1, 3); if (alignment[2] < 0) ScaleN(basis[2], -1, 3); Cross3(basis[2], basis[0], cross); CeedScalar basis_1_orientation = Dot3(cross, basis[1]); if (basis_1_orientation < 0) ScaleN(basis[1], -1, 3); } // @brief Denormalize outputs using min-max (de-)normalization CEED_QFUNCTION_HELPER void DenormalizeDDOutputs(CeedScalar output[6], const CeedScalar new_bounds[6][2], const CeedScalar old_bounds[6][2]) { CeedScalar bounds_ratio; for (int i = 0; i < 6; i++) { bounds_ratio = (new_bounds[i][1] - new_bounds[i][0]) / (old_bounds[i][1] - old_bounds[i][0]); output[i] = bounds_ratio * (output[i] - old_bounds[i][1]) + new_bounds[i][1]; } } /** * @brief Compute model inputs for anisotropic data-driven model * * @param[in] grad_velo_aniso Gradient of velocity in physical (anisotropic) coordinates * @param[in] km_A_ij Anisotropy tensor, in Kelvin-Mandel notation * @param[in] delta Length used to create anisotropy tensor * @param[in] viscosity Kinematic viscosity * @param[out] eigenvectors Eigenvectors of the (anisotropic) velocity gradient * @param[out] inputs Data-driven model inputs * @param[out] grad_velo_magnitude Frobenius norm of the velocity gradient */ CEED_QFUNCTION_HELPER void ComputeSgsDDInputs(const CeedScalar grad_velo_aniso[3][3], const CeedScalar km_A_ij[6], const CeedScalar delta, const CeedScalar viscosity, CeedScalar eigenvectors[3][3], CeedScalar inputs[6], CeedScalar *grad_velo_magnitude) { CeedScalar strain_sframe[3] = {0.}, vorticity_sframe[3] = {0.}; CeedScalar A_ij[3][3] = {{0.}}, grad_velo_iso[3][3] = {{0.}}; // -- Transform physical, anisotropic velocity gradient to isotropic KMUnpack(km_A_ij, A_ij); MatMat3(grad_velo_aniso, A_ij, CEED_NOTRANSPOSE, CEED_NOTRANSPOSE, grad_velo_iso); { // -- Get Eigenframe CeedScalar kmstrain_iso[6], strain_iso[3][3]; CeedInt work_vector[3] = {0}; KMStrainRate(grad_velo_iso, kmstrain_iso); KMUnpack(kmstrain_iso, strain_iso); Diagonalize3(strain_iso, strain_sframe, eigenvectors, work_vector, SORT_DECREASING_EVALS, true, 5); } { // -- Get vorticity in S-frame CeedScalar rotation_iso[3][3]; RotationRate(grad_velo_iso, rotation_iso); CeedScalar vorticity_iso[3] = {-2 * rotation_iso[1][2], 2 * rotation_iso[0][2], -2 * rotation_iso[0][1]}; OrientBasisWithVector(eigenvectors, vorticity_iso); MatVec3(eigenvectors, vorticity_iso, CEED_NOTRANSPOSE, vorticity_sframe); } // -- Calculate DD model inputs *grad_velo_magnitude = VelocityGradientMagnitude(strain_sframe, vorticity_sframe); inputs[0] = strain_sframe[0]; inputs[1] = strain_sframe[1]; inputs[2] = strain_sframe[2]; inputs[3] = vorticity_sframe[0]; inputs[4] = vorticity_sframe[1]; inputs[5] = viscosity / Square(delta); ScaleN(inputs, 1 / (*grad_velo_magnitude + CEED_EPSILON), 6); } /** * @brief Compute the physical SGS stresses from the neural-network output * * @param[in,out] outputs Outputs from the neural-network * @param[in] delta Length used to create anisotropy tensor * @param[in] eigenvectors Eigenvectors of the (anisotropic) velocity gradient * @param[in] new_bounds Bounds used for min-max de-normalization * @param[in] grad_velo_magnitude Magnitude of the velocity gradient * @param[out] kmsgs_stress Physical SGS stresses in Kelvin-Mandel notation */ CEED_QFUNCTION_HELPER void ComputeSgsDDOutputs(CeedScalar outputs[6], const CeedScalar delta, const CeedScalar eigenvectors[3][3], const CeedScalar new_bounds[6][2], const CeedScalar grad_velo_magnitude, CeedScalar kmsgs_stress[6]) { CeedScalar old_bounds[6][2] = {{0}}; for (int j = 0; j < 6; j++) old_bounds[j][1] = 1; DenormalizeDDOutputs(outputs, new_bounds, old_bounds); // Re-dimensionalize sgs_stress ScaleN(outputs, Square(delta) * Square(grad_velo_magnitude), 6); CeedScalar sgs_stress[3][3] = {{0.}}; { // Rotate SGS Stress back to physical frame, SGS_physical = E^T SGS_sframe E CeedScalar Evec_sgs[3][3] = {{0.}}; const CeedScalar sgs_sframe[3][3] = { {outputs[0], outputs[3], outputs[4]}, {outputs[3], outputs[1], outputs[5]}, {outputs[4], outputs[5], outputs[2]}, }; MatMat3(eigenvectors, sgs_sframe, CEED_TRANSPOSE, CEED_NOTRANSPOSE, Evec_sgs); MatMat3(Evec_sgs, eigenvectors, CEED_NOTRANSPOSE, CEED_NOTRANSPOSE, sgs_stress); } KMPack(sgs_stress, kmsgs_stress); }