KOKO\Mimi
499 lines
21 KiB
C++
499 lines
21 KiB
C++
#include "fesa/elements/thermal_shell.h"
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#include <gtest/gtest.h>
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#include <algorithm>
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#include <array>
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#include <cmath>
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#include <limits>
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#include <numeric>
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namespace {
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using fesa::Matrix;
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using fesa::Vector3;
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using Patch = std::array<Vector3, 4>;
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const Patch kSquare{{{0, 0, 0}, {1, 0, 0}, {1, 1, 0}, {0, 1, 0}}};
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double Gamma(int operations) {
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const double product = operations * std::ldexp(1., -53);
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return product / (1 - product);
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}
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double Norm(const Matrix& matrix) {
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double norm = 0;
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for (std::size_t i = 0; i < matrix.Rows(); ++i)
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for (std::size_t j = 0; j < matrix.Columns(); ++j)
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norm = std::hypot(norm, matrix(i, j));
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return norm;
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}
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double Difference(const Matrix& a, const Matrix& b) {
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double norm = 0;
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for (std::size_t i = 0; i < a.Rows(); ++i)
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for (std::size_t j = 0; j < a.Columns(); ++j)
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norm = std::hypot(norm, a(i, j) - b(i, j));
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return norm;
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}
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fesa::Result<fesa::ThermalShell> Kernel(const Patch& patch = kSquare,
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double h = 2, double k = 1) {
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return fesa::ThermalShell::Create({"patch", 19, "0019"}, {{7, 2, 9, 4}},
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patch, k, 1, 1, h, {"kernel.inp", 42});
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}
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struct Oracle {
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Matrix c{20, 20};
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Matrix k{20, 20};
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Matrix pc{20, 20};
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Matrix pk{20, 20};
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};
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// Independently integrated exact rational unit-square matrices, h variable.
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// The signed products are kept separate from their absolute contribution sums.
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Oracle Rational(double h) {
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constexpr int mass[4][4] = {
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{4, 2, 1, 2}, {2, 4, 2, 1}, {1, 2, 4, 2}, {2, 1, 2, 4}};
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constexpr int stiffness[4][4] = {
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{4, -1, -2, -1}, {-1, 4, -1, -2}, {-2, -1, 4, -1}, {-1, -2, -1, 4}};
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constexpr int z0[5][5] = {{4, 2, -1, 0, 0},
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{2, 16, 2, 0, 0},
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{-1, 2, 8, 2, -1},
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{0, 0, 2, 16, 2},
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{0, 0, -1, 2, 4}};
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constexpr int z1[5][5] = {{7, -8, 1, 0, 0},
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{-8, 16, -8, 0, 0},
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{1, -8, 14, -8, 1},
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{0, 0, -8, 16, -8},
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{0, 0, 1, -8, 7}};
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Oracle result;
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for (std::size_t p = 0; p < 20; ++p)
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for (std::size_t q = 0; q < 20; ++q) {
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const auto i = p / 5, j = q / 5, a = p % 5, b = q % 5;
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result.c(p, q) = h * mass[i][j] * z0[a][b] / 2160.;
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const double tangential = h * stiffness[i][j] * z0[a][b] / 360.;
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const double normal = mass[i][j] * z1[a][b] / (54. * h);
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result.k(p, q) = tangential + normal;
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result.pc(p, q) = std::abs(result.c(p, q));
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const double sx[4] = {-1, 1, 1, -1}, sy[4] = {-1, -1, 1, 1};
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const double ps = (sx[i] == sx[j] ? 1. / 3 : 1. / 6) +
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(sy[i] == sy[j] ? 1. / 3 : 1. / 6);
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result.pk(p, q) = h * ps * std::abs(z0[a][b]) / 60 + std::abs(normal);
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}
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return result;
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}
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// Independent 54-point B^T B/H^T H oracle: no exact-Z or production helper.
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Oracle Direct54(const Patch& coordinates, double h) {
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Oracle result;
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const double g = std::sqrt(.6);
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const std::array<double, 3> gauss{{-g, 0, g}},
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weight{{5. / 9, 8. / 9, 5. / 9}};
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constexpr double sx[4] = {-1, 1, 1, -1}, sy[4] = {-1, -1, 1, 1};
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for (std::size_t ix = 0; ix < 3; ++ix)
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for (std::size_t iy = 0; iy < 3; ++iy) {
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std::array<double, 4> n{}, dx{}, dy{};
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Vector3 a1, a2;
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for (std::size_t i = 0; i < 4; ++i) {
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n[i] = (1 + sx[i] * gauss[ix]) * (1 + sy[i] * gauss[iy]) / 4;
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dx[i] = sx[i] * (1 + sy[i] * gauss[iy]) / 4;
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dy[i] = sy[i] * (1 + sx[i] * gauss[ix]) / 4;
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a1 = a1 + dx[i] * coordinates[i];
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a2 = a2 + dy[i] * coordinates[i];
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}
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const double aa = a1.Dot(a1), bb = a2.Dot(a2), ab = a1.Dot(a2);
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const double det = aa * bb - ab * ab;
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const Vector3 normal = a1.Cross(a2) / std::sqrt(det);
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const Vector3 reciprocal1 = (bb * a1 - ab * a2) / det;
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const Vector3 reciprocal2 = (aa * a2 - ab * a1) / det;
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for (std::size_t segment = 0; segment < 2; ++segment)
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for (std::size_t iz = 0; iz < 3; ++iz) {
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const double s = gauss[iz];
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const std::array<double, 3> l{
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{s * (s - 1) / 2, 1 - s * s, s * (s + 1) / 2}};
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const std::array<double, 3> dl{{2 * s - 1, -4 * s, 2 * s + 1}};
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std::array<double, 20> shape{};
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std::array<Vector3, 20> gradient{};
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const double w =
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weight[ix] * weight[iy] * weight[iz] * std::sqrt(det) * h / 4;
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for (std::size_t i = 0; i < 4; ++i)
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for (std::size_t a = 0; a < 3; ++a) {
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const auto p = 5 * i + 2 * segment + a;
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shape[p] = n[i] * l[a];
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gradient[p] = l[a] * (dx[i] * reciprocal1 + dy[i] * reciprocal2) +
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(2 * n[i] * dl[a] / h) * normal;
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}
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for (std::size_t p = 0; p < 20; ++p)
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for (std::size_t q = 0; q < 20; ++q) {
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const double c = shape[p] * shape[q] * w;
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const double k = gradient[p].Dot(gradient[q]) * w;
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result.c(p, q) += c;
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result.k(p, q) += k;
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result.pc(p, q) += std::abs(c);
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// Component products, not absolute signed dot products.
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for (std::size_t axis = 0; axis < 3; ++axis)
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result.pk(p, q) +=
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std::abs(gradient[p][axis] * gradient[q][axis] * w);
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}
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}
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}
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return result;
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}
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// Direct recovery operator and absolute propagation scale, independent of the
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// production cache. Rows preserve the two one-sided center derivatives.
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struct FluxOracle {
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std::array<Vector3, 24> value{};
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std::array<double, 24> scale{};
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};
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FluxOracle Flux(const Patch& patch, double h, const std::array<double, 20>& t) {
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FluxOracle result;
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const double g = 1 / std::sqrt(3.);
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const double sx[4] = {-1, 1, 1, -1}, sy[4] = {-1, -1, 1, 1};
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for (std::size_t ip = 0; ip < 4; ++ip) {
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std::array<double, 4> n{}, dx{}, dy{};
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Vector3 a1, a2;
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for (std::size_t i = 0; i < 4; ++i) {
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n[i] = (1 + sx[i] * sx[ip] * g) * (1 + sy[i] * sy[ip] * g) / 4;
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dx[i] = sx[i] * (1 + sy[i] * sy[ip] * g) / 4;
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dy[i] = sy[i] * (1 + sx[i] * sx[ip] * g) / 4;
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a1 = a1 + dx[i] * patch[i];
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a2 = a2 + dy[i] * patch[i];
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}
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const double aa = a1.Dot(a1), bb = a2.Dot(a2), ab = a1.Dot(a2),
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det = aa * bb - ab * ab;
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const Vector3 normal = a1.Cross(a2) / std::sqrt(det);
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const Vector3 r1 = (bb * a1 - ab * a2) / det,
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r2 = (aa * a2 - ab * a1) / det;
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for (std::size_t trace = 0; trace < 6; ++trace) {
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const double x = static_cast<double>(trace % 3) - 1;
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const double l[3] = {x * (x - 1) / 2, 1 - x * x, x * (x + 1) / 2};
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const double dl[3] = {2 * x - 1, -4 * x, 2 * x + 1};
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const auto row = 6 * ip + trace;
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for (std::size_t i = 0; i < 4; ++i)
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for (std::size_t a = 0; a < 3; ++a) {
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const auto p = 5 * i + (trace < 3 ? 0 : 2) + a;
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const Vector3 first = (l[a] * dx[i]) * r1;
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const Vector3 second = (l[a] * dy[i]) * r2;
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const Vector3 third = (2 * n[i] * dl[a] / h) * normal;
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result.value[row] =
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result.value[row] - t[p] * (first + second + third);
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result.scale[row] +=
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std::abs(t[p]) * (first.Norm() + second.Norm() + third.Norm());
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}
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}
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}
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return result;
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}
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// Jacobi orthogonal diagonalization plus a posteriori residual and
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// orthogonality budget. No guessed eigenvalue epsilon is used for the rank
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// decision.
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std::pair<double, double> EigenExtrema(const Matrix& original,
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double& backward) {
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Matrix a = original;
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const auto n = a.Rows();
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Matrix v{n, n};
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for (std::size_t i = 0; i < n; ++i) v(i, i) = 1;
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for (int sweep = 0; sweep < 100; ++sweep) {
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for (std::size_t p = 0; p < n; ++p)
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for (std::size_t q = p + 1; q < n; ++q) {
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if (a(p, q) == 0) continue;
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const double tau = (a(q, q) - a(p, p)) / (2 * a(p, q));
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const double t =
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std::copysign(1., tau) / (std::abs(tau) + std::hypot(1., tau));
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const double c = 1 / std::hypot(1., t), s = t * c;
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const double off = a(p, q);
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a(p, p) -= t * off;
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a(q, q) += t * off;
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a(p, q) = a(q, p) = 0;
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for (std::size_t j = 0; j < n; ++j) {
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if (j != p && j != q) {
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const double x = a(j, p), y = a(j, q);
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a(j, p) = a(p, j) = c * x - s * y;
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a(j, q) = a(q, j) = s * x + c * y;
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}
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const double x = v(j, p), y = v(j, q);
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v(j, p) = c * x - s * y;
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v(j, q) = s * x + c * y;
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}
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}
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}
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double residual = 0, orthogonality = 0;
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for (std::size_t i = 0; i < n; ++i)
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for (std::size_t j = 0; j < n; ++j) {
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double av = 0, vv = 0, absolute = 0;
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for (std::size_t k = 0; k < n; ++k) {
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av += original(i, k) * v(k, j);
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absolute += std::abs(original(i, k) * v(k, j));
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vv += v(k, i) * v(k, j);
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}
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residual = std::hypot(residual,
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std::abs(av - v(i, j) * a(j, j)) +
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Gamma(2 * static_cast<int>(n) + 2) *
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(absolute + std::abs(v(i, j) * a(j, j))));
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orthogonality =
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std::hypot(orthogonality, std::abs(vv - (i == j ? 1. : 0.)) +
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Gamma(2 * static_cast<int>(n) + 2));
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}
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EXPECT_LT(orthogonality, 1.);
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backward =
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(residual + 2 * Norm(original) * orthogonality) / (1 - orthogonality);
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double low = a(0, 0), high = a(0, 0);
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for (std::size_t i = 1; i < n; ++i) {
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low = std::min(low, a(i, i));
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high = std::max(high, a(i, i));
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}
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return {low, high};
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}
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} // namespace
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TEST(ThermalShell, RationalMatricesSymmetryAndConstantModeUseFixedBudgets) {
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const auto created = Kernel();
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ASSERT_TRUE(created.HasValue());
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const auto& kernel = created.Value();
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const auto exact = Rational(2);
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const double ec = Gamma(4096) * Norm(exact.pc),
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ek = Gamma(4096) * Norm(exact.pk);
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EXPECT_LE(Difference(kernel.Capacity(), exact.c), ec);
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EXPECT_LE(Difference(kernel.Conductivity(), exact.k), ek);
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double cs = 0, ks = 0, null = 0, total = 0;
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for (std::size_t i = 0; i < 20; ++i) {
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double row = 0;
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for (std::size_t j = 0; j < 20; ++j) {
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cs = std::hypot(cs, kernel.Capacity()(i, j) - kernel.Capacity()(j, i));
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ks = std::hypot(
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ks, kernel.Conductivity()(i, j) - kernel.Conductivity()(j, i));
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row += kernel.Conductivity()(i, j);
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total += kernel.Capacity()(i, j);
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}
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null = std::hypot(null, row);
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}
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EXPECT_LE(cs, 2 * ec);
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EXPECT_LE(ks, 2 * ek);
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EXPECT_LE(null, std::sqrt(20.) * ek);
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EXPECT_LE(std::abs(total - 2), 20 * ec);
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EXPECT_EQ(kernel.SourceId().source_label_text, "0019");
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EXPECT_EQ(kernel.NodeIndices(),
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(std::array<fesa::EntityIndex, 4>{{7, 2, 9, 4}}));
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}
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TEST(ThermalShell, CapacityIsPositiveAndConductionHasExactlyOneNullMode) {
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const auto created = Kernel();
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ASSERT_TRUE(created.HasValue());
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const auto exact = Rational(2);
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double backward = 0;
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const auto capacity = EigenExtrema(created.Value().Capacity(), backward);
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EXPECT_GT(capacity.first, Gamma(4096) * Norm(exact.pc) + backward);
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const auto conduction =
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EigenExtrema(created.Value().Conductivity(), backward);
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EXPECT_LE(std::abs(conduction.first),
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Gamma(4096) * Norm(exact.pk) + backward);
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// Helmert columns form an independent orthonormal basis of 1-perp.
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Matrix basis{20, 19};
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for (std::size_t j = 0; j < 19; ++j) {
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const double norm = std::sqrt(static_cast<double>((j + 1) * (j + 2)));
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for (std::size_t i = 0; i <= j; ++i) basis(i, j) = 1 / norm;
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basis(j + 1, j) = -static_cast<double>(j + 1) / norm;
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}
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Matrix restricted{19, 19};
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for (std::size_t i = 0; i < 19; ++i)
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for (std::size_t j = 0; j < 19; ++j)
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for (std::size_t p = 0; p < 20; ++p)
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for (std::size_t q = 0; q < 20; ++q)
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restricted(i, j) +=
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basis(p, i) * created.Value().Conductivity()(p, q) * basis(q, j);
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const auto positive = EigenExtrema(restricted, backward);
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const double projection_budget = Gamma(8192) * Norm(exact.pk) * 20;
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EXPECT_GT(positive.first,
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Gamma(4096) * Norm(exact.pk) + backward + projection_budget);
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}
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TEST(ThermalShell,
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Direct54MatchesSeparatedOperatorsOnActualThreeDimensionalPatches) {
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const std::array<Patch, 5> patches{
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{kSquare,
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{{{0, 0, 0}, {2, 0, 0}, {1.5, 1, 0}, {0, 1, 0}}},
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{{{-1, -1, .1}, {1, -1, -.1}, {1, 1, .1}, {-1, 1, -.1}}},
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{{{-1, -1, .5}, {1, -1, -.5}, {1, 1, .5}, {-1, 1, -.5}}},
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{{{-1, -1, 2}, {1, -1, -2}, {1, 1, 2}, {-1, 1, -2}}}}};
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for (const auto& patch : patches) {
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const auto created = Kernel(patch);
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ASSERT_TRUE(created.HasValue());
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const auto direct = Direct54(patch, 2);
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const auto& kernel = created.Value();
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// General-geometry separation error is the propagated interval enclosure;
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// direct 54's independent arithmetic has the prescribed gamma8192 budget.
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EXPECT_LE(Difference(kernel.Capacity(), direct.c),
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Norm(kernel.CapacityError()) + Gamma(8192) * Norm(direct.pc));
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EXPECT_LE(Difference(kernel.Conductivity(), direct.k),
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Norm(kernel.ConductivityError()) + Gamma(8192) * Norm(direct.pk));
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}
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}
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TEST(ThermalShell, ThicknessScalingPreservesBothConductionTermsAndCapacity) {
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for (double h : {.5, 1., 2.}) {
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const auto created = Kernel(kSquare, h);
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ASSERT_TRUE(created.HasValue());
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const auto exact = Rational(h);
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EXPECT_LE(Difference(created.Value().Capacity(), exact.c),
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Gamma(4096) * Norm(exact.pc));
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EXPECT_LE(Difference(created.Value().Conductivity(), exact.k),
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Gamma(4096) * Norm(exact.pk));
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}
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}
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TEST(ThermalShell, AffineLinearFluxFramesAndSeparateMidsurfaceTraces) {
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const auto created = Kernel();
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ASSERT_TRUE(created.HasValue());
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std::array<double, 20> temperature{};
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for (std::size_t i = 0; i < 4; ++i)
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for (std::size_t a = 0; a < 5; ++a)
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temperature[5 * i + a] =
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2 + 3 * kSquare[i].X() - 2 * kSquare[i].Y() + 4 * (-1 + .5 * a);
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const auto recovery = created.Value().Recover(temperature);
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ASSERT_TRUE(recovery.HasValue());
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const auto linear_scale = Flux(kSquare, 2, temperature);
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for (std::size_t row = 0; row < 24; ++row)
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EXPECT_LE(
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(recovery.Value().global_flux[row] - Vector3{-3, 2, -4}).Norm(),
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Gamma(2048) * (linear_scale.scale[row] + Vector3{3, -2, 4}.Norm()));
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for (const auto& frame : recovery.Value().frames) {
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for (std::size_t i = 0; i < 3; ++i)
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for (std::size_t j = 0; j < 3; ++j)
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EXPECT_NEAR(frame[i].Dot(frame[j]), i == j ? 1. : 0., Gamma(2048));
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EXPECT_NEAR(frame[0].Cross(frame[1]).Dot(frame[2]), 1., Gamma(2048));
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}
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for (std::size_t i = 0; i < 4; ++i)
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for (std::size_t a = 0; a < 5; ++a)
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temperature[5 * i + a] = a < 3 ? 0. : static_cast<double>(a - 2);
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const auto traces = created.Value().Recover(temperature);
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ASSERT_TRUE(traces.HasValue());
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for (std::size_t ip = 0; ip < 4; ++ip) {
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EXPECT_EQ(traces.Value().global_flux[6 * ip + 2].Z(), 0.);
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EXPECT_NEAR(traces.Value().global_flux[6 * ip + 3].Z(), -2.,
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Gamma(2048) * 2);
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}
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temperature.fill(2);
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const auto constant = created.Value().Recover(temperature);
|
|
ASSERT_TRUE(constant.HasValue());
|
|
const auto constant_scale = Flux(kSquare, 2, temperature);
|
|
for (std::size_t row = 0; row < 24; ++row)
|
|
EXPECT_LE(constant.Value().global_flux[row].Norm(),
|
|
Gamma(2048) * constant_scale.scale[row]);
|
|
}
|
|
|
|
TEST(ThermalShell, TranslationRotationAndReversedNormalPreservePhysicalFlux) {
|
|
const auto base = Kernel();
|
|
ASSERT_TRUE(base.HasValue());
|
|
Patch translated = kSquare, rotated = kSquare, reversed{};
|
|
const auto rotate = [](Vector3 p) { return Vector3{p.Z(), p.X(), p.Y()}; };
|
|
for (std::size_t i = 0; i < 4; ++i) {
|
|
translated[i] = kSquare[i] + Vector3{1048576, -1048576, 1048576};
|
|
rotated[i] = rotate(kSquare[i]);
|
|
reversed[i] = kSquare[(4 - i) % 4];
|
|
}
|
|
const auto moved = Kernel(translated), turned = Kernel(rotated),
|
|
flipped = Kernel(reversed);
|
|
ASSERT_TRUE(moved.HasValue());
|
|
ASSERT_TRUE(turned.HasValue());
|
|
ASSERT_TRUE(flipped.HasValue());
|
|
EXPECT_LE(
|
|
Difference(base.Value().Capacity(), moved.Value().Capacity()),
|
|
Norm(base.Value().CapacityError()) + Norm(moved.Value().CapacityError()));
|
|
EXPECT_LE(
|
|
Difference(base.Value().Conductivity(), turned.Value().Conductivity()),
|
|
Norm(base.Value().ConductivityError()) +
|
|
Norm(turned.Value().ConductivityError()));
|
|
std::array<double, 20> temperature{}, reverse_temperature{};
|
|
for (std::size_t i = 0; i < 4; ++i)
|
|
for (std::size_t a = 0; a < 5; ++a) {
|
|
temperature[5 * i + a] =
|
|
2 + 3 * kSquare[i].X() - 2 * kSquare[i].Y() + 4 * (-1 + .5 * a);
|
|
reverse_temperature[5 * i + a] =
|
|
2 + 3 * reversed[i].X() - 2 * reversed[i].Y() - 4 * (-1 + .5 * a);
|
|
}
|
|
const auto flux = turned.Value().Recover(temperature),
|
|
reverse = flipped.Value().Recover(reverse_temperature);
|
|
ASSERT_TRUE(flux.HasValue());
|
|
ASSERT_TRUE(reverse.HasValue());
|
|
const auto turn_scale = Flux(rotated, 2, temperature);
|
|
const auto reverse_scale = Flux(reversed, 2, reverse_temperature);
|
|
for (std::size_t row = 0; row < 24; ++row) {
|
|
EXPECT_LE((flux.Value().global_flux[row] - rotate({-3, 2, -4})).Norm(),
|
|
Gamma(2048) * (turn_scale.scale[row] + Vector3{3, -2, 4}.Norm()));
|
|
EXPECT_LE(
|
|
(reverse.Value().global_flux[row] - Vector3{-3, 2, -4}).Norm(),
|
|
Gamma(2048) * (reverse_scale.scale[row] + Vector3{3, -2, 4}.Norm()));
|
|
}
|
|
}
|
|
|
|
TEST(ThermalShell, CheckedCreationAndRecoveryRetainSourceAndRejectNonfinite) {
|
|
for (double bad : {0., -1., std::numeric_limits<double>::infinity(),
|
|
std::numeric_limits<double>::quiet_NaN()}) {
|
|
EXPECT_FALSE(Kernel(kSquare, bad).HasValue());
|
|
EXPECT_FALSE(Kernel(kSquare, 2, bad).HasValue());
|
|
}
|
|
const auto created = Kernel();
|
|
ASSERT_TRUE(created.HasValue());
|
|
std::array<double, 20> temperature{};
|
|
temperature[3] = std::numeric_limits<double>::quiet_NaN();
|
|
const auto bad = created.Value().Recover(temperature);
|
|
ASSERT_FALSE(bad.HasValue());
|
|
EXPECT_EQ(bad.GetStatus().Diagnostics()[0].entity_identity, "patch:0019");
|
|
EXPECT_EQ(bad.GetStatus().Diagnostics()[0].location.line, 42U);
|
|
temperature.fill(std::numeric_limits<double>::max());
|
|
temperature[0] = -temperature[0];
|
|
EXPECT_FALSE(created.Value().Recover(temperature).HasValue());
|
|
}
|
|
|
|
TEST(ThermalShell, GeneralWarpedFluxUsesActualReciprocalTangentsAndTraceOrder) {
|
|
const Patch saddle{{{-1, -1, 2}, {1, -1, -2}, {1, 1, 2}, {-1, 1, -2}}};
|
|
const auto created = Kernel(saddle, 1);
|
|
ASSERT_TRUE(created.HasValue());
|
|
std::array<double, 20> temperature{};
|
|
for (std::size_t p = 0; p < 20; ++p)
|
|
temperature[p] = static_cast<double>((p * 7) % 11) - 4;
|
|
const auto actual = created.Value().Recover(temperature);
|
|
ASSERT_TRUE(actual.HasValue());
|
|
const auto expected = Flux(saddle, 1, temperature);
|
|
for (std::size_t row = 0; row < 24; ++row)
|
|
EXPECT_LE((actual.Value().global_flux[row] - expected.value[row]).Norm(),
|
|
2 * Gamma(2048) * expected.scale[row]);
|
|
// Local normals vary around this saddle; recovery must retain those frames.
|
|
EXPECT_GT((actual.Value().frames[0][2] - actual.Value().frames[1][2]).Norm(),
|
|
1.);
|
|
}
|
|
|
|
TEST(ThermalShell, PowerOfTwoCoordinateScalingPreservesDimensionedOperators) {
|
|
for (int exponent : {-200, 200}) {
|
|
Patch patch = kSquare;
|
|
const double scale = std::ldexp(1., exponent);
|
|
for (auto& point : patch) point = scale * point;
|
|
const auto created = Kernel(patch, 2);
|
|
ASSERT_TRUE(created.HasValue());
|
|
const auto unit = Rational(2);
|
|
const auto very_thick = Rational(2 / scale);
|
|
// Area multiplies C by s^2; surface conduction is invariant, while the
|
|
// normal term multiplies by s^2. Equivalent expression uses h/s and k*s.
|
|
Matrix c{20, 20}, k{20, 20};
|
|
for (std::size_t p = 0; p < 20; ++p)
|
|
for (std::size_t q = 0; q < 20; ++q) {
|
|
c(p, q) = std::ldexp(unit.c(p, q), 2 * exponent);
|
|
k(p, q) = scale * very_thick.k(p, q);
|
|
}
|
|
EXPECT_LE(Difference(created.Value().Capacity(), c),
|
|
Norm(created.Value().CapacityError()) + Gamma(4096) * Norm(c));
|
|
EXPECT_LE(
|
|
Difference(created.Value().Conductivity(), k),
|
|
Norm(created.Value().ConductivityError()) + Gamma(4096) * Norm(k));
|
|
}
|
|
}
|
|
|
|
TEST(ThermalShell, RejectsUnrepresentablePositivePropertiesWithoutClamping) {
|
|
const auto underflow =
|
|
fesa::ThermalShell::Create({"patch", 19, "0019"}, {{0, 1, 2, 3}}, kSquare,
|
|
1, std::numeric_limits<double>::denorm_min(),
|
|
std::numeric_limits<double>::denorm_min(), 1);
|
|
EXPECT_FALSE(underflow.HasValue());
|
|
const auto overflow =
|
|
fesa::ThermalShell::Create({"patch", 19, "0019"}, {{0, 1, 2, 3}}, kSquare,
|
|
1, std::numeric_limits<double>::max(), 2, 1);
|
|
EXPECT_FALSE(overflow.HasValue());
|
|
}
|