FluidPropertiesUtils Namespace Reference

Functions
template<typename T , typename Functor >
std::pair< T, T >	NewtonSolve (const T &x, const T &y, const Real z_initial_guess, const Real tolerance, const Functor &y_from_x_z, const std::string &caller_name, const unsigned int max_its=100, const bool verbose=false)
	NewtonSolve does a 1D Newton Solve to solve the equation y = f(x, z) for variable z.
template<typename T , typename Functor1 , typename Functor2 >
void	NewtonSolve2D (const T &f, const T &g, const Real x0, const Real y0, T &x_final, T &y_final, const Real f_tol, const Real g_tol, const Functor1 &f_from_x_y, const Functor2 &g_from_x_y, const std::string &caller_name="", const unsigned int max_its=100, bool debug=false)
	NewtonSolve2D does a 2D Newton Solve to solve for the x and y such that: f = f_from_x_y(x, y) and g = g_from_x_y(x, y).

Function Documentation

NewtonSolve()

template<typename T , typename Functor >

std::pair< T, T > FluidPropertiesUtils::NewtonSolve	(	const T &	x,
		const T &	y,
		const Real	z_initial_guess,
		const Real	tolerance,
		const Functor &	y_from_x_z,
		const std::string &	caller_name,
		const unsigned int	max_its = 100,
		const bool	verbose = false
	)

NewtonSolve does a 1D Newton Solve to solve the equation y = f(x, z) for variable z.

Parameters

[in]	x	constant first argument of the f(x, z) term
[in]	y	constant which should be equal to f(x, z) with a converged z
[in]	z_initial_guess	initial guess for return variables
[in]	tolerance	criterion for relative or absolute (if y is sufficiently close to zero) convergence checking
[in]	y_from_x_z	two-variable function returning both values and derivatives as references
[in]	caller_name	name of the fluid properties appended to name of the routine calling the method
[in]	max_its	the maximum number of iterations for Newton's method
[in]	verbose	whether to output Newton iteration data

Returns

a pair in which the first member is the value z such that f(x, z) = y and the second member is dy/dz

Definition at line 44 of file NewtonInversion.h.

{
  // R represents residual
 
  std::function<bool(const T &, const T &)> abs_tol_check =
      [tolerance](const T & R, const T & /*y*/)
  { return std::abs(MetaPhysicL::raw_value(R)) < tolerance; };
  std::function<bool(const T &, const T &)> rel_tol_check = [tolerance](const T & R, const T & y)
  { return std::abs(MetaPhysicL::raw_value(R / y)) < tolerance; };
  auto convergence_check = MooseUtils::absoluteFuzzyEqual(MetaPhysicL::raw_value(y), 0, tolerance)
                               ? abs_tol_check
                               : rel_tol_check;
 
  T z = z_initial_guess, R, new_y, dy_dx, dy_dz;
  unsigned int iteration = 0;
 
  using std::isnan;
  if (verbose)
    Moose::out << "Target value for 1D Newton inversion:\n" << y << std::endl;
 
  do
  {
    y_from_x_z(x, z, new_y, dy_dx, dy_dz);
    R = new_y - y;
 
    // We always want to perform at least one update in order to get derivatives on z correct (z
    // corresponding to the initial guess will have no derivative information), so we don't
    // immediately return if we are converged
    const bool converged = convergence_check(R, y);
 
#ifndef NDEBUG
    static constexpr Real perturbation_factor = 1 + 1e-8;
    T perturbed_y, dummy, dummy2;
    y_from_x_z(x, perturbation_factor * z, perturbed_y, dummy, dummy2);
    // Check the accuracy of the Jacobian
    auto J_differenced = (perturbed_y - new_y) / (1e-8 * z);
    if (!MooseUtils::relativeFuzzyEqual(J_differenced, dy_dz, 1e-2))
      mooseDoOnce(mooseWarning(caller_name + ": Bad Jacobian in NewtonSolve"));
#endif
 
    z += -(R / dy_dz);
 
    if (verbose)
    {
      Moose::out << "Iteration " << iteration << std::endl;
      Moose::out << "Current solution vector: " << z << std::endl;
      Moose::out << "Current (minus) residual: " << -R << std::endl;
      Moose::out << "Current Jacobian: " << dy_dz << std::endl;
    }
 
    // Check for NaNs
    if (isnan(z))
      mooseException(caller_name + ": NaN detected in Newton solve");
 
    if (converged)
      break;
  } while (++iteration < max_its);
 
  // Check for divergence or slow convergence of Newton's method
  if (iteration >= max_its)
    mooseException(caller_name +
                       ": Newton solve convergence failed: maximum number of iterations, ",
                   max_its,
                   ", exceeded");
 
  // z was updated, we need to recompute the derivative
  y_from_x_z(x, z, new_y, dy_dx, dy_dz);
 
  return {z, dy_dz};
}

Referenced by TabulatedFluidProperties::e_from_p_rho(), TabulatedFluidProperties::e_from_p_rho(), TabulatedFluidProperties::e_from_p_T(), TabulatedFluidProperties::e_from_p_T(), TabulatedFluidProperties::e_from_v_h(), TabulatedFluidProperties::e_from_v_h(), TemperaturePressureFunctionFluidProperties::p_from_v_e(), Water97FluidProperties::p_from_v_e_template(), SodiumSaturationFluidProperties::rho_from_p_s(), SodiumSaturationFluidProperties::rho_from_p_s(), Water97FluidProperties::T_drhodT_from_p_rho(), TabulatedFluidProperties::T_from_p_h(), SimpleFluidProperties::T_from_p_h(), SodiumSaturationFluidProperties::T_from_p_h(), TemperaturePressureFunctionFluidProperties::T_from_p_h(), HelmholtzFluidProperties::T_from_p_h(), TabulatedFluidProperties::T_from_p_h(), TabulatedFluidProperties::T_from_p_rho(), TemperaturePressureFunctionFluidProperties::T_from_p_rho(), NaKFluidProperties::T_from_p_rho(), TabulatedFluidProperties::T_from_p_rho(), TabulatedFluidProperties::T_from_p_s(), and TEST().

NewtonSolve2D()

template<typename T , typename Functor1 , typename Functor2 >

void FluidPropertiesUtils::NewtonSolve2D	(	const T &	f,
		const T &	g,
		const Real	x0,
		const Real	y0,
		T &	x_final,
		T &	y_final,
		const Real	f_tol,
		const Real	g_tol,
		const Functor1 &	f_from_x_y,
		const Functor2 &	g_from_x_y,
		const std::string &	caller_name = "",
		const unsigned int	max_its = 100,
		bool	debug = false
	)

NewtonSolve2D does a 2D Newton Solve to solve for the x and y such that: f = f_from_x_y(x, y) and g = g_from_x_y(x, y).

This is done for example in the constant of (v, e) to (p, T) variable set conversion.

Parameters

[in]	f	target value for f_from_x_y
[in]	g	target value for g_from_x_y
[in]	x0	initial guess for first output variable
[in]	y0	initial guess for second output variable
[out]	x_final	output for first variable
[out]	y_final	output for second variable
[in]	f_tol	criterion for relative or absolute (if f is sufficently close to zero) convergence checking
[in]	g_tol	criterion for relative or absolute (if g is sufficently close to zero) convergence checking
[in]	f_from_x_y	two-variable function returning both values and derivatives as references
[in]	g_from_x_y	two-variable function returning both values and derivatives as references
[in]	caller_name	routine calling this solve
[in]	max_its	the maximum number of iterations for Newton's method
[in]	debug	whether to output the solution, residual and Jacobian on every iteration

Definition at line 144 of file NewtonInversion.h.

{
 
  constexpr unsigned int system_size = 2;
  DenseVector<T> targets = {{f, g}};
  DenseVector<Real> tolerances = {{f_tol, g_tol}};
  // R represents a residual equal to y - y_in
  auto convergence_check = [&targets, &tolerances](const auto & minus_R)
  {
    using std::abs;
 
    for (const auto i : index_range(minus_R))
    {
      const auto error = abs(MooseUtils::absoluteFuzzyEqual(targets(i), 0, tolerances(i))
                                 ? minus_R(i)
                                 : minus_R(i) / targets(i));
      if (error >= tolerances(i))
        return false;
    }
    return true;
  };
 
  DenseVector<T> u = {{x0, y0}};
  DenseVector<T> minus_R(system_size), func_evals(system_size), u_update(system_size);
  DenseMatrix<T> J(system_size, system_size);
  unsigned int iteration = 0;
#ifndef NDEBUG
  DenseVector<Real> svs(system_size), evs_real(system_size), evs_imag(system_size);
  DenseMatrix<Real> raw_J(system_size, system_size), raw_J2(system_size, system_size);
#endif
 
  typedef std::function<void(const T &, const T &, T &, T &, T &)> FuncType;
  std::array<FuncType, 2> func = {{f_from_x_y, g_from_x_y}};
 
  auto assign_solution = [&u, &x_final, &y_final]()
  {
    x_final = u(0);
    y_final = u(1);
  };
  auto status_string = [&u, &func_evals, &targets, &minus_R](unsigned int comp) -> std::stringstream
  {
    std::stringstream ss;
    ss << "Current solution for component " << comp << ": " << u(comp)
       << " (current ordinate: " << func_evals(comp) << " -> target: " << targets(comp)
       << ", scaled residual: " << minus_R(comp) << ")";
    return ss;
  };
  if (debug)
    Moose::out << "Target values for 2D Newton inversion:\n" << targets << std::endl;
 
  using std::isnan, std::max, std::abs;
 
  do
  {
    for (const auto i : make_range(system_size))
      func[i](u(0), u(1), func_evals(i), J(i, 0), J(i, 1));
 
    for (const auto i : make_range(system_size))
      minus_R(i) = targets(i) - func_evals(i);
 
    // We always want to perform at least one update in order to get derivatives on z correct (z
    // corresponding to the initial guess will have no derivative information), so we don't
    // immediately return if we are converged
    const bool converged = convergence_check(minus_R);
 
    // Check for NaNs before proceeding to system solve. We may simultaneously not have NaNs in z
    // but have NaNs in the function evaluation
    for (const auto i : make_range(system_size))
      if (isnan(minus_R(i)))
      {
        assign_solution();
        mooseException(caller_name + ": NaN detected in Newton solve");
      }
 
    if (debug)
    {
      Moose::out << "Iteration " << iteration << std::endl;
      Moose::out << "Current solution vector:\n" << u << std::endl;
      Moose::out << "Current (minus) residual:\n" << minus_R << std::endl;
      Moose::out << "Current Jacobian:\n" << J << std::endl;
    }
 
    // Do some Jacobi (rowmax) preconditioning and check for an empty row
    int degenerate_row = -1;
    for (const auto i : make_range(system_size))
    {
      const auto rowmax = max(abs(J(i, 0)), abs(J(i, 1)));
      if (rowmax > 0)
      {
        for (const auto j : make_range(system_size))
          J(i, j) /= rowmax;
        minus_R(i) /= rowmax;
      }
      else
      {
        if (degenerate_row != -1)
          mooseException(caller_name + ": Jacobian is all zeros in NewtonSolve2D");
        degenerate_row = i;
      }
    }
 
#ifndef NDEBUG
    //
    // Check nature of linearized system
    //
    for (const auto i : make_range(system_size))
      for (const auto j : make_range(system_size))
      {
        raw_J(i, j) = MetaPhysicL::raw_value(J(i, j));
        raw_J2(i, j) = MetaPhysicL::raw_value(J(i, j));
      }
    raw_J.svd(svs);
    raw_J2.evd(evs_real, evs_imag);
    if (debug)
      Moose::out << "Jacobian singular values:\n" << svs << std::endl;
#endif
 
    if (degenerate_row == -1)
      J.lu_solve(minus_R, u_update);
    else
    {
      // use a 1D newton when the Jacobian has an empty row
      const auto other_row = system_size - 1 - degenerate_row;
      u_update(other_row) = minus_R(other_row) / J(other_row, other_row);
      u_update(degenerate_row) = 0;
    }
    // reset the decomposition
    J.zero();
    u += u_update;
 
    // Check for NaNs
    for (const auto i : make_range(system_size))
      if (isnan(u(i)))
      {
        assign_solution();
        mooseException(caller_name + ": NaN detected in NewtonSolve2D\n" + status_string(0).str() +
                       "\n" + status_string(1).str());
      }
 
    if (converged)
      break;
  } while (++iteration < max_its);
 
  assign_solution();
 
  // Check for divergence or slow convergence of Newton's method
  if (iteration >= max_its)
    mooseException(caller_name +
                       ": Newton solve convergence failed: maximum number of iterations, ",
                   max_its,
                   ", exceeded.\n" + status_string(0).str() + "\n" + status_string(1).str());
}

Referenced by SinglePhaseFluidProperties::p_T_from_h_s(), SinglePhaseFluidProperties::p_T_from_v_e(), SinglePhaseFluidProperties::p_T_from_v_h(), TEST(), TabulatedFluidProperties::v_from_p_T(), and TabulatedFluidProperties::v_from_p_T().