GeneralSensorPostprocessor.C

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//* This file is part of the MOOSE framework
//* https://mooseframework.inl.gov
//*
//* All rights reserved, see COPYRIGHT for full restrictions
//* https://github.com/idaholab/moose/blob/master/COPYRIGHT
//*
//* Licensed under LGPL 2.1, please see LICENSE for details
//* https://www.gnu.org/licenses/lgpl-2.1.html
#include <vector>
#include <cmath>
#include <algorithm>
#include <numeric>
 
#include "GeneralSensorPostprocessor.h"
#include "Function.h"
#include "MooseRandom.h"
#include "LinearInterpolation.h"
#include "SplineInterpolation.h"
 
registerMooseObject("MiscApp", GeneralSensorPostprocessor);
 
InputParameters
GeneralSensorPostprocessor::validParams()
{
  InputParameters params = GeneralPostprocessor::validParams();
  params.addRequiredParam<PostprocessorName>("input_signal", "The input signal postprocessor");
  params.addParam<FunctionName>(
      "drift_function", 0.0, "Drift function describing signal drift over time");
  params.addParam<Real>("vector_size", 1000000000.0, "The maximum size of vector to be saved");
  params.addClassDescription("This is a GeneralSensorPostprocessor, and functions as a base class "
                             "for other sensor postprocessors");
  params.addParam<FunctionName>(
      "efficiency_function", 0.8, "Efficiency function describing efficiency over time");
  params.addParam<FunctionName>(
      "noise_std_dev_function",
      0.5,
      "Standard deviation of noise function describing noise standard deviation over time");
  params.addParam<FunctionName>(
      "signalToNoise_function", 0.2, "Signal to noise ratio for the modeled sensor");
  params.addParam<FunctionName>(
      "delay_function", 0.1, "Delay function describing the delay over time");
  params.addParam<FunctionName>(
      "uncertainty_std_dev_function",
      0.1,
      "Standard deviation of uncertainty function describing uncertainty std dev over time");
  params.addParam<FunctionName>("R_function", 1, "The function R for the integration term.");
  params.addParam<Real>("proportional_weight", 1, "The weight assigned to the proportional term");
  params.addParam<Real>("integral_weight", 0, "The weight assigned to the integral term");
  params.addParam<unsigned int>("seed", 1, "Seed for the random number generator");
  return params;
}
 
GeneralSensorPostprocessor::GeneralSensorPostprocessor(const InputParameters & parameters)
  : GeneralPostprocessor(parameters),
    _input_signal(getPostprocessorValue("input_signal")),
    _drift_function(getFunction("drift_function")),
    _vector_size(getParam<Real>("vector_size")),
    _pp_old(getPostprocessorValueOld("input_signal")),
    _efficiency_function(getFunction("efficiency_function")),
    _noise_std_dev_function(getFunction("noise_std_dev_function")),
    _delay_function(getFunction("delay_function")),
    _signalToNoise_function(getFunction("signalToNoise_function")),
    _uncertainty_std_dev_function(getFunction("uncertainty_std_dev_function")),
    _R_function(getFunction("R_function")),
    _proportional_weight(getParam<Real>("proportional_weight")),
    _integral_weight(getParam<Real>("integral_weight")),
    _time_values(declareRestartableData<std::vector<Real>>("time_values")),
    _input_signal_values(declareRestartableData<std::vector<Real>>("input_signal_values")),
    _integrand(declareRestartableData<std::vector<Real>>("integrand")),
    _R_function_values(declareRestartableData<std::vector<Real>>("R_function_values")),
    _t_step_old(declareRestartableData<int>("gsp_t_step_old", -1)),
    _seed(getParam<unsigned int>("seed")),
    _delay_value(0) // Initialize delay_value at 0
{
}
 
void
GeneralSensorPostprocessor::initialize()
{
  Real drift_value = _drift_function.value(_t);
  Real efficiency_value = _efficiency_function.value(_t);
  Real signalToNoise_value = _signalToNoise_function.value(_t);
  // setting seed for random number generator
  _rng.seed(_seed);
  Real noise_std_dev = _noise_std_dev_function.value(_t);
  Real noise_value = _rng.randNormal(0, noise_std_dev);
  Real uncertainty_std_dev = _uncertainty_std_dev_function.value(_t);
  Real uncertainty_value = _rng.randNormal(0, uncertainty_std_dev);
  // Added line to calculate delay_value from the delay function
  _delay_value = _delay_function.value(_t);
 
  // if the problem is steady-state
  if (!_fe_problem.isTransient())
  {
    // output
    _sensor_value = drift_value +
                    efficiency_value * (_input_signal + signalToNoise_value * noise_value) +
                    uncertainty_value;
  }
 
  // if the problem is transient
  else
  {
    // Remove last element if we are repeating the timestep
    mooseAssert(_t_step_old <= _t_step,
                "The old time step needs to be behind or the same as the current time step.");
    if (_t_step_old == _t_step)
    {
      _time_values.pop_back();
      _input_signal_values.pop_back();
      _integrand.pop_back();
      _R_function_values.pop_back();
    }
 
    _time_values.push_back(_t);
    _input_signal_values.push_back(_input_signal);
 
    // Check if the size is greater than _vector_size
    if (_time_values.size() > _vector_size && _input_signal_values.size() > _vector_size)
    {
      // Remove the first 10 elements if size is larger
      _time_values.erase(_time_values.begin(), _time_values.begin() + 10);
      _input_signal_values.erase(_input_signal_values.begin(), _input_signal_values.begin() + 10);
    }
    Real _input_signal_delayed = getDelayedInputSignal();
 
    // computing integral term
    Real term_for_integration = _input_signal + signalToNoise_value * noise_value;
    _integrand.push_back(term_for_integration);
    _integration_value = getIntegral(_integrand);
 
    // output
    Real proportional_value = _input_signal_delayed + signalToNoise_value * noise_value;
    _sensor_value = drift_value +
                    efficiency_value * (_proportional_weight * proportional_value +
                                        _integral_weight * _integration_value) +
                    uncertainty_value;
 
    // Update old time step
    _t_step_old = _t_step;
  }
}
 
PostprocessorValue
GeneralSensorPostprocessor::getValue() const
{
  return _sensor_value;
}
 
std::vector<Real>
GeneralSensorPostprocessor::elementwiseMultiply(std::vector<Real> & vec1, std::vector<Real> & vec2)
{
  // Ensure both vectors have the same size
  mooseAssert(vec1.size() == vec2.size(),
              "Vectors must have the same size for element-wise multiplication.");
  // Create a result vector
  std::vector<Real> result;
  // Perform element-wise multiplication
  result.reserve(vec1.size());
  for (const auto i : index_range(vec1))
    result.push_back(vec1[i] * vec2[i]);
  return result;
}
 
Real
GeneralSensorPostprocessor::getDelayedInputSignal()
{
  // delayed input signal
  Real t_desired = _t - _delay_value;
  Real input_signal_delayed;
 
  if (t_desired < _time_values[0])
    input_signal_delayed = 0;
 
  // Modified cases where desired time isin the time value list
  else if (t_desired == _time_values[0])
    input_signal_delayed = _input_signal_values[0];
 
  // linear interpolation
  else if (t_desired > _time_values[0] && t_desired <= _t && t_desired >= _t - _dt)
    input_signal_delayed = _input_signal + (t_desired - _t) * (_pp_old - _input_signal) / (-_dt);
 
  else if (t_desired > _time_values[0] && t_desired < _t - _dt)
  {
    LinearInterpolation time_and_input_signal(_time_values, _input_signal_values);
    input_signal_delayed = time_and_input_signal.sample(t_desired);
  }
 
  else
    mooseError("Unhandled case in interpolation values.");
 
  return input_signal_delayed;
}
 
vector<Real>
GeneralSensorPostprocessor::getRVector()
{
  _R_function_values.push_back(_R_function.value(_t));
  return _R_function_values;
}
 
Real
GeneralSensorPostprocessor::getIntegral(std::vector<Real> integrand)
{
  Real integration_value;
  getRVector();
  // calculate the total integrand vector including previous integrand and exponential terms
  integrand = elementwiseMultiply(integrand, _R_function_values);
 
  // if there are more than two datapoints, do simpson's 3/8 rule for integration
  // else do trapezoidal rule
  if (_time_values.size() > 2)
  {
    SplineInterpolation spline(_time_values, integrand);
    // number of intervals
    Real n;
 
    // if number of datapoints is less than 30, use spline interpolation to get 30 intervals
    if (_time_values.size() < 30)
      n = 30;
    else
    {
      n = _time_values.size();
      // if interval is not a multiple of 3, make it
      while (static_cast<int>(n) % 3 != 0)
        n = n + 1;
    }
 
    Real h = (_time_values.back() - _time_values[0]) / n; // distance between time values
    // time vector for simpson integration
    vector<Real> time_vec_simp;
    // integrand vector for simpson integration
    vector<Real> integrand_vec_simp;
 
    for (Real i = 0; i < n + 1; i++)
    {
      Real new_time = _time_values[0] + i * h;
      time_vec_simp.push_back(new_time);
      Real new_integrand = spline.SplineInterpolation::sample(new_time);
      integrand_vec_simp.push_back(new_integrand);
    }
 
    // initialize integral with endpoints
    integration_value = integrand_vec_simp[0] + integrand_vec_simp[n];
 
    // Sum for points not at endpoints
    for (int i = 1; i < n; ++i)
    {
      if (i % 3 == 0)
        // Every third point
        integration_value += 2 * integrand_vec_simp[i];
      else
        integration_value += 3 * integrand_vec_simp[i];
    }
 
    // Multiply by 3h/8
    integration_value *= (3 * h) / 8.0;
  }
 
  else
  {
    // performing integration by trapezoidal rule, not accurate
    // LinearInterpolation object with two vectors
    LinearInterpolation integral(_time_values, integrand);
    // integrate the vectors
    integration_value = integral.integrate();
  }
 
  return integration_value;
}