time_averaged_method.h

Go to the documentation of this file.

/* -------------------------------------------------------------------------*
*                               SPHinXsys                                   *
* --------------------------------------------------------------------------*
* SPHinXsys (pronunciation: s'finksis) is an acronym from Smoothed Particle *
* Hydrodynamics for industrial compleX systems. It provides C++ APIs for    *
* physical accurate simulation and aims to model coupled industrial dynamic *
* systems including fluid, solid, multi-body dynamics and beyond with SPH   *
* (smoothed particle hydrodynamics), a meshless computational method using  *
* particle discretization.                                                  *
*                                                                           *
* SPHinXsys is partially funded by German Research Foundation               *
* (Deutsche Forschungsgemeinschaft) DFG HU1527/6-1, HU1527/10-1             *
* and HU1527/12-1.                                                          *
*                                                                           *
* Portions copyright (c) 2017-2020 Technical University of Munich and       *
* the authors' affiliations.                                                *
*                                                                           *
* Licensed under the Apache License, Version 2.0 (the "License"); you may   *
* not use this file except in compliance with the License. You may obtain a *
* copy of the License at http://www.apache.org/licenses/LICENSE-2.0.        *
*                                                                           *
* --------------------------------------------------------------------------*/
#pragma once
#include "regression_test_base.hpp"

namespace SPH
{
    template<class ObserveMethodType>
    class RegressionTestTimeAveraged : public RegressionTestBase<ObserveMethodType>
    {
        /* identify the variable type from the parent class. */
        using VariableType = decltype(ObserveMethodType::type_indicator_);

    protected:
        int snapshot_for_converged_ ;                 /* the index of the steady converged starting point. */
        std::string mean_variance_filefullpath_;      /* the file path for mean and variance. (.xml) */
        std::string filefullpath_filter_output_;      /* the file path for filtered output. */
        XmlEngine mean_variance_xml_engine_in_;       /* xml engine for mean and variance input. */
        XmlEngine mean_variance_xml_engine_out_;      /* xml engine for mean and variance output. */
        
        VariableType threshold_mean_, threshold_variance_;  /* the container of threshold value for mean and variance. */
        StdVec<VariableType> meanvalue_, meanvalue_new_;    /* the container of (new) meanvalue. */
        StdVec<VariableType> variance_, variance_new_;      /* the container of (new) variance. */
        StdVec<VariableType> local_meanvalue_;              /* the container of meanvalue for current result. */
        
        void filterLocalResult(DoubleVec<Real> &current_result);
        void filterLocalResult(DoubleVec<Vecd> &current_result);
        void filterLocalResult(DoubleVec<Matd> &current_result);

        void searchSteadyStart(DoubleVec<Real> &current_result);
        void searchSteadyStart(DoubleVec<Vecd> &current_result);
        void searchSteadyStart(DoubleVec<Matd> &current_result);

        void calculateNewVariance(DoubleVec<Real> &current_result, StdVec<Real> &local_meanvalue, StdVec<Real> &variance, StdVec<Real> &variance_new);
        void calculateNewVariance(DoubleVec<Vecd> &current_result, StdVec<Vecd> &local_meanvalue, StdVec<Vecd> &variance, StdVec<Vecd> &variance_new);
        void calculateNewVariance(DoubleVec<Matd> &current_result, StdVec<Matd> &local_meanvalue, StdVec<Matd> &variance, StdVec<Matd> &variance_new);

        int compareParameter(string par_name, StdVec<Real> &parameter, StdVec<Real> &parameter_new, Real &threshold);
        int compareParameter(string par_name, StdVec<Vecd> &parameter, StdVec<Vecd> &parameter_new, Vecd &threshold);
        int compareParameter(string par_name, StdVec<Matd> &parameter, StdVec<Matd> &parameter_new, Matd &threshold);

        int testNewResult(DoubleVec<Real> &current_result, StdVec<Real> &meanvalue, StdVec<Real> &local_meanvalue, StdVec<Real> &variance);
        int testNewResult(DoubleVec<Vecd> &current_result, StdVec<Vecd> &meanvalue, StdVec<Vecd> &local_meanvalue, StdVec<Vecd> &variance);
        int testNewResult(DoubleVec<Matd> &current_result, StdVec<Matd> &meanvalue, StdVec<Matd> &local_meanvalue, StdVec<Matd> &variance);
        
    public:
        template<typename... ConstructorArgs>
        explicit RegressionTestTimeAveraged(ConstructorArgs &&...args) :
            RegressionTestBase<ObserveMethodType>(std::forward<ConstructorArgs>(args)...),
            mean_variance_xml_engine_in_("mean_variance_xml_engine_in_", "meanvariance"),
            mean_variance_xml_engine_out_("mean_variance_xml_engine_out_", "meanvariance")
        {
            mean_variance_filefullpath_ = this->input_folder_path_ + "/" + this->body_name_
                + "_" + this->quantity_name_ + "_time_averaged_mean_variance.xml";
        };
        virtual ~RegressionTestTimeAveraged() {};

        void initializeThreshold(VariableType &threshold_mean, VariableType &threshold_variance);
        void settingupTheTest();  
        void readMeanVarianceFromXml();  
        void searchForStartPoint(); 
        void filterExtremeValues();  
        void updateMeanVariance();  
        void writeMeanVarianceToXml();  
        bool compareMeanVariance();  
        void resultTest();  
        /* the interface for generating the priori converged result with time-averaged meanvalue and variance. */
        void generateDataBase(VariableType threshold_mean, VariableType threshold_variance, string filter = "false")
        {
            this->writeXmlToXmlFile(); /* currently defined in in_output. */
            this->readXmlFromXmlFile();/* currently defined in in_output. */
            initializeThreshold(threshold_mean, threshold_variance);
            if (this->converged == "false")
            {
                settingupTheTest();
                if (filter == "true")
                    filterExtremeValues(); /* Pay attention to use this filter. */
                searchForStartPoint(); /* searching starting point with snapshot*observation data structure, and it is dynamic varying. */
                this->transposeTheIndex(); /* transpose the snapshot and observation, and it is defined in Base. */
                readMeanVarianceFromXml();
                updateMeanVariance();
                this->writeResultToXml(this->number_of_run_ - 1); /* the result is output as separately. */
                writeMeanVarianceToXml();
                compareMeanVariance(); /* To identify whether the current mean and variance are converged or not.*/
            }
            else
                std::cout << "The results have been converged." << endl;
        };

        void newResultTest(string filter = "false")
        {
            this->writeXmlToXmlFile(); /* currently defined in in_output. */
            this->readXmlFromXmlFile(); /* currently defined in in_output. */
            settingupTheTest();
            if (filter == "true")
                filterExtremeValues();
            searchForStartPoint();
            readMeanVarianceFromXml();
            resultTest();
        }
    };
}