#include #include "Eigen/Dense" #include "time.h" #include "Helmholtz.h" #include "CoolProp.h" class EOSFitter; #include "Fitter.h" #include "DataTypes.h" int main() { double n[] = {0.0, 0.5586817e-3, 0.4982230e0, 0.2458698e-0, 0.8570145e-3, 0.4788584e-3, -0.1800808e-1, 0.2671641e0, -0.4781652e1, 0.1423987e1, 0.3324062e0, -0.7485907e-2, 0.1017263e-3, -0.5184567e+0, -0.8692288e-1, 0.2057144e+0, -0.5000457e-2, 0.4603262e-3, -0.3497836e-2, 0.6995038e-2, -0.1452184e-1, -0.1285458e-3}; double d[] = {0, 2, 1, 3, 6, 6, 1, 1, 2, 5, 2, 2, 4, 1, 4, 1, 2, 4, 1, 5, 3, 10}; double t[] = {0.0, -1.0 / 2.0, 0.0, 0.0, 0.0, 3.0 / 2.0, 3.0 / 2.0, 2.0, 2.0, 1.0, 3.0, 5.0, 1.0, 5.0, 5.0, 6.0, 10.0, 10.0, 10.0, 18.0, 22.0, 50.0}; double c[] = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0}; std::vector nv(n, n + sizeof(n) / sizeof(double)); double mm = Props1SI("R134a", "molemass"); double rhoL, rhoV; bool supercritical_T; double Tr = Props1SI("R134a", "Treduce"); EOSFitter* pEOS = new EOSFitterFixedForm(Props1SI("R134a", "Treduce"), Props1SI("R134a", "rhoreduce") / mm * 1000, 8.314471); EOSFitter& EOS = *pEOS; // ---------------------------- // Generate "experimental" data // ---------------------------- for (double T = 250; T < 500; T += 10) { if (T < Tr) { rhoL = PropsSI("D", "T", T, "Q", 0, "R134a"); rhoV = PropsSI("D", "T", T, "Q", 1, "R134a"); supercritical_T = false; } else { rhoL = -1; rhoV = -1; supercritical_T = true; } for (double rho = 1e-10; rho < 1200; rho *= 1.5) { if (!supercritical_T && (rho < rhoL && rho > rhoV)) { continue; } double p = PropsSI("P", "T", T, "D", rho, "R134a"); double rhobar = rho / mm * 1000; double cp = PropsSI("C", "T", T, "D", rho, "R134a"); // [J/kg/K]; convert to J/mol/K by *mm/1000 double variance = 1; // TODO; change this EOS.linear_data_points.push_back(new PressureDataPoint(pEOS, T, rho / mm * 1000, p, variance)); EOS.nonlinear_data_points.push_back(new SpecificHeatCPDataPoint(pEOS, T, rho / mm * 1000, cp * mm / 1000, variance * 100)); } } // Setup the EOS EOS.alphar = phir_power(n, d, t, c, 1, 21, 22); static const double a0[] = { 0.0, //[0] -1.019535, //[1] 9.047135, //[2] -1.629789, //[3] -9.723916, //[4] -3.927170 //[5] }; static const double t0[] = { 0.0, //[0] 0.0, //[1] 0.0, //[2] 0.0, //[3] -1.0 / 2.0, //[4] -3.0 / 4.0 //[5] }; // phi0=log(delta)+a0[1]+a0[2]*tau+a0[3]*log(tau)+a0[4]*pow(tau,-1.0/2.0)+a0[5]*pow(tau,-3.0/4.0); EOS.alpha0.push_back(new phi0_lead(a0[1], a0[2])); EOS.alpha0.push_back(new phi0_logtau(a0[3])); EOS.alpha0.push_back(new phi0_power(a0, t0, 4, 5, 6)); /*for (unsigned int i = 0; i < EOS.nonlinear_data_points.size();i++) { std::cout << EOS.nonlinear_data_points[i]->residual(nv) << std::endl; }*/ // Set the coefficients in the preliminary EOS EOS.set_n(nv); std::cout << format("before fit x2 %g\n", EOS.sum_squares(nv, false)); // Solve for n without nonlinear terms to get an approximate solution EOS.solve_for_n(nv, false); std::cout << format("solved for n x2 %g\n", EOS.sum_squares(nv, false)); EOS.set_n(nv); std::cout << format("applied n x2 %g\n", EOS.sum_squares(nv, true)); for (int iter = 0; iter < 5; iter++) { EOS.set_n(nv); // Turn on the nonlinear terms and try again EOS.solve_for_n(nv, true); std::cout << nv[1] << " " << nv[2] << std::endl; std::cout << format("iter: %d x2 %g\n", iter, EOS.sum_squares(nv, true)); } double rr = 0; }