From 4aded3f1e2135a336adcab8886e1d9ec6258a9f9 Mon Sep 17 00:00:00 2001 From: Ian Bell Date: Mon, 26 Aug 2024 10:57:26 -0400 Subject: [PATCH 1/9] First commits to the multifluid + AC modeling approach Seems to be working properly, notebook looks decent --- doc/source/models/MultifluidPlusAC.ipynb | 266 ++++++++++++++++++ doc/source/models/index.rst | 1 + .../teqp/models/activity/activity_models.hpp | 120 ++++++++ include/teqp/models/cubics.hpp | 102 +------ .../models/multifluid/multifluid_activity.hpp | 72 +++++ .../CPP/model_factory_multifluid_activity.cpp | 11 + interface/CPP/teqp_impl_factory.cpp | 2 + 7 files changed, 474 insertions(+), 100 deletions(-) create mode 100644 doc/source/models/MultifluidPlusAC.ipynb create mode 100644 include/teqp/models/activity/activity_models.hpp create mode 100644 include/teqp/models/multifluid/multifluid_activity.hpp create mode 100644 interface/CPP/model_factory_multifluid_activity.cpp diff --git a/doc/source/models/MultifluidPlusAC.ipynb b/doc/source/models/MultifluidPlusAC.ipynb new file mode 100644 index 0000000..13bf5e3 --- /dev/null +++ b/doc/source/models/MultifluidPlusAC.ipynb @@ -0,0 +1,266 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ef2685a7", + "metadata": {}, + "source": [ + "# Multifluid+Activity\n", + "\n", + "Following the derivations in [Jaeger et al.](https://doi.org/10.1016/j.fluid.2018.04.015), the multifluid plus activity coefficient model can be defined as a the conventional corresponding states portion\n", + "$$\n", + "\\alpha^{\\rm r}_{\\rm CS} = \\sum_i \\alpha^{\\rm r}_{oi}(\\tau, \\delta)\n", + "$$\n", + "plus the departure term coming from the activity coefficient model\n", + "$$\n", + "\\alpha^{\\rm Dep}(T,\\rho,\\bar x) = \\frac{\\ln(1+b_{\\rm mix}\\rho)}{\\ln(1+\\frac{1}{u})}\\left[\\frac{g^{\\rm E,R}_{\\rm GE}(T,\n", + "\\bar x)}{RT}-\\sum_{i=1}^Nx_i\\left(\\alpha^{\\rm r}_{oi}(\\delta_{\\rm ref},\\tau)-\\alpha^{\\rm r}_{oi}(\\delta_{i,ref}, \\tau_i)\\right)\\right]\n", + "$$\n", + "with \n", + "$$\n", + "b_{\\rm mix} = \\sum_i b_ix_i\n", + "$$\n", + "$$\n", + "\\frac{g^{\\rm E,R}_{\\rm GE}(T,\n", + "\\bar x)}{RT} = \\sum_ix_i\\ln\\left(\\gamma_i^{\\rm R}\\right)\n", + "$$\n", + "\n", + "$$\n", + "\\delta_{\\rm ref} = \\frac{1}{ub_{\\rm mix}\\rho_r(\\bar x)}\n", + "$$\n", + "$$\n", + "\\delta_{i,{\\rm ref}} = \\frac{1}{ub_i\\rho_{c,i}}\n", + "$$\n", + "and the conventional reducing function is used to define $T_{\\rm r}(\\bar x)$ and $\\rho_{\\rm r}(\\bar x)$.\n", + "\n", + "Any activity coefficient model can be used for $\\gamma_i^{\\rm R}$, but so far (as of version 0.22) only the Wilson model is available due to how well it works with the advanced cubic mixing rules." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "937c2216", + "metadata": {}, + "outputs": [], + "source": [ + "import io, teqp, numpy as np, CoolProp.CoolProp as CP\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "4e03c647", + "metadata": {}, + "outputs": [], + "source": [ + "# Four isotherms of experimental data for CO2 + Nitrogen from doi: 10.1016/j.fluid.2016.05.015\n", + "import pandas\n", + "dat = pandas.read_csv(io.StringIO(\"\"\"PointID y1 uy1 x1 ux1 p/bar up T/K\n", + "1 0.0274 0.0007 0.0068 0.0002 59.830 0.053 293.10 \n", + "2 0.0664 0.0014 0.0183 0.0004 64.864 0.080 293.10 \n", + "3 0.0978 0.0020 0.0298 0.0007 69.772 0.080 293.10 \n", + "4 0.1199 0.0024 0.0424 0.0009 74.737 0.080 293.10 \n", + "5 0.1219 0.0028 0.1132 0.0023 89.869 0.080 293.10 \n", + "6 0.1339 0.0024 0.0995 0.0022 89.198 0.080 293.10 \n", + "7 0.1399 0.0026 0.0943 0.0020 88.853 0.080 293.10 \n", + "8 0.1461 0.0027 0.0823 0.0019 86.962 0.080 293.10 \n", + "9 0.1466 0.0028 0.0778 0.0017 85.942 0.080 293.10 \n", + "10 0.1466 0.0028 0.0772 0.0016 85.868 0.080 293.10 \n", + "1 0.1378 0.0027 0.0159 0.0004 42.667 0.051 273.08 \n", + "2 0.2143 0.0038 0.0297 0.0007 49.547 0.051 273.08 \n", + "3 0.2612 0.0043 0.0411 0.0009 55.238 0.051 273.08 \n", + "4 0.3209 0.0049 0.0609 0.0013 65.069 0.088 273.08 \n", + "5 0.3554 0.0051 0.0786 0.0016 73.395 0.088 273.08 \n", + "6 0.3758 0.0052 0.0978 0.0019 81.061 0.088 273.08 \n", + "7 0.3903 0.0053 0.1190 0.0023 90.706 0.088 273.08 \n", + "8 0.3914 0.0053 0.1477 0.0028 100.966 0.088 273.08 \n", + "9 0.3879 0.0053 0.1614 0.0030 104.806 0.088 273.08 \n", + "10 0.3724 0.0052 0.1875 0.0033 110.846 0.088 273.08\n", + "11 0.3550 0.0051 0.2068 0.0036 114.105 0.088 273.08\n", + "12 0.2727 0.0044 0.2531 0.0041 118.020 0.088 273.08\n", + "13 0.3343 0.0049 0.2268 0.0038 116.295 0.088 273.08\n", + "1 0.2048 0.0038 0.0106 0.0003 25.754 0.050 253.05 \n", + "2 0.3019 0.0049 0.0217 0.0005 30.479 0.050 253.05 \n", + "3 0.4638 0.0056 0.0436 0.0010 45.352 0.050 253.05 \n", + "4 0.5319 0.0056 0.0647 0.0014 58.188 0.050 253.05 \n", + "5 0.5854 0.0054 0.1077 0.0021 78.315 0.084 253.05 \n", + "6 0.5979 0.0054 0.1497 0.0028 98.276 0.084 253.05\n", + "7 0.5898 0.0054 0.1801 0.0032 109.241 0.084 253.05\n", + "8 0.5042 0.0057 0.0570 0.0012 51.343 0.084 253.05\n", + "9 0.5644 0.0055 0.0861 0.0017 67.594 0.084 253.05\n", + "10 0.5949 0.0054 0.1267 0.0024 86.883 0.084 253.05\n", + "11 0.5826 0.0054 0.2015 0.0035 116.614 0.084 253.05\n", + "12 0.5537 0.0055 0.2431 0.0040 129.873 0.084 253.05\n", + "13 0.4973 0.0055 0.2971 0.0046 139.161 0.084 253.05\n", + "14 0.4971 0.0055 0.2972 0.0046 139.261 0.084 253.05\n", + "1 0.7076 0.0050 0.0257 0.0006 27.983 0.056 223.10\n", + "2 0.7774 0.0041 0.0522 0.0011 44.918 0.056 223.10\n", + "3 0.8077 0.0036 0.0930 0.0019 64.906 0.081 223.10\n", + "4 0.8131 0.0035 0.1261 0.0024 84.799 0.081 223.10\n", + "5 0.8057 0.0035 0.1584 0.0029 104.410 0.081 223.10\n", + "6 0.7843 0.0038 0.1982 0.0035 125.782 0.081 223.10\n", + "7 0.7533 0.0041 0.2380 0.0040 144.287 0.081 223.10\n", + "8 0.7150 0.0045 0.2813 0.0044 159.015 0.081 223.10\n", + "9 0.6942 0.0047 0.3064 0.0047 165.347 0.081 223.10\n", + "\"\"\"), sep='\\s+', engine='python')\n", + "model = teqp.make_model(j)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "74404b8c", + "metadata": {}, + "outputs": [], + "source": [ + "Tc_K = np.array([304.21, 126.19])\n", + "pc_Pa = np.array([7.383e6, 3395800.0])\n", + "\n", + "def get_bi():\n", + " OmegaB = 0.08664 \n", + " R = 8.31446261815324\n", + " return (OmegaB*R*Tc_K/pc_Pa).tolist()\n", + "\n", + "donor = teqp.build_multifluid_model([\"CO2\", \"Nitrogen\"], teqp.get_datapath())\n", + "vc_m3mol = donor.get_vcvec()" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "ab83b1d7", + "metadata": {}, + "outputs": [], + "source": [ + "# Parameter set with linear mixing in the reducing function\n", + "BIP_linear = [{\n", + " \"Name1\": \"CarbonDioxide\",\n", + " \"Name2\": \"Nitrogen\",\n", + " \"betaT\": 1.0,\n", + " \"gammaT\": 0.5*(Tc_K[0]+Tc_K[1])/(Tc_K[0]*Tc_K[1])**0.5,\n", + " \"betaV\": 1.0,\n", + " \"gammaV\": 4*(vc_m3mol[0]+vc_m3mol[1])/(vc_m3mol[0]**(1/3) + vc_m3mol[1]**(1/3))**3,\n", + " \"F\": 0.0\n", + "}]\n", + "\n", + "# Parameter set with Lorentz-Berthelot\n", + "BIP_LorentzBerthelot = [{\n", + " \"Name1\": \"CarbonDioxide\",\n", + " \"Name2\": \"Nitrogen\",\n", + " \"betaT\": 1.0,\n", + " \"gammaT\": 1.0,\n", + " \"betaV\": 1.0,\n", + " \"gammaV\": 1.0,\n", + " \"F\": 0.0\n", + "}]\n", + "\n", + "def get_model(BIPset=None):\n", + " if BIPset is not None and BIPset == 'linear':\n", + " return teqp.make_model({\n", + " 'kind': 'multifluid',\n", + " \"model\":{\n", + " \"components\": [\"CO2\", \"Nitrogen\"],\n", + " \"root\": teqp.get_datapath(),\n", + " \"BIP\": BIP_linear\n", + " }})\n", + "\n", + " # The Wilson activity coefficient model is from Lasala et al.: https://doi.org/10.1016/j.fluid.2016.05.015\n", + " # and then combined with the \n", + " j = {\n", + " \"kind\": \"multifluid-activity\",\n", + " \"model\": {\n", + " \"multifluid\":{\n", + " \"components\": [\"CO2\", \"Nitrogen\"],\n", + " \"root\": teqp.get_datapath(),\n", + " \"BIP\": BIPset\n", + " },\n", + " \"activity\": {\n", + " \"aresmodel\": {\n", + " \"type\": \"Wilson\", \n", + " \"m\": [[0.0, -3.4768], [3.5332, 0.0]], \n", + " \"n\": [[0.0, 825], [-585, 0.0]], \n", + " \"b\": get_bi()\n", + " },\n", + " \"options\": {\"u\": 1.17, \"b\": get_bi()}\n", + " }\n", + " }\n", + " }\n", + "\n", + " return teqp.make_model(j) if BIPset is not None else donor" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "500aa1ae", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "labels = ['linear+Wilson','Lorentz-Berthelot+Wilson','GERG-2008','linear']\n", + "for imod, model in enumerate([get_model(BIP_linear), get_model(BIP_LorentzBerthelot), get_model(), get_model('linear')]):\n", + " lw = 0.5+imod\n", + " for T in [223.15, 253.05, 273.08, 293.1]:\n", + " ipure = 0\n", + "\n", + " [rhoL0, rhoV0] = [CP.PropsSI('Dmolar','T',T,'Q',Q,'CO2') for Q in [0,1]]\n", + "\n", + " rhovecL0 = np.array([0.0, 0.0]); rhovecL0[ipure] = rhoL0\n", + " rhovecV0 = np.array([0.0, 0.0]); rhovecV0[ipure] = rhoV0\n", + "\n", + " J = model.trace_VLE_isotherm_binary(T, rhovecL0, rhovecV0)\n", + " df = pandas.DataFrame(J)\n", + " plt.plot(df['xL_0 / mole frac.'], df['pL / Pa']/1e6,'k', lw=lw, label=labels[imod] if T == 223.15 else '')\n", + " plt.plot(df['xV_0 / mole frac.'], df['pV / Pa']/1e6,'k', lw=lw)\n", + " \n", + "plt.title('CO$_2$ (1) + N$_2$ (2)')\n", + "plt.plot(1-dat['x1'], dat['p/bar']/10, 'o')\n", + "plt.plot(1-dat['y1'], dat['p/bar']/10, '^')\n", + "plt.legend(loc='best')\n", + "plt.gca().set(xlabel='$x_1$ / mole frac.', ylabel='$p$ / MPa', ylim=(0, 25))\n", + "plt.savefig(\"multifluid_Wilson_CO2_N2.pdf\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e1b7813a", + "metadata": {}, + "source": [ + "so we can see that the activity coefficient models are not too bad, clearly an improvement over pure linear mixing, but the model from GERG-2008 is unsurprisingly the most accurate" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/doc/source/models/index.rst b/doc/source/models/index.rst index e723a4e..34133f6 100644 --- a/doc/source/models/index.rst +++ b/doc/source/models/index.rst @@ -28,5 +28,6 @@ Models GERG ECS MultifluidPlusAssociation + MultifluidPlusAC IdealGas \ No newline at end of file diff --git a/include/teqp/models/activity/activity_models.hpp b/include/teqp/models/activity/activity_models.hpp new file mode 100644 index 0000000..b1243f8 --- /dev/null +++ b/include/teqp/models/activity/activity_models.hpp @@ -0,0 +1,120 @@ +#pragma once + + +namespace teqp::activity::activity_models{ + +/** + A residual Helmholtz term that returns nothing (the empty term) + */ +template +class NullResidualHelmholtzOverRT { +public: + template + auto operator () (const TType& /*T*/, const MoleFractions& molefracs) const { + std::common_type_t val = 0.0; + return val; + } +}; + +/** + + \f[ + \frac{a^{E,\gamma}_{total}}{RT} = -sum_iz_i\ln\left(\sum_jz_jOmega_{ij}(T)\right) + \f] + + \f[ + \frac{a^{E,\gamma}_{comb}}{RT} = -sum_iz_i\ln\left(\frac{\Omega_i}{z_i}\right) + \f] + + \f[ + \frac{a^{E,\gamma}_{res}}{RT} = \frac{a^{E,\gamma}_{total}}{RT} - \frac{a^{E,\gamma}_{comb}}{RT} + \f] + + Volume fraction of component \f$i\f$ +\f[ + \phi_i = \frac{z_iv_i}{\sum_j z_j v_j} + \f] + with \f$v_i = b_i\f$ + */ +template +class WilsonResidualHelmholtzOverRT { + +public: + const std::vector b; + const Eigen::ArrayXXd m, n; + WilsonResidualHelmholtzOverRT(const std::vector& b, const Eigen::ArrayXXd& m, const Eigen::ArrayXXd& n) : b(b), m(m), n(n) {}; + + template + auto combinatorial(const TType& /*T*/, const MoleFractions& molefracs) const { + if (b.size() != static_cast(molefracs.size())){ + throw teqp::InvalidArgument("Bad size of molefracs"); + } + + using TYPE = std::common_type_t; + // The denominator in Phi + TYPE Vtot = 0.0; + for (auto i = 0U; i < molefracs.size(); ++i){ + auto v_i = b[i]; + Vtot += molefracs[i]*v_i; + } + + TYPE summer = 0.0; + for (auto i = 0U; i < molefracs.size(); ++i){ + auto v_i = b[i]; + // The ratio phi_i/z_i is expressed like this to better handle + // the case of z_i = 0, which would otherwise be a divide by zero + // in the case that the composition of one component is zero + auto phi_i_over_z_i = v_i/Vtot; + summer += molefracs[i]*log(phi_i_over_z_i); + } + return summer; + } + + template + auto get_Aij(std::size_t i, std::size_t j, const TType& T) const{ + return forceeval(m(i,j)*T + n(i,j)); + } + + template + auto total(const TType& T, const MoleFractions& molefracs) const { + + using TYPE = std::common_type_t; + TYPE summer = 0.0; + for (auto i = 0U; i < molefracs.size(); ++i){ + auto v_i = b[i]; + TYPE summerj = 0.0; + for (auto j = 0U; j < molefracs.size(); ++j){ + auto v_j = b[j]; + auto Aij = get_Aij(i,j,T); + auto Omega_ji = v_j/v_i*exp(-Aij/T); + summerj += molefracs[j]*Omega_ji; + } + summer += molefracs[i]*log(summerj); + } + return forceeval(-summer); + } + + // Returns ares/RT + template + auto operator () (const TType& T, const MoleFractions& molefracs) const { + return forceeval(total(T, molefracs) - combinatorial(T, molefracs)); + } +}; + +using ResidualHelmholtzOverRTVariant = std::variant, WilsonResidualHelmholtzOverRT>; + +inline ResidualHelmholtzOverRTVariant ares_model_factory(const nlohmann::json& armodel) { + + std::string type = armodel.at("type"); + if (type == "Wilson"){ + std::vector b = armodel.at("b"); + auto mWilson = build_square_matrix(armodel.at("m")); + auto nWilson = build_square_matrix(armodel.at("n")); + return WilsonResidualHelmholtzOverRT(b, mWilson, nWilson); + } + else{ + throw teqp::InvalidArgument("bad type of ares model: " + type); + } +}; + +} diff --git a/include/teqp/models/cubics.hpp b/include/teqp/models/cubics.hpp index 10a985d..eabdd6c 100644 --- a/include/teqp/models/cubics.hpp +++ b/include/teqp/models/cubics.hpp @@ -17,6 +17,8 @@ Implementations of the canonical cubic equations of state #include "teqp/math/pow_templates.hpp" #include "nlohmann/json.hpp" +#include "teqp/models/activity/activity_models.hpp" +using namespace teqp::activity::activity_models; #include @@ -460,106 +462,6 @@ inline void from_json(const json& j, AdvancedPRaEOptions& o) { } } -/** - A residual Helmholtz term that returns nothing (the empty term) - */ -template -class NullResidualHelmholtzOverRT { -public: - template - auto operator () (const TType& /*T*/, const MoleFractions& molefracs) const { - std::common_type_t val = 0.0; - return val; - } -}; - -/** - - \f[ - \frac{a^{E,\gamma}_{total}}{RT} = -sum_iz_i\ln\left(\sum_jz_jOmega_{ij}(T)\right) - \f] - - \f[ - \frac{a^{E,\gamma}_{comb}}{RT} = -sum_iz_i\ln\left(\frac{\Omega_i}{z_i}\right) - \f] - - \f[ - \frac{a^{E,\gamma}_{res}}{RT} = \frac{a^{E,\gamma}_{total}}{RT} - \frac{a^{E,\gamma}_{comb}}{RT} - \f] - - Volume fraction of component \f$i\f$ -\f[ - \phi_i = \frac{z_iv_i}{\sum_j z_j v_j} - \f] - with \f$v_i = b_i\f$ - */ -template -class WilsonResidualHelmholtzOverRT { - -public: - const std::vector b; - const Eigen::ArrayXXd m, n; - WilsonResidualHelmholtzOverRT(const std::vector& b, const Eigen::ArrayXXd& m, const Eigen::ArrayXXd& n) : b(b), m(m), n(n) {}; - - template - auto combinatorial(const TType& /*T*/, const MoleFractions& molefracs) const { - if (b.size() != static_cast(molefracs.size())){ - throw teqp::InvalidArgument("Bad size of molefracs"); - } - - using TYPE = std::common_type_t; - // The denominator in Phi - TYPE Vtot = 0.0; - for (auto i = 0U; i < molefracs.size(); ++i){ - auto v_i = b[i]; - Vtot += molefracs[i]*v_i; - } - - TYPE summer = 0.0; - for (auto i = 0U; i < molefracs.size(); ++i){ - auto v_i = b[i]; - // The ratio phi_i/z_i is expressed like this to better handle - // the case of z_i = 0, which would otherwise be a divide by zero - // in the case that the composition of one component is zero - auto phi_i_over_z_i = v_i/Vtot; - summer += molefracs[i]*log(phi_i_over_z_i); - } - return summer; - } - - template - auto get_Aij(std::size_t i, std::size_t j, const TType& T) const{ - return forceeval(m(i,j)*T + n(i,j)); - } - - template - auto total(const TType& T, const MoleFractions& molefracs) const { - - using TYPE = std::common_type_t; - TYPE summer = 0.0; - for (auto i = 0U; i < molefracs.size(); ++i){ - auto v_i = b[i]; - TYPE summerj = 0.0; - for (auto j = 0U; j < molefracs.size(); ++j){ - auto v_j = b[j]; - auto Aij = get_Aij(i,j,T); - auto Omega_ji = v_j/v_i*exp(-Aij/T); - summerj += molefracs[j]*Omega_ji; - } - summer += molefracs[i]*log(summerj); - } - return forceeval(-summer); - } - - // Returns ares/RT - template - auto operator () (const TType& T, const MoleFractions& molefracs) const { - return forceeval(total(T, molefracs) - combinatorial(T, molefracs)); - } -}; - -using ResidualHelmholtzOverRTVariant = std::variant, WilsonResidualHelmholtzOverRT>; - /** Cubic EOS with advanced mixing rules, the EoS/aE method of Jaubert and co-workers diff --git a/include/teqp/models/multifluid/multifluid_activity.hpp b/include/teqp/models/multifluid/multifluid_activity.hpp new file mode 100644 index 0000000..d7ea38c --- /dev/null +++ b/include/teqp/models/multifluid/multifluid_activity.hpp @@ -0,0 +1,72 @@ +#pragma once + +#include "teqp/types.hpp" + +#include "teqp/models/multifluid.hpp" +#include "teqp/models/activity/activity_models.hpp" + +namespace teqp::multifluid::multifluid_activity { + +/** + Implementing the general approach of: + Jaeger et al., + A theoretically based departure function for multi-fluid mixture models, + https://doi.org/10.1016/j.fluid.2018.04.015 +*/ +class MultifluidPlusActivity{ +private: + using multifluid_t = decltype(multifluidfactory(nlohmann::json{})); + const multifluid_t m_multifluid; + const ResidualHelmholtzOverRTVariant m_activity; + const std::vector b; + const double u; +public: + MultifluidPlusActivity(const nlohmann::json &spec) : + m_multifluid(multifluidfactory(spec.at("multifluid"))), + m_activity(ares_model_factory(spec.at("activity").at("aresmodel"))), + b(spec.at("activity").at("options").at("b")), + u(spec.at("activity").at("options").at("u")){} + +// const auto& get_activity() const { return m_activity; } + + template + auto R(const VecType& molefrac) const { + return get_R_gas(); + } + + template + auto alphar_activity(const TType& T, const RhoType& rho, const MoleFractions& molefrac) const { + auto gER_over_RT = std::visit([T, &molefrac](const auto& mod){return mod(T, molefrac); }, m_activity); // dimensionless + if (static_cast(b.size()) != molefrac.size()){ + throw teqp::InvalidArgument("Size of mole fractions is incorrect"); + } + + auto bm = contiguous_dotproduct(b, molefrac); + + const auto& Tcvec = m_multifluid.redfunc.Tc; + const auto& vcvec = m_multifluid.redfunc.vc; + + auto rhor = m_multifluid.redfunc.get_rhor(molefrac); + auto Tr = m_multifluid.redfunc.get_Tr(molefrac); + auto tau = forceeval(Tr/T); + auto delta_ref = forceeval(1.0/(u*bm*rhor)); + + std::decay_t> summer = 0.0; + for (auto i = 0; i < molefrac.size(); ++i){ + auto delta_i_ref = forceeval(1.0/(u*b[i]/vcvec(i))); + auto tau_i = forceeval(Tcvec(i)/T); + summer += molefrac(i)*(m_multifluid.alphar_taudeltai(tau, delta_ref, i) - m_multifluid.alphar_taudeltai(tau_i, delta_i_ref, i)); + } + return forceeval(log(1.0+rho*bm)/log(1.0+1.0/u)*(gER_over_RT - summer)); + } + + template + auto alphar(const TType& T, const RhoType& rho, const MoleFractions& molefrac) const { + return forceeval( + m_multifluid.alphar(T, rho, molefrac) + + alphar_activity(T, rho, molefrac) + ); + } +}; + +}; // namespace teqp diff --git a/interface/CPP/model_factory_multifluid_activity.cpp b/interface/CPP/model_factory_multifluid_activity.cpp new file mode 100644 index 0000000..d4cb242 --- /dev/null +++ b/interface/CPP/model_factory_multifluid_activity.cpp @@ -0,0 +1,11 @@ +#include "teqp/cpp/teqpcpp.hpp" +#include "teqp/cpp/deriv_adapter.hpp" +#include "teqp/models/multifluid/multifluid_activity.hpp" + +namespace teqp{ + namespace cppinterface{ + std::unique_ptr make_multifluid_activity(const nlohmann::json &j){ + return teqp::cppinterface::adapter::make_owned(multifluid::multifluid_activity::MultifluidPlusActivity(j)); + } + } +} diff --git a/interface/CPP/teqp_impl_factory.cpp b/interface/CPP/teqp_impl_factory.cpp index 7dc365c..22a20db 100644 --- a/interface/CPP/teqp_impl_factory.cpp +++ b/interface/CPP/teqp_impl_factory.cpp @@ -24,6 +24,7 @@ namespace teqp { std::unique_ptr make_LKP(const nlohmann::json &); std::unique_ptr make_multifluid(const nlohmann::json &); std::unique_ptr make_multifluid_association(const nlohmann::json &); + std::unique_ptr make_multifluid_activity(const nlohmann::json &); std::unique_ptr make_multifluid_ECS_HuberEly1994(const nlohmann::json &); std::unique_ptr make_AmmoniaWaterTillnerRoth(); std::unique_ptr make_LJ126_TholJPCRD2016(); @@ -78,6 +79,7 @@ namespace teqp { {"multifluid", [](const nlohmann::json& spec){ return make_multifluid(spec);}}, {"multifluid-ECS-HuberEly1994", [](const nlohmann::json& spec){ return make_multifluid_ECS_HuberEly1994(spec);}}, {"multifluid-association", [](const nlohmann::json& spec){ return make_multifluid_association(spec);}}, + {"multifluid-activity", [](const nlohmann::json& spec){ return make_multifluid_activity(spec);}}, {"AmmoniaWaterTillnerRoth", [](const nlohmann::json& /*spec*/){ return make_AmmoniaWaterTillnerRoth();}}, {"LJ126_TholJPCRD2016", [](const nlohmann::json& /*spec*/){ return make_LJ126_TholJPCRD2016();}}, From fc635952311e1735ce648b141518032a4f5df900 Mon Sep 17 00:00:00 2001 From: Ian Bell Date: Mon, 26 Aug 2024 11:01:29 -0400 Subject: [PATCH 2/9] Add dot product function --- include/teqp/types.hpp | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) diff --git a/include/teqp/types.hpp b/include/teqp/types.hpp index a80e5f4..9c688d7 100644 --- a/include/teqp/types.hpp +++ b/include/teqp/types.hpp @@ -225,6 +225,41 @@ namespace teqp { } #endif + /** + \brief Take the dot-product of two vector-like objects that have contiguous memory and support the .size() method + + This allows to mix and match 1D Eigen arrays and vectors and STL vectors + */ + auto contiguous_dotproduct(const auto& x, const auto&y){ + using x_t = std::decay_t; + using y_t = std::decay_t; + using ret_t = std::common_type_t; + if (x.size() != y.size()){ + throw teqp::InvalidArgument("Arguments to contiguous_dotproduct are not the same size"); + } + ret_t summer = 0.0; + for (auto i = 0U; i < x.size(); ++i){ + summer += x[i]*y[i]; + } + return summer; +// auto get_ptr = [](auto& x){ +// using x_t = std::decay_t; +// const x_t* x_0_ptr; +// if constexpr (is_eigen_impl::value){ +// x_0_ptr = x.data(); +// } +// else{ +// x_0_ptr = &(x[0]); +// } +// return x_0_ptr; +// }; +// // element-wise product and then a sum +// return ( +// Eigen::Map>(get_ptr(x), x.size()).template cast() +// * Eigen::Map>(get_ptr(y), y.size()).template cast() +// ).sum(); + } + }; // namespace teqp #endif From b8e65e20bd930f516d564215ede644889e51cd94 Mon Sep 17 00:00:00 2001 From: Ian Bell Date: Mon, 26 Aug 2024 11:05:07 -0400 Subject: [PATCH 3/9] Add missing include --- include/teqp/types.hpp | 2 ++ 1 file changed, 2 insertions(+) diff --git a/include/teqp/types.hpp b/include/teqp/types.hpp index 9c688d7..be8fad0 100644 --- a/include/teqp/types.hpp +++ b/include/teqp/types.hpp @@ -26,6 +26,8 @@ namespace teqp { #include "boost/multiprecision/cpp_bin_float.hpp" #endif +#include "teqp/exceptions.hpp" + // autodiff include #include #include From 4fb6fe255317eea170db2e33747af6a29686e0c2 Mon Sep 17 00:00:00 2001 From: Ian Bell Date: Mon, 26 Aug 2024 13:05:57 -0400 Subject: [PATCH 4/9] Expose calculations for excess Gibbs energy with MF+AC --- include/teqp/models/multifluid/multifluid_activity.hpp | 5 ++++- interface/pybind11_wrapper.cpp | 7 +++++++ 2 files changed, 11 insertions(+), 1 deletion(-) diff --git a/include/teqp/models/multifluid/multifluid_activity.hpp b/include/teqp/models/multifluid/multifluid_activity.hpp index d7ea38c..7a287b3 100644 --- a/include/teqp/models/multifluid/multifluid_activity.hpp +++ b/include/teqp/models/multifluid/multifluid_activity.hpp @@ -27,7 +27,10 @@ class MultifluidPlusActivity{ b(spec.at("activity").at("options").at("b")), u(spec.at("activity").at("options").at("u")){} -// const auto& get_activity() const { return m_activity; } + /// Calculate the dimensionless value of \f$g_{\rm GE}^{\rm E,R}/RT\f$ from the AC model + auto calc_gER_over_RT(double T, const Eigen::ArrayXd& molefrac) const { + return std::visit([T, &molefrac](const auto& mod){return mod(T, molefrac); }, m_activity); + } template auto R(const VecType& molefrac) const { diff --git a/interface/pybind11_wrapper.cpp b/interface/pybind11_wrapper.cpp index f938608..8b28fc8 100644 --- a/interface/pybind11_wrapper.cpp +++ b/interface/pybind11_wrapper.cpp @@ -17,6 +17,7 @@ #include "teqp/algorithms/ancillary_builder.hpp" #include "teqp/models/multifluid_ecs_mutant.hpp" #include "teqp/models/multifluid_association.hpp" +#include "teqp/models/multifluid/multifluid_activity.hpp" #include "teqp/models/saft/genericsaft.hpp" #include "teqp/algorithms/phase_equil.hpp" @@ -152,6 +153,7 @@ using CPA_t = decltype(teqp::CPA::CPAfactory("")); const std::type_index CPA_i{std::type_index(typeid(CPA_t))}; const std::type_index genericSAFT_i{std::type_index(typeid(teqp::saft::genericsaft::GenericSAFT))}; const std::type_index MultiFluidAssociation_i{std::type_index(typeid(MultifluidPlusAssociation))}; +const std::type_index MultiFluidActivity_i{std::type_index(typeid(teqp::multifluid::multifluid_activity::MultifluidPlusActivity))}; /** At runtime we can add additional model-specific methods that only apply for a particular model. We take in a Python-wrapped @@ -319,6 +321,11 @@ void attach_model_specific_methods(py::object& obj){ return get_typed(o).get_association().get_assoc_calcs(T, rhomolar, molefrac); }, "self"_a, "T"_a, "rhomolar"_a, "molefrac"_a), obj)); } + else if (index == MultiFluidActivity_i){ + setattr("calc_gER_over_RT", MethodType(py::cpp_function([](py::object& o, double T, REArrayd& molefrac){ + return get_typed(o).calc_gER_over_RT(T, molefrac); + }, "self"_a, "T"_a, "molefrac"_a), obj)); + } }; /// Instantiate "instances" of models (really wrapped Python versions of the models), and then attach all derivative methods From 540c2f47e538b9d8558d06da0a54fad6830c9656 Mon Sep 17 00:00:00 2001 From: Ian Bell Date: Mon, 26 Aug 2024 13:07:05 -0400 Subject: [PATCH 5/9] Fix signedness in check --- include/teqp/types.hpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/include/teqp/types.hpp b/include/teqp/types.hpp index be8fad0..a7b55da 100644 --- a/include/teqp/types.hpp +++ b/include/teqp/types.hpp @@ -236,7 +236,7 @@ namespace teqp { using x_t = std::decay_t; using y_t = std::decay_t; using ret_t = std::common_type_t; - if (x.size() != y.size()){ + if (static_cast(x.size()) != y.size()){ throw teqp::InvalidArgument("Arguments to contiguous_dotproduct are not the same size"); } ret_t summer = 0.0; From ec323c78b4822acf1308652e98de914b8766d17f Mon Sep 17 00:00:00 2001 From: Ian Bell Date: Mon, 26 Aug 2024 14:09:10 -0400 Subject: [PATCH 6/9] Fix notebook, remove outputs --- doc/source/models/MultifluidPlusAC.ipynb | 26 +++++++----------------- 1 file changed, 7 insertions(+), 19 deletions(-) diff --git a/doc/source/models/MultifluidPlusAC.ipynb b/doc/source/models/MultifluidPlusAC.ipynb index 13bf5e3..393b46d 100644 --- a/doc/source/models/MultifluidPlusAC.ipynb +++ b/doc/source/models/MultifluidPlusAC.ipynb @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "937c2216", "metadata": {}, "outputs": [], @@ -49,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "4e03c647", "metadata": {}, "outputs": [], @@ -103,13 +103,12 @@ "7 0.7533 0.0041 0.2380 0.0040 144.287 0.081 223.10\n", "8 0.7150 0.0045 0.2813 0.0044 159.015 0.081 223.10\n", "9 0.6942 0.0047 0.3064 0.0047 165.347 0.081 223.10\n", - "\"\"\"), sep='\\s+', engine='python')\n", - "model = teqp.make_model(j)" + "\"\"\"), sep='\\s+', engine='python')" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "id": "74404b8c", "metadata": {}, "outputs": [], @@ -128,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "id": "ab83b1d7", "metadata": {}, "outputs": [], @@ -192,21 +191,10 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "id": "500aa1ae", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "labels = ['linear+Wilson','Lorentz-Berthelot+Wilson','GERG-2008','linear']\n", "for imod, model in enumerate([get_model(BIP_linear), get_model(BIP_LorentzBerthelot), get_model(), get_model('linear')]):\n", From 9644e542570a4ba69bccfe14ee3dfde2d9b022e4 Mon Sep 17 00:00:00 2001 From: Ian Bell Date: Tue, 27 Aug 2024 15:39:31 -0400 Subject: [PATCH 7/9] Add a g_{GE}^{E,R}/RT term for binary mixtures from the invariant reducing function And another flawed approach --- .../teqp/models/activity/activity_models.hpp | 49 ++++++++++++++++++- 1 file changed, 48 insertions(+), 1 deletion(-) diff --git a/include/teqp/models/activity/activity_models.hpp b/include/teqp/models/activity/activity_models.hpp index b1243f8..e725afa 100644 --- a/include/teqp/models/activity/activity_models.hpp +++ b/include/teqp/models/activity/activity_models.hpp @@ -101,7 +101,46 @@ class WilsonResidualHelmholtzOverRT { } }; -using ResidualHelmholtzOverRTVariant = std::variant, WilsonResidualHelmholtzOverRT>; +///** +// \note This approach works well except that... the derivatives at the pure fluid endpoints don't. So this is more a record as a failed idea +// */ +//template +//class BinaryBetaResidualHelmholtzOverRT { +// +//public: +// const std::vector c; +// BinaryBetaResidualHelmholtzOverRT(const std::vector& c) : c(c) {}; +// +// // Returns ares/RT +// template +// auto operator () (const TType& /*T*/, const MoleFractions& molefracs) const { +// if (molefracs.size() != 2){ +// throw teqp::InvalidArgument("Must be size of 2"); +// } +// std::decay_t> out = c[0]*pow(molefracs[0], c[1])*pow(molefracs[1], c[2]); +// return out; +// } +//}; + +template +class BinaryInvariantResidualHelmholtzOverRT { + +public: + const std::vector c; + BinaryInvariantResidualHelmholtzOverRT(const std::vector& c) : c(c) {}; + + // Returns ares/RT + template + auto operator () (const TType& /*T*/, const MoleFractions& molefracs) const { + if (molefracs.size() != 2){ + throw teqp::InvalidArgument("Must be size of 2"); + } + std::decay_t> out = c[0]*molefracs[0]*molefracs[1]*(c[1] + c[2]*molefracs[1]); + return out; + } +}; + +using ResidualHelmholtzOverRTVariant = std::variant, WilsonResidualHelmholtzOverRT, BinaryInvariantResidualHelmholtzOverRT>; inline ResidualHelmholtzOverRTVariant ares_model_factory(const nlohmann::json& armodel) { @@ -112,6 +151,14 @@ inline ResidualHelmholtzOverRTVariant ares_model_factory(const nlohmann::json& a auto nWilson = build_square_matrix(armodel.at("n")); return WilsonResidualHelmholtzOverRT(b, mWilson, nWilson); } +// else if (type == "binaryBeta"){ +// std::vector c = armodel.at("c"); +// return BinaryBetaResidualHelmholtzOverRT(c); +// } + else if (type == "binaryInvariant"){ + std::vector c = armodel.at("c"); + return BinaryInvariantResidualHelmholtzOverRT(c); + } else{ throw teqp::InvalidArgument("bad type of ares model: " + type); } From 3a871b3e19ad7ee03bca35ec3d7a650fea3ef98b Mon Sep 17 00:00:00 2001 From: Ian Bell Date: Wed, 28 Aug 2024 12:16:30 -0400 Subject: [PATCH 8/9] Add COSMO-SAC-2010 as an option Other COSMO-SAC variants are possible, would require a bit of refactoring --- include/teqp/models/activity/COSMOSAC.hpp | 360 ++++++++++++++++++ .../teqp/models/activity/activity_models.hpp | 21 +- 2 files changed, 380 insertions(+), 1 deletion(-) create mode 100644 include/teqp/models/activity/COSMOSAC.hpp diff --git a/include/teqp/models/activity/COSMOSAC.hpp b/include/teqp/models/activity/COSMOSAC.hpp new file mode 100644 index 0000000..cd05e50 --- /dev/null +++ b/include/teqp/models/activity/COSMOSAC.hpp @@ -0,0 +1,360 @@ +/** + This header is derived from the code in https://github.com/usnistgov/COSMOSAC, and the methodology is described in the paper: + + Ian H. Bell, Erik Mickoleit, Chieh-Ming Hsieh, Shiang-Tai Lin, Jadran Vrabec, Cornelia Breitkopf, and Andreas Jäger + Journal of Chemical Theory and Computation 2020 16 (4), 2635-2646 + DOI: 10.1021/acs.jctc.9b01016 + */ + +namespace teqp::activity::activity_models::COSMOSAC{ + +/** + A single sigma profile. In this data structure (for consistency with the 2005 Virginia Tech + database of COSMO-SAC parameters), the first column is the electron density in [e/A^2], and the second column + is the probability of finding a segment with this electron density, multiplied by the segment area, in A^2 + */ +class SigmaProfile { +public: + Eigen::ArrayXd m_sigma, m_psigmaA; + /// Default constructor + SigmaProfile() {}; + /// Copy-by-reference constructor + SigmaProfile(const Eigen::ArrayXd &sigma, const Eigen::ArrayXd &psigmaA) : m_sigma(sigma), m_psigmaA(psigmaA) {}; + /// Copy-by-reference constructor with std::vector + SigmaProfile(const std::vector &sigma, const std::vector &psigmaA) : m_sigma(Eigen::Map(&(sigma[0]), sigma.size())), m_psigmaA(Eigen::Map(&(psigmaA[0]), psigmaA.size())) {}; + /// Move constructor + SigmaProfile(Eigen::ArrayXd &&sigma, Eigen::ArrayXd &&psigmaA) : m_sigma(sigma), m_psigmaA(psigmaA) {}; + const Eigen::ArrayXd &psigmaA() const { return m_psigmaA; } + const Eigen::ArrayXd &sigma() const { return m_sigma; } + const Eigen::ArrayXd psigma(double A_i) const { return m_psigmaA/A_i; } +}; + +// A holder class for the sigma profiles for a given fluid +struct FluidSigmaProfiles { + SigmaProfile nhb, ///< The profile for the non-hydrogen-bonding segments + oh, ///< The profile for the OH-bonding segments + ot; ///< The profile for the "other" segments +}; + +struct COSMO3Constants { + double + AEFFPRIME = 7.25, // [A^2] + c_OH_OH = 4013.78, // [kcal A^4/(mol e^2)] + c_OT_OT = 932.31, // [kcal A^4 /(mol e^2)] + c_OH_OT = 3016.43, // [kcal A^4 /(mol e^2)] + A_ES = 6525.69, // [kcal A^4 /(mol e^2)] + B_ES = 1.4859e8, // [kcal A^4 K^2/(mol e^2)] + N_A = 6.022140758e23, // [mol^{-1}] + k_B = 1.38064903e-23, // [J K^{-1}] + R = k_B*N_A/4184, // [kcal/(mol*K)]; Universal gas constant of new redefinition of 2018, https://doi.org/10.1088/1681-7575/aa99bc + Gamma_rel_tol = 1e-8; // relative tolerance for Gamma in iterative loop + bool fast_Gamma = false; + std::string to_string() { + return "NOT IMPLEMENTED YET"; + //return "c_hb: " + std::to_string(c_hb) + " kcal A^4 /(mol*e^2) \nsigma_hb: " + std::to_string(sigma_hb) + " e/A^2\nalpha_prime: " + std::to_string(alpha_prime) + " kcal A^4 /(mol*e^2)\nAEFFPRIME: " + std::to_string(AEFFPRIME) + " A\nR: " + std::to_string(R) + " kcal/(mol*K)"; + } +}; + +enum class profile_type { NHB_PROFILE, OH_PROFILE, OT_PROFILE }; + +class COSMO3 { +private: + std::vector A_COSMO_A2; ///< The area per fluid, in \AA^2 + std::vector profiles; ///< The vector of profiles, one per fluid + COSMO3Constants m_consts; + Eigen::Index ileft, w; +public: + COSMO3(const std::vector& A_COSMO_A2, const std::vector &SigmaProfiles, const COSMO3Constants &constants = COSMO3Constants()) + : A_COSMO_A2(A_COSMO_A2), profiles(SigmaProfiles), m_consts(constants) { + Eigen::Index iL, iR; + std::tie(iL, iR) = get_nonzero_bounds(); + this->ileft = iL; this->w = iR - iL + 1; + }; + + /// Determine the left and right bounds for the psigma that is nonzero to accelerate the iterative calculations + std::tuple get_nonzero_bounds(){ + // Determine the range of entries in p(sigma) that are greater than zero, we + // will only calculate segment activity coefficients for these segments + Eigen::Index min_ileft = 51, max_iright = 0; + for (auto i = 0U; i < profiles.size(); ++i) { + const Eigen::ArrayXd psigma = profiles[i].nhb.psigma(A_COSMO_A2[i]); + Eigen::Index ileft = 0, iright = psigma.size(); + for (auto ii = 0; ii < psigma.size(); ++ii) { if (std::abs(psigma(ii)) > 1e-16) { ileft = ii; break; } } + for (auto ii = psigma.size() - 1; ii > ileft; --ii) { if (std::abs(psigma(ii)) > 1e-16) { iright = ii; break; } } + if (ileft < min_ileft) { min_ileft = ileft; } + if (iright > max_iright) { max_iright = iright; } + } + return std::make_tuple(min_ileft, max_iright); + } + + template + auto get_psigma_mix(const MoleFractions &z, profile_type type = profile_type::NHB_PROFILE) const { + Eigen::ArrayX> psigma_mix(51); psigma_mix.fill(0); + std::decay_t xA = 0.0; + for (auto i = 0; i < z.size(); ++i) { + switch (type) { + case profile_type::NHB_PROFILE: + psigma_mix += z[i] * profiles[i].nhb.psigmaA(); break; + case profile_type::OH_PROFILE: + psigma_mix += z[i] * profiles[i].oh.psigmaA(); break; + case profile_type::OT_PROFILE: + psigma_mix += z[i] * profiles[i].ot.psigmaA(); break; + } + xA += z[i] * A_COSMO_A2[i]; + } + psigma_mix /= xA; + return psigma_mix; + } + + /// Get access to the parameters that are in use + COSMO3Constants &get_mutable_COSMO_constants() { return m_consts; } + + std::tuple get_ileftw() const { return std::make_tuple(ileft, w);} ; + + double get_c_hb(profile_type type1, profile_type type2) const{ + + if (type1 == type2){ + if (type1 == profile_type::OH_PROFILE) { + return m_consts.c_OH_OH; + } + else if (type1 == profile_type::OT_PROFILE) { + return m_consts.c_OT_OT; + } + else { + return 0.0; + } + } + else if ((type1 == profile_type::OH_PROFILE && type2 == profile_type::OT_PROFILE) + || (type1 == profile_type::OT_PROFILE && type2 == profile_type::OH_PROFILE)) { + return m_consts.c_OH_OT; + } + else { + return 0.0; + } + } + template + auto get_DELTAW(const TType& T, profile_type type_t, profile_type type_s) const { + auto delta_sigma = 2*0.025/50; + Eigen::ArrayXX DELTAW(51, 51); + double cc = get_c_hb(type_t, type_s); + for (auto m = 0; m < 51; ++m) { + for (auto n = 0; n < 51; ++n) { + double sigma_m = -0.025 + delta_sigma*m, + sigma_n = -0.025 + delta_sigma*n, + c_hb = (sigma_m*sigma_n >= 0) ? 0 : cc; + auto c_ES = m_consts.A_ES + m_consts.B_ES/(T*T); + DELTAW(m, n) = c_ES*POW2(sigma_m + sigma_n) - c_hb*POW2(sigma_m-sigma_n); + } + } + return DELTAW; + } + template + auto get_DELTAW_fast(TType T, profile_type type_t, profile_type type_s) const { + auto delta_sigma = 2 * 0.025 / 50; + Eigen::ArrayXX DELTAW(51, 51); DELTAW.setZero(); + double cc = get_c_hb(type_t, type_s); + for (auto m = ileft; m < ileft+w+1; ++m) { + for (auto n = ileft; n < ileft+w+1; ++n) { + double sigma_m = -0.025 + delta_sigma*m, + sigma_n = -0.025 + delta_sigma*n, + c_hb = (sigma_m*sigma_n >= 0) ? 0 : cc; + auto c_ES = m_consts.A_ES + m_consts.B_ES / (T*T); + DELTAW(m, n) = c_ES*POW2(sigma_m + sigma_n) - c_hb*POW2(sigma_m - sigma_n); + } + } + return DELTAW; + } + + template + auto get_AA(const TType& T, PSigma psigmas){ + // Build the massive \Delta W matrix that is 153*153 in size + Eigen::ArrayXX DELTAW(51, 51); // Depends on temperature + double R = m_consts.R; + std::vector types = { profile_type::NHB_PROFILE, profile_type::OH_PROFILE, profile_type::OT_PROFILE }; + for (auto i = 0; i < 3; ++i) { + for (auto j = 0; j < 3; ++j) { + DELTAW.matrix().block(51 * i, 51 * j, 51, 51) = get_DELTAW(T, types[i], types[j]); + } + } + return Eigen::exp(-DELTAW/(R*T)).rowwise()*psigmas.transpose(); + } + /** + Obtain the segment activity coefficients \f$\Gamma\f$ for given a set of charge densities + + If fast_Gamma is enabled, then only segments that have some contribution are included in the iteration (can be a significant acceleration for nonpolar+nonpolar mixtures) + + \param T Temperature, in K + \param psigmas Charge densities, in the order of NHB, OH, OT. Length is 153. + */ + template + auto get_Gamma(const TType& T, PSigmaType psigmas) const { + auto startTime = std::chrono::high_resolution_clock::now(); + + using TXType = std::decay_t>; + + double R = m_consts.R; + Eigen::ArrayX Gamma(153), Gammanew(153); Gamma.setOnes(); Gammanew.setOnes(); + + // A convenience function to convert double values to string in scientific format + auto to_scientific = [](double val) { std::ostringstream out; out << std::scientific << val; return out.str(); }; + + auto max_iter = 2000; + if (!m_consts.fast_Gamma){ + // The slow and simple branch + + // Build the massive \Delta W matrix that is 153*153 in size + Eigen::ArrayXX DELTAW(153, 153); + std::vector types = { profile_type::NHB_PROFILE, profile_type::OH_PROFILE, profile_type::OT_PROFILE }; + for (auto i = 0; i < 3; ++i) { + for (auto j = 0; j < 3; ++j) { + DELTAW.matrix().block(51*i, 51*j, 51, 51) = get_DELTAW(T, types[i], types[j]); + } + } + + auto AA = (Eigen::exp(-DELTAW / (R*T)).template cast().rowwise()*psigmas.template cast().transpose()).eval(); + for (auto counter = 0; counter <= max_iter; ++counter) { + Gammanew = 1 / (AA.rowwise()*Gamma.transpose()).rowwise().sum(); + Gamma = (Gamma + Gammanew) / 2; + double maxdiff = getbaseval(((Gamma - Gammanew) / Gamma).cwiseAbs().real().maxCoeff()); + if (maxdiff < m_consts.Gamma_rel_tol) { + break; + } + if (counter == max_iter) { + throw std::invalid_argument("Could not obtain the desired tolerance of " + + to_scientific(m_consts.Gamma_rel_tol) + + " after " + + std::to_string(max_iter) + + " iterations in get_Gamma; current value is " + + to_scientific(maxdiff)); + } + } + return Gamma; + } + else { + // The fast branch! + // ---------------- + + // Build the massive AA matrix that is 153*153 in size + auto midTime = std::chrono::high_resolution_clock::now(); + std::vector types = { profile_type::NHB_PROFILE, profile_type::OH_PROFILE, profile_type::OT_PROFILE }; + std::vector offsets = {0*51, 1*51, 2*51}; + Eigen::ArrayXX AA(153, 153); + for (auto i = 0; i < 3; ++i) { + for (auto j = 0; j < 3; ++j) { + Eigen::Index rowoffset = offsets[i], coloffset = offsets[j]; + AA.matrix().block(rowoffset + ileft, coloffset + ileft, w, w) = Eigen::exp(-get_DELTAW_fast(T, types[i], types[j]).block(ileft, ileft, w, w).array() / (R*T)).template cast().rowwise()*psigmas.template cast().segment(coloffset+ileft,w).transpose(); + } + } + auto midTime2 = std::chrono::high_resolution_clock::now(); + + for (auto counter = 0; counter <= max_iter; ++counter) { + for (Eigen::Index offset : {51*0, 51*1, 51*2}){ + Gammanew.segment(offset + ileft, w) = 1 / ( + AA.matrix().block(offset+ileft,51*0+ileft,w,w).array().rowwise()*Gamma.segment(51*0+ileft, w).transpose() + + AA.matrix().block(offset+ileft,51*1+ileft,w,w).array().rowwise()*Gamma.segment(51*1+ileft, w).transpose() + + AA.matrix().block(offset+ileft,51*2+ileft,w,w).array().rowwise()*Gamma.segment(51*2+ileft, w).transpose() + ).rowwise().sum(); + } + for (Eigen::Index offset : {51 * 0, 51 * 1, 51 * 2}) { + Gamma.segment(offset + ileft, w) = (Gamma.segment(offset + ileft, w) + Gammanew.segment(offset + ileft, w)) / 2; + } + double maxdiff = getbaseval(((Gamma - Gammanew) / Gamma).cwiseAbs().real().maxCoeff()); + if (maxdiff < m_consts.Gamma_rel_tol) { + break; + } + if (counter == max_iter){ + throw std::invalid_argument("Could not obtain the desired tolerance of " + + to_scientific(m_consts.Gamma_rel_tol) + +" after " + +std::to_string(max_iter) + +" iterations in get_Gamma; current value is " + + to_scientific(maxdiff)); + } + } + auto endTime = std::chrono::high_resolution_clock::now(); + //std::cout << std::chrono::duration(midTime - startTime).count() << " s elapsed (DELTAW)\n"; + //std::cout << std::chrono::duration(midTime2 - midTime).count() << " s elapsed (AA)\n"; + //std::cout << std::chrono::duration(endTime - midTime2).count() << " s elapsed (comps)\n"; + //std::cout << std::chrono::duration(endTime - startTime).count() << " s elapsed (total)\n"; + return Gamma; + } + } + + /** + The residual part of ln(γ_i), for the i-th component + */ + template + auto get_lngamma_resid(std::size_t i, TType T, const Array &lnGamma_mix) const + { + double AEFFPRIME = m_consts.AEFFPRIME; + Eigen::ArrayX psigmas(3*51); // For a pure fluid, p(sigma) does not depend on temperature + double A_i = A_COSMO_A2[i]; + psigmas << profiles[i].nhb.psigma(A_i), profiles[i].oh.psigma(A_i), profiles[i].ot.psigma(A_i); + // double check_sum = psigmas.sum(); //// Should sum to 1.0 + auto lnGammai = get_Gamma(T, psigmas).log().eval(); + return A_i/AEFFPRIME*(psigmas*(lnGamma_mix - lnGammai)).sum(); + } + /** + This overload is a convenience overload, less computationally + efficient, but simpler to use and more in keeping with the other + contributions. It does not require the lnGamma_mix to be pre-calculated + and passed into this function + */ + + template + auto get_lngamma_resid(TType T, const MoleFracs& molefracs) const + { + using TXType = std::decay_t>; + + Eigen::Array psigmas; + psigmas << get_psigma_mix(molefracs, profile_type::NHB_PROFILE), get_psigma_mix(molefracs, profile_type::OH_PROFILE), get_psigma_mix(molefracs, profile_type::OT_PROFILE); + + Eigen::ArrayX lngamma(molefracs.size()); + // double check_sum = psigmas.sum(); //// Should sum to 1.0 + Eigen::ArrayX lnGamma_mix = get_Gamma(T, psigmas).log(); + for (Eigen::Index i = 0; i < molefracs.size(); ++i) { + lngamma(i) = get_lngamma_resid(i, T, lnGamma_mix); + } + return lngamma; + } + + // EigenArray get_lngamma_disp(const EigenArray &x) const { + // if (x.size() != 2){ throw std::invalid_argument("Multi-component mixtures not supported for dispersive contribution yet"); } + // double w = 0.27027; // default value + // auto cls0 = m_fluids[0].dispersion_flag, cls1 = m_fluids[1].dispersion_flag; + // using d = FluidProfiles::dispersion_classes; + // if ((cls0 == d::DISP_WATER && cls1 == d::DISP_ONLY_ACCEPTOR) + // | + // (cls1 == d::DISP_WATER && cls0 == d::DISP_ONLY_ACCEPTOR)){ + // w = -0.27027; + // } + // else if ((cls0 == d::DISP_WATER && cls1 == d::DISP_COOH) + // | + // (cls1 == d::DISP_WATER && cls0 == d::DISP_COOH)) { + // w = -0.27027; + // } + // else if ((cls0 == d::DISP_COOH && (cls1 == d::DISP_NHB || cls1 == d::DISP_DONOR_ACCEPTOR)) + // | + // (cls1 == d::DISP_COOH && (cls0 == d::DISP_NHB || cls0 == d::DISP_DONOR_ACCEPTOR))) { + // w = -0.27027; + // } + // + // double ekB0 = m_fluids[0].dispersion_eoverkB, ekB1 = m_fluids[1].dispersion_eoverkB; + // double A = w*(0.5*(ekB0+ekB1) - sqrt(ekB0*ekB1)); + // EigenArray lngamma_dsp(2); + // lngamma_dsp(0) = A*x[1]*x[1]; + // lngamma_dsp(1) = A*x[0]*x[0]; + // return lngamma_dsp; + // } + // EigenArray get_lngamma(double T, const EigenArray &x) const override { + // return get_lngamma_comb(T, x) + get_lngamma_resid(T, x) + get_lngamma_disp(x); + // } + + // Returns ares/RT + template + auto operator () (const TType& T, const MoleFractions& molefracs) const { + return contiguous_dotproduct(molefracs, get_lngamma_resid(T, molefracs)); + } +}; + +}; diff --git a/include/teqp/models/activity/activity_models.hpp b/include/teqp/models/activity/activity_models.hpp index e725afa..c5ea73d 100644 --- a/include/teqp/models/activity/activity_models.hpp +++ b/include/teqp/models/activity/activity_models.hpp @@ -1,5 +1,6 @@ #pragma once +#include "teqp/models/activity/COSMOSAC.hpp" namespace teqp::activity::activity_models{ @@ -140,7 +141,7 @@ class BinaryInvariantResidualHelmholtzOverRT { } }; -using ResidualHelmholtzOverRTVariant = std::variant, WilsonResidualHelmholtzOverRT, BinaryInvariantResidualHelmholtzOverRT>; +using ResidualHelmholtzOverRTVariant = std::variant, WilsonResidualHelmholtzOverRT, BinaryInvariantResidualHelmholtzOverRT, COSMOSAC::COSMO3>; inline ResidualHelmholtzOverRTVariant ares_model_factory(const nlohmann::json& armodel) { @@ -159,6 +160,24 @@ inline ResidualHelmholtzOverRTVariant ares_model_factory(const nlohmann::json& a std::vector c = armodel.at("c"); return BinaryInvariantResidualHelmholtzOverRT(c); } + else if (type == "COSMO-SAC-2010"){ + std::vector A_COSMOSAC_A2 = armodel.at("A_COSMOSAC / A^2"); + std::vector profiles; + for (auto& el : armodel.at("profiles")){ + COSMOSAC::FluidSigmaProfiles prof; + auto get_ = [](const auto& j){ + return COSMOSAC::SigmaProfile{ + j.at("sigma / e/A^2").template get>(), + j.at("p(sigma)*A / A^2").template get>() + }; + }; + prof.nhb = get_(el.at("nhb")); + prof.oh = get_(el.at("oh")); + prof.ot = get_(el.at("ot")); + profiles.push_back(prof); + } + return COSMOSAC::COSMO3(A_COSMOSAC_A2, profiles); + } else{ throw teqp::InvalidArgument("bad type of ares model: " + type); } From f273f6c0c356ebaf9255334161b27d034812217e Mon Sep 17 00:00:00 2001 From: Ian Bell Date: Thu, 29 Aug 2024 09:15:57 -0400 Subject: [PATCH 9/9] Update cyclopentane to be the updated EOS Was changed after the preprint, fixed also in CoolProp --- teqp/fluiddata/dev/fluids/Cyclopentane.json | 124 ++++++++++---------- 1 file changed, 65 insertions(+), 59 deletions(-) diff --git a/teqp/fluiddata/dev/fluids/Cyclopentane.json b/teqp/fluiddata/dev/fluids/Cyclopentane.json index 5a12d17..cccae6a 100644 --- a/teqp/fluiddata/dev/fluids/Cyclopentane.json +++ b/teqp/fluiddata/dev/fluids/Cyclopentane.json @@ -200,9 +200,9 @@ "T_units": "K", "hmolar": 38057.35612816187, "hmolar_units": "J/mol", - "p": 4571200, + "p": 4582800, "p_units": "Pa", - "rhomolar": 3820.000000000001, + "rhomolar": 3920.000000000001, "rhomolar_units": "mol/m^3", "smolar": 87.99746047649523, "smolar_units": "J/mol/K" @@ -240,26 +240,26 @@ "acentric_units": "-", "alpha0": [ { - "a1": 3.2489131288, - "a2": 2.6444166315, + "a1": -0.3946233253, + "a2": 2.4918910143, "type": "IdealGasHelmholtzLead" }, { - "a": 0.96, + "a": 3.0, "type": "IdealGasHelmholtzLogTau" }, { "n": [ - 3.34, - 18.6, - 13.9, - 4.86 + 1.34, + 13.4, + 17.4, + 6.65 ], "t": [ - 0.2345032439615415, - 2.540451809583366, - 5.276322989134683, - 10.35722660830141 + 0.4494645509262878, + 2.3059485656218244, + 4.299226139294927, + 10.161807238333463 ], "type": "IdealGasHelmholtzPlanckEinstein" } @@ -291,73 +291,79 @@ 1 ], "n": [ - 0.0536938, - 1.60394, - -2.41244, - -0.474009, - 0.203482, - -0.965616, - -0.344543, - 0.353975, - -0.231373, - -0.0379099 + 0.0630928, + 1.50365, + -2.37099, + -0.484886, + 0.191843, + -0.835582, + -0.435929, + 0.545607, + -0.209741, + -0.0387635 ], "t": [ - 1, + 1.0, 0.29, - 0.8, - 1.14, - 0.5, - 2, + 0.85, + 1.185, + 0.45, + 2.28, + 1.8, 1.5, - 1, - 3.36, - 0.95 - ], + 2.9, + 0.93], "type": "ResidualHelmholtzPower" }, { "beta": [ - 1.15, - 1.61, - 0.66, - 2.72 + 0.63, + 2.8, + 0.5, + 0.95, + 0.23 ], "d": [ 1, 1, 3, - 3 + 3, + 2 ], "epsilon": [ - 0.68, - 0.97, - 0.84, - 0.66 + 0.684, + 0.7, + 0.77, + 0.625, + 0.42 ], "eta": [ - 0.82, - 1.19, - 0.79, - 1.52 + 0.86, + 0.85, + 0.86, + 1.53, + 5.13 ], "gamma": [ - 1.08, - 0.36, - 0.09, - 1.48 + 1.22, + 0.32, + 0.22, + 1.94, + 1.21 ], "n": [ - 0.867586, - -0.381827, - -0.108741, - -0.0976984 + 0.677674, + -0.137043, + -0.0852862, + -0.128085, + -0.00389381 ], "t": [ - 1, - 2.5, - 2.5, - 1.5 + 1.05, + 4.0, + 2.33, + 1.5, + 1.0 ], "type": "ResidualHelmholtzGaussian" } @@ -426,7 +432,7 @@ "hmolar_units": "J/mol", "p": 4571200.0, "p_units": "Pa", - "rhomolar": 3820.000000000001, + "rhomolar": 3920.000000000001, "rhomolar_units": "mol/m^3", "smolar": 87.98268208465164, "smolar_units": "J/mol/K" @@ -557,4 +563,4 @@ "type": "Chung" } } -} \ No newline at end of file +}