{ "cells": [ { "cell_type": "markdown", "id": "03058ac5", "metadata": {}, "source": [ "# Multifluid+Association\n", "\n", "Version 0.22 of teqp adds a new sort of hybrid model -- a combination of multifluid model plus an association model. Conceptually the pure fluid EOS consider the self-association implicitly, so you may want to consider the association model to only allow cross-assocation, which be enforced by supplying the \"self_assocation_mask\" logical mask in the \"options\" of the model. See below" ] }, { "cell_type": "code", "execution_count": 1, "id": "ae2f6b60", "metadata": { "execution": { "iopub.execute_input": "2025-10-15T23:08:36.692266Z", "iopub.status.busy": "2025-10-15T23:08:36.692060Z", "iopub.status.idle": "2025-10-15T23:08:37.189309Z", "shell.execute_reply": "2025-10-15T23:08:37.188266Z" } }, "outputs": [], "source": [ "import teqp, json, numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas \n", "\n", "BIP = [{\n", " \"Name1\": \"Water\",\n", " \"Name2\": \"Ammonia\",\n", " \"betaT\": 1.0,\n", " \"gammaT\": 1.0,\n", " \"betaV\": 1.0,\n", " \"gammaV\": 1.0,\n", " \"F\": 0.0\n", "}]\n", "jmf = {\n", " \"components\": [\"Water\", \"Ammonia\"],\n", " \"root\": teqp.get_datapath(),\n", " \"BIP\": BIP, \n", "}\n", "\n", "jassoc = {\n", " \"kind\": \"Dufal\",\n", " \"model\": {\n", " \"sigma / m\": [3.0555e-10, 3.3309e-10],\n", " \"epsilon / J/mol\": [3475.445374388054, 323.70*8.3124462618],\n", " \"lambda_r\": [35.823, 36.832],\n", " \n", " # Note the scaling factors of 0.2 on the bonding energy to yield more reasonable behavior\n", " \"epsilon_HB / J/mol\": [0.2*13303.140189045183, 0.2*1105.0*8.314462618], \n", " \n", " \"K_HB / m^3\": [496.66e-30, 560.73e-30],\n", " \"kmat\": [[0.0,0.0],[0.0, 0.0]],\n", " \"Delta_rule\": \"Dufal\",\n", " \"molecule_sites\": [[\"e\",\"e\",\"H\",\"H\"],[\"e\",\"H\",\"H\",\"H\"]],\n", " \"options\": {\"self_association_mask\": [False, False]}\n", " }\n", "}\n", " \n", "j = {\n", " 'kind': 'multifluid-association',\n", " 'model': {\n", " 'multifluid': jmf,\n", " 'association': jassoc\n", " }\n", "}\n", "\n", "model = teqp.make_model(j)" ] }, { "cell_type": "code", "execution_count": 2, "id": "469bdad6", "metadata": { "execution": { "iopub.execute_input": "2025-10-15T23:08:37.190943Z", "iopub.status.busy": "2025-10-15T23:08:37.190679Z", "iopub.status.idle": "2025-10-15T23:08:37.193405Z", "shell.execute_reply": "2025-10-15T23:08:37.192814Z" } }, "outputs": [], "source": [ "# model.get_assoc_calcs(300, 300, np.array([0.5, 0.5]))" ] }, { "cell_type": "code", "execution_count": 3, "id": "3d9a26b9", "metadata": { "execution": { "iopub.execute_input": "2025-10-15T23:08:37.194489Z", "iopub.status.busy": "2025-10-15T23:08:37.194373Z", "iopub.status.idle": "2025-10-15T23:08:37.409552Z", "shell.execute_reply": "2025-10-15T23:08:37.409048Z" } }, "outputs": [], "source": [ "T = 293.15 # K\n", "pure = teqp.build_multifluid_model([\"Ammonia\"], teqp.get_datapath())\n", "anc = pure.build_ancillaries()\n", "j = model.trace_VLE_isotherm_binary(T, np.array([0, anc.rhoL(T)]), np.array([0, anc.rhoV(T)]))\n", "df = pandas.DataFrame(j)" ] }, { "cell_type": "code", "execution_count": 4, "id": "e9760392", "metadata": { "execution": { "iopub.execute_input": "2025-10-15T23:08:37.410888Z", "iopub.status.busy": "2025-10-15T23:08:37.410738Z", "iopub.status.idle": "2025-10-15T23:08:37.637801Z", "shell.execute_reply": "2025-10-15T23:08:37.637312Z" } }, "outputs": [ { "data": { 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(df['xL_0 / mole frac.'], df['pL / Pa']/1e3)\n", "plt.plot(df['xV_0 / mole frac.'], df['pL / Pa']/1e3)\n", "plt.yscale('log')\n", "plt.gca().set(xlabel='$x_1,y_1$ / mole frac.', ylabel='p / kPa')\n", "plt.title(f'Water(1) + Ammonia(2) @ {T} K')\n", "plt.show()" ] } ], "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": "3.11.14" } }, "nbformat": 4, "nbformat_minor": 5 }