diff --git a/intermediateFoodTests/Ex1.py b/intermediateFoodTests/Ex1.py index 189054665..15dfd0789 100644 --- a/intermediateFoodTests/Ex1.py +++ b/intermediateFoodTests/Ex1.py @@ -18,7 +18,7 @@ import matplotlib.pyplot as plt from tespy.components import Separator,Merge,CycleCloser,Valve -from tespy.components.newcomponents import DiabaticSimpleHeatExchanger,MergeWithPressureLoss,SeparatorWithSpeciesSplits +from tespy.components.newcomponents import DiabaticSimpleHeatExchanger,MergeWithPressureLoss,SeparatorWithSpeciesSplits,SimpleHeatExchanger import logging #logging.basicConfig(level=logging.DEBUG) @@ -45,7 +45,9 @@ # set conditions around boiler # fluid_back_ends={'Water': "INCOMP", "PHE": "INCOMP", "S800": "INCOMP"} c1.set_attr(fluid={'INCOMP::Water': 0.80,'INCOMP::PHE': 0.15,'INCOMP::S800': 0.05}, m=100, h=h0, p=p0, mixing_rule="incompressible") -c2.set_attr(h=h0,p=p0) +#c2.set_attr(h=h0,p=p0) +c2.set_attr(h=h0) +boiler.set_attr(pr=1) # solve and print results network.solve('design') diff --git a/intermediateFoodTests/Ex1tespy070.csv b/intermediateFoodTests/Ex1tespy070.csv index f5ff45442..91593cccb 100644 --- a/intermediateFoodTests/Ex1tespy070.csv +++ b/intermediateFoodTests/Ex1tespy070.csv @@ -1,3 +1,3 @@ -,m,m_unit,v,v_unit,p,p_unit,h,h_unit,T,T_unit,Td_bp,Td_bp_unit,vol,vol_unit,x,x_unit,s,s_unit,Water,PHE,S800 -source:out1_boiler:in1,100.0,kg / s,0.10392670186561417,m3 / s,2.0,bar,100.0,kJ / kg,46.94571580233958,C,0.0,C,0.0010392670186561417,m3 / kg,0.0,-,326.0023871536029,J / kgK,0.8,0.15,0.05 -boiler:out1_sink:in1,100.0,kg / s,0.10392670186561417,m3 / s,2.0,bar,100.0,kJ / kg,46.94571580233958,C,0.0,C,0.0010392670186561417,m3 / kg,0.0,-,326.0023871536029,J / kgK,0.8,0.15,0.05 +,m,m_unit,v,v_unit,p,p_unit,h,h_unit,T,T_unit,Td_bp,Td_bp_unit,vol,vol_unit,x,x_unit,s,s_unit,Water,S800,PHE +source:out1_boiler:in1,100.0,kg / s,0.10392670186561417,m3 / s,2.0,bar,100.0,kJ / kg,46.94571580233958,C,0.0,C,0.0010392670186561417,m3 / kg,0.0,-,326.0023871536029,J / kgK,0.8,0.05,0.15 +boiler:out1_sink:in1,100.0,kg / s,0.10392670186561417,m3 / s,2.0,bar,100.0,kJ / kg,46.94571580233958,C,0.0,C,0.0010392670186561417,m3 / kg,0.0,-,326.0023871536029,J / kgK,0.8,0.05,0.15 diff --git a/intermediateFoodTests/diff.ipynb b/intermediateFoodTests/diff.ipynb index 0a127bf5f..e003dabae 100644 --- a/intermediateFoodTests/diff.ipynb +++ b/intermediateFoodTests/diff.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 27, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -11,13 +11,13 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "m 0.000000e+00\n", + "m 2.220446e-16\n", "p 0.000000e+00\n", "h 6.494361e-12\n", "T 2.223999e-12\n", @@ -32,7 +32,7 @@ "dtype: float64" ] }, - "execution_count": 28, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -68,7 +68,7 @@ "dtype: float64" ] }, - "execution_count": 29, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -83,28 +83,28 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "m 7.105427e-15\n", + "m 1.065814e-14\n", "p 0.000000e+00\n", "h 1.131468e-10\n", "T 1.203944e-10\n", "v 8.985868e-16\n", "vol 9.996344e-17\n", - "s 1.285571e-09\n", + "s 1.285741e-09\n", "x 0.000000e+00\n", "Td_bp 0.000000e+00\n", - "PHE 8.326673e-17\n", - "S800 2.220446e-16\n", - "Water 1.110223e-16\n", + "PHE 2.775558e-16\n", + "S800 3.330669e-16\n", + "Water 6.661338e-16\n", "dtype: float64" ] }, - "execution_count": 30, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -119,28 +119,28 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "m 7.105427e-15\n", + "m 1.065814e-14\n", "p 0.000000e+00\n", "h 1.131468e-10\n", "T 1.203944e-10\n", "v 8.985868e-16\n", "vol 9.996344e-17\n", - "s 1.285571e-09\n", + "s 1.285741e-09\n", "x 0.000000e+00\n", "Td_bp 0.000000e+00\n", - "PHE 8.326673e-17\n", - "S800 2.220446e-16\n", - "Water 1.110223e-16\n", + "PHE 2.775558e-16\n", + "S800 3.330669e-16\n", + "Water 6.661338e-16\n", "dtype: float64" ] }, - "execution_count": 31, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -155,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -170,13 +170,13 @@ "s 3.728383e-08\n", "x 0.000000e+00\n", "Td_bp 0.000000e+00\n", - "Water 5.551115e-16\n", + "Water 6.661338e-16\n", "PHE 2.220446e-16\n", "S800 1.040834e-16\n", "dtype: float64" ] }, - "execution_count": 32, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -206,7 +206,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.15" + "version": "3.10.11" }, "orig_nbformat": 4 },