980 lines
39 KiB
Python
980 lines
39 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Mon Dec 22 15:35:07 2025
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"""
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import ctREFPROP.ctREFPROP as ct
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from scipy.optimize import minimize_scalar
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import numpy as np
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import matplotlib.pyplot as plt
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class CO2PropertyCalculator():
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"""二氧化碳物性计算"""
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def __init__(self, refprop_path = None):
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"""初始化库"""
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self.rp = ct.REFPROPFunctionLibrary(refprop_path)
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# 设置流体文件
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self.rp.SETUPdll(1, 'CO2.FLD', 'HMX.BNC', 'DEF')
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# 设置单位
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self.rp.SETUPdll(2, 'SI', 'SI', 'DEF')
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self.z = [1.0]
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self.mw = self.rp.WMOLdll(self.z)
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def calculate_properties(self, T=None, P=None, h=None, s=None):
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"""计算二氧化碳物性"""
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if T is not None and P is not None:
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# 已知Tp
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result = self.rp.TPFLSHdll(T, P, self.z)
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properties = {
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'T': T,
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'P': P,
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'h': result.h,
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's': result.s,
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}
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elif P is not None and h is not None:
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# 已知Ph
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result = self.rp.PHFLSHdll(P, h, self.z)
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properties = {
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'T': result.T,
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'P': P,
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'h': h,
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's': result.s,
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}
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elif T is not None and h is not None:
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# 已知Th
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result = self.rp.THFLSHdll(T, h, self.z)
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properties = {
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'T': T,
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'P': result.P,
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'h': h,
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's': result.s,
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}
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elif P is not None and s is not None:
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# 已知Ps
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result = self.rp.PSFLSHdll(P, s, self.z)
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properties = {
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'T': result.T,
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'P': P,
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'h': result.h,
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's': s,
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}
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else:
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raise ValueError("提供的参数不足")
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if result.ierr > 0:
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raise ValueError(f"REFPROP计算错误:{result.ierr}")
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# 补充提取的物性，这里由于后续还要使用，不进行参数变换
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properties['D'] = result.D
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properties['cp'] = result.Cp
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properties['cv'] = result.Cv,
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return properties
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class Compressor():
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"""压缩机类"""
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def __init__(self, name, eff):
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"""
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初始化参数
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name: 名称
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eff: 等熵效率
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Wc: 压缩功
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inlet_state: 入口参数
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outlet_state: 出口参数
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outlet_state_is: 等熵状态下出口参数
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"""
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self.name = name
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self.eff = eff
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self.variables = None
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def calculator(self, p_in, T_in, p_out, property_calculator):
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# 先计算熵值
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inlet_state = property_calculator.calculate_properties(T=T_in, P=p_in)
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mw = property_calculator.mw
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s = inlet_state['s']
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h_in = inlet_state['h']
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outlet_state_is = property_calculator.calculate_properties(P=p_out, s=s)
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h_out_is = outlet_state_is['h']
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h_out = h_in + (h_out_is - h_in) / self.eff
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outlet_state = property_calculator.calculate_properties(P=p_out, h=h_out)
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Wc = h_out - h_in
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T_out = outlet_state['T']
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# 计算结果
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self.variables = {
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'name': self.name,
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'inlet_state':{
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'P': p_in, # 压强(kPa)
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'T': T_in,
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'h': h_in/mw, # 比焓(J/mol)->(J/kg)
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's': s/mw, # 比熵(J/mol.K)->(J/kg.K)
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},
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'outlet_state':{
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'P': p_out, # 压强(kPa)
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'T': T_out,
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'h': h_out/mw, # 比焓(kJ/mol)->(kJ/kg)
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's': s/mw, # 比熵(kJ/mol.K)->(kJ/kg.K)
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},
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'eff': self.eff,
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'Wc': Wc/mw, # 压缩功(kJ/mol)->(kJ/kg)
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'pi': p_out / p_in
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}
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class Turbine():
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"""透平类"""
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def __init__(self, name, eff):
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"""
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初始化参数
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name: 名称
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eff: 透平效率
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"""
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self.name = name
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self.eff = eff
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self.variables = None
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def calculator(self, p_in, T_in, p_out, property_calculator):
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"""涡轮参数计算"""
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inlet_state = property_calculator.calculate_properties(P=p_in, T=T_in)
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mw = property_calculator.mw
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s = inlet_state['s']
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h_in = inlet_state['h']
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outlet_state_is = property_calculator.calculate_properties(P=p_out, s=s)
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h_out_is = outlet_state_is['h']
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h_out = h_in - (h_in - h_out_is) * self.eff
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outlet_state = property_calculator.calculate_properties(P=p_out, h=h_out)
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T_out = outlet_state['T']
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Wt = h_in - h_out
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self.variables = {
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'name': self.name,
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'inlet_state':{
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'P': p_in, # 压强(kPa)
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'T': T_in,
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'h': h_in/mw, # 比焓(J/mol)->(J/kg)
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's': s/mw, # 比熵(J/mol.K)->(J/kg.K)
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},
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'outlet_state':{
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'P': p_out, # 压强(kPa)
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'T': T_out,
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'h': h_out/mw, # 比焓(J/mol)->(J/kg)
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's': s/mw, # 比熵(J/mol.K)->(J/kg.K)
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},
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'eff': self.eff,
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'Wt': Wt/mw, # 透平做功(J/mol)->(J/kg)
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'pi': p_in / p_out
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}
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class Recuperator():
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"""换热器类"""
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def __init__(self, name, eff, x=0):
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"""
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初始化参数
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name: 名称
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eff: 换热效率(基于焓的计算方法)
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"""
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self.name = name
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self.eff = eff
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self.Q_ex = None
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self.variables = None
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self.x = x
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def calculator(self, cold_inlet_state, hot_inlet_state, bypass_info, ploss, property_calculator):
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"""
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计算换热器两侧参数，默认逆流
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bypass_info: 是否存在分流，0为不存在，1为存在
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下标含义
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----------
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1: 换热器冷端入口
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2: 换热器冷端出口
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3: 换热器热端入口
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4: 换热器热端出口
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ass: 迭代中间变量，假设值
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"""
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# 计算两入口参数, 这里单位是kg
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p1 = cold_inlet_state['P']
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T1 = cold_inlet_state['T']
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h1 = cold_inlet_state['h']
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p2 = p1 * (1 - ploss)
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p3 = hot_inlet_state['P']
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T3 = hot_inlet_state['T']
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h3 = hot_inlet_state['h']
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p4 = p3 * (1 - ploss)
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mw = property_calculator.mw
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# 设定质量流量
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m_cold = (1-self.x) if bypass_info == 1 else 1.0
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m_hot = 1.0
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# =============================================================================
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# # 采用焓差效能的方式来计算换热器进出口参数
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# # 假设最大温差发生在冷端, 计算冷端出口温度和比焓
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# cold_outlet_state = property_calculator.calculate_properties(T=T3, P=p2)
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# h2 = cold_outlet_state['h'] / mw
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# Q_ass_cold = m_cold * abs(h2 - h1)
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#
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# # 假设最大温差发生在热端, 计算热端出口温度和比焓
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# hot_outlet_state = property_calculator.calculate_properties(T=T1, P=p4)
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# h4 = hot_outlet_state['h'] / mw
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# Q_ass_hot = m_hot * abs(h3 - h4)
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#
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# # 比较两个可能的Q，取最小值与焓差效能的乘积作为实际换热量
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# self.Q_ex = min(Q_ass_hot, Q_ass_cold) * self.eff
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# # 由实际换热量计算出口焓和出口状态
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# h2 = h1 + self.Q_ex / m_cold
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# h4 = h3 - self.Q_ex / m_hot
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# cold_outlet_state = property_calculator.calculate_properties(P=p2, h=h2*mw)
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# hot_outlet_state = property_calculator.calculate_properties(P=p4, h=h4*mw)
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# T2 = cold_outlet_state['T']
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# T4 = hot_outlet_state['T']
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# =============================================================================
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# 采用温差效能的方式来计算换热器进出口参数
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T_ass_max = abs(T1 - T3)
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# 假设最大温差发生在冷端, 计算冷端出口温度和比焓
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T2 = T1 + T_ass_max * self.eff
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cold_outlet_state = property_calculator.calculate_properties(T=T2, P=p2)
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h2 = cold_outlet_state['h'] / mw
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Q_ass_cold = m_cold * abs(h2 - h1)
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# 假设最大温差发生在热端, 计算热端出口温度和比焓
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T4 = T3 - T_ass_max * self.eff
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hot_outlet_state = property_calculator.calculate_properties(T=T4, P=p4)
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h4 = hot_outlet_state['h'] / mw
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Q_ass_hot = m_hot * abs(h3 - h4)
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# 比较两个可能的Q，取最小值与焓差效能的乘积作为实际换热量
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self.Q_ex = min(Q_ass_hot, Q_ass_cold)
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# 由实际换热量计算出口焓和出口状态
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h2 = h1 + self.Q_ex / m_cold
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h4 = h3 - self.Q_ex / m_hot
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cold_outlet_state = property_calculator.calculate_properties(P=p2, h=h2*mw)
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hot_outlet_state = property_calculator.calculate_properties(P=p4, h=h4*mw)
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T2 = cold_outlet_state['T']
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T4 = hot_outlet_state['T']
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# 拼装变量
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self.variables = {
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'name': self.name,
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'cold_inlet_state':{
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'P': p1,
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'T': T1,
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'h': h1,
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's': cold_inlet_state['s']
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},
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'cold_outlet_state':{
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'P': p2,
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'T': cold_outlet_state['T'],
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'h': h2,
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's': cold_outlet_state['s']/mw
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},
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'hot_inlet_state':{
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'P': p3,
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'T': T3,
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'h': h3,
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's': hot_inlet_state['s']
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},
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'hot_outlet_state':{
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'P': p4,
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'T': T4,
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'h': h4,
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's': hot_outlet_state['s']/mw
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},
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'eff': self.eff,
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'Q_exchange': self.Q_ex
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}
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def check_pinch_point(self, property_calculator, num_segments=20):
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"""
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换热器内部夹点校验
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将换热量均分为 num_segments 段，检查内部每个微元的冷热流体温度
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"""
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h_cold_in = self.variables['cold_inlet_state']['h']
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h_hot_in = self.variables['hot_inlet_state']['h']
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p_cold = self.variables['cold_inlet_state']['P']
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p_hot = self.variables['hot_inlet_state']['P']
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m_cold = (1 - self.x) if self.name == "Low Temperature recuprerator" else 1.0
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m_hot = 1.0
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dQ = self.Q_ex / num_segments
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# 沿冷流体流动方向步进检查
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for i in range(num_segments + 1):
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q_current = i * dQ
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# 当前微元截面的焓值
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h_cold_local = h_cold_in + q_current / m_cold
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h_hot_local = (h_hot_in - self.Q_ex / m_hot) + q_current / m_hot
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# 查温度
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T_cold_local = property_calculator.calculate_properties(P=p_cold, h=h_cold_local * property_calculator.mw)['T']
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T_hot_local = property_calculator.calculate_properties(P=p_hot, h=h_hot_local * property_calculator.mw)['T']
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# 如果热流体温度低于等于冷流体温度 (设定一个 0.1K 的最小逼近温差容差)
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if T_hot_local - T_cold_local < 0.1:
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return False # 发生温度交叉，物理不可行！
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return True # 校验通过
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class Heater():
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"""加热器类"""
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def __init__(self, name):
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self.name = name
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self.variables = None
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def calculator(self, inlet_state, outlet_state):
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h_in = inlet_state['h']
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h_out = outlet_state['h']
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Q_input = h_out - h_in
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self.variables = {
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'name': self.name,
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'inlet_state':{
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'P': inlet_state['P'], # 压强(kPa)
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'T': inlet_state['T'],
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'h': inlet_state['h'],
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's': inlet_state['s'],
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},
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'outlet_state':{
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'P': outlet_state['P'], # 压强(kPa)
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'T': outlet_state['T'],
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'h': outlet_state['h'],
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's': outlet_state['s'],
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},
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'Q_in': Q_input,
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}
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class Condenser():
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"""冷凝器类"""
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def __init__(self, name):
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self.name = name
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self.variables = None
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def calculator(self, inlet_state, outlet_state):
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h_in = inlet_state['h']
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h_out = outlet_state['h']
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Q_output = h_in - h_out
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self.variables = {
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'name': self.name,
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'inlet_state':{
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'P': inlet_state['P'], # 压强(kPa)
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'T': inlet_state['T'],
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'h': inlet_state['h'],
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's': inlet_state['s'],
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},
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'outlet_state':{
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'P': outlet_state['P'], # 压强(kPa)
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'T': outlet_state['T'],
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'h': outlet_state['h'],
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's': outlet_state['s'],
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},
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'Q_out': Q_output,
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}
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class Concentrator():
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"""汇流组件"""
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def __init__(self, name):
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self.name = name
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self.variables = None
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def calculator(self, inlet_state_bypass, inlet_state_mroad, x, property_calculator):
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h_in_bypass = inlet_state_bypass['h']
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h_in_mroad = inlet_state_mroad['h']
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p_in = inlet_state_bypass['P']
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mw = property_calculator.mw
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h_out = x * h_in_bypass + (1-x) * h_in_mroad
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outlet_state = property_calculator.calculate_properties(P=p_in, h=h_out*mw)
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self.variables = {
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'name': self.name,
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'inlet_state_bypass': inlet_state_bypass,
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'inlet_state_mroad': inlet_state_mroad,
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'outlet_state':{
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'P': p_in,
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'T': outlet_state['T'],
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'h': h_out,
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's': outlet_state['s']/mw
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}
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}
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class BraytonCycle():
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"""循环计算"""
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def __init__(self, name, refprop_path = "C:/Program Files (x86)/REFPROP 10.0+/REFPROP"):
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self.name = name
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self.property_calculator = None
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self.compressor = None
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self.turbine = None
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self.recuperator = None
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self.heater = None
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self.condenser = None
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self.concentrator = None
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self.refprop_path = refprop_path
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self.property_calculator = CO2PropertyCalculator(self.refprop_path)
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def cycle_eff_calculator(self):
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if type(self.compressor) == list:
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Wc = (self.compressor[0].variables['Wc'] +
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self.compressor[1].variables['Wc'])
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else:
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Wc = self.compressor.variables['Wc']
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Wt = self.turbine.variables['Wt']
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Q_input = self.heater.variables['Q_in']
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cycle_eff = (Wt - Wc) / Q_input
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return cycle_eff
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def SC(self, T_low, T_high, p_low, p_high, param = None):
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if param == None:
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param = {
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'compressor_eff': 0.98,
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'turbine_eff': 0.95
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}
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# 定义循环组件
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self.heater = Heater(name = "Main heater")
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self.condenser = Condenser(name = "Main condenser")
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self.compressor = Compressor(name = "Main compressor", eff = param['compressor_eff'])
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self.turbine = Turbine(name = "Turbine", eff = param['turbine_eff'])
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# 计算循环参数
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self.compressor.calculator(p_low, T_low, p_high, self.property_calculator)
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self.turbine.calculator(p_high, T_high, p_low, self.property_calculator)
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self.heater.calculator(self.compressor.variables['outlet_state'], self.turbine.variables['inlet_state'])
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self.condenser.calculator(self.turbine.variables['outlet_state'], self.compressor.variables['inlet_state'])
|
||
|
||
# 计算循环效率
|
||
cycle_eff = self.cycle_eff_calculator()
|
||
return cycle_eff
|
||
|
||
def SRC(self, T_low, T_high, p_low, p_high, param = None):
|
||
if ('recuperator_eff' not in param) or param == None:
|
||
param = {
|
||
'compressor_eff': 0.98,
|
||
'turbine_eff': 0.95,
|
||
'recuperator_eff': 0.85
|
||
}
|
||
|
||
# 定义循环组件
|
||
self.heater = Heater(name = "Main heater")
|
||
self.condenser = Condenser(name = "Main condenser")
|
||
self.compressor = Compressor(name = "Main compressor", eff = param['compressor_eff'])
|
||
self.turbine = Turbine(name = "Turbine", eff = param['turbine_eff'])
|
||
self.recuperator = Recuperator(name = "Recuperator", eff = param['recuperator_eff'])
|
||
|
||
# 计算循环参数
|
||
self.compressor.calculator(p_low, T_low, p_high, self.property_calculator)
|
||
self.turbine.calculator(p_high, T_high, p_low, self.property_calculator)
|
||
self.recuperator.calculator(self.compressor.variables['outlet_state'],
|
||
self.turbine.variables['outlet_state'], 0,
|
||
self.property_calculator)
|
||
self.condenser.calculator(self.recuperator.variables['hot_outlet_state'],
|
||
self.compressor.variables['inlet_state'])
|
||
self.heater.calculator(self.recuperator.variables['cold_outlet_state'],
|
||
self.turbine.variables['inlet_state'])
|
||
|
||
# 计算循环效率
|
||
cycle_eff = self.cycle_eff_calculator()
|
||
return cycle_eff
|
||
|
||
def RC(self, T_low, T_high, p_low, p_high, ploss, param = None):
|
||
if ('recompressor_eff' not in param or
|
||
'highT_recuperator_eff' not in param or
|
||
'x' not in param or
|
||
param == None):
|
||
param = {
|
||
'compressor_eff': 0.98,
|
||
'turbine_eff': 0.95,
|
||
'recuperator_eff': 0.85,
|
||
'recompressor_eff': 0.98,
|
||
'highT_recuperator_eff': 0.85,
|
||
'x': 0.9
|
||
}
|
||
self.x = param['x']
|
||
# 定义循环组件
|
||
self.heater = Heater(name = "Main heater")
|
||
self.condenser = Condenser(name = "Main_condenser")
|
||
self.turbine = Turbine(name = "Main Turbine",
|
||
eff = param['turbine_eff'])
|
||
main_compressor = Compressor(name = "Main compressor",
|
||
eff = param['compressor_eff'])
|
||
recompressor = Compressor(name = "Recompressor",
|
||
eff = param['recompressor_eff'])
|
||
lrecuperator = Recuperator(name = "Low Temperature recuprerator",
|
||
eff = param['recuperator_eff'],
|
||
x = self.x)
|
||
hrecuperator = Recuperator(name = "High Temperature recuperator",
|
||
eff = param['highT_recuperator_eff'])
|
||
self.concentrator = Concentrator(name = "Concentrator")
|
||
self.recuperator = []
|
||
self.compressor = []
|
||
|
||
|
||
# 计算组件进出口参数
|
||
# 先算压缩机和涡轮
|
||
main_compressor.calculator(p_low, T_low, p_high, self.property_calculator)
|
||
self.turbine.calculator(p_high*(1-ploss)**3,
|
||
T_high, p_low*(1-ploss)**(-3),
|
||
self.property_calculator)
|
||
|
||
# 假定低温回热器出口参数并进行迭代
|
||
mw = self.property_calculator.mw
|
||
T_hr_inlet = ((self.turbine.variables['outlet_state']['T']
|
||
+ main_compressor.variables['outlet_state']['T'])/2)
|
||
# T_lr_inlet = self.turbine.variables['outlet_state']['T'] * 1.01
|
||
max_iter = 300
|
||
relax_fac = 0.4
|
||
for i in range(max_iter):
|
||
hr_inlet_state_hot_mol = self.property_calculator.calculate_properties(P=p_high*(1-ploss), T=T_hr_inlet)
|
||
# 单位转换成mol
|
||
hr_inlet_state_hot = {
|
||
'P': hr_inlet_state_hot_mol['P'],
|
||
'T': hr_inlet_state_hot_mol['T'],
|
||
'h': hr_inlet_state_hot_mol['h']/mw,
|
||
's': hr_inlet_state_hot_mol['s']/mw
|
||
}
|
||
|
||
hrecuperator.calculator(hr_inlet_state_hot,
|
||
self.turbine.variables['outlet_state'], 0,
|
||
ploss, self.property_calculator)
|
||
lrecuperator.calculator(main_compressor.variables['outlet_state'],
|
||
hrecuperator.variables['hot_outlet_state'], 1,
|
||
ploss, self.property_calculator)
|
||
recompressor.calculator(p_in = p_low/(1-ploss), p_out = p_high*(1-ploss),
|
||
T_in = lrecuperator.variables['hot_outlet_state']['T'],
|
||
property_calculator = self.property_calculator)
|
||
self.concentrator.calculator(recompressor.variables['outlet_state'],
|
||
lrecuperator.variables['cold_outlet_state'],
|
||
self.x, self.property_calculator)
|
||
|
||
Tc_outlet = self.concentrator.variables['outlet_state']['T']
|
||
err = abs(Tc_outlet - T_hr_inlet)
|
||
if err <= 1e-5:
|
||
break
|
||
if i == max_iter - 1:
|
||
raise ValueError(f"迭代次数超过范围，当前误差{err:.4f}")
|
||
|
||
T_hr_inlet = relax_fac * Tc_outlet + (1 - relax_fac) * T_hr_inlet
|
||
|
||
hrecuperator.calculator(self.concentrator.variables['outlet_state'],
|
||
self.turbine.variables['outlet_state'], 0,
|
||
ploss, self.property_calculator)
|
||
lrecuperator.calculator(main_compressor.variables['outlet_state'],
|
||
hrecuperator.variables['hot_outlet_state'], 1,
|
||
ploss, self.property_calculator)
|
||
|
||
main_compressor.variables['Wc'] *= (1-self.x)
|
||
recompressor.variables['Wc'] *= self.x
|
||
self.recuperator.append(lrecuperator)
|
||
self.recuperator.append(hrecuperator)
|
||
self.compressor.append(main_compressor)
|
||
self.compressor.append(recompressor)
|
||
self.condenser.calculator(lrecuperator.variables['hot_outlet_state'],
|
||
main_compressor.variables['inlet_state'])
|
||
self.condenser.variables['Q_out'] *= (1-self.x)
|
||
self.heater.calculator(hrecuperator.variables['cold_outlet_state'],
|
||
self.turbine.variables['inlet_state'])
|
||
# 计算循环效率
|
||
cycle_eff = self.cycle_eff_calculator()
|
||
return cycle_eff
|
||
|
||
def base_params_single_optimize(self, fixed_var, base_params, target_var_name, bounds):
|
||
"""
|
||
组件性能单变量优化器
|
||
: 固定边界条件
|
||
: 默认参数
|
||
: 优化变量名称
|
||
: 变量范围
|
||
"""
|
||
|
||
if target_var_name not in base_params:
|
||
raise ValueError(f"参数{target_var_name}不在参数字典中")
|
||
|
||
def opt_fun(opt_var):
|
||
# 复制变量字典
|
||
opt_param = base_params.copy()
|
||
# 修改要优化的变量为参数
|
||
opt_param[target_var_name] = opt_var
|
||
# 带入循环参数计算
|
||
try:
|
||
eff = self.RC(
|
||
T_low = fixed_var['T_low'],
|
||
T_high= fixed_var['T_high'],
|
||
p_low = fixed_var['p_low'],
|
||
p_high = fixed_var['p_high'],
|
||
ploss = fixed_var['ploss'],
|
||
param = opt_param)
|
||
return -eff
|
||
except Exception as e:
|
||
return 0.0 # 遇到物性计算崩溃时返回极差值
|
||
res = minimize_scalar(opt_fun, bounds=bounds, method='bounded')
|
||
if res.success:
|
||
print(f"✅ 优化完成！")
|
||
print(f"👉 最佳 {target_var_name} = {res.x:.4f}")
|
||
print(f"👉 此时系统最高效率 = {-res.fun:.2%}\n")
|
||
else:
|
||
print("❌ 优化失败。")
|
||
|
||
return res
|
||
|
||
def fixed_params_single_optimize(self, fixed_params, target_var_name, params, bounds):
|
||
"""
|
||
边界条件单变量优化器
|
||
: 固定边界条件
|
||
: 默认参数
|
||
: 优化变量名称
|
||
: 变量范围
|
||
"""
|
||
if target_var_name not in fixed_params:
|
||
raise ValueError(f"参数{target_var_name}不在参数字典中")
|
||
|
||
def opt_fun(opt_var):
|
||
# 复制变量
|
||
opt_params = fixed_params.copy()
|
||
# 变量替换
|
||
opt_params[target_var_name] = opt_var
|
||
# 带入循环
|
||
try:
|
||
eff = self.RC(T_low = opt_params['T_low'],
|
||
T_high = opt_params['T_high'],
|
||
p_low = opt_params['p_low'],
|
||
p_high = opt_params['p_high'],
|
||
ploss = opt_params['ploss'],
|
||
param = params)
|
||
return -eff
|
||
except Exception as e:
|
||
return 0.0
|
||
res = minimize_scalar(opt_fun, bounds=bounds, method='bounded')
|
||
if res.success:
|
||
print(f"✅ 优化完成！")
|
||
print(f"👉 最佳 {target_var_name} = {res.x:.4f}")
|
||
print(f"👉 此时系统最高效率 = {-res.fun:.2%}\n")
|
||
else:
|
||
print("❌ 优化失败。")
|
||
return res
|
||
|
||
def plot_optimization_landscape(self, fixed_params, params, target_var_name, bounds, res, num_points=50):
|
||
"""
|
||
绘制单变量优化地形图
|
||
:param target_var_name: 要扫描和优化的变量名（如 'x'）
|
||
:param bounds: 扫描和优化的范围 (min, max)
|
||
:param num_points: 扫描的采样点数量，越大曲线越平滑，但计算越慢
|
||
"""
|
||
print(f"开始对【{target_var_name}】进行区间扫描，共计算 {num_points} 个点...")
|
||
plt.rcParams['font.sans-serif'] = ['SimHei'] # Windows 用黑体
|
||
plt.rcParams['axes.unicode_minus'] = False # 正常显示负号
|
||
# 1. 生成扫描数组
|
||
x_vals = np.linspace(bounds[0], bounds[1], num_points)
|
||
eff_vals = []
|
||
valid_x = [] # 记录那些没有报错的 x
|
||
if target_var_name not in fixed_params:
|
||
# 2. 遍历计算曲线上的点
|
||
for val in x_vals:
|
||
current_param = params.copy()
|
||
current_param[target_var_name] = val
|
||
try:
|
||
# 调用你的黑盒物理模型（注意：这里取正效率用于画图）
|
||
eff = self.RC(
|
||
T_low=fixed_params['T_low'],
|
||
T_high=fixed_params['T_high'],
|
||
p_low=fixed_params['p_low'],
|
||
p_high=fixed_params['p_high'],
|
||
ploss=fixed_params['ploss'],
|
||
param=current_param
|
||
)
|
||
# 如果系统加了夹点校验且没通过，可能会返回 None 或者抛异常
|
||
# 这里确保只有成功的点才画上去
|
||
eff_vals.append(eff * 100) # 乘以 100 转换为百分比
|
||
valid_x.append(val)
|
||
except Exception as e:
|
||
# 如果某个 x 导致计算崩溃，我们跳过这个点，不画它
|
||
pass
|
||
else:
|
||
for val in x_vals:
|
||
current_param = fixed_params.copy()
|
||
current_param[target_var_name] = val
|
||
try:
|
||
# 调用你的黑盒物理模型（注意：这里取正效率用于画图）
|
||
eff = self.RC(
|
||
T_low=current_param['T_low'],
|
||
T_high=current_param['T_high'],
|
||
p_low=current_param['p_low'],
|
||
p_high=current_param['p_high'],
|
||
ploss=current_param['ploss'],
|
||
param=params
|
||
)
|
||
# 如果系统加了夹点校验且没通过，可能会返回 None 或者抛异常
|
||
# 这里确保只有成功的点才画上去
|
||
eff_vals.append(eff * 100) # 乘以 100 转换为百分比
|
||
valid_x.append(val)
|
||
except Exception as e:
|
||
# 如果某个 x 导致计算崩溃，我们跳过这个点，不画它
|
||
pass
|
||
print("扫描完成！正在使用优化器寻找精确最高点...")
|
||
|
||
# 4. 开始绘图
|
||
plt.figure(figsize=(8, 6), dpi=120) # 设置画布大小和清晰度
|
||
|
||
# 画出目标函数曲线
|
||
plt.plot(valid_x, eff_vals, linestyle='-', color='#1f77b4', linewidth=2, label='系统热效率曲线')
|
||
|
||
# 如果优化成功，用醒目的红星标出最优点
|
||
if res.success:
|
||
best_x = res.x
|
||
best_eff = -res.fun * 100
|
||
plt.scatter(best_x, best_eff, color='red', marker='*', s=200, zorder=5, label=f'最优点 ({best_x:.4f}, {best_eff:.2f}%)')
|
||
|
||
# 画辅助虚线对齐坐标轴
|
||
plt.axvline(x=best_x, color='gray', linestyle='--', alpha=0.6)
|
||
plt.axhline(y=best_eff, color='gray', linestyle='--', alpha=0.6)
|
||
else:
|
||
print("优化结果未输入!")
|
||
# 设置图表装饰
|
||
plt.title(f'系统热效率随 {target_var_name} 的变化趋势', fontsize=14)
|
||
plt.xlabel(f'优化变量: {target_var_name}', fontsize=12)
|
||
plt.ylabel('循环热效率 η (%)', fontsize=12)
|
||
plt.grid(True, linestyle=':', alpha=0.7)
|
||
plt.legend(fontsize=11)
|
||
|
||
# 显示图像
|
||
plt.tight_layout()
|
||
plt.show()
|
||
|
||
# 调用测试
|
||
# plot_optimization_landscape('x', bounds=(0.6, 0.95), num_points=40)
|
||
|
||
|
||
def sweep_and_optimize(self, fixed_params, params, sweep_var, sweep_bounds, opt_var, opt_bounds, num_points=50):
|
||
"""
|
||
带内部动态优化的单变量扫描器 (极度通用版)
|
||
|
||
:param fixed_params: 固定的边界条件字典
|
||
:param params: 组件性能参数字典
|
||
:param sweep_var: 你要扫描/遍历的变量名 (例如 'T_low')
|
||
:param sweep_bounds: 扫描变量的范围 (min, max)
|
||
:param opt_var: 在每个扫描点下，你需要动态寻找最优值的变量名 (例如 'x')
|
||
:param opt_bounds: 优化变量的搜索范围 (min, max)
|
||
:param num_points: 扫描点数
|
||
"""
|
||
print(f"\n🚀 开始执行嵌套扫描：")
|
||
print(f" - 扫描变量 (X轴): 【{sweep_var}】 范围 {sweep_bounds}")
|
||
print(f" - 内部动态优化变量: 【{opt_var}】 范围 {opt_bounds}")
|
||
|
||
# 1. 生成扫描节点
|
||
sweep_vals = np.linspace(sweep_bounds[0], sweep_bounds[1], num_points)
|
||
|
||
# 记录数据的列表
|
||
valid_sweep_vals = []
|
||
best_effs = []
|
||
best_opt_vals = []
|
||
|
||
# 2. 开始逐点扫描
|
||
for s_val in sweep_vals:
|
||
|
||
# 【核心1：每次必须使用干净的字典副本】
|
||
current_fixed = fixed_params.copy()
|
||
current_param = params.copy()
|
||
|
||
# 判断扫描变量是属于 fixed_params 还是 params，并赋值
|
||
if sweep_var in current_fixed:
|
||
current_fixed[sweep_var] = s_val
|
||
elif sweep_var in current_param:
|
||
current_param[sweep_var] = s_val
|
||
else:
|
||
raise ValueError(f"找不到扫描变量: {sweep_var}")
|
||
|
||
# 3. 定义内部优化目标函数 (闭包)
|
||
def inner_objective(guess_val):
|
||
# 将优化器猜的值赋给 opt_var
|
||
current_param[opt_var] = guess_val
|
||
|
||
try:
|
||
# 调用黑盒计算
|
||
eff = self.RC(
|
||
T_low=current_fixed['T_low'],
|
||
T_high=current_fixed['T_high'],
|
||
p_low=current_fixed['p_low'],
|
||
p_high=current_fixed['p_high'],
|
||
ploss=current_fixed['ploss'],
|
||
param=current_param
|
||
)
|
||
# 【预留口：此处可加入换热器内部夹点校验】
|
||
# 取出两个换热器进行夹点校验
|
||
for rec in self.recuperator:
|
||
if not rec.check_pinch_point(self.property_calculator):
|
||
return 0.0 # 核心！如果交叉了，直接返回 0 效率，强迫优化器换参数
|
||
return -eff
|
||
except Exception:
|
||
return 0.0
|
||
|
||
# 4. 调用一维优化器
|
||
res = minimize_scalar(inner_objective, bounds=opt_bounds, method='bounded')
|
||
|
||
# 5. 结果校验与存储
|
||
if res.success and -res.fun > 0:
|
||
best_eff = -res.fun * 100
|
||
best_opt_val = res.x
|
||
|
||
valid_sweep_vals.append(s_val)
|
||
best_effs.append(best_eff)
|
||
best_opt_vals.append(best_opt_val)
|
||
|
||
print(f"✔️ {sweep_var} = {s_val:.2f} | 寻得最优 {opt_var} = {best_opt_val:.4f} | 最高效率 = {best_eff:.2f}%")
|
||
else:
|
||
print(f"❌ {sweep_var} = {s_val:.2f} | 优化失败或物理无解，已跳过")
|
||
|
||
# 6. 调用画图方法 (将画图剥离，保持代码干净)
|
||
self._plot_results(sweep_var, valid_sweep_vals, best_effs, opt_var, best_opt_vals)
|
||
|
||
return valid_sweep_vals, best_effs, best_opt_vals
|
||
|
||
def _plot_results(self, sweep_var, x_data, y_eff_data, opt_var, y_opt_data):
|
||
"""专门用来画图的内部方法，支持双Y轴"""
|
||
if not x_data:
|
||
print("没有有效数据可供绘制！")
|
||
return
|
||
|
||
plt.rcParams['font.sans-serif'] = ['SimHei']
|
||
plt.rcParams['axes.unicode_minus'] = False
|
||
|
||
fig, ax1 = plt.subplots(figsize=(9, 6), dpi=120)
|
||
|
||
# 画左Y轴：最高效率曲线
|
||
color1 = '#1f77b4'
|
||
ax1.set_xlabel(f'扫描变量: {sweep_var}', fontsize=12)
|
||
ax1.set_ylabel('最优循环热效率 η (%)', color=color1, fontsize=12)
|
||
ax1.plot(x_data, y_eff_data, color=color1, linewidth=2.5, label='系统热效率')
|
||
ax1.tick_params(axis='y', labelcolor=color1)
|
||
ax1.grid(True, linestyle=':', alpha=0.6)
|
||
|
||
# 画右Y轴：对应的最优分流量走势
|
||
ax2 = ax1.twinx()
|
||
color2 = '#d62728'
|
||
ax2.set_ylabel(f'匹配的最优动态变量: {opt_var}', color=color2, fontsize=12)
|
||
ax2.plot(x_data, y_opt_data, color=color2, linestyle='--', linewidth=2, label=f'最优 {opt_var} 值')
|
||
ax2.tick_params(axis='y', labelcolor=color2)
|
||
|
||
plt.title(f'系统最高效率及对应的最优 {opt_var} 随 {sweep_var} 的变化', fontsize=14)
|
||
fig.tight_layout()
|
||
plt.show()
|
||
|
||
if __name__ == "__main__":
|
||
brayton1 = BraytonCycle(name = "simple brayton cycle test")
|
||
T_high = 650+273.15
|
||
T_low = 42+273.15
|
||
p_high = 20.0e6
|
||
p_low = 9.09e6
|
||
fixed_var = {
|
||
'T_high': 650+273.15,
|
||
'T_low': 42+273.15,
|
||
'p_high': 20.0e3,
|
||
'p_low': 8.16e3,
|
||
'ploss': 0.01
|
||
}
|
||
# param = {
|
||
# 'compressor_eff': 0.9,
|
||
# 'turbine_eff': 0.93,
|
||
# 'recuprator_eff': 0.96
|
||
# }
|
||
|
||
# =========================================================================
|
||
# 焓差效能参数定义
|
||
# param = {
|
||
# 'x': 0.279,
|
||
# 'compressor_eff': 0.9,
|
||
# 'recompressor_eff': 0.855,
|
||
# 'turbine_eff': 0.93,
|
||
# 'recuperator_eff': 0.9354,
|
||
# 'highT_recuperator_eff': 0.8857
|
||
# }
|
||
# =========================================================================
|
||
# eff = brayton1.simple_brayton_cycle(T_low, T_high, p_low/1e3, p_high/1e3, param)
|
||
#
|
||
param = {
|
||
'x': 0.279,
|
||
'compressor_eff': 0.9,
|
||
'recompressor_eff': 0.9,
|
||
'turbine_eff': 0.93,
|
||
'recuperator_eff': 0.94,
|
||
'highT_recuperator_eff': 0.96
|
||
}
|
||
ploss = 0.01
|
||
eff = brayton1.RC(T_low, T_high, p_low/1e3, p_high/1e3, ploss, param)
|
||
c = brayton1.compressor
|
||
h = brayton1.heater
|
||
cond = brayton1.condenser
|
||
conc = brayton1.concentrator.variables
|
||
t = brayton1.turbine
|
||
r = brayton1.recuperator
|
||
vl = brayton1.recuperator[0].variables
|
||
vh = brayton1.recuperator[1].variables
|
||
Wc = brayton1.compressor[0].variables['Wc'] + brayton1.compressor[1].variables['Wc']
|
||
# Wc = brayton1.compressor.variables['Wc']
|
||
Wt = brayton1.turbine.variables['Wt']
|
||
Q_input = brayton1.heater.variables['Q_in']
|
||
Q_output = brayton1.condenser.variables['Q_out']
|
||
print(Q_input+Wc-(Q_output+Wt))
|
||
print((Wt - Wc)/Q_input)
|
||
|
||
# res_x = brayton1.base_params_single_optimize(fixed_var = fixed_var,
|
||
# base_params = param,
|
||
# target_var_name = 'x',
|
||
# bounds = (0.1,0.5))
|
||
# res_min_T = brayton1.fixed_params_single_optimize(fixed_params = fixed_var,
|
||
# target_var_name = 'T_low',
|
||
# params = param,
|
||
# bounds = (30+273.15, 60+273.15))
|
||
|
||
x_vals, eff_vals, opt_vals = brayton1.sweep_and_optimize(
|
||
fixed_params=fixed_var,
|
||
params=param,
|
||
sweep_var='T_low', # 扫描变量
|
||
sweep_bounds=(30+273.15, 60+273.15),
|
||
opt_var='x', # 动态优化变量
|
||
opt_bounds=(0.1, 0.5),
|
||
num_points=30)
|
||
|
||
# refprop_path = "C:/Program Files (x86)/REFPROP 10.0+/REFPROP"
|
||
# recuprator = Recuperator(name='Main Recuprator', eff=param['recuprator_eff'])
|
||
# calculator = CO2PropertyCalculator(refprop_path)
|
||
# cold_inlet_state = brayton1.compressor.variables['outlet_state']
|
||
# hot_inlet_state = brayton1.turbine.variables['outlet_state']
|
||
# recuprator.calculator(hot_inlet_state, cold_inlet_state, calculator)
|
||
# print(recuprator.variables)
|
||
|
||
|
||
|
||
|
||
# self.cycle_eff = None
|
||
# self.compressors = []
|
||
# self.turbines = []
|
||
# self.recuperators = []
|
||
# self.heaters = []
|
||
# self.condensers = []
|
||
|
||
# def add_compressor(self, name, eff):
|
||
# """添加压缩机"""
|
||
# compressor = Compressor(name, eff)
|
||
# self.compressors.append(compressor)
|
||
# return compressor
|
||
|
||
# def add_turbine(self, name, eff):
|
||
# """添加透平"""
|
||
# turbine = Turbine(name, eff)
|
||
# self.turbines.append(turbine)
|
||
# return turbine
|
||
|
||
# def add_heater(self, name):
|
||
# heater = Heater(name)
|
||
# self.heaters.append(heater)
|
||
# return heater
|
||
|
||
# def add_condenser(self,name):
|
||
# condenser = Condenser(name)
|
||
# self.condensers.append(condenser)
|
||
# return condenser
|
||
|
||
# def efficiency_calculation(self):
|
||
# cycle_Wc = 0.0
|
||
# for compressor in self.compressors:
|
||
# cycle_Wc += compressor.Wc
|
||
# cycle_Wt = 0.0
|
||
# for turbine in self.turbines:
|
||
# cycle_Wt += turbine.Wt
|
||
# cycle_Q_in = 0.0
|
||
# for heater in self.heaters:
|
||
# cycle_Q_in += self.heater.Q_input
|
||
# cycle_Q_out = 0.0
|
||
# for condenser in self.condensers:
|
||
# cycle_Q_out += self.condenser.Q_output
|
||
# cycle_eff = (cycle_Wt - cycle_Wc - cycle_Q_out) / cycle_Q_in
|
||
# return cycle_eff
|
||
|