摘要
热电联产过程中的蒸汽输送过程难以监测,流量和压力变化比较复杂,管网运维依赖人员经验,容易造成能源浪费。本文建立了蒸汽供热管网的动态仿真模型,模型将蒸汽管网假设为单相可压缩流体网络,由节点和管路组成。使用管网质量方程、动量方程和能量方程建立管网物理模型,并采用隐式欧拉算法、稀疏矩阵算法求解由物理模型所得的非线性微分方程组。此外,本文建立了简单管网,并结合Fluent仿真对算法进行了验证。最后,建立了实际管网模型,与某企业蒸汽管网实际运行数据进行对比,结果表明所提出的模型不仅具有计算准确性,而且有实际工程应用价值。
In the process of cogeneration of heat and power,many factors affect the energy efficiency,such as the difficulty to monitor the steam transmission process,the complex change of flow rate and pressure,and the requirement of experience of personnel for the operation and maintenance of pipe network.This paper develops a dynamic simulation model of steam heating pipe network.The model presumes a single-phase compressible steam pipe network composed of nodes and pipelines.The mass equation,momentum equation and energy equation of the pipe network are used to establish the physical model of the pipe network.The implicit Euler algorithm and sparse matrix algorithm are used to solve the nonlinear differential equations obtained from the physical model.In addition,this paper constructed a simple pipe network as an example,and numerical simulation with Fluent was carried out to verify the algorithm.Finally,the actual pipe network model is established and compared with the actual operation data of a steam pipe network in an enterprise.The results show that the proposed model is not only accurate in calculation,but also valuable in practical engineering application.
作者
王安然
曾波
张勍
杨荣超
张凯
赵瑶
张明
赵晓东
宿彬
李祎萍
张鹏飞
WANG Anran;ZENG Bo;ZHANG Qing;YANG Rongchao;ZHANG Kai;ZHAO Yao;ZHANG Ming;ZHAO Xiaodong;SU Bin;LI Yiping;ZHANG Pengfei(College of Metrology&Measurement Engineering,China Jiliang University,Hangzhou 310018,China;China Tobacco Standardization Research Center,Zhengzhou Tobacco Research Institute,Zhengzhou 450001,China;Henan Electric Power Co.,Ltd.,State Power Investment Corporation,Zhengzhou 450018,China)
出处
《力学与实践》
北大核心
2021年第6期905-913,共9页
Mechanics in Engineering
基金
国家自然科学基金资助项目(11472260)。
关键词
蒸汽管网
动态仿真
网络拓扑
算法研究
steam pipe network
dynamic simulation
network topology
algorithm research