Integral identification of fluid specific heat capacity and heat transfer coefficient distribution in heat exchangers based on multiple-case joint analysis

Abstract

Estimating detailed heat transfer characteristics efficiently is crucial for analysis and optimization of thermal systems. However, there still lacks accurate methods for identifying local parameters in heat exchangers, mainly due to the hindrance of sensor placement. This work proposes a method for identifying fluid specific heat capacity and heat transfer coefficient distribution in heat exchangers from lumped datasets consisting of multiple working cases, which only include mass flow rates of fluids and their inlet and outlet temperatures. Dividing the fluid temperature range in the dataset and combining energy conservation equations of all cases gives a matrix equation. Solving it with the least squares method gives the specific heat capacity distribution. Combining this method and reinforcement learning offers a two-stage strategy for identifying the heat transfer coefficient distribution. The first stage gives an approximation of the distribution, and the second stage refines the approximation by considering secondary effects such as radial property variation. A heat transfer process between supercritical carbon dioxide (sCO2) and water is used to validate the method. Results show that relative identification errors of sCO2 specific heat capacity and heat transfer coefficient distributions are within 3.1% and 6.3%, respectively. The satisfying accuracy proves the proposed method’s efficacy.