廣川 智己

Abstract This paper presents an experimental investigation of local heat transfer characteristics of single-phase flow in a plate heat exchanger (PHE). The local heat transfer coefficient is evaluated using a test section with PHE geometry for measuring wall temperature distribution. The test section of 1.5 mm thickness is employed to consider the heat conduction effect of the heat transfer plate. The results indicated that the local heat transfer coefficient is influenced by the development of the thermal boundary layer along the flow direction and the maldistribution of water flows along both the direction perpendicular to the flow and the stacking direction. The harmonic mean heat transfer coefficient calculated by the measured local heat transfer coefficient agrees with the average heat transfer coefficient evaluated by the modified Wilson plot method within ±25% and within ±16% for the hot side and the cold side, respectively.

Heat exchanger modeling has been widely employed in recent years for performance calculation, design optimizations, real-time simulations for control analysis, as well as transient performance predictions. Among these applications, the model’s computational speed and robustness are of great interest, particularly for the purpose of optimization studies. Machine learning models built upon experimental or numerical data can contribute to improving the state-of-the-art simulation approaches, provided careful consideration is given to algorithm selection and implementation, to the quality of the database, and to the input parameters and variables. This comprehensive review covers machine learning methods applied to heat exchanger applications in the last 8 years. The reviews are generally categorized based on the types of heat exchangers and also consider common factors of concern, such as fouling, thermodynamic properties, and flow regimes. In addition, the limitations of machine learning methods for heat exchanger modeling and potential solutions are discussed, along with an analysis of emerging trends. As a regression classification tool, machine learning is an attractive data-driven method to estimate heat exchanger parameters, showing a promising prediction capability. Based on this review article, researchers can choose appropriate models for analyzing and improving heat exchanger modeling.