A new analytical method for thermal performance prediction of multi-pass serpentine tube heat exchangers

Abstract

Accurate and computationally efficient prediction of thermal performance in serpentine tube heat exchangers (STHEs) is paramount for advanced aeroengine thermal management systems. Existing methods, however, often oversimplify complex flow phenomena, rendering them inadequate for identifying intricate local cross-flow interactions and their consequences, which directly impact system reliability and safety. To address this critical research gap, this paper introduces a novel analytical model for multi-pass STHEs that achieves both high accuracy and superior computational efficiency. Our model employs a unit-based approach, discretizing the STHE into repeatable single-tube row modules. By sequentially solving these modules, we achieve an accurate resolution of detailed, two-dimensional temperature distributions across the entire heat exchanger. A significant contribution of this work is the model's capability to predict and mechanistically explain the observed local inverse heat transfer phenomenon in parallel-flow configurations. This critical behavior, largely overlooked by conventional models, directly influences overall heat transfer performance. Rigorous numerical validation confirms the model's accuracy, with average deviations below 1% for the total heat transfer rate and 3.49% for the temperature distribution. Furthermore, comparative analyses reveal that our proposed analytical model significantly outperforms traditional methods in predictive accuracy. The improvement is larger than 5% when μ > 0.30 and NTU₂ > 2.83 for parallel-flow STHEs, and μ < 0.43 and NTU₂ > 0.56 counter-flow STHEs. These findings collectively demonstrate that the developed framework not only provides new physical insights into the heat transfer mechanisms governing multi-pass STHEs but also serves as an efficient computational tool for performance optimization of aerospace thermal management systems where stringent reliability constraints exist.