本文主要是利用場協同理論對不同幾何的山型紋板式熱交換器在流場及熱場的數值模擬結果進行探討，並且進行最佳化設計。以往場協同理論應用於熱交換器上僅探討熱場，本研究加入流場一同探討。板式熱交換器板片的深寬比及幾何角度作為設計參數，產生多組不同幾何的熱交換器板片，在固定雷諾數、冷熱流體入口定溫的條件下模擬逆向流，為了使邊界條件符合物理現象而外推出絕熱段，以網格獨立測性測試確認解趨近穩定及能量守恆公式(誤差<2%)確定模擬的正確性，數值模擬的結果利用摩擦因子、紐賽數、協同角alpha及協同角theta進行探討，並得出幾何形狀對熱傳及壓降的關係及原因。再由類神經網路建立熱交換器性能模型，並透過遺傳演算法進行最佳化方法：(1)以熱交換器性能係數(PEC)為目標，尋找到的結果與數值模擬驗證並且得到高於訓練組的熱交換器性能係數(PEC)，顯示熱最佳化方法的準確度以及尋找新穎答案的潛力(2)搭配NSGA-II建立以紐賽數及摩擦因子為目標的柏拉圖前沿。本研究提供山型紋板式熱交換器設計者權衡紐賽數及摩擦因子的設計參考。

This paper mainly discusses the numerical simulations of thermal-hydraulic performance for different geometries chevron-type plate heat exchangers with filed synergy theory, and optimization design. The dimensionless geometric parameters Pc/b and beta are used as design parameters. The counter flow is simulated under the boundary conditions of a fixed Reynolds number and a constant temperature at the inlet of the cold and hot fluid. In order to make the boundary conditions satisfy physic property, the adiabatic section is added. The grid independent test was used to confirm the solution stability and the energy conservation formula to determine the correctness. The results is evaluated by fiction factor, Nusselt number and filed synergy theory and obtained the relationship of geometric parameters to heat transfer and pressure drop. The optimization design is conducted by using the neural network(NN) and the genetic algorithm(GA), we obtained the following results. (1) Considering the performance evaluation criteria(PEC) as an objective function, the results found are verified by numerical simulations and better than the training set, showing the accuracy of the optimization method and the potential for finding novel answers. (2) Using NSGA-II to establish a Pareto front with Nusselt number and friction factor. We provide design suggestions and design parameter tables of the Pareto front for reference.

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