本文研究超橢圓(superellipse)之渦捲式熱交換器進行熱流數值模擬，討論在固定旋轉匝數時，導圓角的幾何參數變化，以及將阿基米德螺線設計成與超橢圓有相同傳熱面積，兩者情況對於流場與熱場上的影響，並且在設計參數一定範圍內，選定適應值函數做單目標最佳化。其中在固定旋轉匝數，利用不同的導圓角幾何尺寸，以及固定傳熱面積時對應不同阿基米德螺線匝數，設計出不同的渦卷式熱交換器結構。在不同雷諾數、固定冷熱流體入口溫度的條件下，藉由總熱傳係數與熱流體進出口溫度差來分析數值模擬的結果，並觀察幾何參數變化對於其熱交換器熱傳與壓降的關係。由類神經網路建立熱交換器的性能模型，再透過基因演算法做最佳化設計，以熱傳綜合性能係數作為適應性函數搜尋在設計參數內的最佳結果。最佳化預測結果 再與數值模擬進行驗證，確定結果的適當性，並與建模使用的設計參數組比較，確定其具有更好的熱交換性能係數。顯示基因演算法最佳化的準確性以及模擬結果搭配類神經網路的實用性，設計方法可運用於各種不同的熱交換器研究之參考。而利用NSGA-II，所建立的柏拉圖前緣，則能夠針對最大總熱傳係數與最小壓降的目標來探討設計參數的變化對於性能的影響，以提供渦捲式熱交換器設計者權衡這些參數做參考依據。

This paper studies the thermal and fluid numerical simulation of a superellipse spiral heat exchanger, discussing the impact of geometric parameter variations of the rounding parameter under a fixed number of turns and fixed heat transfer area on the flow and thermal fields. A fitness function is selected for single-objective optimization within a specific range of design parameters. The study involves designing different spiral heat exchanger structures using various geometric sizes of the rounding parameter under a fixed number of turns and different numbers of Archimedean spiral turns under a fixed heat transfer area. Under conditions of different Reynolds numbers and fixed inlet temperatures of hot and cold fluids, the results of numerical simulations are analyzed using the heat transfer rate and temperature difference between the inlet and outlet of the thermal fluid, observing the relationship between geometric parameter variations and the heat transfer and pressure drop of the heat exchanger. A performance model of the heat exchanger is established using a neural network, and the design is optimized using a genetic algorithm. The comprehensive heat transfer performance coefficient is the fitness function to search for optimal results within the design parameters. The optimization prediction results are then verified with numerical simulations to ensure their appropriateness and compared with the design parameters used in the modeling to confirm a better heat exchange performance coefficient. This demonstrates the accuracy of single-objective optimization and the practicality of simulation results combined with neural networks. The design method can be used as a reference for various heat exchanger research.

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