本研究目的在於結合計算流體力學(Computational fluid dynamic, CFD) 軟體與最佳化方法，進行空氣軸承(Air bearing) 的最佳化設計，使得空氣軸承系統的承載力達到最大以提升該系統的性能。本研究利用現存的商業軟體進行流場的數值模擬分析，以作為三維流場的求解器; 另外利用一徑向基礎函數(Radial basis function) 的代理模型(Surrogate model)，從數值模擬結果中歸納出目標函數與設計變數之間的函數關係; 同時使用該函數關係，藉由簡易共軛梯度法(Simplified conjugate gradient method, SCGM) 搜尋最佳化的設計變數組合。在尋求空氣軸承系統之最佳化設計過程中，RBF 方法可以大量降低求解過程所需之計算量，而簡易共軛梯度法則可以進行多變數搜尋，使目標函數達到最小值。此外，為了結合商用軟體(求解器) 與自建最佳化程式(代理模型與共軛梯度法最佳化) 以進行不間斷搜尋，在最佳化過程中，另建立一Python 介面來作為商用軟體求解器與最佳化程式間數據傳送的介面。最後，本研究將所得的最佳化空氣軸承和初始的空氣軸承比較，可發現最佳化空氣軸承的承載力可提高43.13%。另外，研究發現即使改變最佳化初始點的位置或使用不同的數據點來產生代理模型函數關係，最佳化過程仍皆趨向同一最佳化參數組合，由此顯示本方法之強建性與準確性。

This paper aims to apply Computational Fluid Dynamics (CFD) combined with an optimization method to find the optimum performances of a flat externally pressurized air bearing system.
After briefly introducing the topic and the air bearing technologies, this paper presents the numerical model that have been developed with the commercial package ANSYS. The paper then focuses on the optimization method that has been used in this study, and after briefly reminds the bases of optimization and CFD-based optimization, the Radial Basis Function (RBF) surrogate model is introduced. This surrogate model is used to reduce the number of CFD computation required to find the optimal design of the air bearing. The Simplified Conjugate Gradient Method (SCGM) is used to find the estimated minimum response of the objective function.
This paper therefore describes the equations used by SCGM and details the Python program that has been developed to ensure the data transmission between the optimization module and the CFD solver.
The analyses of the optimization results, carried out in the last part of this paper, allow to compare the optimized design and the base-line case. The design found thanks to the combination of the optimization method and CFD allows to increase of 43.13 % the load capacity of the bearing.

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