本研究以DrivAer Fastback車型後照鏡進行計算流體力學（CFD）數值模擬與局部幾何形變最佳化，採用ANSYS Fluent內建之SST k−ω湍流模型及伴隨場（Adjoint Method）結合徑向基函數（Radial Basis Function, RBF）與梯度式最佳化（Gradient-Based Optimization, GBO）技術，探討後照鏡外型之局部形變與氣動減阻效益。
研究分別於不可壓縮理想氣體（Incompressible Ideal Gas, IG）與密度常數（Constant Density）兩種物性假設下，針對低速（17.95 m/s）與高速（30.6 m/s）行駛條件執行四組形變最佳化案例，並由 Fluent 自動執行多次靈敏度分析與信任區域步長收縮機制，驗證該流程於局部幾何曲面形變之穩定性與可行性。
模擬結果顯示，密度常數模式相較不可壓縮理想氣體模式，省略能量方程耦合，能在相同條件下獲得穩定之收斂性與略高的阻力係數（Cd）改善幅度（低速約2.66%，高速約2.94%），驗證物性設定對形變靈敏度求解具明顯影響。形變後幾何主要集中於尾端曲面、上緣稜線與外側後緣，有效縮小高壓區域、延後分離點並抑制尾擴散，使壓力恢復更順暢、降低壓差阻力，進一步提升後照鏡氣動效能與行駛穩定性。
本研究證實Fluent伴隨場–RBF–梯度式最佳化流程可應用於局部複雜曲面外型之氣動設計，數值結果具一致性與可重現性，為後續多目標、多工況耦合及全車外型氣動最佳化提供可行之方法學基礎與工程參考，並提醒未來如應用於極高速（如賽車或高速列車）或熱效應顯著之條件，仍應考量採用不可壓縮理想氣體或壓縮流模型以確保模擬精度。

This study investigates the rearview mirror of the DrivAer Fastback model through Computational Fluid Dynamics (CFD) simulations combined with local geometry deformation optimization. The SST k–ω turbulence model and the built-in Adjoint Method of ANSYS Fluent were integrated with Radial Basis Function (RBF) deformation and Gradient-Based Optimization (GBO) to evaluate aerodynamic drag reduction from local shape modifications.
Two physical assumptions, Incompressible Ideal Gas (IG) and Constant Density, were examined under low-speed (17.95 m/s) and high-speed (30.6 m/s) conditions. Four optimization cases were conducted, with sensitivity analyses and trust-region step-size controls used to ensure workflow stability and feasibility.
Results show that the Constant Density mode, by omitting energy equation coupling, achieved more stable convergence and slightly greater drag reduction (2.66% at low speed, 2.94% at high speed) compared to IG. The optimized deformations occurred mainly at the rear surface, upper edge, and outer trailing edge of the mirror, reducing high-pressure zones, delaying separation, and suppressing wake vortices.
This study demonstrates that the Fluent Adjoint–RBF–GBO workflow provides a reliable methodology for local aerodynamic design of complex curved surfaces and establishes a basis for future multi-objective and full-vehicle aerodynamic optimization.

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