本研究將比對兩種計算流體力學(CFD)軟體 Ansys Fluent 與 SU2 的準確度與 SU2 程式穩健性，並透過一系列的驗證模型來判斷 SU2 作為開源軟體的可靠性 以及對比 Fluent 之模擬準確性，以及測試 SU2 在透過伴隨離散求解器的外型優 化功能。Fluent 之模擬將使用壓力速度耦合的隱式(Coupled)方法來求解可壓縮 納維爾·斯托克斯方程式(Navier-Stoke equation)，SU2 與 Fluent 皆會使用 SST k- ?紊流模型對 RAE-2822、ONERA M6 以及 CRM-HL 等三種模型進行比對，比 對內容包括:升力係數、阻力係數、俯仰力矩與壓力係數分佈等，並探討兩種程 式模擬結果之誤差進行探討。 再者，針對 SU2 的外型優化功能做實際測試與比較優化前後之差異，其 中，本研究之一將專注於壓力係數回推之反向設計研究，此研究的核心目的是 將隨意的翼型透過目標翼型的壓力係數分佈圖，透過離散伴隨求解器將隨意的 翼型變形回目標翼型，達成反向設計。另一項研究則是利用 SU2 中的 FFD 旋 轉功能，將 CRM-HL 的前緣縫翼與襟翼角度在特定流場條件下進行優化，並比 對優化前後升阻力係數變化以及角度之差異，以及透過設定不同的目標函數來 觀察其優化之結果之差異。

The validation of SU2 code was conducted in this study by using the verification cases from NASA turbulence model website, including RAE-2822 airfoil and OneraM6 wing, the simulations were carried out by both SU2 code and commercial software Ansys Fluent, to better understand the differences between them, a side by side comparison of Lift and Drag coefficients in addition to pressure coefficient distributions on all verification cases. The pressure-velocity coupling scheme were used in Ansys Fluent and k-? SST turbulence model for both code. Overall the outcomes from both code fit close with each other very well, and we concluded that SU2 is capable of predicting the coefficients just like Ansys Fluent. On the other hand, the development of SU2’s shape optimization which can efficiently optimize the aerodynamic profile by using discrete adjoint method, makes it easier for designer to devise aircraft under specific condition, such as maximize Lift or minimize Drag. In this study, we focused on two topic which used the same optimize process, at first, the Inverse Pressure design that took user’s input pressure coefficient distribution and convert the other different airfoil back to user input’s airfoil, according to the result and comparisons on airfoil outline, we successfully achieved the inverse design on the optimized airfoil which had reversed back to NACA0012 smoothly. At last, the optimization with Slat/Flap angles of HL-CRM had a stunning result from the process, a significant increase, up 10% on the aerodynamic efficiency in one of the results showed that SU2 is indeed a useful tool suitable for aerodynamic optimization.

[1]Economon, T.D., et al., SU2: An open-source suite for multiphysics simulation and design. 828-846. 54(3): p. Aiaa Journal, 2016.
[2]Economon, T.D., et al. Adjoint formulation investigations of benchmark aerodynamic design cases in su2. in 35th AIAA Applied Aerodynamics Conference. 2017.
[3]Gomes, P., T.D. Economon, and R. Palacios. Sustainable high-performance optimizations in SU2. in AIAA Scitech 2021 Forum. 2021.
[4]Palacios, F., T.D. Economon, and J.J. Alonso. Large-scale aircraft design using SU2. in 53rd AIAA aerospace sciences meeting. 2015.
[5]Palacios, F., et al. Stanford university unstructured (su 2): an open-source integrated computational environment for multi-physics simulation and design. in 51st AIAA aerospace sciences meeting including the new horizons forum and aerospace exposition. 2013.
[6]Palacios, F., et al. Stanford university unstructured (SU2): Analysis and design technology for turbulent flows. in 52nd Aerospace Sciences Meeting. 2014.
[7]Yang, G. and A. Da Ronch. Aerodynamic shape optimisation of benchmark problems using SU2. in 2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. 2018.
[8]Yang, G., et al., Sensitivity assessment of optimal solution in aerodynamic design optimisation using SU2. 362-374. 81: p. Aerospace Science and Technology, 2018.
[9]Becker, G.G. and R. Granzoto. DPW-6 and HiLiftPW-3 using the Stanford University Unstructured (SU2). in 2018 Applied Aerodynamics Conference. 2018.
[10]Sachdeva, A., Implementation and application of AUSM+-up and AUSM+-up2 schemes in open-source CFD code SU2. p.
[11]Morelli, M., T. Bellosta, and A. Guardone, Development and preliminary assessment of the open-source CFD toolkit SU2 for rotorcraft flows. 113340. 389: p. Journal of Computational and Applied Mathematics, 2021.
[12]Straathof, M.H. and M.J. van Tooren, Extension to the class-shape-transformation method based on B-splines. 780-790. 49(4): p. AIAA journal, 2011.
[13]Straathof, M. and M. van Tooren. Adjoint optimization of a wing using the CSRT method. in 29th AIAA Applied Aerodynamics Conference. 2011.
[14]Durrani, N. and N. Qin. Comparison of RANS, DES and DDES results for ONERA M-6 Wing at transonic flow speed using an in-house parallel code. in 49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition. 2011.
[15]Araya, G., Turbulence model assessment in compressible flows around complex geometries with unstructured grids. 81. 4(2): p. Fluids, 2019.
[16]Schmitt, V., Pressure distributions on the ONERA M6-wing at transonic mach numbers, experimental data base for computer program assessment. p. AGARD AR-138, 1979.
[17]Albring, T.A., M. Sagebaum, and N.R. Gauger. Efficient aerodynamic design using the discrete adjoint method in SU2. in 17th AIAA/ISSMO multidisciplinary analysis and optimization conference. 2016.
[18]Rogers, S., et al. A comparison of turbulence models in computing multi-element airfoil flows. in 32nd Aerospace Sciences Meeting and Exhibit. 1994.
[19]Murayama, M., et al., CFD validation for high-lift devices: Three-element airfoil. 40-48. 49(163): p. Transactions of the Japan Society for Aeronautical and Space Sciences, 2006.
[20]Wilcox, D.C., Reassessment of the scale-determining equation for advanced turbulence models. 1299-1310. 26(11): p. AIAA journal, 1988.
[21]Chien, K.-Y., Predictions of channel and boundary-layer flows with a low-Reynolds-number turbulence model. 33-38. 20(1): p. AIAA journal, 1982.
[22]Menter, F.R., Two-equation eddy-viscosity turbulence models for engineering applications. 1598-1605. 32(8): p. AIAA journal, 1994.
[23]Menter, F.R., M. Kuntz, and R. Langtry, Ten years of industrial experience with the SST turbulence model. 625-632. 4(1): p. Turbulence, heat and mass transfer, 2003.
[24]Cook, P., M. Firmin, and M. McDonald, Aerofoil RAE 2822: pressure distributions, and boundary layer and wake measurements: RAE. 1977.
[25]Michal, T., et al., Comparing unstructured adaptive mesh solutions for the high lift common research airfoil. 3566-3584. 59(9): p. AIAA Journal, 2021.
[26]Ursachi, C.-I., et al. Output-based adaptive RANS solutions using higher-order FEM on a multi-element airfoil. in Aiaa Aviation 2020 Forum. 2020.
[27]Agarwal, D., S. Marques, and T.T. Robinson, Aerodynamic Shape Optimisation Using Parametric CAD and Discrete Adjoint. 743. 9(12): p. Aerospace, 2022.
[28]Anderson, J.D. and M.L. Bowden, Introduction to flight: McGraw-Hill Higher Education New York. Vol. 582. 2005.