臺灣博碩士論文加值系統

前翼與尾翼是開輪式賽車產生下壓力的主要元件，佔了總體下壓力約6成，設計好壞會大大影響賽車的過彎速度與煞車性能。影響前翼與尾翼的設計變數除了翼形的外型之外，翼形攻角是最重要且容易改變的參數。如何建立一套經驗模型方便後續比賽調整至符合賽道的需求是本研究重點。
本研究針對前翼與尾翼的攻角進行探討，期望達成增加下壓力的同時阻力最小化。研究首先對單一翼形以Ansys Fluent做二維的CFD模擬，將結果與實驗做對照來確認計算方式與計算值的正確性；同時，為了設計參數範圍，先以田口法對尾翼做參數敏感度試驗來確認攻角範圍，接著利用Box-Behnken設計方法對尾翼設計參數樣本點之範圍。前翼則利用中央合成設計方法在原始設計的攻角選擇樣本點。將模擬後得到的參數匯入Minitab軟體配適出升力係數與阻力係數的經驗模型，並對模型做樣本範圍內預測力測試，接著，利用願望函數法對下壓力與阻力進行最適化分析。最後，比較設計前後的CFD模擬差異，結果顯示，在二維情況下，最適化後的尾翼其阻力係數減少400%之多，升力係數則增加了200%；前翼的阻力係數減少19%，升力係數則增加12%。從前面的討論來看，前翼與尾翼的經驗模型可在樣本範圍內預測升力係數與阻力係數之變動趨勢，可有效幫助設計不同攻角的配置。

Front wing and rear wing are playing an important role in open-wheel racecar, these two main elements account for 60% of vehicle downforce, and consequently affect the vehicle cornering ability and breaking performance. There are plenty of design parameters in design-ing multi-element wing, such as airfoil shape, however, angle of attack is the easiest method on wing design. Therefore, to build an empirical model for adjusting the angle of attack is the purpose of this study.
This paper focus on the angle of attack of front wing and rear wing to anticipate a bet-ter downforce but lower drag force. The first section is doing 2D CFD simulation by Ansys Fluent for testing computational method and compare experiment data with numerical result to confirm the accuracy of simulation data; meanwhile, we use Taguchi orthogonal array to test the sensitivity of rear wing parameters, and then goes on to choose sample point of angle by using Box-Behnken design. The sample point design for front wing is using central com-posite design. After CFD simulation, the numerical results are inputted into statistics soft-ware Minitab to build lift coefficient and drag coefficient empirical models, then we verify the accuracy of these two models. In the following part, we optimize downforce and reduce drag force by desirability function approach. Comparing the two result of optimal design and original design of rear wing and front wing, it can be seen that rear wing increase 400% of drag coefficient and 200% of lift coefficient; front wing increase 19% of drag coefficient and 12% of lift coefficient. From the previous discussion, the empirical models of front wing and rear wing can predict drag coefficient and lift coefficient within sample range, and provide reference values for angle adjustment.

摘要 i
ABSTRACT ii
目錄 v
表目錄 vii
圖目錄 ix
符號表 xii
第一章 緒論 1
1.1 研究背景 1
1.2 文獻回顧 7
1.3 研究目的 8
1.4 研究流程 9
1.5 論文架構 10
第二章 計算流體力學之數值模式 11
2.1 統御方程式 11
2.2 紊流模型 13
第三章 車輛模型介紹與邊界條件 15
3.1 車輛幾何模型 15
3.1.1 全車模型 15
3.1.2 尾翼模型 16
3.1.3 前翼模型 17
3.1.4 二維計算模型 19
3.2 二維網格設置 21
3.3 邊界條件 23
3.4 網格敏感度分析 24
第四章 實驗設計法 29
4.1 實驗流程與實驗規劃 29
4.2 田口法實驗設計簡介 30
4.2.1 品質特性與因子水準 30
4.2.2 田口直交表 30
4.2.3 信噪比與因子效應 31
4.3 以田口法進行尾翼參數敏感度分析 31
4.3.1 尾翼之直交表參數設計 31
4.3.2 尾翼田口實驗結果分析 33
4.4 反應曲面法 35
4.4.1 迴歸分析概論 35
4.4.2 Minitab軟體簡介 37
4.4.3 反應曲面法之實驗設計 38
第五章 反應曲面分析與參數最適化 43
5.1 尾翼反應曲面之結果分析與檢驗 43
5.1.1 尾翼之Box-Behnken參數模擬結果 43
5.1.2 尾翼阻力係數數學模型與等高線圖 44
5.1.3 尾翼升力係數數學模型與等高線圖 48
5.1.4 尾翼之阻力係數與升力係數數學模型檢驗 52
5.2 前翼反應曲面之結果分析與檢驗 54
5.2.1 前翼中央合成設計模擬結果 54
5.2.2 前翼阻力係數數學模型與等高線圖 55
5.2.3 前翼升力係數數學模型與等高線圖 57
5.2.4 前翼之阻力係數與升力係數數學模型檢驗 60
5.3 二維模型攻角參數最適化 62
5.3.1 尾翼最適化分析 64
5.3.2 前翼最適化分析 68
第六章 結論 71
參考文獻 72

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