臺灣博碩士論文加值系統

本研究以數值模擬方式針對無附面層隔道超音速進氣道進行優化，其中無附面層隔道超音速進氣道由進氣道與鼓包構成，而由文獻得知鼓包對進道之氣動與匿蹤性能會有所影響。因此本研究首先對於若干幾何進行分析，其研究參數包括鼓包錐形夾角、流線方程式常數和鼓包與進氣道之間的距離等，以計算其對流況與雷達散射截面之影響，再依模擬所得數據組，利用機器學習建立參數間的數學模型。最後以以進氣道之總壓恢復與雷達散射截面作為優化目標，藉由基因演算法對整體進氣道進行優化，以提供一個有效且快速的優化組合。經研究發現鼓包與進氣道之間的距離，對進氣道之流況與雷達散射截面有顯著的影響，而最終優化構型相較原始構型的進氣道，其總壓恢復提升24.01%，雷達散射截面之水平極化與垂直極化則分別下降23.78%與18.71%。

This study used numerical simulation to optimize the diverterless supersonic inlet (DSI), which DSI consists of an inlet and bump. From the literature, it is known that the bump will affect DSI's aerodynamic and stealth performance. Firstly, this research conducts a parametric study with the bump cone's angle, the streamline equation's constant, the distance between the pump and intake, etc., to investigate their influences on flow conditions and the performance of radar cross-section. Then we use machine learning to construct the model between the parameters based on the simulation results. After that, the genetic algorithm is used to optimize the DSI with the objectives of total pressure recovery and performance of radar cross-section to provide an efficient, optimized parametric combination. It was found that the distance between the pump and intake significantly impacts the DSI performance. Compared with the original design, the optimized configuration improves the total pressure recovery by 24.01%, and the horizontal and vertical polarization of the radar cross-section decrease by 23.78% and 18.71%.

第一章 緒論 1
1.1 研究動機及背景 1
1.2 文獻回顧 4
1.3 研究目的 8
第二章 研究方法 9
2.1 DSI幾何設計 9
2.1.1 鼓包理論 9
2.1.2 幾何參數 11
2.1.3 原始構型 14
2.2 流場數值模擬 15
2.2.1 數學模式 15
2.2.2 數值方法 17
2.2.3 網格配置 20
2.2.4 數值模擬 23
2.3 雷達散射截面 25
2.3.1 基本電磁波理論 25
2.3.2 雷達系統 31
2.3.3 雷達散射截面定義 34
2.3.4 雷達散射截面計算法 36
2.3.5 彈跳射線追蹤法 38
2.3.6 雷達散射截面模擬 41
2.4 類神經網路 42
2.4.1 類神經網路簡介 42
2.4.2 倒傳遞類神經網路 42
2.5 基因演算法 47
2.5.1 基因演算法簡介 47
2.5.2 基因演算架構 47
2.5.6 非支配型排序基因演算法 49
第三章 結果與討論 52
3.1 數值模結果 52
3.1.1 流場數值模擬驗證 52
3.1.2 雷達散射截面驗證 54
3.1.3 參數對流況與RCS之影響 54
3.2 類神經網路與基因演算法 60
3.2.1 訓練與優化參數設定 60
3.2.2 設定基因演算法 63
3.2.3 綜合比較 67
第四章 結論與建議 70
4.1 結論 70
4.2 建議 71
參考文獻 192
附件 200
附件1 1.6馬赫類神經網路訓練資料 200
附件2 0.8馬赫類神經網路訓練資料 207

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