臺灣博碩士論文加值系統

本研究旨在探討兩棲輪型甲車之泵噴推進器構型設計，以模型自推實驗執行數據蒐整及分析，甲車構型以我國研發之雲豹甲車第二代車體CM-32為原型，而泵噴推進器則以SOLAS公司所生產之小艇用泵噴推進器為模型，運用逆向工程及3D列印技術，仿製出1/8縮小比例之車體及泵噴推進器模型，藉由「雷射督卜勒測速儀」進行泵噴推進器出口速度之分佈量測，以估算出流量及推力，並藉由負荷計量測推力值以比較估算之推力值。研究目標為設計出具有較高泵效率及推力之泵噴推進器，而影響其效能之因素甚多；本研究以「轉子葉片數」及「定子葉片數」等兩項重要的控制因子進行初步探討，另為了能有效提升品質並降低成本，採取田口式實驗設計法，藉由系統化的參數水準組合，節省實驗的時間及成本，透過統計方法ANOVA分析探討各因子之影響性及彼此間交互作用，接著利用類神經網路(ANN)中「徑向基底函數網路(RBFN)」建構非線型迴歸模型，並運用自適應粒子群算法(APSO)求得最佳因子水準組合，進而得到最適化設計之泵噴推進器構型。

The purpose of this study is to discuss the design of pump-jet for amphibious vehicle. Get and analysis data by model test. The geometry design of the amphibious vehicle is based on the second type of Taiwan Infantry Fighting Vehicle, and taking the pump-jet produced by SOLAS to be the design of prototype. Adopt the reverse engineering and 3D printing technology to imitate the 1/8 scale model. Use “Laser Doppler Velocimetry (LDV)” to measure the velocity distribution of nozzle, the flow rate and the estimation of thrust. Then use “Load Cell” to measure the thrust to compare the estimated value. The research goal is to design a pump-jet with high pump efficiency and thrust. However, there are many factors influencing the performance, two significant control factors, such as “number of rotor blades”, ”number of stator blades” are discussed in this study. In order to effectively improve the quality and reduce the cost, adopting the Taguchi method, one of the designs of experiments (DOE). Time and cost of the experiment are reduced by systematic parameter combination. The influence and the interaction of each factor are discussed through statistical method ANOVA analysis. Then, this work uses the Radial Basis Function Network (RBFN) in ANN to construct a non-linear regression model; the optimum factor-level combination is obtained by using the adaptive particle swarm optimization (APSO) in order to get the parameter design of the best thrust of pump-jet.

誌謝
摘要
Abstract
表目錄
圖目錄
符號表
1. 緒論
1.1 研究背景與動機
1.2 研究目的
1.3 文獻回顧
1.4 研究方法及架構
2. 實驗設備與方法
2.1 實驗設計規劃
2.2 實驗模型設計
2.3 實驗裝備
2.3.1 雷射都卜勒測速儀
2.3.2 負荷計
2.4 實驗配置
2.4.1 實驗模型架設及環境設定
2.4.2實驗步驟
2.5 實驗數據換算方法
2.5.1 辛普森積分法則及推力估算
2.5.2 泵效率計算方法
2.6 實驗誤差及不準確度估算
3. 智慧型參數設計
3.1 實驗設計法
3.1.1 全因子實驗法
3.1.2 部分因子實驗法
3.2 田口直交表實驗法
3.3 田口動態分析方法
3.3.1動態分析方法簡介
3.3.2零點比例式
3.3.3動態SN比
3.4 變異數分析
3.5類神經網路
3.5.1類神經網路架構
3.5.2徑向基底函數網路(RBFN)簡介
3.6自適應粒子群優化算法
3.7總期望值
4. 實驗結果分析
4.1 實驗量測數據
4.1.1 LDV量測數據
4.1.2負荷計量測數據
4.1.3 LDV與負荷計數據比較分析
4.2 田口動態分析
4.2.1 計算SN比、斜率
4.2.2 非線性轉換
4.2.3 變異數分析及SN最大化
4.2.4 調整感度至最大值
4.2.5 田口方法推力最佳因子水準組合
4.3推力最佳化設計
4.3.1徑向基網路建模
4.3.2總期望值最佳化結果
4.3.3確認實驗
4.4 泵效率換算
4.5泵噴推進器無因次分析
5. 結論
6. 未來與展望
參考文獻
論文發表
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