本研究主旨在利用級堆疊 (Stage-Stacking) 法配合通用級特徵曲線(Generalized Characteristic Curves)，求解出壓縮機各級之性能參數，以重建壓縮機總體性能圖。本文以一維非定常歐拉(Euler)方程式為壓縮機流場統御方程式。數值方法採用有限體積法及修正後的Osher-Chakravarthy MUSCL型上風總變量縮減TVD法。時間積分採四步階的Runge-Kutta方法，並加入次疊代步驟與遲滯源項(Time-Lagged Source Term)法配合顯式(Explicit)時間步進計算來解決數值運算不穩定現象。進出口邊界則以無反射邊界條件來處理。本文以NASA兩級壓縮機作為參考對象，並模擬在已知某操作點之性能曲線資料或是實際量測數據的條件下，利用基因演算法全域搜尋的能力估算出各級之最佳性能參數參考值。結果顯示，由本研究方法建立之壓縮比性能曲線與實驗值相當吻合，雖然在效率性能曲線與實驗值間有些微差異，但此兩者誤差在8%以下。本研究提供了發動機使用者在分析壓縮機操作性能時若遭遇元件特性數據不足時如何重建壓縮機性能圖之方法。

The objective of the present research is to reconstruct the compressor performance map based on the stage stacking technique augmented by the generalized stage characteristics. The multi-stage compressor flow field is assumed uniformly distributed and one dimensional unsteady Euler equations are used as the governing equations. Finite-volume method and modified Osher-Chakaravarthy MUSCL type upwind TVD scheme are employed as the numerical methods. A four-step Runge-Kutta time stepping is used and the numerical stability is enhanced using Newton’s subiteration and an explicit, time-lagged treatment of the source term. Inflow/Outflow boundary condition are implemented by a characteristic-based nonreflecting boundary condition treatment. In this work, a NASA two-stage compressor with which the performance curves and/or the experimental data that are known is simulated. A genetic algorithm(GA) is adopted to search for the optimal performance reference states for each constituent stage. Result shows that the pressure ratio performance curve agrees well with the experimental data. However, there exists a small discrepancy of 8 percent in the efficiency performance matching. The present study provides the engine users a tool in analyzing the compressor performance when the information of component characteristics are insufficient or unavailable.

中文摘要i
英文摘要ii
致謝iii
目錄iv
表目錄vii
圖目錄viii
符號說明x

第一章 簡介1
1-1 前言1
1-2 故障樣本產生方式3
1-3 發動機數值模擬簡介4
1-4 研究動機與目的7
1-5 壓縮機性能圖繪製方法文獻回顧8
1-6 研究內容9

第二章 壓縮機流場方程式之建立11
2-1 級堆疊統御方程式11
2-2 級特徵14
2-3 數值方法16
2-3-1 數值通量之計算17
2-3-2 時間步進積分20
2-3-3 邊界條件23
2-3-3-1 入口邊界23
2-3-3-2 出口邊界25

第三章 基因演算法27
3-1 基因演算法簡介27
3-2 編碼型基因演算法28
3-3 實數行基因演算操作流程28
3-3-1 定義染色體29
3-3-2 初始族群29
3-3-3 定義適應值30
3-3-4 挑選30
3-3-5 交配31
3-3-5 突變31

第四章 基因演算法於壓縮機性能圖建立之應用33
4-1 壓縮機模擬程式驗證33
4-2 基因演算法程式驗證35
4-3 基因演算法於壓縮機性能圖建立之應用36
4-3-1 基因演算法於壓縮機性能圖建立之應用:案例一37
4-3-2 基因演算法於壓縮機性能圖建立之應用:案例二39

第五章 結論42
5-1 結論42
5-2 未來發展與建議43
參考文獻44
表49
圖56
自述
著作權聲明

[1] 陳大光, “發動機狀態監視及故障診斷系統的經濟效益分析,” 幹線客機用發動機狀態監視及故障診斷系統的分析研究, 北京航空航天大學動力系, 1993年5月.
[2] Myers, D. A., and Hogg, G. W., “F100-PW-220 Engine Monitoring System,” AGARD Conference Proceedings No. 448, pp. 18-1~18-9, 1988.
[3] Muir, D. E., Rudnitski, D. M., and Cue, R. W., “CF-18 Engine Performance Monitoring,” AGARD Conference Proceedings No. 448, pp. 7-1~7-20, 1988.
[4] Smetana, F. O., “Turbojet Engine Gas Path Analysis A Review,” AGARD Conference Proceedings No. 165, 4-5 April, 1974.
[5] Urban, L. A., “Gas Path Analysis Applied to Turbine Engine Condition Monitoring,” AIAA Paper 72-1082, 1972.
[6] Cifald, M. L., and Chokani, N., “Engine Monitoring Using Neural Network,” AIAA Paper 98-3548, 1998.
[7] Eustace, R., “Neural Network Fault Diagnosis of A Turbofan Engine,” Proceedings of the International Symposium on Air Breathing Engines, Vol. 2, Tokyo, Japan, Sept, 20-24, 1993
[8] Zedda, M., and Singh, R., “Fault Diagnosis of a Turbofan Engine Using Neural Network : A Quantitative Approach,” AIAA Paper 98-3602, 1998.
[9] 陳大光, 劉福生, “燃氣渦輪發動機故障診斷的人工神經網路法,” CSAA 98-P-184(P), 北京航空航天大學動力系, 1993年五月.
[10] P. J. Lu, M. C. Zhang, T. C. Hsu and J. Zhang, “An Evaluation of Engine Faults Diagnostics Using Artificial Neural Networks,” ASME Journal of Engineering for Gas Turbines and Power, Vol. 123, No. 2, April, pp. 340-346, 2001.
[11] MacIsaac, B. D., “Engine Performance and Health Monitoring Models Using Steady State and Transient Prediction Methods,” AGARD Lecture Series 183, May, pp. 9-1—9-21, 1992.
[12] Fishbach, L. H., and Caddy, M. J., “NNEP – The Navy NASA Engine Program,” NASA Technical Memorandum X-71857, 1975.
[13] Sellers, J. F., and Daniele, C. J., “DYNGEN – A Program for Calculating Steady-State and Transient Performance of Turbojet and Turbofan Engines,” NASA TN D-7901, 1975.
[14] Daniele, C. J., Krosel, S. M., Szuch, J. R., and Westerkamp, E. J., “Digital Computer Program for Generating Dynamic Turbofan Engine Models (DIGTEM),” NASA Technical Memorandum 83446, 1983.
[15] 郭兆書, “軍用渦輪扇發動機性能診斷之數值模擬,” 國立成功大學航空太空工程研究所碩士論文, 2000.
[16] Johnson, M. S., “One-Dimensional, Stage-By-Stage, Axial Compressor Performance Model,” ASME Paper 91-GT-192, 1991
[17] Saravanamutto, H. I. H., and Lakshminarasimha, A. N., “Predictiom of Fouled Compressor Performance Using Stage Stacking Techniques,” Proceeding of the Fourth Joint Fluid Mechanics, Plasma Dynamics, and Lasers Conference, Atlanta, GA, May, pp. 59-66, 1986.
[18] Attia, M. S., and Schoberiri, M. T., “A New Method for the Prediction of Compressor Performance Maps Using One-Dimensional Row-By-Row Analysis,” ASME Paper 95-GT-434, 1995
[19] Muir, D. E., Saravanamutto, H. I. H., and Marchall, D. J., “Health Monitoring of Variable Geometry Gas Turbines for the Canadian Navy,” ASME Journal of Engineering for Gas Turbines and Power, Vol. 111, No. 2, April, pp. 244-250, 1989.
[20] Davis, M. W., “A Stage-By-Stage Post-Stall Compression System Modeling Technique : Methodology, Validation, And Application,” Ph.D Dissertation, Virginia Polytechnic Institute and State University, December 1986.
[21] Carchedi, F., and Wood, G. R., “Design and Development of a 12:1 Pressure Ratio Compressor for the Ruston 6-MW Gas Turbine,” ASME Journal of Engineering for Gas Turbines and Power, Vol. 97, pp. 549-560, 1975.
[22] Balsa, T. F., and Mellor, G. L., “The Simulation of Axial Compressor Performance Using an Annulus Wall Boundary Layer Theory,” ASME Paper 74-GT-56, April 1974.
[23] Milner, E. J., and Wenzel, L. M., “Performance of a J85-13 Compressor with Clean and Distorted Inlet Flow,” NASA TMX-3304, 1975.
[24] Budinger, R. E., and Kaufman, H. R., “Investigation of the Performance of a Turbojet Engine with Variable-Position Compressor Inlet Guide Vanes,” NACA RM-E54L23a, 1955.
[25] Howell, A. R., and Bonham, R. P., “Overall and Stage Characteristics of Axial Flow Compressor,” Proceedings of Institute of Mechanical Engineering, Vol. 163, 1950.
[26] Roe, P. L., “Approximate Riemann Solver, Parameter Vector and Difference Schemes,” J. Comput. Phys., Vol. 43, pp. 357-372, 1981.
[27] Osher, S., and Chakravarthy, S. R., “A New Class of High Accuracy TVD Schemes for Hyperbolic Conservation Laws,” AIAA Paper 85-0363, 1985.
[28] 游義地, “三維穿音速葉柵之聲波激擾流場,” 國立成功大學航空太空工程研究所博士論文, 2000
[29] Goldberg, D. E., Genetic Algorithms and Engineering Design, John Wiley, New York, 1989
[30] Farag, W. A., Quintana, H., and Lambert, T. G., “A Genetic-Based Neuro-Fuzzy Approach for Modeling and Control of Dynamical System,” IEEE Trans. Neural Networks, Vol. 9, No. 5, Sep., pp. 756-767, 1998.
[31] 許智淵, “基因演算法於類神經網路之應用,” 國立成功大學航空太空工程研究所碩士論文, 2000
[32] 謝昇蓉, “基因演算法於亂流中飛行軌跡重建之應用,” 國立成功大學航空太空工程研究所碩士論文, 2000
[33] Urasek, D. C., Gorrell, W. T., and Cunnan, W. S., “Performance of Two-Stage Fan Having Low-Aspect-Ratio First-Stage Rotor Blading,” NASA TP-1493, 1979.
[34] William, W. B., Fundamentals of Gas Turbines 2nd ed., John Wiley &Sons, Inc., New York, 1995.
[35] Shoichiro, N., Applied Numerical Methods with Software, Prentice Hall, Englewood Cliffs, New Jersey, 1991.
[36] 蘇木春, 張孝德, 機器學習：類神經網路、模糊系統以及基因演算法則, 全華科技, 1997.