淡江大學覺生紀念圖書館 (TKU Library)
進階搜尋


下載電子全文限經由淡江IP使用) 
系統識別號 U0002-2808200612062400
中文論文名稱 翼尖渦流下飛機最佳飛行軌跡之研究
英文論文名稱 Optimum Flight Trajectory Study under the Influence of Wake Vortices
校院名稱 淡江大學
系所名稱(中) 航空太空工程學系碩士班
系所名稱(英) Department of Aerospace Engineering
學年度 94
學期 2
出版年 95
研究生中文姓名 邱德溢
研究生英文姓名 Te-Yi Chiu
學號 694370031
學位類別 碩士
語文別 英文
口試日期 2006-06-26
論文頁數 66頁
口試委員 指導教授-宛同
委員-宛同
委員-蕭飛賓
委員-瀋大知
中文關鍵字 渦流  翼尖渦流  基因演算法  WV參數 
英文關鍵字 Wake  Vortices  Genetic Algorithm  WV-Factor 
學科別分類 學科別應用科學航空太空
中文摘要 現今大型航空器的使用及生產已趨於成熟,所以翼尖渦流的影響也越來越受到重視,但在現今的法規中,僅用較為寬鬆的法規來制定飛行器之間的安全飛行距離。本研究最主要的目的,是藉由所新定義的WV參數來找出最佳的閃避軌跡,以訂出更為嚴謹的安全飛行間距。
第一步我們使用MATLAB來建立不同大小的翼尖渦流,並且從參考文獻中選擇二架不同大小的飛行器做為受影響的飛行器,使用剛體運動方程式建立飛行模型,並用四階的Runge-Kutta解法來解飛行力學方程式,以求得飛行器受到翼尖渦流後應有的飛行姿態及飛行軌跡。
其次,我們所建立一新的WV參數,其中分別為第一項,風場對於垂直運動的危害程度,第二項為風場對於水平運動的危害程度,第三項為風場對於旋轉運動的影響。在之前所計算出的飛行姿態及飛行軌跡,將其轉變為WV參數,這可以將翼尖渦流對於飛行器的影響做參數化的研究。
最後,為了要求出飛行器在翼尖渦流中的最佳逃離飛行軌跡,我們使用最佳化的方法來求解,我們所選用的方法為基因演算法(GA)。在本論文中所採用的基因演算法是實數型基因演算法,理由是實數型的基因演算法的計算效率較高,且對系統的環境要求較不嚴苛。本文中所訂定的目標函數有二:其一是WV參數值,其二是三個方位角大小限制,分別針對飛行安全與同時考慮飛安和飛行品質二者。用上述的兩種方法求兩個目標函數的最小值即為最佳飛行軌跡。
本研究中此方式所找出之最佳飛行軌跡,能逹到飛行安全及飛行舒適的二項要求,並且能有效的減少飛行間距,使得越來越繁忙的空中交通得到舒緩。
英文摘要 In modern airline’s operation, the larger aircraft’s procreation and usage is a common practice. So the influence of wake vortices is more important upon flight safety. But the rules of flight separation distance are legislating somewhat outdated. In our study, the principal purpose is use the newly created WV-factor to find the optimum flight trajectory, so that the separation distance between two flights could be greatly reduced.
First, we use the MATLAB tool to create three kinds of wake vortices, and two aircrafts (major transport and business jet) are chosen to regard as the aircraft that encounter the wake vortices. And the classical rigid body, mass/mass distribution fixed flight dynamics equations are solved by standard 4th order Runge-Kutta method. It can found the flight path and flight posture when the aircrafts encounter the wake vortices.
Secondly, we are inventing a new factor, the WV-factor. In this factor, it has three parts. First part is the harm of vertical direction of the wake vortices, second part is the harm of horizontal direction of the wake vortices, and third part is the harm of rotation motion of the wake vortices. Thus we could implement this new factor to fully investigate the effects of the flight path and flight posture when aircraft encounter wake vortices.
Finally, in order to achieve an optimum flight trajectory of wake vortex, a steering tool has been employed, namely, the genetic algorithm. In our work the real-value GA approach is chosen due to its computation efficiency and the similarity to the natural world. Our GA process is implemented as follow: both WV-factor and Euler angle values are assigned as the objective functions. The optimum flight trajectory thus computed is conforming to flight safety and flight comfort. It is believed that the concepts and procedures developed will be effective to reduce flight separation distance, and increase the airline’s operation efficiency.
論文目次 Contents

Chapter Page
中文簡介 I
ABSTRACT: II
FIGURE OF CONTENTS IV
TABLE OF CONTENTS VI
1. INTRODUCTION 1
2. MODELING 6
2.1. WAKE VORTEX MODELING 6
2.2. FLIGHT SIMULATION 8
Coordinate System 8
Moment equation 10
2.3. WV-FACTOR 13
2.4. GENETIC ALGORITHM [17][18][19] 15
Elitism 17
Crossover 18
Mutation 18
3. RESULTS AND DISCUSSION 19
3.1. INITIAL FLIGHT CONDITIONS 19
3.2. CALCULATE TRIM CONDITIONS 20
3.3. CALCULATE WV-FACTOR WITHOUT G.A. 20
3.4. AVOIDANCE STRATEGY 25
3.5. CALCULATE OPTIMUM FLIGHT TRAJECTORY 26
4. CONCLUSION 32
REFERENCES 34
APPENDIX I 37
APPENDIX II 47
APPENDIX III 57
APPENDIX IV 61

Figure of Contents

Figure Page
FIG. 2.1 VORTEX FLOW TAKEN FROM HTTP://LAVA.LARC.NASA.GOV/IMAGES/SMALL/EL-1996-00130.JPEG 7
FIG. 2.2 IT WAS SHOW THE THREE KINDS OF WAKE VORTEX 8
FIG. 2.3 EARTH-FIXED COORDINATE SYSTEM 9
FIG. 2.4 BODY AXES COORDINATE SYSTEM 9
FIG. 2.5 WIND AXES COORDINATE SYSTEM HTTP://HISTORY.NASA.GOV/SP-367/F166.HTM 10
FIG. 2.6 PROCESS OF GENETIC ALGORITHM 17
FIG. 3.1 IT IS SHOWN TREE DIFFERENT WAKE VORTICES THAT WE USED IN THIS PART. 21
FIG. 3.2 (A) THE POSITION OF 19-PERSON BUSINESS JET ENCOUNTER WAKE VORTEX AT THE ORIGIN. (B) THE POSITION OF 19-PERSON BUSINESS JET ENCOUNTER WAKE VORTEX AT THE RIGHT. (C) THE POSITION OF 19-PERSON BUSINESS JET ENCOUNTER WAKE VORTEX AT THE LEFT. 23
FIG. 3.3 THE WV-FACTOR OF =2500 FT/SEC2 AND K=5 FT-2 24
FIG. 3.4 THE WV-FACTOR OF =4000 FT/SEC2 AND K=40 FT-2 25
FIG. 3.5 IT IS SHOWING US THE TANGENTIAL VELOCITY OF DIFFERENT SEPARATION DISTANCE OF WAKE VORTEX THAT =1500 FT2/M. 26
FIG. 3.6 THE 3-D FLIGHT PATH OF BOEING 747-100 ENCOUNTER WAKE VORTEX =1500FT2/SEC AT (0,0) 28
FIG. 3.7 THE 3-D FLIGHT PATH OF BOEING 747-100 ENCOUNTER WAKE VORTEX =2500FT2/SEC AT (0,0) 28
FIG. 3.8 THE 3-D FLIGHT PATH OF BOEING 747-100 ENCOUNTER WAKE VORTEX =1500FT2/SEC AT(-16.4,0) 29
FIG. 3.9 THE 3-D FLIGHT PATH OF BOEING 747-100 ENCOUNTER WAKE VORTEX =2500FT2/SEC AT (-16.4) 29
FIG. 3.10 THE 3-D FLIGHT PATH OF 19-PERSONE BUSINESS JET ENCOUNTER WAKE VORTEX =1500FT2/SEC AT (0,0) 29
FIG. 3.11 THE 3-D FLIGHT PATH OF 19-PERSONE BUSINESS JET ENCOUNTER WAKE VORTEX =2500FT2/SEC AT (0,0) 30
FIG. 3.12 THE 3-D FLIGHT PATH OF 19-PERSONE BUSINESS JET ENCOUNTER WAKE VORTEX =4000FT2/SEC AT (0,0) 30
FIG. 3.13 THE 3-D FLIGHT PATH OF 19-PERSONE BUSINESS JET ENCOUNTER WAKE VORTEX =1500FT2/SEC AT (-16.4,0) 30
FIG. 3.14 THE 3-D FLIGHT PATH OF 19-PERSONE BUSINESS JET ENCOUNTER WAKE VORTEX =2500FT2/SEC AT (-16.4,0) 31
FIG. 3.15 THE 3-D FLIGHT PATH OF 19-PERSONE BUSINESS JET ENCOUNTER WAKE VORTEX =4000FT2/SEC AT (-16.4,0) 31

Table of Contents

Table Page
TABLE 1.1 KIND OF AIRCRAFT 3
TABLE 1.2 FAA FOR HORIZONTAL SEPARATION REQUIREMENTS (UNIT: MILES) 3
TABLE 1.3 ICAO FOR HORIZONTAL SEPARATION REQUIREMENTS (UNIT: MILES) 4
TABLE 3.1 THE MAXIMUM WV-FACTOR OF DIFFERENT LOCATION THAT AIRCRAFT ENCOUNTER WAKE VORTICES 21
TABLE 3.2 THE MAXIMUM WV-FACTOR OF DIFFERENT WAKE VORTEX 23
TABLE 3.3 THE DISTANCE OF BEHIND AIRCRAFT ENCOUNTER WAKE VORTEX FROM LEAD AIRCRAFT. 27
參考文獻 [1] Roberts, L., “On Wake Vortex Alleviation,” NASA University Conference on Aeronautics-Theme: The Future of Aeronautics, Univ. of Kansas, October 1974.
[2] McCormick, B. W., “Aircraft Wakes: A Survey of the Problem,” FAA Symposium on Turbulence, Washington, D.C., March 1971.
[3] Sammonds, R. I. And G. W. Stinnett, Jr., “Hazard Criteria for Wake Vortex Encounters,” NASA TM X-62473, Aug. 1975.
[4] Sammonds, R. I., G. W. Stinnett, Jr. and W. E. Larsen, “Wake Vortex Encounter Hazard Criteria for Two Aircraft Classes,” NASA TM X-73113, June, 1976.
[5] Kantha, L. H., “Empirical Model of Transport and Decay of Wake Vortices Between Parallel Runways,” AIAA Journal, Vol. 33, No. 4, July-August, 1996.
[6] Switzer; G. F., and F. H. Proctor, “Wake Vortex Prediction Models for Decay and Transport within Stratified Environments,” AIAA-2002-0945.
[7] Bowles, R. L., “Windshear Detection, Warning, and Flight Guidance”, NASA CP 10004, 1987.
[8] Wan, T., and H.F. Huang, “Clear Air Turbulence Avoidance Strategy Analysis via Genetic Algorithm and Neural Network Methods,” AIAA- 2002-0941.
[9] Roskam, J., Airplane Flight Dynamics and Automatic Flight Controls, PartⅡ, Roskam Aviation and Engineering Corporation, Kansas, 1979.
[10] Nelson, R. C., Flight Stability and Automatic Control, 2nd edition, McGraw- Hill, 1998.
[11] Visser, H. G., “Optimal Lateral-Escape Maneuvers for Microburst Encounters During Final Approach,” Journal of Guidance, Control, and Dynamics, Vol. 17, 1994.
[12] Arbuckle, D. P., M. S. Lewis, and D. A. Hinton, “Airborne Systems Technology Application to the Windshear Threat,” Proceedings of the 20th ICAS, Sorrento, Napoli, Italy, 1996.
[13] Descatoire, F., D. Guffond, and H.T. Huynh, “Parametric Study of Performance of Aircraft Equipped with Airborne Reactive and Forward Looking Sensor During Microburst Encounter Including Raining Effect,” Proceedings of the 20th ICAS, Sorrento, Napoli, Italy 1996.
[14] Nelson, R. C., Flight Stability and Automatic Control, 2nd ed., WCB/McGraw-Hill, 1998.
[15] Barnes W. McCormick, Aerodynamics, Aeronautics, and Flight Mechanics, 2nd ed., JOHN WILEY & SONS, INC., 1995.
[16] Goldberg, D. E., Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, 1989.
[17] 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”, 金華科技圖書公司, 1999.
[18] 張吉禮, “模糊-神經網絡控制原理與工程應用”, 哈爾濱工業大學出版社, 2004.6.
[19] 周鵬程, “遺傳演算法原理與應用—活用Matlab”, 全華科技圖書股份有限公司, 2005.12.
論文使用權限
  • 同意紙本無償授權給館內讀者為學術之目的重製使用,於2008-08-28公開。
  • 同意授權瀏覽/列印電子全文服務,於2008-08-28起公開。


  • 若您有任何疑問,請與我們聯絡!
    圖書館: 請來電 (02)2621-5656 轉 2281 或 來信