结冰条件下人-机-环系统的飞行风险概率
收稿日期: 2016-01-03
修回日期: 2016-03-16
网络出版日期: 2016-05-04
基金资助
国家“973”计划（2015CB755802）；国家自然科学基金（61503406）
Flight risk probability of pilot-aircraft-environment system under icing conditions
Received date: 2016-01-03
Revised date: 2016-03-16
Online published: 2016-05-04
Supported by
National Basic Research Program of China (2015CB755802); National Natural Science Foundation of China (61503406)
以结冰条件下的飞行风险量化概率为研究对象，基于蒙特卡罗飞行仿真实验对结冰条件下人-机-环系统的耦合特性进行了分析，并获取了飞行参数极值样本。构建了飞行风险发生的判定条件；对飞行参数极值样本进行了统计特性分析，验证了其厚尾分布特征。一维分布类型辨识结果表明广义极值分布对相对速度和迎角极值的描述精度最高。为描述二维变量对相关性的各自影响程度，提出了一种新的双参数变权重Copula模型；辨识结果表明该Copula模型能以较高的精度通过假设检验。相关性分析的结果表明相对速度和迎角同时出现极大值和极小值的概率较大。基于二维极值样本的Copula分布模型求出了不同结冰程度下的飞行风险概率值，探讨了飞行风险的非线性增长趋势。
薛源 , 徐浩军 , 胡孟权 . 结冰条件下人-机-环系统的飞行风险概率[J]. 航空学报, 2016 , 37(11) : 3328 -3339 . DOI: 10.7527/S1000-6893.2016.0086
The quantitative flight risk probability under icing conditions is set as the research object. Based on Monte Carlo flight simulation experiment, coupling characteristics of pilot-aircraft-environment system after icing are studied, and the extreme flight parameters are extracted. The judgement conditions for flight risk are given. And statistical properties of the extreme samples are analyzed. The fact that extreme flight parameters have heavy tail distribution characteristics is verified. Identification results indicate that generalized extreme value model can best describe the distribution characteristics of relative velocity and angle of attack. In order to describe the influence level of each two-dimensional parameter on the correlations, a new Copula model that has two changeable weights is proposed; identification result shows that this new Copula model can pass the hypothesis testing with high accuracy. Correlation analysis results reveal that relative velocity and angle of attack would both show up extreme values with greater probability. Flight risk probabilities under different icing levels are calculated based on Copula distribution model with two-dimensional extreme values. The nonlinear growth trend of flight risk is also discussed.
[1] CONNOR P, DEA A, KENNEDY Q, et al. Measuring safety climate in aviation:A review and recommendations for the future[J]. Safety Science, 2011, 49(2):128-138.
[2] BRAGG M B, PERKINS W R, SARTER N B, et al. An interdisciplinary approach to inflight aircraft icing safety:AIAA-1998-0095[R]. Reston:AIAA, 1998.
[3] HUI K, WOLDE M, BROWN A. Flight dynamics model of turboprop transport aircraft icing effects based on preliminary flight data:AIAA-2005-1068[R]. Reston:AIAA, 2005.
[4] KRZYSZTOF S, MACIEJ L, EDYTA L, et al. Aircraft flight dynamics with simulated ice accretion:AIAA-2004-4948[R]. Reston:AIAA, 2004.
[5] FRANK T L, ADBOLLAH K. Effects of ice accretions on aircraft aerodynamics[J]. Progress in Aerospace Sciences, 2001, 37(8):669-767.
[6] THOMAS P R, BILLY P B, SAM L. Current methods for modeling and simulating icing effects on aircraft performance stability and control:AIAA-2008-6204[R]. Reston:AIAA, 2008.
[7] 王明丰, 王立新, 黄成涛. 积冰对飞机纵向操稳特性的量化影响[J]. 北京航空航天大学学报, 2008, 34(5):592-595. WANG M F, WANG L X, HUANG C T. Computational effects of ice accretion on aircraft longitudinal stability and control[J]. Journal of Beijng University of Aeronautics and Astronautics, 2008, 34(5):592-595(in Chinese).
[8] LAMPTON A, VALASEK J. Prediction of icing effects on the dynamic response of light airplanes[J]. Journal of Guidance, Control, and Dynamics, 2007, 30(3):722-732.
[9] LAMPTON A, VALASEK J. Prediction of icing effects on the coupled dynamic response of light airplanes[J]. Journal of Guidance, Control, and Dynamics, 2008, 31(3):656-673.
[10] LAMPTON A, VALASEK J. Prediction of icing effects on the lateral directional stability and control of light[J]. Aerospace Science and Technology, 2012, 23:305-311.
[11] BRAGG M B, BASAR T, PERKINS W R, et al. Smart icing systems for aircraft icing safety:AIAA-2002-0813[R]. Reston:AIAA, 2002.
[12] ROBERT W D, GLEN A D. Icing encounter flight simulator[J]. Journal of Aircraft, 2006, 43(5):1528-1537.
[13] DAVID R G, BILLY B, RICHARD R, et al. Development and implementation of a model-driven envelope protection system for in-flight ice contamination:AIAA-2010-8141[R]. Reston:AIAA, 2010.
[14] RICHARD R, BORJA M, BILLY N, et al. Piloted simulation to evaluate the utility of a real time envelope protection system for mitigating in-flight icing hazards:AIAA-2010-7987[R]. Reston:AIAA, 2010.
[15] DAVID R G. Requirements and modeling of in-flight icing effects for flight training:AIAA-2013-5075[R]. Reston:AIAA, 2013.
[16] Society of Automotive Engineers. Guidelines and methods for conducting the safety assessment process on civil airborne systems and equipment:ARP 4761[S]. Washington, D.C.:SAE,1996.
[17] Society of Automotive Engineers. Certification considerations for high-integrated or complex aircraft systems:ARP 4754[S]. Washington, D.C.:SAE, 2010.
[18] USA Department of Defense. Airworthiness certification criteria:MIL-HDBK-516B[S]. Washington, D.C.:DOD, 2005.
[19] USA Department of Defense. Standard practice for system safety:MIL-STD-882E[S]. Washington, D.C.:DOD, 2012.
[20] USA Department of Defense. Flying qualities of piloted aircraft:MIL-STD-1797B[S]. Washington, D.C.:DOD, 2012:673-695.
[21] BROOKER P. Experts, Bayesian belief networks, rare events and aviation risk estimates[J]. Safety Science, 2011, 49(8):1142-1155.
[22] WANG W H, JIANG X B, XIA S C. Incident tree model and incident tree analysis method for quantified risk assessment:An in-depth accident study in traffic operation[J]. Safety Science, 2010, 48(10):1248-1262.
[23] MATTHEWS B, DAS S, BHADURI K, et al. Discovering anomalous aviation safety events using scalable data mining algorithms[J]. Journal of Aerospace Information Systems, 2013, 10(10):467-475.
[24] OCAMPO J, MILLWATER H, SINGH G, et al. Development of a probabilistic linear damage methodology for small aircraft[J]. Journal of Aircraft, 2011, 48(6):2090-2106.
[25] BALACHANDRAN S, ATKINS E M. A constrained Markova decision process framework for flight safety assessment and management:AIAA-2015-0115[R]. Reston:AIAA, 2015.
[26] NELSEN R B. An introduction to copulas[M]. 2rd ed. New York:Springer, 2006:51-108.
[27] DIKS C, PANCHENKO V, SOKOLINSKIY O, et al. Comparing the accuracy of multivariate density forecasts in selected regions of the copula support[J]. Journal of Economic Dynamics & Control, 2014, 48:79-94.
[28] SUKCHAROEN K, ZOHRABYAN T, LEATHAM D, et al. Interdependence of oil prices and stock market indices:A copula approach[J]. Energy Economics, 2014, 44:331-339.
[29] JÄSCHKE S. Estimation of risk measures in energy portfolios using modern copula techniques[J]. Computational Statistics and Data Analysis, 2014, 76:359-376.
[30] YASMIN S, ELURU N, ABDUL R, et al. Examining driver injury severity in two vehicle crashes-A copula based approach[J]. Accident Analysis and Prevention, 2014, 66(3):120-135.
[31] MASIN M, LAMBERTI A, ARCHETTI R. Coastal flooding:A copula based approach for estimating the joint probability of water levels and waves[J]. Coastal Engineering, 2015, 97:37-52.
[32] BESSA R J, MIRANDA V, BOTTERUD A, et al. Time-adaptive quantile-copula for wind power probabilistic forecasting[J]. Renewable Energy, 2012, 40(1):29-39.
[33] ERYILMAZ S. Estimation in coherent reliability systems through Copulas[J]. Reliability Engineering and System Safety, 2011, 96(5):564-568.
[34] BERGER T. Forecasting value-at-risk using time varying copulas and EVT return distributions[J]. International Economics, 2013, 133:93-106.
[35] MOAZAMI S, GOLIAN S, KAVIANPOUR M R, et al. Uncertainty analysis of bias from satellite rainfall estimates using copula method[J]. Atmospheric Research, 2014, 137(2):145-166.
[36] XUE Y, XU H J, WANG X L. Build probability distribution maps of flight risk during wake encountering[J]. Journal of Aircraft, 2015, 52(3):805-818.
/
| 〈 |
|
〉 |
CJA