考虑重心位置不确定性的无尾无人机优化设计
收稿日期: 2014-09-01
修回日期: 2014-12-22
网络出版日期: 2015-01-20
基金资助
西北工业大学基础研究基金(JCY20130103)
Tailless UAV design optimization under center of gravity location uncertainty
Received date: 2014-09-01
Revised date: 2014-12-22
Online published: 2015-01-20
Supported by
NPU Foundation for Fundamental Research (JCY20130103)
飞机的飞行性能与重心(CG)位置密切相关,尤其是后掠式无尾飞机的重心位置对其飞行性能影响更甚,如果重心位置发生变化,升力分布随之改变,进而影响飞机航时。针对这个问题,从气动布局和设计方法两方面,设计了一种航时对重心位置不敏感的无尾无人机(UAV)。气动布局上,提出了利用螺旋桨动力配平纵向力矩的鸥翼(GW)布局,以减小重心位置变化对升阻特性的影响;设计方法上,采用稳健性优化设计(RDO)理论,分析重心位置不确定时的航时低敏感度问题。以一架小型电动无人机为研究对象,建立了无尾无人机稳健性优化设计环境,包括总体设计、代理模型构造以及稳健性优化。分析结果表明:利用螺旋桨动力配平的鸥翼布局使重心可用范围增加了5%;静安定裕度在5%~15%变化时,该布局可以有效提高航时稳健性。采用稳健性优化得到的无人机几何参数,大幅度降低了重心位置对航时的影响,显著提升了满足约束的概率。
王刚 , 胡峪 , 宋笔锋 . 考虑重心位置不确定性的无尾无人机优化设计[J]. 航空学报, 2015 , 36(7) : 2214 -2224 . DOI: 10.7527/S1000-6893.2014.0358
There is a strict connection between aircraft performance and center of gravity (CG) location. This is especially true for a swept tailless aircraft. The shape of lift distribution deviates much from ellipse due to the presence of CG fluctuations. Then it leads to the endurance deterioration. Hence, the tailless unmanned aerial vehicle (UAV) whose endurance is insensitive to CG location is designed. In the current paper, two scenarios namely aerodynamic configuration and design method are presented. In configuration, the gull wing (GW) involving propeller thrust to trim the pitching moment is proposed in order to reduce the effect of CG location variation on lift to drag ratio. While in terms of method, the robust design optimization (RDO) theory is applied to solving the problem of insensitive endurance under CG location uncertainty. A small electric-powered UAV is taken as a case study. The RDO environment is established including UAV conceptual design, surrogate model construction as well as robust optimization. The analytical results show that the proposed GW configuration increases CG admissible region by 5% and the endurance is insensitive when static margin varies between 5% and 15%. The optimal parameters based on RDO largely improve the endurance robustness of tailless UAV. The constraints of UAV design requirements are satisfied with much higher probabilities.
[1] Lee K. Development of unmanned aerial vehicle (UAV) for wildlife surveillance[D]. Gainesville: University of Florida, 2004.
[2] Christiansen R S. Design of an autopilot for small unmanned aerial vehicles[D]. Provo: Brigham Young University, 2004.
[3] Joowon P. A prototype design, test and evaluation of a small unmanned aerial vehicle for short operations, AIAA-2004-6536[R]. Reston: AIAA, 2004.
[4] Kroo I. Design and development of the swift: a foot-launched sailplane, AIAA-2000-4336[R]. Reston: AIAA, 2000.
[5] Wang G, Hu Y, Song B F. A flying wing UAV trimmed with propeller thrust[J]. Journal of Northwestern Polytechnical University, 2014, 32(2): 181-187 (in Chinese). 王刚, 胡峪, 宋笔锋. 利用螺旋桨动力配平的飞翼布局无人机[J]. 西北工业大学学报, 2014, 32(2): 181-187.
[6] Dimitri N M, Oliver B, Daniel A D. Robust design simulation: a probabilistic approach to multidisciplinary design[J]. Journal of Aircraft, 1999, 36(1): 298-307.
[7] Wang Y, Yu X Q. Optimization method for aircraft conceptual design under uncertainty[J]. Acta Aeronautica et Astronautica Sinica, 2009, 30(10): 1883-1888 (in Chinese). 王宇, 余雄庆. 考虑不确定性的飞机总体参数优化方法[J]. 航空学报, 2009, 30(10): 1883-1888.
[8] Meng W G, Ma D L, Chu L. Wing aerodynamic robustness optimization based on neural network response surface[J]. Acta Aeronautica et Astronautica Sinica, 2010, 31(6): 1135-1140 (in Chinese). 蒙文巩, 马东立, 楚亮. 基于神经网络响应面的机翼气动稳健优化设计[J]. 航空学报, 2010, 31(6): 1135-1140.
[9] Flachsbart O, Krober G. Experimental investigation of aircraft propellers exposed to oblique air currents, NACA TM-562[R]. Washington, D.C.: NACA, 1930.
[10] Nickl K, Wohlfahrt M. Tailless aircraft in theory and practice[M]. Reston: AIAA Education Series, 1994: 74, 81-82, 92-93, 369-371.
[11] Daniel S A, Nicolaas J T, Huyssen R J. Pitch handling qualities investigation of the tailless gull-wing configuration[J]. Journal of Aircraft, 2009, 46(2): 683-691.
[12] Li J Z, Gao Z H. The application of multi-objective evolutionary algorithm and surrogate model to aerodynamic robust optimization design[J]. Acta Aerodynamic Sinica, 2012, 30(1): 47-51 (in Chinese). 李焦赞, 高正红. 多目标进化算法和代理模型技术在气动稳健优化设计中的应用[J]. 空气动力学学报, 2012, 30(1): 47-51.
[13] Liang Y, Cheng X Q, Li Z N, et al. Multi-object aerodynamic configuration parameter design using Kriging approximation[J]. Acta Aeronautica et Astronautica Sinica, 2010, 31(6): 1141-1148 (in Chinese). 梁煜, 程小全, 郦正能, 等. 基于代理模型的气动外形平面参数多目标匹配设计[J]. 航空学报, 2010, 31(6): 1141-1148.
[14] Garrison P, Pinella D. CMARC user's guide[M]. Los Angeles: Aerologic Inc., 2009: 21-31.
[15] Timothy T, Brian G. Zero lift drag and drag divergence prediction for finite wings in aircraft design, AIAA-2010-0846[R]. Reston: AIAA, 2010.
[16] Wang G, Hu Y, Song B F. Improving performance of flying wing UAV with propeller thrust involved trimming the pitching moment, AIAA-2013-4421[R]. Reston: AIAA, 2013.
[17] Charles N A, Robert H L. Design of optimum propellers[J]. Journal of Propulsion and Power, 1994, 10(5): 676-682.
[18] Phillips W F, Anderson E A, Kelly Q J. Predicting the contribution of running propellers to aircraft stability derivatives[J]. Journal of Aircraft, 2003, 40(6): 1107-1114.
[19] Lawrence D A, Mohseni K. Efficiency analysis for long-endurance electric MAVs, AIAA-2005-7090[R]. Reston: AIAA, 2005.
[20] Traub L W. Range and endurance estimates for battery-powered aircraft[J]. Journal of Aircraft, 2011, 48(2): 703-707.
[21] Zhang K S. Multidisciplinary design optimization method and its application in aircraft design[D]. Xi'an: Northwestern Polytechnical University, 2006 (in Chinese). 张科施. 飞机设计的多学科优化方法研究[D]. 西安: 西北工业大学, 2006.
/
| 〈 |
|
〉 |
CJA