期刊文献+

飞翼布局飞机控制/气动/隐身多学科优化设计 被引量:8

Multidisciplinary optimization of control-aerodynamic-stealth for flying wing aircraft design
下载PDF
导出
摘要 飞翼布局是先进飞机广泛应用的布局形式.飞翼布局飞机总体设计阶段不仅要考虑其隐身和气动要求,还必须高度重视控制系统的影响.以飞翼布局飞机为对象,研究控制、气动与隐身多学科优化的策略和方法.建立了适合在各学科的子空间进行多目标优化的流程,基于学科分析分配各个子空间的设计变量,并通过变量综合形成系统级的设计变量.在优化过程中,综合采用了改进的遗传算法和近似模型构造方法.针对飞翼布局飞机的特点,采用基于控制分配的控制系统结构,以时域指标作为控制学科的优化目标和约束条件.优化结果验证了所用方法的有效性,为将控制学科纳入飞翼布局飞机多学科优化提供了可行的途径. Flying wing is a widely applied configuration for advanced military aircraft.Not only should aerodynamic and stealth requirements be taken into account at its conceptual design stage,but also the flight control system should be well designed.A flying wing aircraft was investigated,and the strategy and methods for integrated control-aerodynamic-stealth were studied.A new flow suitable for conducting multi-objective optimization in each discipline's subspace was proposed.Design variables in subspace were assigned based on disciplinary analyses,and then system level design variables were formed by variable synthesis.According to the characteristics of flying wing configuration,control allocation based control system architecture was adopted,and time domain indexes were used as objectives and constraints in control discipline.Results of optimization show the effectiveness of new methods,which supply a feasible way for incorporating control discipline into the multidisciplinary design optimization of flying wing configuration aircraft.
出处 《北京航空航天大学学报》 EI CAS CSCD 北大核心 2009年第11期1357-1360,共4页 Journal of Beijing University of Aeronautics and Astronautics
基金 国家973基金资助项目(5132004)
关键词 多学科设计优化 并行子空间优化 飞机设计 飞行控制 multidisciplinary design optimization concurrent subspace optimization aircraft design flight control
  • 相关文献

参考文献5

二级参考文献45

  • 1吴志刚,杨超.机翼的气动伺服弹性设计优化研究[J].航空学报,2006,27(4):570-573. 被引量:6
  • 2Wang Z G, Chen X Q, Luo W C, et al. Research on the theory and application of multidisciplinary design optimization of flight vehicles. Beijing: National Defence Industry Press, 2006.
  • 3Rao S S, Dhingra A K. Applications of fuzzy theories to multiobjective system optimization. NASA CR 177573, 1991.
  • 4Soland R M. Multicriteria optimization: a general characterization of efficient solutions. Decision Sciences 1979; 10(1): 26-38.
  • 5Cui X X. Multiobjective evolutionary algorithm and their application. Beijing: National Defence Industry Press, 2006.
  • 6Coello C A C. An updated survey of evolutionary multiobjective optimization techniques: state of the art and future trends. Proceedings of the 1999 Congress on Evolutionary Computation. 1999; 1: 3-13.
  • 7Schaffer J D. Some experiments in machine learning using vector evaluated genetic algorithms. PhD thesis, Vanderbilt University, 1984.
  • 8Horn J, Nafpliotis N, Goldberg D E. A niched Pareto genetic algorithm for multiobjective optimization. Proc of the 1st IEEE Conf on Evolutionary Computation. 1994; 82-87.
  • 9Zitzler E, Thiele L. Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans on Evolutionary Computation 1999; 3(4): 257-271.
  • 10Zitzler E, Marco L, Lothar T. SPEA2: improving the strength Pareto evolutionary algorithm. Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology Technical Report 103, 2001.

共引文献9

同被引文献73

  • 1王晓锋,席光.基于Kriging模型的翼型气动性能优化设计[J].航空学报,2005,26(5):545-549. 被引量:39
  • 2马松辉,吴成富,陈怀民.飞翼飞机稳定性与操纵性研究[J].飞行力学,2006,24(3):17-21. 被引量:31
  • 3Han Zhonghua He Fei Song Wenping Qiao Zhide.A Preconditioned Multigrid Method for Efficient Simulation of Three-dimensional Compressible and Incompressible Flows[J].Chinese Journal of Aeronautics,2007,20(4):289-296. 被引量:14
  • 4Hicks R M, Murman E M, Vanderplaats G N. An assessment of airfoil design by numerical optimization. NASA-TM-X-3092, 1974.
  • 5Hicks R M, Henne P. Wing design by numerical optimization[J]. Journal of Aircraft, 1978, 15(7): 407-412.
  • 6Jameson A. Aerodynamic design via control theory[J]. Journal of Scientific Computing, 1988, 13(3): 233-260.
  • 7Samareh J A. A survey of shape parameterization techniques for high-fidelity multidisciplinary shape optimization[J]. AIAA Journal, 2001, 39(5): 877-884.
  • 8Wataru Y, Sylvain M, Gérald C. Geometry parameterization and computational mesh deformation by physics-based direct manipulation approaches[J]. AIAA Journal, 2010, 48(8): 1817-1832.
  • 9Jason E H, David W Z. Aerodynamic optimization algorithm with integrated geometry parameterization and mesh movement[J]. AIAA Journal, 2010, 48(2): 400-413.
  • 10Vijay N J, Felipe A C V, Raphael T H, et al. Design optimization of a bendable UAV wing under uncertainty. AIAA-2010-2761, 2010.

引证文献8

二级引证文献40

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部