Robust Airfoil Optimization Based on Improved Particle Swarm Optimization Method
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摘要: 基于标准粒子群算法,将位移变化作为影响微粒速度的变量,使得粒子群算法关于粒子位置为二阶精度函数,加快了收敛速度;进一步地在粒子速度更新公式中引入振荡环节,提高了群体多样性,改善了算法的全局收敛性.以改进粒子群算法为基础,结合气动分析程序、代理模型以及翼型参数化方法,构建了翼型稳健型气动优化设计系统.针对某型客机的基本翼型以及翼梢小翼翼型气动优化设计结果表明,优化后的翼型气动特性相对于初始翼型在较宽的设计范围内都有了大幅度提高.Abstract: A robust airfoil optimization platform was constructed based on modified particle swarm optimization method(i.e.second-order oscillating particle swarm method),which consists of an efficient optimization algorithm,a precise aero dynamic analysis program,a highac-curacy surrogate model and a classical airfoil parametric method.There are two improvements for the modified particle swarm method compared to standard particle swarm method.Firstly,particle velocity was represented by the combination of particle position and variation of position,which makes the particle swarm algorithm become a second-order precision method with respect to particle position.Secondly,for the sake of adding diversity to the swarm and enlarging parameter searching domain to improve the global convergence performance of the algorithm,an oscillating term was introduced to the update formula of particle velocity.At last,taking two airfoils as examples,the aerodynamic shapes were optimized on this optimization platform.It is shown from the optimization results that the aerodynamic characteristic of the airfoils was greatly improved at a broad design range.
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[1] Kennedy J, Eberhart R C. Particle swarm optimization[C]Proceedings of the 1995 IEEE International Conference on Neural Networks. Vol 4. Perth, Australia, 1995: 1942-1948. [2] Eberhart R C, Kennedy J. A new optimizer using particles swarm theory[C]Proceedings of the Sixth International Symposium on Micro Machine and Human Science. Nagoya, Japan, 1995: 39-43. [3] Eberhart R C, Shi Y. Comparing inertia weights and constriction factors in particle swarm optimization[C]Proceedings of IEEE Congress on Evolutionary Computation. San Diego, America, 2000: 84-88. [4] Eberhart R C, Shi Y. Particle swarm optimization: developments, applications and resources[C]Proceedings of IEEE Congress on Evolutionary Computation. Vol 1. Seoul, Korea, 2001: 81-86. [5] Abido A A. Particle swarm optimization for multimachine power system stabilizer design[C]Proc Power Engineering Soc Summer Meeting. Vol 3. 2001:1346-1351. [6] Boering D W, Werner D H. Particle swarm optimization versus genetic algorithms for phased array synthesis[J]. IEEE Transactions on Anetnas and Propagation, 2004, 52(3):771-779. doi: 10.1109/TAP.2004.825102 [7] 高尚, 韩斌, 吴小俊, 杨靖宇.求解旅行商问题的混合粒子群优化算法[J]. 控制与决策, 2004, 19(11): 1286-1289.(GAO Shang, HAN Bin, WU Xiao-jun, YANG Jing-yu. Solving traveling salesman problem by hybrid particle swarm optimization algorithm[J]. Control and Decision, 2004, 19(11):1286-1289. (in Chinese)) [8] 丁继峰, 李为吉, 张勇, 唐伟. 基于响应面的翼型稳健设计研究[J]. 空气动力学学报, 2007, 25(1): 19-22.(DING Ji-feng, LI Wei-ji, ZHANG Yong, TANG Wei. Robust airfoil optimization based on response surface method[J]. Acta Aerodynamica Sinica, 2007, 25(1): 19-22. (in Chinese)) [9] Patnaik S N. Neural network and regression approximations in high-speed civil transport aircraft design optimization[J]. Journal of Aircraft, 1998, 35(6):839-850. [10] McKay M D, Beckman R J. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code[J].Technometrics, 1979, 21(2): 239-245. [11] Hinks R M, Henne P A. Wing design by numerical optimization[J]. Journal of Aircraft, 1978, 15(7): 407-412. doi: 10.2514/3.58379
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