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论文:2015,Vol:33,Issue(2):178-184 |
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引用本文： |
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夏露, 张阳, 孙腾腾. 基于寄生模型的粒子群算法在气动优化中的应用[J]. 西北工业大学学报 |
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Xia Lu, Zhang Yang, Sun Tengteng. Applying a New SPPSO Algorithm to Airfoil Aerodynamic Optimization[J]. Northwestern polytechnical university |
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| 基于寄生模型的粒子群算法在气动优化中的应用 |
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| 夏露, 张阳, 孙腾腾 |
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| 西北工业大学航空学院, 陕西西安 710072 |
| 摘要: |
| 为了解决粒子群算法(PSO)在寻优过程中全局最优和局部最优的矛盾,通过在粒子群算法中加入寄生模型,发展了一种基于寄生模型的改进粒子群算法(SPPSO)。对提出的模拟寄生算法(SP)进行了分析与验证,并将其引入到粒子群算法中,丰富了粒子之间的优势信息源,增强了粒子的信息共享能力,使得SPPSO算法能够有效地跳出局部最优。函数测试表明,该算法显著提高了PSO算法的寻优性能。将SP及SPPSO算法应用于翼型的气动优化设计中,取得了良好的效果,从而表明提出的算法准确有效,具有良好的实用性。 |
| 关键词:
空气动力学
阻力系数
全局优化
粒子群算法
寄生模型
模拟寄生算法
翼型气动优化设计
攻角
压力分布
雷诺数
数值方法的收敛性
有效性
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| Applying a New SPPSO Algorithm to Airfoil Aerodynamic Optimization |
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| Xia Lu, Zhang Yang, Sun Tengteng |
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| College of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China |
| Abstract: |
| In order to solve the contradiction between global optimization and local optimization in the particle swarm optimization (PSO), a new algorithm (SPPSO) combining particle swarm optimization with simulated parasitic model is presented. The simulated parasitic (SP) algorithm, after being analyzed and validated, is introduced into the particle swarm algorithm. It enriches the source of information among the particles and enhances the information sharing ability so that the SPPSO algorithm can effectively avoid local optimization. Function tests show preliminarily that, this algorithm can improve the performance of the PSO algorithm. Applying SPPSO algorithm to the airfoil aerodynamic optimization design, we achieve good results, thus showing that the proposed algorithm is effective and practical. |
| Key words:
aerodynamics
drag coefficient
global optimization
particle swarm optimization(PSO)
SP(Simulated Parasitic)
SPPSO algorithm
airfoil aerodynamic optimization design
angle of attack
pressure distribution
Reynold number
convergence of numerical methods
efficiency
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| 收稿日期: 2014-09-30
修回日期:
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| DOI: |
| 基金项目: 国家自然科学基金(11172242)资助 |
| 通讯作者:
Email： |
| 作者简介: 夏露(1977-),女,西北工业大学副教授,主要从事飞行器设计研究。
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| 相关功能 |
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| 作者相关文章 |
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| 夏露 在本刊中的所有文章 |
| 张阳 在本刊中的所有文章 |
| 孙腾腾 在本刊中的所有文章 |
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参考文献: |
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[5] 孙腾腾,夏露.基于代理模型的群智能算法在气动优化设计中的应用[D].西安:西北工业大学,2014 Sun Tengteng, Xia Lu. Swarm Intelligence Algorithms Based on Surrogate Model for Aerodynamic Optimization Design[D]. Xi'an:Northwestern Polytechnical University, 2014(in Chinese)
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