The Design of Multi-Element Airfoils Through Multi-Objective Optimization Techniques

被引:0
作者
Trapani, G. [1 ]
Kipouros, T. [1 ]
Savill, A. M. [1 ]
机构
[1] Cranfield Univ, Dept Power & Prop, Cranfield MK43 0AL, Beds, England
来源
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES | 2012年 / 88卷 / 02期
基金
英国工程与自然科学研究理事会;
关键词
multi-objective optimization; tabu search; NSGA-II; high-lift airfoil design; robust optimization; HIGH-LIFT; SHAPE OPTIMIZATION; AERODYNAMIC DESIGN; ALGORITHM;
D O I
暂无
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This paper presents the development and the application of a multi-objective optimization framework for the design of two-dimensional multi-element high-lift airfoils. An innovative and efficient optimization algorithm, namely Multi-Objective Tabu Search (MOTS), has been selected as core of the framework. The flow-field around the multi-element configuration is simulated using the commercial computational fluid dynamics (cfd) suite Ansys cfx. Elements shape and deployment settings have been considered as design variables in the optimization of the Garteur A310 airfoil, as presented here. A validation and verification process of the cfd simulation for the Garteur airfoil is performed using available wind tunnel data. Two design examples are presented in this study: a single-point optimization aiming at concurrently increasing the lift and drag performance of the test case at a fixed angle of attack and a multi-point optimization. The latter aims at introducing operational robustness and off-design performance into the design process. Finally, the performance of the MOTS algorithm is assessed by comparison with the leading NSGA-II (Non-dominated Sorting Genetic Algorithm) optimization strategy. An equivalent framework developed by the authors' within the industrial sponsor environment is used for the comparison. To eliminate cfd solver dependencies three optimum solutions from the Pareto optimal set have been cross-validated. As a result of this study MOTS has been demonstrated to be an efficient and effective algorithm for aerodynamic optimizations.
引用
收藏
页码:107 / 139
页数:33
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