Aerodynamic Optimization Algorithm with Integrated Geometry Parameterization and Mesh Movement

AIAA Journal ◽  
2010 ◽  
Vol 48 (2) ◽  
pp. 400-413 ◽  
Author(s):  
Jason E. Hicken ◽  
David W. Zingg
Keyword(s):  
Optimization Algorithm ◽  
Aerodynamic Optimization ◽  
Mesh Movement ◽  
Geometry Parameterization

scholarly journals COMPUTATIONAL AERODYNAMIC OPTIMIZATION OF LOW-SPEED WING

2014 ◽  
Vol 54 (6) ◽  
pp. 420-425 ◽  
Author(s):  
Martin Lahuta ◽  
Zdeněk Pátek ◽  
András Szöllös
Keyword(s):  
Optimization Algorithm ◽  
Optimization Method ◽  
Aerodynamic Optimization ◽  
Low Speed

An optimization method consisting of genetic and evolution optimization algorithm and a solver using nonlinear aerodynamics was applied on design of low-speed wing. Geometric parameterization of wing uses standard geometric quantities commonly used for the description of wing geomtery. The method seems to provide good teliable results at low computer capacity requirements.

scholarly journals MULTI OBJECTIVE AERODYNAMIC OPTIMIZATION USING PARALLEL NASH EVOLUTIONARY/DETERMINISTIC HYBRID ALGORITHMS

2016 ◽  
Vol 42 ◽  
pp. 1660168
Author(s):  
ZHILI TANG
Keyword(s):  
Drag Reduction ◽  
Optimization Algorithm ◽  
Aerodynamic Drag ◽  
Hybrid Algorithms ◽  
Hybrid Optimization ◽  
Aerodynamic Optimization ◽  
Multi Objective ◽  
Hybrid Optimization Algorithm ◽  
Aerodynamic Drag Reduction ◽  
Global Parameters

This paper solved aerodynamic drag reduction of transport wing fuselage configuration in transonic regime by using a parallel Nash evolutionary/deterministic hybrid optimization algorithm. Two sets of parameters are used, namely globally and locally. It is shown that optimizing separately local and global parameters by using Nash algorithms is far more efficient than considering these variables as a whole.

scholarly journals Nonconsistent Mesh Movement and Sensitivity Calculation on Adjoint Aerodynamic Optimization

AIAA Journal ◽  
2018 ◽  
Vol 56 (4) ◽  
pp. 1541-1553 ◽  
Author(s):  
Gabriele Luigi Mura ◽  
Benjamin Lee Hinchliffe ◽  
Ning Qin ◽  
Joël Brezillon
Keyword(s):  
Aerodynamic Optimization ◽  
Sensitivity Calculation ◽  
Mesh Movement

Modified teaching-learning-based optimization algorithm for multi-objective optimization problems

2022 ◽  
pp. 1-10
Author(s):  
Zhi Wang ◽  
Shufang Song ◽  
Hongkui Wei
Keyword(s):  
Optimization Algorithm ◽  
Learning Strategy ◽  
Optimization Problems ◽  
Teaching Strategy ◽  
Multi Objective Optimization ◽  
Aerodynamic Optimization ◽  
Multi Objective ◽  
Teaching Learning Based Optimization ◽  
Benchmark Test Functions ◽  
Teaching Learning

When solving multi-objective optimization problems, an important issue is how to promote convergence and distribution simultaneously. To address the above issue, a novel optimization algorithm, named as multi-objective modified teaching-learning-based optimization (MOMTLBO), is proposed. Firstly, a grouping teaching strategy based on pareto dominance relationship is proposed to strengthen the convergence efficiency. Afterward, a diversified learning strategy is presented to enhance the distribution. Meanwhile, differential operations are incorporated to the proposed algorithm. By the above process, the search ability of the algorithm can be encouraged. Additionally, a set of well-known benchmark test functions including ten complex problems proposed for CEC2009 is used to verify the performance of the proposed algorithm. The results show that MOMTLBO exhibits competitive performance against other comparison algorithms. Finally, the proposed algorithm is applied to the aerodynamic optimization of airfoils.

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