Application of Nontraditional Optimization Techniques for Airfoil Shape Optimization
The method of optimization algorithms is one of the most important parameters which will strongly influence the fidelity of the solution during an aerodynamic shape optimization problem. Nowadays, various optimization methods, such as genetic algorithm (GA), simulated annealing (SA), and particle sw...
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | Modelling and Simulation in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2012/636135 |
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| author | R. Mukesh K. Lingadurai U. Selvakumar |
| author_facet | R. Mukesh K. Lingadurai U. Selvakumar |
| author_sort | R. Mukesh |
| collection | DOAJ |
| description | The method of optimization algorithms is one of the most important parameters which will strongly influence the fidelity of the solution during an aerodynamic shape optimization problem. Nowadays, various optimization methods, such as genetic algorithm (GA), simulated annealing (SA), and particle swarm optimization (PSO), are more widely employed to solve the aerodynamic shape optimization problems. In addition to the optimization method, the geometry parameterization becomes an important factor to be considered during the aerodynamic shape optimization process. The objective of this work is to introduce the knowledge of describing general airfoil geometry using twelve parameters by representing its shape as a polynomial function and coupling this approach with flow solution and optimization algorithms. An aerodynamic shape optimization problem is formulated for NACA 0012 airfoil and solved using the methods of simulated annealing and genetic algorithm for 5.0 deg angle of attack. The results show that the simulated annealing optimization scheme is more effective in finding the optimum solution among the various possible solutions. It is also found that the SA shows more exploitation characteristics as compared to the GA which is considered to be more effective explorer. |
| format | Article |
| id | doaj-art-79b63f54ca9d40b294eed9d7732df53c |
| institution | Kabale University |
| issn | 1687-5591 1687-5605 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Modelling and Simulation in Engineering |
| spelling | doaj-art-79b63f54ca9d40b294eed9d7732df53c2025-08-20T03:35:48ZengWileyModelling and Simulation in Engineering1687-55911687-56052012-01-01201210.1155/2012/636135636135Application of Nontraditional Optimization Techniques for Airfoil Shape OptimizationR. Mukesh0K. Lingadurai1U. Selvakumar2Department of Mechanical Engineering, Anna University, Tamil Nadu, Dindigul 624622, IndiaDepartment of Mechanical Engineering, Anna University, Tamil Nadu, Dindigul 624622, IndiaDepartment of Information Technology, IBBT, Ghent University, 9050 Ghent, BelgiumThe method of optimization algorithms is one of the most important parameters which will strongly influence the fidelity of the solution during an aerodynamic shape optimization problem. Nowadays, various optimization methods, such as genetic algorithm (GA), simulated annealing (SA), and particle swarm optimization (PSO), are more widely employed to solve the aerodynamic shape optimization problems. In addition to the optimization method, the geometry parameterization becomes an important factor to be considered during the aerodynamic shape optimization process. The objective of this work is to introduce the knowledge of describing general airfoil geometry using twelve parameters by representing its shape as a polynomial function and coupling this approach with flow solution and optimization algorithms. An aerodynamic shape optimization problem is formulated for NACA 0012 airfoil and solved using the methods of simulated annealing and genetic algorithm for 5.0 deg angle of attack. The results show that the simulated annealing optimization scheme is more effective in finding the optimum solution among the various possible solutions. It is also found that the SA shows more exploitation characteristics as compared to the GA which is considered to be more effective explorer.http://dx.doi.org/10.1155/2012/636135 |
| spellingShingle | R. Mukesh K. Lingadurai U. Selvakumar Application of Nontraditional Optimization Techniques for Airfoil Shape Optimization Modelling and Simulation in Engineering |
| title | Application of Nontraditional Optimization Techniques for Airfoil Shape Optimization |
| title_full | Application of Nontraditional Optimization Techniques for Airfoil Shape Optimization |
| title_fullStr | Application of Nontraditional Optimization Techniques for Airfoil Shape Optimization |
| title_full_unstemmed | Application of Nontraditional Optimization Techniques for Airfoil Shape Optimization |
| title_short | Application of Nontraditional Optimization Techniques for Airfoil Shape Optimization |
| title_sort | application of nontraditional optimization techniques for airfoil shape optimization |
| url | http://dx.doi.org/10.1155/2012/636135 |
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