Optimization of Wind Turbine Airfoil Using Nondominated Sorting Genetic Algorithm and Pareto Optimal Front
A Computational Fluid Dynamics (CFD) and response surface-based multiobjective design optimization were performed for six different 2D airfoil profiles, and the Pareto optimal front of each airfoil is presented. FLUENT, which is a commercial CFD simulation code, was used to determine the relevant ae...
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | International Journal of Chemical Engineering |
| Online Access: | http://dx.doi.org/10.1155/2012/193021 |
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| author | Ziaul Huque Ghizlane Zemmouri Donald Harby Raghava Kommalapati |
| author_facet | Ziaul Huque Ghizlane Zemmouri Donald Harby Raghava Kommalapati |
| author_sort | Ziaul Huque |
| collection | DOAJ |
| description | A Computational Fluid Dynamics (CFD) and response surface-based multiobjective design optimization were performed for six different 2D airfoil profiles, and the Pareto optimal front of each airfoil is presented. FLUENT, which is a commercial CFD simulation code, was used to determine the relevant aerodynamic loads. The Lift Coefficient (CL) and Drag Coefficient (CD) data at a range of 0° to 12° angles of attack (α) and at three different Reynolds numbers (Re=68,459, 479, 210, and 958, 422) for all the six airfoils were obtained. Realizable k-ε turbulence model with a second-order upwind solution method was used in the simulations. The standard least square method was used to generate response surface by the statistical code JMP. Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) was used to determine the Pareto optimal set based on the response surfaces. Each Pareto optimal solution represents a different compromise between design objectives. This gives the designer a choice to select a design compromise that best suits the requirements from a set of optimal solutions. The Pareto solution set is presented in the form of a Pareto optimal front. |
| format | Article |
| id | doaj-art-10f3cdabf815493db30a757fad6b6112 |
| institution | Kabale University |
| issn | 1687-806X 1687-8078 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Chemical Engineering |
| spelling | doaj-art-10f3cdabf815493db30a757fad6b61122025-02-03T01:28:12ZengWileyInternational Journal of Chemical Engineering1687-806X1687-80782012-01-01201210.1155/2012/193021193021Optimization of Wind Turbine Airfoil Using Nondominated Sorting Genetic Algorithm and Pareto Optimal FrontZiaul Huque0Ghizlane Zemmouri1Donald Harby2Raghava Kommalapati3Department of Mechanical Engineering, Prairie View A&M University, P.O. Box 519, Mail Stop 2525, Prairie View, TX 77446, USADepartment of Mechanical Engineering, Prairie View A&M University, P.O. Box 519, Mail Stop 2525, Prairie View, TX 77446, USADepartment of Mechanical Engineering, Prairie View A&M University, P.O. Box 519, Mail Stop 2525, Prairie View, TX 77446, USACenter for Energy and Environmental Sustainability, Prairie View A&M University, P.O. Box 519, Mail Stop 2500, Prairie View, TX 77446, USAA Computational Fluid Dynamics (CFD) and response surface-based multiobjective design optimization were performed for six different 2D airfoil profiles, and the Pareto optimal front of each airfoil is presented. FLUENT, which is a commercial CFD simulation code, was used to determine the relevant aerodynamic loads. The Lift Coefficient (CL) and Drag Coefficient (CD) data at a range of 0° to 12° angles of attack (α) and at three different Reynolds numbers (Re=68,459, 479, 210, and 958, 422) for all the six airfoils were obtained. Realizable k-ε turbulence model with a second-order upwind solution method was used in the simulations. The standard least square method was used to generate response surface by the statistical code JMP. Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) was used to determine the Pareto optimal set based on the response surfaces. Each Pareto optimal solution represents a different compromise between design objectives. This gives the designer a choice to select a design compromise that best suits the requirements from a set of optimal solutions. The Pareto solution set is presented in the form of a Pareto optimal front.http://dx.doi.org/10.1155/2012/193021 |
| spellingShingle | Ziaul Huque Ghizlane Zemmouri Donald Harby Raghava Kommalapati Optimization of Wind Turbine Airfoil Using Nondominated Sorting Genetic Algorithm and Pareto Optimal Front International Journal of Chemical Engineering |
| title | Optimization of Wind Turbine Airfoil Using Nondominated Sorting Genetic Algorithm and Pareto Optimal Front |
| title_full | Optimization of Wind Turbine Airfoil Using Nondominated Sorting Genetic Algorithm and Pareto Optimal Front |
| title_fullStr | Optimization of Wind Turbine Airfoil Using Nondominated Sorting Genetic Algorithm and Pareto Optimal Front |
| title_full_unstemmed | Optimization of Wind Turbine Airfoil Using Nondominated Sorting Genetic Algorithm and Pareto Optimal Front |
| title_short | Optimization of Wind Turbine Airfoil Using Nondominated Sorting Genetic Algorithm and Pareto Optimal Front |
| title_sort | optimization of wind turbine airfoil using nondominated sorting genetic algorithm and pareto optimal front |
| url | http://dx.doi.org/10.1155/2012/193021 |
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