An Improved Multi-Objective Particle Swarm Optimization Method for Rotor Airfoil Design
In this study, a multi-objective aerodynamic optimization is performed on the rotor airfoil via an improved MOPSO (multi-objective particle swarm optimization) method. A database of rotor airfoils containing both geometric and aerodynamic parameters is established, where the geometric parameters are...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/10/9/820 |
| _version_ | 1797581739365761024 |
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| author | Yongchuan Wu Gang Sun Jun Tao |
| author_facet | Yongchuan Wu Gang Sun Jun Tao |
| author_sort | Yongchuan Wu |
| collection | DOAJ |
| description | In this study, a multi-objective aerodynamic optimization is performed on the rotor airfoil via an improved MOPSO (multi-objective particle swarm optimization) method. A database of rotor airfoils containing both geometric and aerodynamic parameters is established, where the geometric parameters are obtained via the CST (class shape transformation) method and the aerodynamic parameters are obtained via CFD (computational fluid dynamics) simulations. On the basis of the database, a DBN (deep belief network) surrogate model is proposed and trained to accurately predict the aerodynamic parameters of the rotor airfoils. In order to improve the convergence rate and global searching ability of the standard MOPSO algorithm, an improved MOPSO framework is established. By embedding the DBN surrogate model into the improved MOPSO framework, multi-objective and multi-constraint aerodynamic optimization for the rotor airfoil is performed. Finally, the aerodynamic performance of the optimized rotor airfoil is validated through CFD simulations. The results indicate that the aerodynamic performance of the optimized rotor airfoil is improved dramatically compared with the baseline rotor airfoil. |
| first_indexed | 2024-03-10T23:09:52Z |
| format | Article |
| id | doaj.art-cef388e13aa34dcb9ff62e8563d8816a |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-10T23:09:52Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-cef388e13aa34dcb9ff62e8563d8816a2023-11-19T09:05:18ZengMDPI AGAerospace2226-43102023-09-0110982010.3390/aerospace10090820An Improved Multi-Objective Particle Swarm Optimization Method for Rotor Airfoil DesignYongchuan Wu0Gang Sun1Jun Tao2Department of Aeronautics & Astronautics, Fudan University, Shanghai 200433, ChinaDepartment of Aeronautics & Astronautics, Fudan University, Shanghai 200433, ChinaDepartment of Aeronautics & Astronautics, Fudan University, Shanghai 200433, ChinaIn this study, a multi-objective aerodynamic optimization is performed on the rotor airfoil via an improved MOPSO (multi-objective particle swarm optimization) method. A database of rotor airfoils containing both geometric and aerodynamic parameters is established, where the geometric parameters are obtained via the CST (class shape transformation) method and the aerodynamic parameters are obtained via CFD (computational fluid dynamics) simulations. On the basis of the database, a DBN (deep belief network) surrogate model is proposed and trained to accurately predict the aerodynamic parameters of the rotor airfoils. In order to improve the convergence rate and global searching ability of the standard MOPSO algorithm, an improved MOPSO framework is established. By embedding the DBN surrogate model into the improved MOPSO framework, multi-objective and multi-constraint aerodynamic optimization for the rotor airfoil is performed. Finally, the aerodynamic performance of the optimized rotor airfoil is validated through CFD simulations. The results indicate that the aerodynamic performance of the optimized rotor airfoil is improved dramatically compared with the baseline rotor airfoil.https://www.mdpi.com/2226-4310/10/9/820rotor airfoilaerodynamicimproved MOPSO algorithmdeep belief network |
| spellingShingle | Yongchuan Wu Gang Sun Jun Tao An Improved Multi-Objective Particle Swarm Optimization Method for Rotor Airfoil Design Aerospace rotor airfoil aerodynamic improved MOPSO algorithm deep belief network |
| title | An Improved Multi-Objective Particle Swarm Optimization Method for Rotor Airfoil Design |
| title_full | An Improved Multi-Objective Particle Swarm Optimization Method for Rotor Airfoil Design |
| title_fullStr | An Improved Multi-Objective Particle Swarm Optimization Method for Rotor Airfoil Design |
| title_full_unstemmed | An Improved Multi-Objective Particle Swarm Optimization Method for Rotor Airfoil Design |
| title_short | An Improved Multi-Objective Particle Swarm Optimization Method for Rotor Airfoil Design |
| title_sort | improved multi objective particle swarm optimization method for rotor airfoil design |
| topic | rotor airfoil aerodynamic improved MOPSO algorithm deep belief network |
| url | https://www.mdpi.com/2226-4310/10/9/820 |
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