Machine Learning Assisted Prediction of Airfoil Lift-to-Drag Characteristics for Mars Helicopter
The aerodynamic properties of rotor systems operating within low Reynolds number flow field conditions are profoundly influenced by their geometric and flight parameters. Precise estimation of optimal airfoil parameters at different angles of attack is indispensable for enhancing these aerodynamic p...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/10/7/614 |
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| author | Pengyue Zhao Xifeng Gao Bo Zhao Huan Liu Jianwei Wu Zongquan Deng |
| author_facet | Pengyue Zhao Xifeng Gao Bo Zhao Huan Liu Jianwei Wu Zongquan Deng |
| author_sort | Pengyue Zhao |
| collection | DOAJ |
| description | The aerodynamic properties of rotor systems operating within low Reynolds number flow field conditions are profoundly influenced by their geometric and flight parameters. Precise estimation of optimal airfoil parameters at different angles of attack is indispensable for enhancing these aerodynamic properties. This study presents a technique for optimizing the airfoil parameters of a Mars helicopter by employing machine learning methods in conjunction with computational fluid dynamics (CFD) simulations, thereby circumventing the need for expensive experiments and simulations. The effectiveness of diverse machine learning algorithms for prediction is evaluated, and the resultant models are utilized for airfoil optimization. Ultimately, the aerodynamic properties of the optimized airfoil are experimentally validated. The experimental findings exhibit agreement with the simulated predictions, indicating the successful optimization of the aerodynamic properties. This research offers valuable insights into the influence of airfoil parameters on the aerodynamic properties of the Mars helicopter, along with guidance for airfoil optimization. |
| first_indexed | 2024-03-11T01:24:39Z |
| format | Article |
| id | doaj.art-977f5157444044eb92937ebd6355908c |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-11T01:24:39Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-977f5157444044eb92937ebd6355908c2023-11-18T17:50:47ZengMDPI AGAerospace2226-43102023-07-0110761410.3390/aerospace10070614Machine Learning Assisted Prediction of Airfoil Lift-to-Drag Characteristics for Mars HelicopterPengyue Zhao0Xifeng Gao1Bo Zhao2Huan Liu3Jianwei Wu4Zongquan Deng5Center of Ultra-Precision Optoelectronic Instrumentation Engineering, Harbin Institute of Technology, Harbin 150001, ChinaCenter of Ultra-Precision Optoelectronic Instrumentation Engineering, Harbin Institute of Technology, Harbin 150001, ChinaCenter of Ultra-Precision Optoelectronic Instrumentation Engineering, Harbin Institute of Technology, Harbin 150001, ChinaCenter of Ultra-Precision Optoelectronic Instrumentation Engineering, Harbin Institute of Technology, Harbin 150001, ChinaCenter of Ultra-Precision Optoelectronic Instrumentation Engineering, Harbin Institute of Technology, Harbin 150001, ChinaState Key Laboratory of Robotics and System, Harbin Institute of Technology, No. 92, Xidazhi Street, Nangang District, Harbin 150001, ChinaThe aerodynamic properties of rotor systems operating within low Reynolds number flow field conditions are profoundly influenced by their geometric and flight parameters. Precise estimation of optimal airfoil parameters at different angles of attack is indispensable for enhancing these aerodynamic properties. This study presents a technique for optimizing the airfoil parameters of a Mars helicopter by employing machine learning methods in conjunction with computational fluid dynamics (CFD) simulations, thereby circumventing the need for expensive experiments and simulations. The effectiveness of diverse machine learning algorithms for prediction is evaluated, and the resultant models are utilized for airfoil optimization. Ultimately, the aerodynamic properties of the optimized airfoil are experimentally validated. The experimental findings exhibit agreement with the simulated predictions, indicating the successful optimization of the aerodynamic properties. This research offers valuable insights into the influence of airfoil parameters on the aerodynamic properties of the Mars helicopter, along with guidance for airfoil optimization.https://www.mdpi.com/2226-4310/10/7/614machine learningMars helicoptercomputational fluid dynamicsairfoilaerodynamic properties |
| spellingShingle | Pengyue Zhao Xifeng Gao Bo Zhao Huan Liu Jianwei Wu Zongquan Deng Machine Learning Assisted Prediction of Airfoil Lift-to-Drag Characteristics for Mars Helicopter Aerospace machine learning Mars helicopter computational fluid dynamics airfoil aerodynamic properties |
| title | Machine Learning Assisted Prediction of Airfoil Lift-to-Drag Characteristics for Mars Helicopter |
| title_full | Machine Learning Assisted Prediction of Airfoil Lift-to-Drag Characteristics for Mars Helicopter |
| title_fullStr | Machine Learning Assisted Prediction of Airfoil Lift-to-Drag Characteristics for Mars Helicopter |
| title_full_unstemmed | Machine Learning Assisted Prediction of Airfoil Lift-to-Drag Characteristics for Mars Helicopter |
| title_short | Machine Learning Assisted Prediction of Airfoil Lift-to-Drag Characteristics for Mars Helicopter |
| title_sort | machine learning assisted prediction of airfoil lift to drag characteristics for mars helicopter |
| topic | machine learning Mars helicopter computational fluid dynamics airfoil aerodynamic properties |
| url | https://www.mdpi.com/2226-4310/10/7/614 |
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