Visual Explainable Convolutional Neural Network for Aerodynamic Coefficient Prediction
Recently, aerodynamic performance analysis has been widely studied due to its importance in aircraft design. Most works adopted computational fluid dynamics (CFD) simulation to compute the aerodynamic forces, which is time consuming. To reduce the simulation time, several works proposed to use deep...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | International Journal of Aerospace Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/9873112 |
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| _version_ | 1832561611317968896 |
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| author | Yanxuan Zhao Chengwen Zhong Fang Wang Yueqing Wang |
| author_facet | Yanxuan Zhao Chengwen Zhong Fang Wang Yueqing Wang |
| author_sort | Yanxuan Zhao |
| collection | DOAJ |
| description | Recently, aerodynamic performance analysis has been widely studied due to its importance in aircraft design. Most works adopted computational fluid dynamics (CFD) simulation to compute the aerodynamic forces, which is time consuming. To reduce the simulation time, several works proposed to use deep learning model as the surrogate model of CFD simulation. However, the explainability of deep learning models is poor and has been widely criticized, which limits the further development of deep learning in aerodynamic performance analysis. In this paper, a novel neural network is proposed to predict the aerodynamic forces of airfoils. To improve the explainability, the circular padding is proposed to replace traditional zero padding in the convolutional layers. Moreover, the saliency map of the predicted aerodynamic force on the input airfoil is shown in a more intuitive way. In this manner, the influence of different parts of airfoil on the final aerodynamic force can be easily analyzed. Extensive experiments on different data sets show that our work is efficient and effective. Most importantly, these results explain the potential relationship between the airfoil and the aerodynamic force. |
| format | Article |
| id | doaj-art-07140f73e7ac48899404105536f48a5c |
| institution | Kabale University |
| issn | 1687-5974 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Aerospace Engineering |
| spelling | doaj-art-07140f73e7ac48899404105536f48a5c2025-02-03T01:24:38ZengWileyInternational Journal of Aerospace Engineering1687-59742022-01-01202210.1155/2022/9873112Visual Explainable Convolutional Neural Network for Aerodynamic Coefficient PredictionYanxuan Zhao0Chengwen Zhong1Fang Wang2Yueqing Wang3School of AeronauticsSchool of AeronauticsChina Aerodynamics Research and Development CenterChina Aerodynamics Research and Development CenterRecently, aerodynamic performance analysis has been widely studied due to its importance in aircraft design. Most works adopted computational fluid dynamics (CFD) simulation to compute the aerodynamic forces, which is time consuming. To reduce the simulation time, several works proposed to use deep learning model as the surrogate model of CFD simulation. However, the explainability of deep learning models is poor and has been widely criticized, which limits the further development of deep learning in aerodynamic performance analysis. In this paper, a novel neural network is proposed to predict the aerodynamic forces of airfoils. To improve the explainability, the circular padding is proposed to replace traditional zero padding in the convolutional layers. Moreover, the saliency map of the predicted aerodynamic force on the input airfoil is shown in a more intuitive way. In this manner, the influence of different parts of airfoil on the final aerodynamic force can be easily analyzed. Extensive experiments on different data sets show that our work is efficient and effective. Most importantly, these results explain the potential relationship between the airfoil and the aerodynamic force.http://dx.doi.org/10.1155/2022/9873112 |
| spellingShingle | Yanxuan Zhao Chengwen Zhong Fang Wang Yueqing Wang Visual Explainable Convolutional Neural Network for Aerodynamic Coefficient Prediction International Journal of Aerospace Engineering |
| title | Visual Explainable Convolutional Neural Network for Aerodynamic Coefficient Prediction |
| title_full | Visual Explainable Convolutional Neural Network for Aerodynamic Coefficient Prediction |
| title_fullStr | Visual Explainable Convolutional Neural Network for Aerodynamic Coefficient Prediction |
| title_full_unstemmed | Visual Explainable Convolutional Neural Network for Aerodynamic Coefficient Prediction |
| title_short | Visual Explainable Convolutional Neural Network for Aerodynamic Coefficient Prediction |
| title_sort | visual explainable convolutional neural network for aerodynamic coefficient prediction |
| url | http://dx.doi.org/10.1155/2022/9873112 |
| work_keys_str_mv | AT yanxuanzhao visualexplainableconvolutionalneuralnetworkforaerodynamiccoefficientprediction AT chengwenzhong visualexplainableconvolutionalneuralnetworkforaerodynamiccoefficientprediction AT fangwang visualexplainableconvolutionalneuralnetworkforaerodynamiccoefficientprediction AT yueqingwang visualexplainableconvolutionalneuralnetworkforaerodynamiccoefficientprediction |