Modeling and validation of base pressure for aerodynamic vehicles based on machine learning models
The application of abruptly enlarged flows to adjust the drag of aerodynamic vehicles using machine learning models has not been investigated previously. The process variables (Mach number (M), nozzle pressure ratio (η), area ratio (α), and length-to-diameter ratio (γ )) were numerically explored to...
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2023
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my.iium.irep.1054952023-11-07T03:21:09Z http://irep.iium.edu.my/105495/ Modeling and validation of base pressure for aerodynamic vehicles based on machine learning models Quadros, Jaimon Dennis Khan, Sher Afghan Aabid, Abdul Baig, Muneer TL Motor vehicles. Aeronautics. Astronautics The application of abruptly enlarged flows to adjust the drag of aerodynamic vehicles using machine learning models has not been investigated previously. The process variables (Mach number (M), nozzle pressure ratio (η), area ratio (α), and length-to-diameter ratio (γ )) were numerically explored to address several aspects of this process, namely base pressure (β) and base pressure with the cavity (βcav). In this work, the optimal base pressure is determined using the PCA-BAS-ENN-based algorithm to modify the base pressure presetting accuracy, thereby regulating the base drag required for the smooth flow of aerodynamic vehicles. Based on the identical dataset, the GA-BP and PSO-BP algorithms are also compared to the PCA-BAS-ENN algorithm. The data for training and testing the algorithms was derived using the regression equation developed using the Box-Behnken Design (BBD). The results show that the PCA-BAS-ENN model delivered highly accurate predictions when compared to the other two models. As a result, the advantages of these results are two-fold, providing: (i) a detailed examination of the efficiency of different neural network algorithms in dealing with a genuine aerodynamic problem, and (ii) helpful insights for regulating process variables to improve technological, operational, and financial factors, simultaneously. Tech Science Press 2023-08-03 Article PeerReviewed application/pdf en http://irep.iium.edu.my/105495/7/105495_Modeling%20and%20validation%20of%20base%20pressure.pdf application/pdf en http://irep.iium.edu.my/105495/13/105495_Modeling%20and%20validation%20of%20base%20pressure_Scopus.pdf Quadros, Jaimon Dennis and Khan, Sher Afghan and Aabid, Abdul and Baig, Muneer (2023) Modeling and validation of base pressure for aerodynamic vehicles based on machine learning models. Computer Modeling in Engineering & Sciences, 137 (3). pp. 2331-2352. ISSN 1526-1492 E-ISSN 1526-1506 https://www.techscience.com/CMES/online/detail/19273/pdf http://dx.doi.org/10.32604/cmes.2023.028925 |
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TL Motor vehicles. Aeronautics. Astronautics Quadros, Jaimon Dennis Khan, Sher Afghan Aabid, Abdul Baig, Muneer Modeling and validation of base pressure for aerodynamic vehicles based on machine learning models |
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The application of abruptly enlarged flows to adjust the drag of aerodynamic vehicles using machine learning models has not been investigated previously. The process variables (Mach number (M), nozzle pressure ratio (η), area ratio (α), and length-to-diameter ratio (γ )) were numerically explored to address several aspects of this process, namely base pressure (β) and base pressure with the cavity (βcav). In this work, the optimal base pressure is determined using the PCA-BAS-ENN-based algorithm to modify the base pressure presetting accuracy, thereby regulating the base drag required for the smooth flow of aerodynamic vehicles. Based on the identical dataset, the GA-BP and PSO-BP algorithms are also compared to the PCA-BAS-ENN algorithm. The data for training and testing the algorithms was derived using the regression equation developed using the Box-Behnken Design (BBD). The results show that the PCA-BAS-ENN model delivered highly accurate predictions when compared to the other two models. As a result, the advantages of these results are two-fold, providing: (i) a detailed examination of the efficiency of different neural network algorithms in dealing with a genuine aerodynamic problem, and (ii) helpful insights for regulating process variables to improve technological, operational, and financial factors, simultaneously. |
format |
Article |
author |
Quadros, Jaimon Dennis Khan, Sher Afghan Aabid, Abdul Baig, Muneer |
author_facet |
Quadros, Jaimon Dennis Khan, Sher Afghan Aabid, Abdul Baig, Muneer |
author_sort |
Quadros, Jaimon Dennis |
title |
Modeling and validation of base pressure for aerodynamic vehicles based on machine learning models |
title_short |
Modeling and validation of base pressure for aerodynamic vehicles based on machine learning models |
title_full |
Modeling and validation of base pressure for aerodynamic vehicles based on machine learning models |
title_fullStr |
Modeling and validation of base pressure for aerodynamic vehicles based on machine learning models |
title_full_unstemmed |
Modeling and validation of base pressure for aerodynamic vehicles based on machine learning models |
title_sort |
modeling and validation of base pressure for aerodynamic vehicles based on machine learning models |
publisher |
Tech Science Press |
publishDate |
2023 |
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http://irep.iium.edu.my/105495/7/105495_Modeling%20and%20validation%20of%20base%20pressure.pdf http://irep.iium.edu.my/105495/13/105495_Modeling%20and%20validation%20of%20base%20pressure_Scopus.pdf http://irep.iium.edu.my/105495/ https://www.techscience.com/CMES/online/detail/19273/pdf http://dx.doi.org/10.32604/cmes.2023.028925 |
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