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Modeling and Validation of Base Pressure for Aerodynamic Vehicles Based on Machine Learning Models

Computer Modeling in Engineering & Sciences 2023年第12期137卷 2331-2352页

作者： Jaimon Dennis Quadros Sher Afghan Khan Abdul Aabid muneer Baig Senior Lecturer of Mechanical Engineering University of BoltonRAK Academic CenterRas Al Khaimah16038UAE Department of Mechanical and Aerospace Engineering Faculty of EngineeringInternational Islamic University MalaysiaSelangor53100Malaysia Department of Engineering Management College of EngineeringPrince Sultan UniversityRiyadh11586Saudi Arabia

The application of abruptly enlarged flows to adjust the drag of aerodynamic vehicles using machine learning models has not been investigated *** 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 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 smooth flow of aerodynamic *** on the identical dataset,the ga-BP and PSO-BP algorithms are also compared to *** data for training and testing the algorithmswas 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 *** 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.

关键词： High speed flow Mach number machine learning PCA-BAS-ENN algorithm

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