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Base drag estimation in suddenly expanded supersonic flows using backpropagation genetic and recurrent neural networks

Jaimon, Dennis Quadros and Prashanth, T. and Khan, Sher Afghan (2022) Base drag estimation in suddenly expanded supersonic flows using backpropagation genetic and recurrent neural networks. Journal of Aerospace Engineering, 236 (3). pp. 1-28. ISSN 0954-4100 E-ISSN 2041-3025 (In Press)

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In recent years, base pressure management has gained a lot of industrial importance due to its applications in missiles and projectiles. For certain aerodynamic vehicles, the base pressure becomes a critical factor in regulating the base drag. That prompted the current work to develop input-output relationships for a suddenly expanded flow process using experiments and neural network-based forward and reverse mapping. The objective of forwarding mapping (FM) is to predict the responses, namely base pressure (β), base pressure with the cavity (βcav), and base pressure with rib (βrib), for a known combination of flow and geometric parameters, namely Mach number (M), nozzle pressure ratio (η), area ratio (α), and length to diameter ratio (ψ). On the other hand, an effort is made to decide the optimal set of flow and geometric parameters for achieving the desired base pressure by reverse mapping (RM). Neural network-controlled backpropagation and recurrent and genetic algorithms have been employed to carry out the forward and reverse mapping trials. A batch mode of training was employed to conduct a parametric study for adjusting and optimizing the neural network parameters. Due to the requirement of massive data for batch mode training, the data required for training was achieved using the response equations developed through response surface methodology. Further, the forecasting performances of the neural network algorithms are compared with the regression models (FM) and among themselves (RM) through random test cases. The findings indicate that all evolved neural network (NN) models could make accurate predictions in both forward and reverse mappings. The results obtained would help aerodynamic engineers control various parameters and their values that affect base drag.

Item Type: Article (Journal)
Uncontrolled Keywords: base pressure, base drag, suddenly expanded flows, cavity, rib, neural networks, backpropagation, genetic algorithm, recurrent neural network
Subjects: T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL500 Aeronautics
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering
Kulliyyah of Engineering > Department of Mechanical Engineering
Depositing User: Prof. Dr. Sher Afghan Khan
Date Deposited: 28 Feb 2022 12:15
Last Modified: 16 Mar 2022 14:49
URI: http://irep.iium.edu.my/id/eprint/96933

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