Title: CFD-based optimisation of base pressure behaviour on suddenly expanded flows at supersonic Mach numbers
Authors: Jaimon Dennis Quadros; S.A. Khan; T. Prashanth
Addresses: Department of Mechanical Engineering, Birla Institute of Technology, Ras-Al-Khaimah Campus, 41222 Ras-Al-Khaimah, UAE ' Department of Mechanical Engineering, International Islamic University Malaysia, Jalan Gombak, 53100, Selangor, Malaysia ' Department of Mechanical Engineering, Global Academy of Technology, RR Nagar, 560098, Bengaluru, India
Abstract: The base pressure developed in a suddenly expanded flow process majorly depends on Mach number (M), nozzle pressure ratio (NPR), area ratio (AR), and length to diameter ratio (L/D). Numerical analysis of the flow process was carried out using the computational fluid dynamics (CFD) technique, and was validated by experiments. The input-output test cases for CFD analysis were developed by two statistical methods, namely central composite design (CCD) and Box-Behnken design (BBD). The BBD model yielded better prediction accuracy and was used for generating data that trained the recurrent and backpropagation neural networks. The recurrent neural network outperformed both the backpropagation neural network and Box-Behnken design. Furthermore, to assess the right range of conditions for maximising base pressure, the genetic algorithm (GA), desirability function approach (DFA), and particle swarm optimisation (PSO) techniques were implemented. The PSO and GA techniques were found to be better, as they carried out search operations in many directions at multi-dimensional space simultaneously.
Keywords: base pressure; CFD; response surface methodology; RSM; neural networks; optimisation.
Progress in Computational Fluid Dynamics, An International Journal, 2022 Vol.22 No.3, pp.159 - 173
Received: 19 Nov 2020
Accepted: 24 Apr 2021
Published online: 01 Jun 2022 *