Performance evaluation of supersonic flow for variable geometry radial ejector through CFD models based on DES-turbulence models, GPR machine learning, and MPA optimization

Performance evaluation of supersonic flow for variable geometry radial ejector through CFD models based on DES-turbulence models, GPR machine learning, and MPA optimization

This study aims to derive valuable insights for utilizing computational fluid dynamics (CFD) based on reynolds-averaged navier–stokes (RANS) and detached eddy simulation (DES) turbulence models (TMs) to analyze a specific radial ejector configuration known as the variable geometry radial ejector (VG...

Full description

Bibliographic Details
Main Authors: Raed Al-Rbaihat, Khalid Saleh, Ray Malpress, David Buttsworth, Hussein Alahmer, Ali Alahmer
Format: Article
Language:English
Published: Elsevier 2023-11-01
Series:International Journal of Thermofluids
Subjects:
Radial flow ejectors
Supersonic flow
RANS turbulence models
Detached eddy simulation
Gaussian process regression
Machine learning
Online Access:http://www.sciencedirect.com/science/article/pii/S2666202723002021

Internet

http://www.sciencedirect.com/science/article/pii/S2666202723002021

Similar Items