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...

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Detaylı Bibliyografya

Asıl Yazarlar:	Raed Al-Rbaihat, Khalid Saleh, Ray Malpress, David Buttsworth, Hussein Alahmer, Ali Alahmer
Materyal Türü:	Artigo
Dil:	Inglês
Baskı/Yayın Bilgisi:	Elsevier 2023-11-01
Seri Bilgileri:	International Journal of Thermofluids
Konular:	Radial flow ejectors Supersonic flow RANS turbulence models Detached eddy simulation Gaussian process regression Machine learning
Online Erişim:	http://www.sciencedirect.com/science/article/pii/S2666202723002021
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