A computational investigation of shock wave train in diverse duct geometries using machine learning-adopted k-ω turbulence model-Reference-Cited by-同舟云学术

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A computational investigation of shock wave train in diverse duct geometries using machine learning-adopted k-ω turbulence model

Published:2024-04 Issue: Volume:217 Page:27-36
ISSN:0094-5765
Container-title:Acta Astronautica
language:en
Short-container-title:Acta Astronautica

Author:

Mirjalily Seyed Ali Agha

Publisher

Elsevier BV

Reference34 articles.

1. Behavior of shock trains in a diverging duct;Weiss;Exp. Fluid,2010

2. Collision partner selection schemes in DSMC: from micro/nano flows to hypersonic flows;Roohi;Phys. Rep.,2016

3. Study of subsonic–supersonic gas flow through micro/nanoscale nozzles using unstructured DSMC solver;Darbandi;Microfluid. Nanofluidics,2011

4. Investigation of aerodynamic characteristics of rarefied flow around NACA 0012 airfoil using DSMC and NS solvers;Shoja-Sani;Eur. J. Mech. B Fluid,2014

5. Fundamentals of Gas Dynamics B. One-Dimensional Treatment of Steady Gas Dynamics;Crocco,1958

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