This book presents selected, peer-reviewed papers of the Aeronautical Society of India (AeSI) CFD Symposium 2024, held at the Birla Institute of Technology, Mesra, Ranchi during 11–13 August 2024. The symposium was organized by the CFD Division of AeSI in association with the Birla Institute of Technology. The AeSI CFD Division has been involved in promoting computational fluid dynamics research and applications in India since its inception in 1998.
The volume brings together research contributions covering a wide spectrum of emerging areas in computational fluid dynamics. The selected papers highlight the advances in algorithms and numerical schemes. Emphasis is placed on applications in turbomachinery and internal flows, high-speed and compressible flows, unsteady flow simulations, and industrial CFD applications. Some aspects of recent developments in artificial intelligence and machine learning for CFD-driven design and optimization have also been highlighted.
This volume will be of significant value to researchers, engineers, academicians, and graduate and post graduate students in aerospace, mechanical, and related engineering disciplines.
This book presents selected, peer-reviewed papers of the Aeronautical Society of India (AeSI) CFD Symposium 2024, held at the Birla Institute of Technology, Mesra, Ranchi during 11–13 August 2024. The symposium was organized by the CFD Division of AeSI in association with the Birla Institute of Technology. The AeSI CFD Division has been involved in promoting computational fluid dynamics research and applications in India since its inception in 1998.
The volume brings together research contributions covering a wide spectrum of emerging areas in computational fluid dynamics. The selected papers highlight the advances in algorithms and numerical schemes. Emphasis is placed on applications in turbomachinery and internal flows, high-speed and compressible flows, unsteady flow simulations, and industrial CFD applications. Some aspects of recent developments in artificial intelligence and machine learning for CFD-driven design and optimization have also been highlighted.
This volume will be of significant value to researchers, engineers, academicians, and graduate and post graduate students in aerospace, mechanical, and related engineering disciplines.
Priyank Kumar
Algorithm and Numerical scheme Mesh free and Cartesian methods Grid generation and adaptation Convergence acceleration High performance parallel computing Turbomachinery and Internal Flows High speed flows Unsteady flows: URANS, DES, LES and DNS AI and ML in Turbulence modelling Emerging areas of CFD Design and Optimization: CFD in MDO Industrial CFD - Metallurgy, Biofluids, and others CFD for experimental setup