Computational Aerodynamic Modeling of Aerospace Vehicles

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Mehdi Ghoreyshi, Karl Jenkins
MDPI, Mar 8, 2019 - Technology & Engineering - 294 pages

 Currently, the use of computational fluid dynamics (CFD) solutions is considered as the state-of-the-art in the modeling of unsteady nonlinear flow physics and offers an early and improved understanding of air vehicle aerodynamics and stability and control characteristics. This Special Issue covers recent computational efforts on simulation of aerospace vehicles including fighter aircraft, rotorcraft, propeller driven vehicles, unmanned vehicle, projectiles, and air drop configurations. The complex flow physics of these configurations pose significant challenges in CFD modeling. Some of these challenges include prediction of vortical flows and shock waves, rapid maneuvering aircraft with fast moving control surfaces, and interactions between propellers and wing, fluid and structure, boundary layer and shock waves.

Additional topic of interest in this Special Issue is the use of CFD tools in aircraft design and flight mechanics. The problem with these applications is the computational cost involved, particularly if this is viewed as a brute-force calculation of vehicle’s aerodynamics through its flight envelope. To make progress in routinely using of CFD in aircraft design, methods based on sampling, model updating and system identification should be considered.
 

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About the author (2019)

 Mehdi Ghoreyshi is a senior aerospace engineer at the United States Air Force Academy in Colorado. He is also a visiting lecturer at University of Colorado, Colorado Springs at the Department of Mechanical and Aerospace engineering and serves the President of DANSI Engineering Company. Dr. Ghoreyshi is an active member of numerous NATO applied vehicle technology activities including vortex interaction effects, design of agile NATO vehicles, investigation of shipboard launch and recovery of vehicles, and Reynolds number scaling effects in swept wing flows. Mehdi has been the principal investigator of many projects supported by the U.S. Air Force Academy, U.S. Army, U.S. Navy, and National Academy of Sciences investigating the aerodynamics of ram-air parachutes, airdrop configurations, tiltrotor–obstacle wake interactions, and Dynamic Modeling of Non-linear Databases with Computational Fluid Dynamics (DyMOND-CFD). Mehdi is an active member of the AIAA Applied Aerodynamics Technical Committee and currently is serving as an Associate Editor for Journal of Aerospace Science and Technology and a Guest Editor for Journal of Aerospace. His research interests include reduced order modeling, system identification, and computational aerodynamic modeling. He is an author of nearly 100 publications in refereed journals and conferences. Karl Jenkins gained a PhD from the University of Manchester that focused on computational and experimental water waves breaking interacting with coastal structures. This expertise in CFD and High Performance Computing was extended in a post-doctoral position at Cambridge University as the Sir Arthur Marshall Research Fellow, where he studied turbulent combustion using Direct Numerical Simulation. Dr. Jenkins has published over 60 papers and has won the Gaydon prize for the most significant paper contribution at a leading symposium on combustion held in Chicago. He is a member of the United Kingdom Consortium on Turbulent Reacting Flows (UKCTRF). He has been invited to give numerous international and domestic seminars and to participate as a discussion panel member at international HPC DNS/LES conferences in the US. Dr. Jenkins has also worked in industry for Allot and Lomax Consulting Engineers and Davy Distington Ltd., working on various commercial CFD codes and training engineers in their use. He has worked on adaptive parallel grid techniques, and has developed parallel codes for academic use and for blue chip companies such as Rolls Royce plc. He has also been actively involved in one of the Cambridge regional e-Science center projects entitled Grid Technology for Distance CFD.

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