Häftad, Engelska, 2027
Modeling and Simulation of Multiphase and Reactive Flows
From Fundamentals to Engineering Applications
Av Yu Lv, Bing Wang, Zheng Qiao, John A. Ekaterinaris
Del i serien Aerospace Fundamentals, Applications, and Exploration
2260 kr
Kommande
Beskrivning
Further cognizance of how to control and manipulate these phenomena is enabled by leveraging powerful technologies for numerical modeling and simulation techniques, with worked-out computational exercises and sample case studies also being featured to encourage practical endeavors to promisingly enhance the performance of many propulsion and energy systems.
- Covers fluid dynamics and its modeling, along with in-depth to ground understanding of key characteristics and behaviors of multi-component flows
- Fosters a case-based learning approach to survey all relevant types of fluid flows and their engineering applications, investigating common issues and remedies through helpful computational methods
- Includes a MATLAB toolbox that visualizes high-fidelity simulation results
Produktinformation
- Utgivningsdatum: 2027-09-01
- Mått: 152 x 229 x undefined mm
- Vikt: 450 g
- Format: Häftad
- Språk: Engelska
- Serie: Aerospace Fundamentals, Applications, and Exploration
- Antal sidor: 450
- Förlag: Elsevier Science
- ISBN: 9780443366451
Mer om författaren
Dr. Lv received his PhD from Stanford University in 2016. He worked as a Postdoc Research Fellow at the Center for Turbulence Research (Stanford University) before joining the Department of Aerospace Engineering of Mississippi State University. Since 2021, he has been an Associate Professor at the Institute of Mechanics, Chinese Academy of Sciences, Beijing China. His research is mainly focused on computational modeling of complex fluid flows. Dr. Wang is a tenured professor of Aerospace Science and Technology & Fluid Mechanics at Tsinghua University. He graduated from the Department of Engineering Mechanics at Tsinghua University in 2000 and earned his doctorate in 2005. He was an Av Humboldt Visiting Scholar at the Technical University of Munich from 2006 to 2008. His research interests span from multiphase and reaction flow modeling, detonation propulsion, to artificial intelligence in aerospace science. He currently leads the Spray Combustion and Propulsion Laboratory and MOE Key Laboratory of Advanced Power and Propulsion and serves on the editorial boards of several top journals. Professor Wang is a prolific author and holds numerous patents. He has received several national science and technology awards and four international invention gold awards. He is an Associate Fellow of the AIAA and a TUM Ambassador. Dr. Qiao is currently a Research Scientist at the Institute of Mechanics, Chinese Academy of Sciences. He earned his PhD in 2020 from Mississippi State University, where he specialized in combustion instability, thermoacoustic, and computational fluid dynamics. Dr. Qiao is deeply committed to advancing combustion science, with a particular emphasis on turbulent combustion modeling and thermoacoustic instability. His research is dedicated to developing predictive models and innovative solutions aimed at controlling and mitigating instabilities in combustion systems. Dr. Ekaterinaris received his MSc in Mechanical Engineering in 1982 and his PhD from the School of Aerospace Engineering in 1987, both at the Georgia Institute of Technology. Dr. Ekaterinaris has an outstanding academic career in aerospace engineering and was affiliated previously with NASA, FORTH/IACM, University of Patras and many other renowned institutes and organizations. He joined the faculty of Embry-Riddle Aeronautical University in 2012 where he is currently teaching and performing research. He serves as the Editor-in-Chief of Aerospace Science and Technology and the Associate Editor of Progress in Aerospace Sciences, both Elsevier high-impact journals. His interests are computational mechanics (including aerodynamics, magnetogasdynamics, electromagnetics, aeroacoustics, flow transition, turbulence research, and flow structure interaction), high order methods for PDEs, and more recently machine learning and uncertainty quantification.
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