Data Driven Analysis And Modeling Of Turbulent Flows
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Data Driven Analysis and Modeling of Turbulent Flows
Data-driven Analysis and Modeling of Turbulent Flows provides an integrated treatment of modern data-driven methods to describe, control, and predict turbulent flows through the lens of both physics and data science.The book is organized into three parts:• Exploration of techniques for discovering coherent structures within turbulent flows, introducing advanced decomposition methods• Methods for estimation and control using data assimilation and machine learning approaches• Finally, novel modeling techniques that combine physical insights with machine learningThis book is intended for students, researchers, and practitioners in fluid mechanics, though readers from related fields such as applied mathematics, computational science, and machine learning will find it also of interest.• Exploration of techniques for discovering coherent structures within turbulent flows, introducing advanced decomposition methods• Methods for estimation and control using data assimilation and machine learning approaches• Finally, novel modeling techniques that combine physical insights with machine learning
Turbulent Flow
Author: Peter S. Bernard
language: en
Publisher: John Wiley & Sons
Release Date: 2002-11-14
Provides unique coverage of the prediction and experimentationnecessary for making predictions. Covers computational fluid dynamics and its relationship todirect numerical simulation used throughout the industry. Covers vortex methods developed to calculate and evaluateturbulent flows. Includes chapters on the state-of-the-art applications ofresearch such as control of turbulence.
Statistical Theory and Modeling for Turbulent Flows
Providing a comprehensive grounding in the subject of turbulence, Statistical Theory and Modeling for Turbulent Flows develops both the physical insight and the mathematical framework needed to understand turbulent flow. Its scope enables the reader to become a knowledgeable user of turbulence models; it develops analytical tools for developers of predictive tools. Thoroughly revised and updated, this second edition includes a new fourth section covering DNS (direct numerical simulation), LES (large eddy simulation), DES (detached eddy simulation) and numerical aspects of eddy resolving simulation. In addition to its role as a guide for students, Statistical Theory and Modeling for Turbulent Flows also is a valuable reference for practicing engineers and scientists in computational and experimental fluid dynamics, who would like to broaden their understanding of fundamental issues in turbulence and how they relate to turbulence model implementation. Provides an excellent foundation to the fundamental theoretical concepts in turbulence. Features new and heavily revised material, including an entire new section on eddy resolving simulation. Includes new material on modeling laminar to turbulent transition. Written for students and practitioners in aeronautical and mechanical engineering, applied mathematics and the physical sciences. Accompanied by a website housing solutions to the problems within the book.