Data-Driven Fluid Mechanics: Combining First Principles and Machine Learning New Edition

Data-Driven Fluid Mechanics: Combining First Principles and Machine Learning (New Edition) is a pioneering resource for fluid dynamicists, offering an essential blend of traditional principles and modern data-driven methods. This comprehensive text is designed for students and practitioners eager to deepen their understanding of fluid dynamics by integrating concepts from computer science, statistics, optimization, signal processing, and nonlinear dynamics.

This edition originates from a one-week lecture series at the von Karman Institute for Fluid Dynamics, providing a pedagogical approach to the latest research and methodologies in fluid mechanics. The book covers critical areas such as model-order reduction, system identification, flow control, and data-driven turbulence closures, offering practical insights into hybrid approaches that marry data-driven techniques with fundamental physics.

The text is divided into six parts, beginning with motivation and foundational concepts, progressing through advanced methods from signal processing, data-driven decompositions, dynamical systems, and practical applications, and concluding with perspectives on the future of computational fluid dynamics. Authored by leading experts in the field, this book is richly illustrated and meticulously organized to serve as both a learning tool and a reference for ongoing research.

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Whether you’re exploring the integration of machine learning into fluid mechanics or seeking to refine your understanding of traditional methods, Data-Driven Fluid Mechanics is an invaluable resource for pushing the boundaries of research and application in the field.

Shortened Table of Contents

• Part I: Motivation
  • Analysis, Modeling, and Control of the Cylinder Wake
  • Coherent Structures in Turbulence
  • Machine Learning in Fluid Dynamics
• Part II: Methods from Signal Processing
  • LTI Systems
  • Time-Frequency Analysis and Wavelets
• Part III: Data-Driven Decompositions
  • Proper Orthogonal Decomposition
  • Dynamic Mode Decomposition
  • Generalized and Multiscale Modal Analysis
• Part IV: Dynamical Systems
  • Linear and Nonlinear Dynamical Systems
  • System Identification
  • Stability Analysis of Fluid Flows
• Part V: Applications
  • Machine Learning for Reduced-Order Modeling
  • Data-Driven Models in Reacting Flow Simulations
  • Turbulence Control and Reinforcement Learning
• Part VI: Perspectives
  • The Computer as Scientist

What is Lower Case “r” Out in Fluid Mechanics?

In fluid mechanics, the lowercase “r” typically represents the radial coordinate in cylindrical coordinate systems. The term “r out” or “r_out” is often used to denote the outer radius of a circular or cylindrical object, such as a pipe or a rotating disk. This measurement is crucial in fluid dynamics calculations, as it helps define the geometry of the flow domain, especially in problems involving rotational symmetry or boundary layer analysis.

The outer radius “r out” is essential in determining flow characteristics like velocity profiles, pressure distribution, and shear stress along the surface of the object. It plays a significant role in the study of flows within pipes, around cylinders, or in annular regions, where fluid behavior is influenced by the distance from the center axis (the radial distance).

Product Details:

• Publisher: Cambridge University Press
• Published: February 2023
• ISBN: 9781108902267
• Title: Data-Driven Fluid Mechanics
• Author: Miguel A. Mendez (ed.); Andrea Ianiro (ed.); Bernd R. Noack (ed.); Steven L. Brunton (ed.)
• Imprint: Cambridge University Press
• Language: English
• Number of Pages: 470