This unique book gives a general unified presentation of the use of the multiscale/multiresolution approaches in the field of turbulence. The coverage ranges from statistical models developed for engineering purposes to multiresolution algorithms for the direct computation of turbulence. It provides the only available up-to-date reviews dealing with the latest and most advanced turbulence models (including LES, VLES, hybrid RANS/LES, DES) and numerical strategies. The book aims at providing the reader with a comprehensive description of modern strategies for turbulent flow simulation, ranging from turbulence modeling to the most advanced multilevel numerical methods. Sample Chapter(s). Chapter 1: A Brief Introduction to Turbulence (4,125 KB). Contents: A Brief Introduction to Turbulence; Turbulence Simulation and Scale Separation; Statistical Multiscale Modeling; Multiscale Subgrid Models: Self-Adaptivity; Structural Multiscale Subgrid Models: Small Scale Estimations; Unsteady Turbulence Simulation on Self-Adaptive Grids; Global Hybrid RANS/LES Methods; Zonal RANS/LES Methods. Readership: Researchers and engineers in academia and industry in aerospace, automotive and other aerodynamics-oriented fields; masters-level students in fluid mechanics, computational fluid dynamics and applied mathematics.
The book aims to provide the reader with an updated general presentation of multiscale/multiresolution approaches in turbulent flow simulations. All modern approaches (LES, hybrid RANS/LES, DES, SAS) are discussed and recast in a global comprehensive framework. Both theoretical features and practical implementation details are addressed. Some full scale applications are described, to provide the reader with relevant guidelines to facilitate a future use of these methods./a
The book aims to provide the reader with an updated general presentation of multiscale/multiresolution approaches in turbulent flow simulations. All modern approaches (LES, hybrid RANS/LES, DES, SAS) are discussed and recast in a global comprehensive framework. Both theoretical features and practical implementation details are addressed. Some full scale applications are described, to provide the reader with relevant guidelines to facilitate a future use of these methods./a
This unique book gives a general unified presentation of the use of the multiscale/multiresolution approaches in the field of turbulence. The coverage ranges from statistical models developed for engineering purposes to multiresolution algorithms for the direct computation of turbulence. It provides the only available up-to-date reviews dealing with the latest and most advanced turbulence models (including LES, VLES, hybrid RANS/LES, DES) and numerical strategies. The book aims at providing the reader with a comprehensive description of modern strategies for turbulent flow simulation, ranging from turbulence modeling to the most advanced multilevel numerical methods. Sample Chapter(s). Chapter 1: A Brief Introduction to Turbulence (4,125 KB). Contents: A Brief Introduction to Turbulence; Turbulence Simulation and Scale Separation; Statistical Multiscale Modeling; Multiscale Subgrid Models: Self-Adaptivity; Structural Multiscale Subgrid Models: Small Scale Estimations; Unsteady Turbulence Simulation on Self-Adaptive Grids; Global Hybrid RANS/LES Methods; Zonal RANS/LES Methods. Readership: Researchers and engineers in academia and industry in aerospace, automotive and other aerodynamics-oriented fields; masters-level students in fluid mechanics, computational fluid dynamics and applied mathematics.
The comprehensive compendium furnishes a quick and efficient entry point to many multiresolution techniques and facilitates the transition from an idea into a real project. It focuses on methods combining several soft computing techniques (fuzzy logic, neural networks, genetic algorithms) in a multiresolution framework.Illustrated with numerous vivid examples, this useful volume gives the reader the necessary theoretical background to decide which methods suit his/her needs.New materials and applications for multiresolution analysis are added, including notable research topics such as deep learning, graphs, and network analysis.
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