Data Driven Analysis and Modeling of Turbulent Flows: Computation and Analysis of Turbulent Flows
Editat de Karthik Duraisamyen Limba Engleză Paperback – 7 mar 2025
Turbulence simulations generate large data sets, and the extraction of useful information from these data fields is an important and challenging task. Statistical learning and machine learning have provided many ways of helping, and this book explains how to use such methods for extracting, treating, and optimizing data to improve predictive turbulence models. These include methods such as POD, SPOD and DMD, for the extraction of modes peculiar to the data, as well as several reduced order models.
This resource is essential reading for those developing turbulence models, performing turbulence simulations or interpreting turbulence simulation results.
- Provides instructions for the most important statistical learning techniques
- Explains the basics of reduced order modeling
- Describes a wide range of data analysis methods evolving from linear and non-linear flow decomposition
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Specificații
ISBN-13: 9780323950435
ISBN-10: 0323950434
Pagini: 414
Dimensiuni: 152 x 229 mm
Editura: ELSEVIER SCIENCE
Seria Computation and Analysis of Turbulent Flows
ISBN-10: 0323950434
Pagini: 414
Dimensiuni: 152 x 229 mm
Editura: ELSEVIER SCIENCE
Seria Computation and Analysis of Turbulent Flows
Cuprins
1. A roadmap for data-driven analysis and modeling of turbulent flows
2. Modal decomposition: POD, SPOD, DMD
3. Statistical learning: Neural nets, sparse regression, Lasso
4. Resolvents
5. Projection-based Reduced Order Modeling
6. Data-assimilation and flow estimation
7. Data-driven control
8. Model-consistent inference and learning
9. Parameter estimation and uncertainty quantification
10. Stress representations
11. Evolutionary optimization
12. Emerging topics: Super resolution
2. Modal decomposition: POD, SPOD, DMD
3. Statistical learning: Neural nets, sparse regression, Lasso
4. Resolvents
5. Projection-based Reduced Order Modeling
6. Data-assimilation and flow estimation
7. Data-driven control
8. Model-consistent inference and learning
9. Parameter estimation and uncertainty quantification
10. Stress representations
11. Evolutionary optimization
12. Emerging topics: Super resolution