Data-driven Analysis and Modeling of Turbulent Flows explains methods for the analysis of large fields of data, and uncovering models and model improvements from numerical or experimental data on turbulence.

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.

Table of Contents:

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