Data Driven Analysis and Modeling of Turbulent Flows

Capture the future of turbulence research with Data Driven Analysis and Modeling of Turbulent Flows by Karthik Duraisamy. This authoritative work brings together modern data-driven techniques and classical turbulence theory to help engineers, researchers, and graduate students tackle the hardest problems in fluid dynamics.

Combining clear exposition with practical insight, the book explains how machine learning, statistical inference, and high-fidelity simulation data can be harnessed to improve turbulence models used in computational fluid dynamics (CFD). Readers gain a grounded understanding of how data informs closure models, uncertainty quantification, and predictive simulation for turbulent flows across engineering applications—from aerospace and automotive to energy and environmental engineering.

Every chapter is written to be accessible yet rigorous: expect lucid explanations of core concepts, guided examples showing how data and models interact, and discussions of real-world implications for design and analysis. The text is equally valuable for those focused on Reynolds-averaged Navier–Stokes (RANS), large-eddy simulation (LES), or hybrid modeling approaches, making it a go-to reference for CFD practitioners and academics in the US, Europe, India, and worldwide.

If you need to modernize your turbulence modeling toolkit or want a comprehensive resource that bridges theory and data science, this book delivers actionable strategies and a clear research roadmap. Ideal for classroom adoption, professional development, or personal study, it positions you at the intersection of fluid mechanics and data-driven innovation.

Secure your copy of Data Driven Analysis and Modeling of Turbulent Flows by Karthik Duraisamy today and advance your capabilities in turbulence analysis, modeling, and simulation.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.