EBOOK: Fluid Mechanics Fundamentals and Applications (SI units)


Book Description

Fluid Mechanics: Fundamentals and Applications is written for the first fluid mechanics course for undergraduate engineering students, with sufficient material for a two-course sequence. This Third Edition in SI Units has the same objectives and goals as previous editions: Communicates directly with tomorrow’s engineers in a simple yet precise manner Covers the basic principles and equations of fluid mechanics in the context of numerous and diverse real-world engineering examples and applications Helps students develop an intuitive understanding of fluid mechanics by emphasizing the physical underpinning of processes and by utilizing numerous informative figures, photographs, and other visual aids to reinforce the basic concepts Encourages creative thinking, interest and enthusiasm for fluid mechanics New to this edition All figures and photographs are enhanced by a full color treatment. New photographs for conveying practical real-life applications of materials have been added throughout the book. New Application Spotlights have been added to the end of selected chapters to introduce industrial applications and exciting research projects being conducted by leaders in the field about material presented in the chapter. New sections on Biofluids have been added to Chapters 8 and 9. Addition of Fundamentals of Engineering (FE) exam-type problems to help students prepare for Professional Engineering exams.







Bubbles, Drops, and Particles in Non-Newtonian Fluids


Book Description

The third edition of Bubbles, Drops, and Particles in Non-Newtonian Fluids provides comprehensive coverage of the scientific foundations and the latest advances in particle motion in non-Newtonian media. Thoroughly updating and expanding its best-selling predecessor, this edition addresses numerical and experimental developments in non-Newtonian particulate systems. It includes a new chapter on heat transfer in non-Newtonian fluids in the free and mixed convection regimes and thus covers forced convection regimes separately in this edition. Salient Features: Demonstrates how dynamic behavior of single particles can yield useful information for modeling transport processes in complex multiphase flows Addresses heat transfer in Generalized Newtonian Fluid (GNF), visco-plastic and visco-elastic fluids throughout the book and outlines potential strategies for heat transfer enhancement Provides a new detailed section on the effect of confinement on heat transfer from bluff-bodies in non-Newtonian fluids Written in a clear and concise manner, this book remains an excellent handbook and reference. It is essential reading for students and researchers interested in exploring particle motion in different types of non-Newtonian systems encountered in disciplines across engineering and the sciences.







Machine Learning Control – Taming Nonlinear Dynamics and Turbulence


Book Description

This is the first textbook on a generally applicable control strategy for turbulence and other complex nonlinear systems. The approach of the book employs powerful methods of machine learning for optimal nonlinear control laws. This machine learning control (MLC) is motivated and detailed in Chapters 1 and 2. In Chapter 3, methods of linear control theory are reviewed. In Chapter 4, MLC is shown to reproduce known optimal control laws for linear dynamics (LQR, LQG). In Chapter 5, MLC detects and exploits a strongly nonlinear actuation mechanism of a low-dimensional dynamical system when linear control methods are shown to fail. Experimental control demonstrations from a laminar shear-layer to turbulent boundary-layers are reviewed in Chapter 6, followed by general good practices for experiments in Chapter 7. The book concludes with an outlook on the vast future applications of MLC in Chapter 8. Matlab codes are provided for easy reproducibility of the presented results. The book includes interviews with leading researchers in turbulence control (S. Bagheri, B. Batten, M. Glauser, D. Williams) and machine learning (M. Schoenauer) for a broader perspective. All chapters have exercises and supplemental videos will be available through YouTube.