Applied Computational Fluid Dynamics and Turbulence Modeling

1. Motivation for CFD and Turbulence Modeling.1.1 Computational Advantages over the Experimental Approach1.2 Jobs and Future Employment Trends2. Review of Basics2.1 Conservation of Mass2.2 Conservation of Momentum2.3 Conservation of Energy2.4 Turbulence Theory2.5 Turbulence Modeling2.6 The Crucial Importance of the Reynolds Number2.7 Isotropic Turbulence Decay and Applications3. Applied Theory-Practical Turbulence Estimates3.1 Integral Eddies3.2 Taylor Eddies3.3 Kolmogorov Eddies3.4 The "LIKE" Algorithm-Computing Your Own Epsilon, Turbulent Kinetic Energy, and Turbulence Kinematic Viscosity, and Reynolds Stresses3.5 Turbulence and Surface Dimpling4. Turbulence Model Selection4.1 Top RANS Turbulence Models4.2 Avoid the Standard k- Model4.3 SST vs. 2006 k-4.4 The Increasing Power of LES and DNS Modeling5. Best Practices of the CFD and Turbulence Trade5.1 Developing a Bullet-Proof Mesh5.2 Boundary Condition Recommendations5.3 RANS Modeling Recommendations5.4 LES Modeling Recommendations5.5 DNS Modeling Recommendations5.6 Miscellaneous Do's and Don'ts of the Trade5.7 Turbulent Natural Circulation Modeling Hints5.8 Data Visualization Tips5.9 Extracting Fluctuating Properties6. Generating Reliable Simulations6.1 Verification and Validation Terminology6.2 Code Verification: Order of Accuracy, Exact Solutions, Method of Manufactured Solutions (MMS)6.3 Solution Verification: Richardson Extrapolation, Roache's grid convergence index (GCI)6.4 Sensitivity Analysis 6.5 Model Validation

About the Author: Sal Rodriguez conducts research in turbulent flow, swirl, and computational fluid dynamics (CFD), with applications in light water and non-light water reactors, small modular reactors, and micro reactors. He is currently a Principal Member of the Technical Staff at Sandia National Laboratories, where he leads turbulence seminars and turbulence workshops. His over 30 years' experience in CFD and turbulence modeling applied to nuclear engineering and other energy systems at Idaho National Laboratory, Los Alamos National Laboratory, and Sandia National Laboratories is reflected in this book. Dr. Rodriguez has taught CFD, turbulence modeling, and advanced turbulence modeling at the University of New Mexico as a Research Associate Professor of the Nuclear Engineering Department for nearly ten years. Dr. Rodriguez earned a BS in Nuclear Engineering from UC Santa Barbara, an MS in Mechanical Engineering from the University of Idaho, an MS in Applied Mathematics from the University of New Mexico, and a PhD in Nuclear Engineering from the University of New Mexico.

His recent research advances include the development of the right-sized dimpling algorithm for reduced flow drag and increased heat transfer, development of the first dynamic code for modeling hydrogen production in integral nuclear reactor systems, aerodynamic drag reduction for race cars, an isotropic turbulence decay model, and the development of a vortex-unification theory. Other research interests include advanced manufacturing of drag-reducing surfaces with enhanced heat transfer, as well as the development and laser-engineered manufacturing of high entropy refractory alloys (RHEAs) for nuclear reactor and aerodynamic applications.