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This succinct introduction to the fundamental physical principles of turbulence provides a modern perspective through statistical theory, experiments, and high-fidelity numerical simulations. It describes classical concepts of turbulence and offers new computational perspectives on their interpretation based on numerical simulation databases, introducing students to phenomena at a wide range of scales. Unique, practical, multi-part physics-based exercises use realistic data of canonical turbulent flows developed by the Stanford Center for Turbulence Research to equip students with hands-on experience with practical and predictive analysis tools. Over 20 case studies spanning real-world settings such as wind farms and airplanes, color illustrations, and color-coded pedagogy support student learning. Accompanied by downloadable datasets, and solutions for instructors, this is the ideal introduction for students in aerospace, civil, environmental, and mechanical engineering and the physical sciences studying a graduate-level one-semester course on turbulence, advanced fluid mechanics, and turbulence simulation.
All fields of science benefit from gathering and analyzing network data. This chapter summarizes a small portion of the ways networks are found in research fields thanks to increasing volumes of data and the computing resources needed to work with that data. Epidemiology, dynamical systems, materials science, and many more fields than we can discuss here, use networks and network data. Well encounter many more examples during the rest of this book.
Drawing examples from real-world networks, this essential book traces the methods behind network analysis and explains how network data is first gathered, then processed and interpreted. The text will equip you with a toolbox of diverse methods and data modelling approaches, allowing you to quickly start making your own calculations on a huge variety of networked systems. This book sets you up to succeed, addressing the questions of what you need to know and what to do with it, when beginning to work with network data. The hands-on approach adopted throughout means that beginners quickly become capable practitioners, guided by a wealth of interesting examples that demonstrate key concepts. Exercises using real-world data extend and deepen your understanding, and develop effective working patterns in network calculations and analysis. Suitable for both graduate students and researchers across a range of disciplines, this novel text provides a fast-track to network data expertise.
This innovative approach to teaching the finite element method blends theoretical, textbook-based learning with practical application using online and video resources. This hybrid teaching package features computational software such as MATLAB®, and tutorials presenting software applications such as PTC Creo Parametric, ANSYS APDL, ANSYS Workbench and SolidWorks, complete with detailed annotations and instructions so students can confidently develop hands-on experience. Suitable for senior undergraduate and graduate level classes, students will transition seamlessly between mathematical models and practical commercial software problems, empowering them to advance from basic differential equations to industry-standard modelling and analysis. Complete with over 120 end-of chapter problems and over 200 illustrations, this accessible reference will equip students with the tools they need to succeed in the workplace.
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