Dimensional Analysis And Self Similarity Methods For Engineers And Scientists
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Dimensional Analysis and Self-Similarity Methods for Engineers and Scientists
· Provides innovative techniques for solving complex nonlinear partial differential equations, previously only available to scientists involved in classified government funded projects. · Goes beyond the traditional Pi (Buckingham) Theorem method to apply dimensional analysis to gas dynamics and thermal hydraulics problems where both laminar and turbulent fluids come into play · Includes specific examples demonstrating how dimensional analysis can shed light on applications from shock wave impact prediction to plasma confinement. · Presents a unique approach to similarity methods by discussing Chaos, Fractals and Arcadia, in addition to the more common Self-Similarity and Fractals Techniques This ground-breaking reference provides an overview of key concepts in dimensional analysis and the scientific approach of similarity methods, including a uniquely robust discussion on self-similarity solutions of the First and Second kinds. The coverage pushes well beyond traditional applications in fluid mechanics and gas dynamics to demonstrate how powerful self-similarity can be in solving complex problems across many diverse fields, using nonlinear Partial Differential Equations (PDEs) by reducing them to Ordinary Differential Equations (ODEs) with a simple traditional analytical solution approach. Of particular interest is the book's coverage of dimensional analysis and self-similarity methods in nuclear and energy engineering from Heat Transfer and Thermal Hydraulic points of view. Numerous practical examples of dimensional analysis problems are presented throughout each chapter, with additional problems presented in each appendix, allowing readers to link the book's theoretical explanations and step-by-step mathematical solutions to practical implementations.
Dimensional Analysis and Self-Similarity Methods for Engineers and Scientists
This ground-breaking reference provides an overview of key concepts in dimensional analysis, and then pushes well beyond traditional applications in fluid mechanics to demonstrate how powerful this tool can be in solving complex problems across many diverse fields. Of particular interest is the book’s coverage of dimensional analysis and self-similarity methods in nuclear and energy engineering. Numerous practical examples of dimensional problems are presented throughout, allowing readers to link the book’s theoretical explanations and step-by-step mathematical solutions to practical implementations.
Dimensional Analysis Beyond the Pi Theorem
Dimensional Analysis and Physical Similarity are well understood subjects, and the general concepts of dynamical similarity are explained in this book. Our exposition is essentially different from those available in the literature, although it follows the general ideas known as Pi Theorem. There are many excellent books that one can refer to; however, dimensional analysis goes beyond Pi theorem, which is also known as Buckingham’s Pi Theorem. Many techniques via self-similar solutions can bound solutions to problems that seem intractable. A time-developing phenomenon is called self-similar if the spatial distributions of its properties at different points in time can be obtained from one another by a similarity transformation, and identifying one of the independent variables as time. However, this is where Dimensional Analysis goes beyond Pi Theorem into self-similarity, which has represented progress for researchers. In recent years there has been a surge of interest in self-similar solutions of the First and Second kind. Such solutions are not newly discovered; they have been identified and named by Zel’dovich, a famous Russian Mathematician in 1956. They have been used in the context of a variety of problems, such as shock waves in gas dynamics, and filtration through elasto-plastic materials. Self-Similarity has simplified computations and the representation of the properties of phenomena under investigation. It handles experimental data, reduces what would be a random cloud of empirical points to lie on a single curve or surface, and constructs procedures that are self-similar. Variables can be specifically chosen for the calculations.