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Expanded to cover more advanced applications where statistical properties of data can be nonstationary and the physical systems nonlinear as opposed to only linear. Stresses the practical use and interpretation of analyzed data to solve problems. Special attention is given to bias and random errors involved in desired estimates and the proper interpretation of results from specific applications. Includes numerous case studies concerned with dynamic problems which can occur in a variety of fields.
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This comprehensively updated new edition covers the fundamental concepts and main methods of modern Computational Fluid Dynamics (CFD). With expert guidance and a wealth of useful techniques, the book offers a clear, concise, and accessible account of the essentials needed to perform and interpret a CFD analysis.The new edition adds a plethora of new information on such topics as the techniques of interpolation, finite volume discretization on unstructured grids, projection methods, and RANS turbulence modeling. The book has been thoroughly edited to improve clarity and to reflect the recent changes in the practice of CFD. It also features a large number of new end-of-chapter problems.
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Munson, Young, and Okiishi's Fundamentals of Fluid Mechanics is intended for undergraduate engineering students for use in a first course on fluid mechanics. Building on the well-established principles of fluid mechanics, the book offers improved and evolved academic treatment of the subject. Each important concept or notion is considered in terms of simple and easy-to-understand circumstances before more complicated features are introduced. The presentation of material allows for the gradual development of student confidence in fluid mechanics problem solving. This International Adaptation of the book comes with some new topics and updates on concepts that clarify, enhance, and expand certain ideas and concepts. The new examples and problems build upon the understanding of engineering applications of fluid mechanics and the edition has been completely updated to use SI units.
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This book is intended for self-study or as a companion of lectures delivered to post-graduate students on the subject of the computational prediction of complex turbulent flows. There are several books in the extensive literature on turbulence that deal, in statistical terms, with the phenomenon itself, as well its many manifestations in the context of fluid dynamics. Statistical Turbulence Modelling for Fluid Dynamics - Demystified differs from these and focuses on the physical interpretation of a broad range of mathematical models used to represent the time-averaged effects of turbulence in computational prediction schemes for fluid flow and related transport processes in engineering and the natural environment. It dispenses with complex mathematical manipulations and instead gives physical and phenomenological explanations.
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This new edition updates Durbin & Koopman's important text on the state space approach to time series analysis. The distinguishing feature of state space time series models is that observations are regarded as made up of distinct components such as trend, seasonal, regression elements and disturbance terms, each of which is modelled separately. The techniques that emerge from this approach are very flexible and are capable of handling a much wider range of problems than the main analytical system currently in use for time series analysis, the Box-Jenkins ARIMA system. Additions to this second edition include the filtering of nonlinear and non-Gaussian series. Part I of the book obtains the mean and variance of the state, of a variable intended to measure the effect of an interaction and of regression coefficients, in terms of the observations. Part II extends the treatment to nonlinear and non-normal models. For these, analytical solutions are not available so methods are based on simulation.
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