Data Driven Fluid Mechanics
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Data Driven Fluid Mechanics
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Author : Miguel A. Mendez
language : en
Publisher: Cambridge University Press
Release Date : 2022-12-31
Data Driven Fluid Mechanics written by Miguel A. Mendez and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-31 with Science categories.
Data-driven methods have become an essential part of the methodological portfolio of fluid dynamicists, motivating students and practitioners to gather practical knowledge from a diverse range of disciplines. These fields include computer science, statistics, optimization, signal processing, pattern recognition, nonlinear dynamics, and control. Fluid mechanics is historically a big data field and offers a fertile ground for developing and applying data-driven methods, while also providing valuable shortcuts, constraints, and interpretations based on its powerful connections to basic physics. Thus, hybrid approaches that leverage both methods based on data as well as fundamental principles are the focus of active and exciting research. Originating from a one-week lecture series course by the von Karman Institute for Fluid Dynamics, this book presents an overview and a pedagogical treatment of some of the data-driven and machine learning tools that are leading research advancements in model-order reduction, system identification, flow control, and data-driven turbulence closures.
Data Driven Science And Engineering
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Author : Steven L. Brunton
language : en
Publisher: Cambridge University Press
Release Date : 2022-05-05
Data Driven Science And Engineering written by Steven L. Brunton and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-05 with Computers categories.
A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
On The Data Driven Reduced Order Modelling In Fluid Dynamics
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Author : Antonio Colanera
language : en
Publisher:
Release Date : 2024
On The Data Driven Reduced Order Modelling In Fluid Dynamics written by Antonio Colanera and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with Science categories.
Experimental Aerodynamics
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Author : Stefano Discetti
language : en
Publisher: CRC Press
Release Date : 2017-03-16
Experimental Aerodynamics written by Stefano Discetti and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-16 with Technology & Engineering categories.
Experimental Aerodynamics provides an up to date study of this key area of aeronautical engineering. The field has undergone significant evolution with the development of 3D techniques, data processing methods, and the conjugation of simultaneous measurements of multiple quantities. Written for undergraduate and graduate students in Aerospace Engineering, the text features chapters by leading experts, with a consistent structure, level, and pedagogical approach. Fundamentals of measurements and recent research developments are introduced, supported by numerous examples, illustrations, and problems. The text will also be of interest to those studying mechanical systems, such as wind turbines.
Data Driven Fluid Mechanics
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Author : Miguel A. Mendez
language : en
Publisher: Cambridge University Press
Release Date : 2023-01-31
Data Driven Fluid Mechanics written by Miguel A. Mendez and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-31 with Science categories.
This is the first book dedicated to data-driven methods for fluid dynamics, with applications in analysis, modeling, control, and closures.
Higher Order Dynamic Mode Decomposition And Its Applications
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Author : Jose Manuel Vega
language : en
Publisher: Academic Press
Release Date : 2020-09-22
Higher Order Dynamic Mode Decomposition And Its Applications written by Jose Manuel Vega and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-22 with Technology & Engineering categories.
Higher Order Dynamic Mode Decomposition and Its Applications provides detailed background theory, as well as several fully explained applications from a range of industrial contexts to help readers understand and use this innovative algorithm. Data-driven modelling of complex systems is a rapidly evolving field, which has applications in domains including engineering, medical, biological, and physical sciences, where it is providing ground-breaking insights into complex systems that exhibit rich multi-scale phenomena in both time and space. Starting with an introductory summary of established order reduction techniques like POD, DEIM, Koopman, and DMD, this book proceeds to provide a detailed explanation of higher order DMD, and to explain its advantages over other methods. Technical details of how the HODMD can be applied to a range of industrial problems will help the reader decide how to use the method in the most appropriate way, along with example MATLAB codes and advice on how to analyse and present results. Includes instructions for the implementation of the HODMD, MATLAB codes, and extended discussions of the algorithm Includes descriptions of other order reduction techniques, and compares their strengths and weaknesses Provides examples of applications involving complex flow fields, in contexts including aerospace engineering, geophysical flows, and wind turbine design
Machine Learning Control Taming Nonlinear Dynamics And Turbulence
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Author : Thomas Duriez
language : en
Publisher: Springer
Release Date : 2016-11-02
Machine Learning Control Taming Nonlinear Dynamics And Turbulence written by Thomas Duriez and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-02 with Technology & Engineering categories.
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.
Two Lectures
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Author : Werner Heisenberg
language : en
Publisher: CUP Archive
Release Date : 1949
Two Lectures written by Werner Heisenberg and has been published by CUP Archive this book supported file pdf, txt, epub, kindle and other format this book has been release on 1949 with Electric conductivity categories.
Whither Turbulence And Big Data In The 21st Century
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Author : Andrew Pollard
language : en
Publisher: Springer
Release Date : 2016-08-30
Whither Turbulence And Big Data In The 21st Century written by Andrew Pollard and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-30 with Technology & Engineering categories.
This volume provides a snapshot of the current and future trends in turbulence research across a range of disciplines. It provides an overview of the key challenges that face scientific and engineering communities in the context of huge databases of turbulence information currently being generated, yet poorly mined. These challenges include coherent structures and their control, wall turbulence and control, multi-scale turbulence, the impact of turbulence on energy generation and turbulence data manipulation strategies. The motivation for this volume is to assist the reader to make physical sense of these data deluges so as to inform both the research community as well as to advance practical outcomes from what is learned. Outcomes presented in this collection provide industry with information that impacts their activities, such as minimizing impact of wind farms, opportunities for understanding large scale wind events and large eddy simulation of the hydrodynamics of bays and lakes thereby increasing energy efficiencies, and minimizing emissions and noise from jet engines. Elucidates established, contemporary, and novel aspects of fluid turbulence - a ubiquitous yet poorly understood phenomena; Explores computer simulation of turbulence in the context of the emerging, unprecedented profusion of experimental data,which will need to be stewarded and archived; Examines a compendium of problems and issues that investigators can use to help formulate new promising research ideas; Makes the case for why funding agencies and scientists around the world need to lead a global effort to establish and steward large stores of turbulence data, rather than leaving them to individual researchers.
Dynamic Mode Decomposition
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Author : J. Nathan Kutz
language : en
Publisher: SIAM
Release Date : 2016-11-23
Dynamic Mode Decomposition written by J. Nathan Kutz and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-23 with Science categories.
Data-driven dynamical systems is a burgeoning field?it connects how measurements of nonlinear dynamical systems and/or complex systems can be used with well-established methods in dynamical systems theory. This is a critically important new direction because the governing equations of many problems under consideration by practitioners in various scientific fields are not typically known. Thus, using data alone to help derive, in an optimal sense, the best dynamical system representation of a given application allows for important new insights. The recently developed dynamic mode decomposition (DMD) is an innovative tool for integrating data with dynamical systems theory. The DMD has deep connections with traditional dynamical systems theory and many recent innovations in compressed sensing and machine learning. Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems, the first book to address the DMD algorithm, presents a pedagogical and comprehensive approach to all aspects of DMD currently developed or under development; blends theoretical development, example codes, and applications to showcase the theory and its many innovations and uses; highlights the numerous innovations around the DMD algorithm and demonstrates its efficacy using example problems from engineering and the physical and biological sciences; and provides extensive MATLAB code, data for intuitive examples of key methods, and graphical presentations.