Whither Turbulence and Big Data in the 21st Century?

Whither Turbulence and Big Data in the 21st Century?

Author: Andrew Pollard

Publisher: Springer

Published: 2016-08-30

Total Pages: 570

ISBN-13: 3319412175

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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.


Machine Learning Control – Taming Nonlinear Dynamics and Turbulence

Machine Learning Control – Taming Nonlinear Dynamics and Turbulence

Author: Thomas Duriez

Publisher: Springer

Published: 2016-11-02

Total Pages: 229

ISBN-13: 3319406248

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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.


High Performance Computing

High Performance Computing

Author: Heike Jagode

Publisher: Springer Nature

Published: 2021-11-12

Total Pages: 515

ISBN-13: 303090539X

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This book constitutes the refereed post-conference proceedings of 9 workshops held at the 35th International ISC High Performance 2021 Conference, in Frankfurt, Germany, in June-July 2021: Second International Workshop on the Application of Machine Learning Techniques to Computational Fluid Dynamics and Solid Mechanics Simulations and Analysis; HPC-IODC: HPC I/O in the Data Center Workshop; Compiler-assisted Correctness Checking and Performance Optimization for HPC; Machine Learning on HPC Systems;4th International Workshop on Interoperability of Supercomputing and Cloud Technologies;2nd International Workshop on Monitoring and Operational Data Analytics;16th Workshop on Virtualization in High-Performance Cloud Computing; Deep Learning on Supercomputers; 5th International Workshop on In Situ Visualization. The 35 papers included in this volume were carefully reviewed and selected. They cover all aspects of research, development, and application of large-scale, high performance experimental and commercial systems. Topics include high-performance computing (HPC), computer architecture and hardware, programming models, system software, performance analysis and modeling, compiler analysis and optimization techniques, software sustainability, scientific applications, deep learning.


50 Years of CFD in Engineering Sciences

50 Years of CFD in Engineering Sciences

Author: Akshai Runchal

Publisher: Springer Nature

Published: 2020-03-09

Total Pages: 950

ISBN-13: 9811526702

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Prof. D. Brian Spalding, working with a small group of students and colleagues at Imperial College, London in the mid-to late-1960’s, single-handedly pioneered the use of Computational Fluid Dynamics (CFD) for engineering practice.​This book brings together advances in computational fluid dynamics in a collection of chapters authored by leading researchers, many of them students or associates of Prof. Spalding. The book intends to capture the key developments in specific fields of activity that have been transformed by application of CFD in the last 50 years. The focus is on review of the impact of CFD on these selected fields and of the novel applications that CFD has made possible. Some of the chapters trace the history of developments in a specific field and the role played by Spalding and his contributions. The volume also includes a biographical summary of Brian Spalding as a person and as a scientist, as well as tributes to Brian Spalding by those whose life was impacted by his innovations. This volume would be of special interest to researchers, practicing engineers, and graduate students in various fields, including aerospace, energy, power and propulsion, transportation, combustion, management of the environment, health and pharmaceutical sciences.


Progress in Turbulence IX

Progress in Turbulence IX

Author: Ramis Örlü

Publisher: Springer Nature

Published: 2021-10-23

Total Pages: 308

ISBN-13: 3030807169

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This volume collects the edited and reviewed contribution presented in the 9th iTi Conference that took place virtually, covering fundamental and applied aspects in turbulence. In the spirit of the iTi conference, the volume is produced after the conference so that the authors had the opportunity to incorporate comments and discussions raised during the meeting. In the present book, the contributions have been structured according to the topics: I Experiments II Simulations and Modelling III Data Processing and Scaling IV Theory V Miscellaneous topics


Data-Driven Science and Engineering

Data-Driven Science and Engineering

Author: Steven L. Brunton

Publisher: Cambridge University Press

Published: 2022-05-05

Total Pages: 615

ISBN-13: 1009098489

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A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.


Handbook of Wind Energy Aerodynamics

Handbook of Wind Energy Aerodynamics

Author: Bernhard Stoevesandt

Publisher: Springer Nature

Published: 2022-08-04

Total Pages: 1495

ISBN-13: 3030313077

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This handbook provides both a comprehensive overview and deep insights on the state-of-the-art methods used in wind turbine aerodynamics, as well as their advantages and limits. The focus of this work is specifically on wind turbines, where the aerodynamics are different from that of other fields due to the turbulent wind fields they face and the resultant differences in structural requirements. It gives a complete picture of research in the field, taking into account the different approaches which are applied. This book would be useful to professionals, academics, researchers and students working in the field.


Snapshot-Based Methods and Algorithms

Snapshot-Based Methods and Algorithms

Author: Peter Benner

Publisher: Walter de Gruyter GmbH & Co KG

Published: 2020-12-16

Total Pages: 369

ISBN-13: 3110671506

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An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This two-volume handbook covers methods as well as applications. This second volume focuses on applications in engineering, biomedical engineering, computational physics and computer science.


Mathematical Modelling of Energy Systems and Fluid Machinery

Mathematical Modelling of Energy Systems and Fluid Machinery

Author: Mirko Morini

Publisher: MDPI

Published: 2021-06-04

Total Pages: 256

ISBN-13: 3036505504

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The ongoing digitalization of the energy sector, which will make a large amount of data available, should not be viewed as a passive ICT application for energy technology or a threat to thermodynamics and fluid dynamics, in the light of the competition triggered by data mining and machine learning techniques. These new technologies must be posed on solid bases for the representation of energy systems and fluid machinery. Therefore, mathematical modelling is still relevant and its importance cannot be underestimated. The aim of this Special Issue was to collect contributions about mathematical modelling of energy systems and fluid machinery in order to build and consolidate the base of this knowledge.


Dynamic Mode Decomposition

Dynamic Mode Decomposition

Author: J. Nathan Kutz

Publisher: SIAM

Published: 2016-11-23

Total Pages: 241

ISBN-13: 1611974496

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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.