Download or read book Turbulent Flow Simulations for Machine Learning written by and published by . This book was released on 2015 with total page 1 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine Learning for Uncertainty Quantification in Turbulent Flow Simulations written by and published by . This book was released on 2016 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Data Analysis for Direct Numerical Simulations of Turbulent Combustion written by Heinz Pitsch and published by Springer Nature. This book was released on 2020-05-28 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents methodologies for analysing large data sets produced by the direct numerical simulation (DNS) of turbulence and combustion. It describes the development of models that can be used to analyse large eddy simulations, and highlights both the most common techniques and newly emerging ones. The chapters, written by internationally respected experts, invite readers to consider DNS of turbulence and combustion from a formal, data-driven standpoint, rather than one led by experience and intuition. This perspective allows readers to recognise the shortcomings of existing models, with the ultimate goal of quantifying and reducing model-based uncertainty. In addition, recent advances in machine learning and statistical inferences offer new insights on the interpretation of DNS data. The book will especially benefit graduate-level students and researchers in mechanical and aerospace engineering, e.g. those with an interest in general fluid mechanics, applied mathematics, and the environmental and atmospheric sciences.

Download or read book Using Machine Learning for Error Detection in Turbulent Flow Simulations written by and published by . This book was released on 2015 with total page 1 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Using Machine Learning to Detect and Reduce Uncertainty in Turbulent Flow Simulations written by and published by . This book was released on 2016 with total page 1 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Data Analysis for Direct Numerical Simulations of Turbulent Combustion written by and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents methodologies for analysing large data sets produced by the direct numerical simulation (DNS) of turbulence and combustion. It describes the development of models that can be used to analyse large eddy simulations, and highlights both the most common techniques and newly emerging ones. The chapters, written by internationally respected experts, invite readers to consider DNS of turbulence and combustion from a formal, data-driven standpoint, rather than one led by experience and intuition. This perspective allows readers to recognise the shortcomings of existing models, with the ultimate goal of quantifying and reducing model-based uncertainty. In addition, recent advances in machine learning and statistical inferences offer new insights on the interpretation of DNS data. The book will especially benefit graduate-level students and researchers in mechanical and aerospace engineering, e.g. those with an interest in general fluid mechanics, applied mathematics, and the environmental and atmospheric sciences.

Download or read book Machine Learning Methods for Modeling Turbulence in Large Eddy Simulations written by Marius Kurz and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The reliable prediction of turbulent flows is of crucial importance since turbulence is prevalent in the majority of flows found in science and engineering. Turbulence is a multi-scale phenomenon, for which flow features can span several orders of magnitude in size. This results in enormous resolution requirements in numerical simulations of turbulent flow. The framework of large eddy simulation relaxes these resolution demands by resolving only the largest, most energetic features of the flow and approximating the dynamics of the smaller, unresolved scales with turbulence models. The goal of this thesis is to leverage the recent advances in machine learning methods to formulate data-driven modeling strategies for implicitly filtered large eddy simulation. To this end, two modeling strategies are devised based on the supervised and the reinforcement learning paradigms. First, artificial neural networks are trained using supervised learning to recover the unknown closure terms from the filtered flow field. It is demonstrated that recurrent neural networks can predict the unknown closure terms with excellent accuracy. The second modeling strategy is based on the reinforcement learning paradigm. For this, Relexi is introduced as a novel reinforcement learning framework that allows to employ legacy flow solvers as training environments at scale. With Relexi, artificial neural networks are trained within forced homogeneous isotropic turbulence to adapt the parameters of traditional turbulence models dynamically in space and time. The trained models provide accurate and stable simulations and generalize well to other resolutions and higher Reynolds numbers. It is demonstrated within this thesis that machine learning methods can be applied to derive data-driven turbulence models for implicitly filtered large eddy simulation and that these models can be trained and incorporated efficiently into practical simulations on high-performance computing systems.

Download or read book High Performance Computing written by Heike Jagode and published by Springer Nature. This book was released on 2020-10-19 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed post-conference proceedings of 10 workshops held at the 35th International ISC High Performance 2020 Conference, in Frankfurt, Germany, in June 2020: First Workshop on Compiler-assisted Correctness Checking and Performance Optimization for HPC (C3PO); First International Workshop on the Application of Machine Learning Techniques to Computational Fluid Dynamics Simulations and Analysis (CFDML); HPC I/O in the Data Center Workshop (HPC-IODC); First Workshop \Machine Learning on HPC Systems" (MLHPCS); First International Workshop on Monitoring and Data Analytics (MODA); 15th Workshop on Virtualization in High-Performance Cloud Computing (VHPC). The 25 full 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.

Download or read book Machine Learning Control Taming Nonlinear Dynamics and Turbulence written by Thomas Duriez and published by Springer. This book was released on 2016-11-02 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Constraint Programming and Decision Making Theory and Applications written by Martine Ceberio and published by Springer. This book was released on 2017-09-07 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes new algorithms and ideas for making effective decisions under constraints, including applications in control engineering, manufacturing (how to optimally determine the production level), econometrics (how to better predict stock market behavior), and environmental science and geosciences (how to combine data of different types). It also describes general algorithms and ideas that can be used in other application areas. The book presents extended versions of selected papers from the annual International Workshops on Constraint Programming and Decision Making (CoProd’XX) from 2013 to 2016. These workshops, held in the US (El Paso, Texas) and in Europe (Würzburg, Germany, and Uppsala, Sweden), have attracted researchers and practitioners from all over the world. It is of interest to practitioners who benefit from the new techniques, to researchers who want to extend the ideas from these papers to new application areas and/or further improve the corresponding algorithms, and to graduate students who want to learn more – in short, to anyone who wants to make more effective decisions under constraints.

Download or read book Machine Learning for Model Uncertainties in Turbulence Models and Monte Carlo Integral Approximation written by Brendan D. Tracey and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: While computational fluid dynamics (CFD) is playing an ever-increasing role in the design process, physical experiments are still required for final verification. There is a demand for certification through simulation, but there is a gap in predictive quality. Reynolds-averaged Navier-Stokes flow simulations have known deficiencies, especially for high Reynolds number flows with turbulent transition and separation, and higher fidelity Large Eddy Simulations (LES) and Direct Numerical Simulations (DNS) are not generally affordable. Quantification and reduction of uncertainty in simulation results is necessary, and yet it is rare for error bounds to be returned by a simulation, and progress towards more accurate turbulent closures in RANS models seems to have stalled. Today, however, the community is better equipped than ever to address this challenge. The rise in data science has driven the creation of tools and techniques to analyze and synthesize massive data sets. Most importantly, the data needed for statistical inference is available; computational budgets allow for RANS calculations on a number of input conditions and design settings, LES advances to increasingly complex geometries, and DNS continues to expand its Reynolds-number range. This dissertation harnesses data-driven approaches to address issues of uncertainty in predictive tools. First, the dissertation explores creating accurate models from data by replicating the behavior of a known model. Computational data is collected from the Spalart-Allmaras turbulence model, a neural network algorithm is trained on this data, and the learned model is re-embedded within a CFD flow solver. The robustness and accuracy of this procedure is explored as influenced by loss function choice, feature selection, and training data. Next, the dissertation considers model uncertainty in low-fidelity models. High-fidelity data from DNS of combustion (using finite-rate chemistry) are used to augment the low-fidelity flamelet progress variable-based RANS approach (FPVA). Supervised learning approaches are used to construct two error models, one for the local inaccuracies in the model and a second addressing the spatial correlation of these errors. These uncertainty models are combined to estimate the uncertainty in the FPVA model. Finally, a methodology is presented for quantifying the effects of input uncertainty on an output variable of interest. This is done by constructing an approximate model of the system using available data samples, and then using this as a control variate to reduced the squared estimation error in the output. Results are presented which demonstrate improved accuracy for a wide range of problem dimensions, function types, and sampling types. Taken together, these approaches indicate the potential of data-driven techniques to identify and reduce uncertainties in complex flow simulations.

Download or read book Data Driven Science and Engineering written by Steven L. Brunton and published by Cambridge University Press. This book was released on 2022-05-05 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

Download or read book Machine Learning and Its Application to Reacting Flows written by Nedunchezhian Swaminathan and published by Springer Nature. This book was released on 2023-01-01 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation.

Download or read book Numerical Prediction of Flow Heat Transfer Turbulence and Combustion written by D. Brian Spalding and published by Elsevier. This book was released on 2015-07-14 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: Numerical Prediction of Flow, Heat Transfer, Turbulence and Combustion: Selected Works of Professor D. Brian Spalding focuses on the many contributions of Professor Spalding on thermodynamics. This compilation of his works is done to honor the professor on the occasion of his 60th birthday. Relatively, the works contained in this book are selected to highlight the genius of Professor Spalding in this field of interest. The book presents various research on combustion, heat transfer, turbulence, and flows. His thinking on separated flows paved the way for the multi-dimensional modeling of turbulence. Arguments on the universality of the models of turbulence and the problems that are associated with combustion engineering are clarified. The text notes the importance of combustion science as well as the problems associated with it. Mathematical computations are also presented in determining turbulent flows in different environments, including on curved pipes, curved ducts, and rotating ducts. These calculations are presented to further strengthen the claims of Professor Spalding in this discipline. The book is a great find for those who are interested in studying thermodynamics.

Download or read book Turbulence written by Uriel Frisch and published by Cambridge University Press. This book was released on 1995-11-30 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook presents a modern account of turbulence, one of the greatest challenges in physics. The state-of-the-art is put into historical perspective five centuries after the first studies of Leonardo and half a century after the first attempt by A. N. Kolmogorov to predict the properties of flow at very high Reynolds numbers. Such 'fully developed turbulence' is ubiquitous in both cosmical and natural environments, in engineering applications and in everyday life. The intended readership for the book ranges from first-year graduate students in mathematics, physics, astrophysics, geosciences and engineering, to professional scientists and engineers. Elementary presentations of dynamical systems ideas, of probabilistic methods (including the theory of large deviations) and of fractal geometry make this a self-contained textbook.

Download or read book Accelerating the Simulation of Chemically Reacting Turbulent Flows Via Machine Learning Techniques written by Opeoluwa Olawale Owoyele and published by . This book was released on 2018 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Cities and Climate Change written by Harriet Bulkeley and published by Routledge. This book was released on 2013-05-07 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: Climate change is one of the most significant global challenges facing the world today. It is also a critical issue for the world’s cities. Now home to over half the world’s population, urban areas are significant sources of greenhouse gas emissions and are vulnerable to the impacts of climate change. Responding to climate change is a profound challenge. A variety of actors are involved in urban climate governance, with municipal governments, international organisations, and funding bodies pointing to cities as key arenas for response. This book provides the first critical introduction to these challenges, giving an overview of the science and policy of climate change at the global level and the emergence of climate change as an urban policy issue. It considers the challenges of governing climate change in the city in the context of the changing nature of urban politics, economics, society and infrastructures. It looks at how responses for mitigation and adaptation have emerged within the city, and the implications of climate change for social and environmental justice. Drawing on examples from cities in the north and south, and richly illustrated with detailed case-studies, this book will enable students to understand the potential and limits of addressing climate change at the urban level and to explore the consequences for our future cities. It will be essential reading for undergraduate students across the disciplines of geography, politics, sociology, urban studies, planning and science and technology studies.