Download or read book Machine Learning for Coherent Structure Identification and Super Resolution in Turbulent Flows written by Sahil Kommalapati and published by . This book was released on 2021 with total page 88 pages. Available in PDF, EPUB and Kindle. Book excerpt: Particle image velocimetry (PIV) techniques provide high fidelity measurements of mutliscale turbulent fluid motion. The research presented in this thesis broadly explores the utilization of Machine Learning for processing and analyzing non-time resolved PIV measurements of turbulent flows. The primary goal is to utilize bayesian inference to automate turbulent coherent structure identification in boundary layer flows. Automated identification was hitherto hindered by the lack of a priori information about the convecting velocities of the coherent structures. A Rankine vortex model is utilized to implement a Markov chain Monte Carlo-based vector matching to overcome this problem. Ultimately, a framework was developed for robust identification of vortices that is capable of visualizing the cumulative distributions of properties of all vortex structures in the flow to provide a rich description of multiscale turbulent coherent structures. Additionally, Bayesian inference is proven to outperform traditional optimization methods based on various loss metrics. The other major goal of this thesis is to implement super resolution in turbulent separated flows using neural network architectures. Machine learning based super resolution using PIV measurements is relatively unexplored, compared to its counterpart with CFD simulation. This is partly due to the unavailability of training datasets with sufficient magnitude. A large PIV dataset of non-time resolved measurements of turbulent separated flow with 50,000 snapshots was collected to overcome this problem. A neural network architecture was trained on this dataset to successfully outperform traditional interpolation based super resolution techniques. Finally, it has been shown that the current approach also accurately reproduced the properties of turbulent derived quantities, like the stream wise variance of velocity fluctuations, with better accuracy in comparison with interpolation based super resolution methods.
Download or read book Proceedings of the 1st International Conference on Fluid Thermal and Energy Systems written by Sudev Das and published by Springer Nature. This book was released on 2024-01-17 with total page 840 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book comprises the proceedings of the 1st International Conference on Fluid, Thermal and Energy Systems. The contents of this book focus on phase change heat transfer, advanced energy systems, separated flows, turbulence and multi-phase modeling, computational fluid flow and heat transfer, thermal energy storage systems, integrated energy systems, nuclear thermal hydraulics, heat transfer in nanofluids, etc. This book serves as a useful reference to researchers, academicians, and students interested in the broad field of thermo-fluid science and engineering.
Download or read book A method for identifying coherent structures in turbulent flows written by Bedri Şefik and published by . This book was released on 1991 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Tezin özeti, teknik nedenlerden dolayı alınamamıştır.
Download or read book Turbulence Structure and Modulation written by Alfredo Soldati and published by Springer Science & Business Media. This book was released on 2001-07-19 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: Controlling turbulence is an important issue for a number of technological applications. Several methods to modulate turbulence are currently being investigated. All of them are based on the introduction of some sort of perturbation into the flow field which affect turbulence coherent structures responsible for turbulence transfer mechanisms. The scope of the book is to describe several aspects of turbulence structure and modulation and to explain and discuss the most promising techniques in detail.
Download or read book Data driven Identification and Modelling of Coherent Dynamics in Turbulent Flows written by Moritz Alexander Sieber and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Study of Coherent Structures in Turbulent Flows Using Proper Orthogonal Decomposition written by Maziar Eslami Samani and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Data driven modeling and optimization in fluid dynamics From physics based to machine learning approaches written by Michel Bergmann and published by Frontiers Media SA. This book was released on 2023-01-05 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Characterization of Coherent Structures in Turbulent Flows written by Alex Liberzon and published by . This book was released on 2003 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1995 with total page 602 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lists citations with abstracts for aerospace related reports obtained from world wide sources and announces documents that have recently been entered into the NASA Scientific and Technical Information Database.
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 Engineering Turbulence Modelling and Experiments 4 written by D. Laurence and published by Elsevier. This book was released on 1999-04-14 with total page 975 pages. Available in PDF, EPUB and Kindle. Book excerpt: These proceedings contain the papers presented at the 4th International Symposium on Engineering Turbulence Modelling and Measurements held at Ajaccio, Corsica, France from 24-26 May 1999. It follows three previous conferences on the topic of engineering turbulence modelling and measurements. The purpose of this series of symposia is to provide a forum for presenting and discussing new developments in the area of turbulence modelling and measurements, with particular emphasis on engineering-related problems. Turbulence is still one of the key issues in tackling engineering flow problems. As powerful computers and accurate numerical methods are now available for solving the flow equations, and since engineering applications nearly always involve turbulence effects, the reliability of CFD analysis depends more and more on the performance of the turbulence models. Successful simulation of turbulence requires the understanding of the complex physical phenomena involved and suitable models for describing the turbulent momentum, heat and mass transfer. For the understanding of turbulence phenomena, experiments are indispensable, but they are equally important for providing data for the development and testing of turbulence models and hence for CFD software validation.
Download or read book Computational Fluid Dynamics written by Takeo Kajishima and published by Springer. This book was released on 2016-10-01 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook presents numerical solution techniques for incompressible turbulent flows that occur in a variety of scientific and engineering settings including aerodynamics of ground-based vehicles and low-speed aircraft, fluid flows in energy systems, atmospheric flows, and biological flows. This book encompasses fluid mechanics, partial differential equations, numerical methods, and turbulence models, and emphasizes the foundation on how the governing partial differential equations for incompressible fluid flow can be solved numerically in an accurate and efficient manner. Extensive discussions on incompressible flow solvers and turbulence modeling are also offered. This text is an ideal instructional resource and reference for students, research scientists, and professional engineers interested in analyzing fluid flows using numerical simulations for fundamental research and industrial applications.
Download or read book Advanced Technologies in Flow Dynamics and Combustion in Propulsion and Power written by Lei Luo and published by Frontiers Media SA. This book was released on 2022-08-05 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 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 Dynamic Mode Decomposition written by J. Nathan Kutz and published by SIAM. This book was released on 2016-11-23 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
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®.
