Download or read book Data Driven Analysis and Modeling of Turbulent Flows written by Karthik Duraisamy and published by Academic Press. This book was released on 2025-03-01 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data-driven Analysis and Modeling of Turbulent Flows explains methods for the analysis of large fields of data, and uncovering models and model improvements from numerical or experimental data on turbulence. Turbulence simulations generate large data sets, and the extraction of useful information from these data fields is an important and challenging task. Statistical learning and machine learning have provided many ways of helping, and this book explains how to use such methods for extracting, treating, and optimizing data to improve predictive turbulence models. These include methods such as POD, SPOD and DMD, for the extraction of modes peculiar to the data, as well as several reduced order models. This resource is essential reading for those developing turbulence models, performing turbulence simulations or interpreting turbulence simulation results.

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 Data driven Dynamic Nonlocal Subgrid scale Modeling for the Large Eddy Simulation of Turbulent Flows written by Seyedhadi Seyedi and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study aims to propose novel solutions to the complex problem of turbulent flows using data-driven statistical and mathematical models. The proposed models reduce the huge computational cost of the direct numerical simulations and make them tractable while maintaining the important statistical features of the chaotic flows. Unlike the conventional models in the literature, the new proposed dynamic models take into account the inherent nonlocality of turbulence and predict the final statistical quantities with higher accuracy and correlations. First, we developed a novel autonomously dynamic nonlocal turbulence model for the large and very large eddy simulation (LES, VLES) of the homogeneous isotropic turbulent flows (HIT). The model is based on a generalized (integer-to-noninteger) order Laplacian of the filtered velocity field, and a novel dynamic model has been formulated to avoid the need for tuning the model constant. Three data-driven approaches were introduced for the determination of the fractional-order to have a model which is totally free of any tuning parameter. Our analysis includes both the a priori and the a posteriori tests. In the former test, using a high-fidelity and well-resolved dataset from direct numerical simulations (DNS), we computed the correlation coefficients for the stress components of the subgrid-scale (SGS) stress tensor and the one we get directly from the DNS results. Moreover, we compared the probability density function of the ensemble-averaged SGS forces for different filter sizes. In the latter, we employed our new model along with other conventional models including static and dynamic Smagorinsky into our pseudo-spectral solver and tested the final predicted quantities. The results of the newly developed model exhibit an expressive agreement with the ground-truth DNS results in all components of the SGS stress and forces. Also, the model exhibits promising results in the VLES region as well as the LES region, which could be remarkably important for the cost-efficient nonlocal turbulence modeling e.g., in meteorological and environmental applications.Afterwards, we extend the same dynamic nonlocal idea to the scalar turbulence. To this end, we formulate the underlying nonlocal model starting from the filtered Boltzmann kinetic transport equation, where the divergence of subgrid-scale scalar fluxes emerges as a fractional-order Laplacian term in the filtered advection-diffusion model, coding the corresponding super-diffusive nature of scalar turbulence. Subsequently, we develop a robust data-driven algorithm for estimation of the fractional (non-integer) Laplacian exponent, where we on-the-fly calculate the corresponding model coefficient employing a new dynamic procedure. Our a priori tests show that our new dynamically nonlocal LES paradigm provides better agreements with the ground-truth filtered DNS data in comparison to the conventional static and dynamic Prandtl-Smagorisnky models. Moreover, in order to analyze the numerical stability and assessing the model's performance, we carry out a comprehensive a posteriori tests. They unanimously illustrate that our new model considerably outperforms other existing functional models, correctly predicting the backscattering phenomena at the same time and providing higher correlations at small-to-large filter sizes. We conclude that our proposed nonlocal subgrid-scale model for scalar turbulence is amenable for coarse LES and VLES frameworks even with strong anisotropies, applicable to environmental applications.Finally, we developed a new dynamic tempered fractional subgrid-scale model, DTF, for the large and very large eddy simulation of turbulent flows. The nonlocality of the turbulent flows is the innate feature that can be seen in the non-Gaussian statistics of the velocity increments and can be addressed properly by the nonlocal models in terms of the fractional operators. Using kinetic transport, we developed a dynamic tempered fractional model that encompasses the three main characteristics of an ideal turbulence model: (i) nonlocal nature, (ii) dynamic model constant computations, and (iii) tempered and finite variance property. Several simulations of forced homogeneous isotropic and multi-layer temporal shear layer turbulent flows have been done in the a priori and a posteriori analyses. The results show that the new model is not only numerically stable and can maintain low- and high-order structures in long-range simulations, but it also provides better predictions than local models and nontempered models.

Download or read book Advanced Approaches in Turbulence written by Paul Durbin and published by Elsevier. This book was released on 2021-07-24 with total page 554 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced Approaches in Turbulence: Theory, Modeling, Simulation and Data Analysis for Turbulent Flows focuses on the updated theory, simulation and data analysis of turbulence dealing mainly with turbulence modeling instead of the physics of turbulence. Beginning with the basics of turbulence, the book discusses closure modeling, direct simulation, large eddy simulation and hybrid simulation. The book also covers the entire spectrum of turbulence models for both single-phase and multi-phase flows, as well as turbulence in compressible flow. Turbulence modeling is very extensive and continuously updated with new achievements and improvements of the models. Modern advances in computer speed offer the potential for elaborate numerical analysis of turbulent fluid flow while advances in instrumentation are creating large amounts of data. This book covers these topics in great detail. Covers the fundamentals of turbulence updated with recent developments Focuses on hybrid methods such as DES and wall-modeled LES Gives an updated treatment of numerical simulation and data analysis

Download or read book Experimental Aerodynamics written by Stefano Discetti and published by CRC Press. This book was released on 2017-03-16 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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 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 Turbulent Flows written by G. Biswas and published by CRC Press. This book was released on 2002 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book allows readers to tackle the challenges of turbulent flow problems with confidence. It covers the fundamentals of turbulence, various modeling approaches, and experimental studies. The fundamentals section includes isotropic turbulence and anistropic turbulence, turbulent flow dynamics, free shear layers, turbulent boundary layers and plumes. The modeling section focuses on topics such as eddy viscosity models, standard K-E Models, Direct Numerical Stimulation, Large Eddy Simulation, and their applications. The measurement of turbulent fluctuations experiments in isothermal and stratified turbulent flows are explored in the experimental methods section. Special topics include modeling of near wall turbulent flows, compressible turbulent flows, and more.

Download or read book Fundamental Mechanics of Fluids Third Edition written by Iain G. Currie and published by CRC Press. This book was released on 2002-12-12 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: Retaining the features that made previous editions perennial favorites, Fundamental Mechanics of Fluids, Third Edition illustrates basic equations and strategies used to analyze fluid dynamics, mechanisms, and behavior, and offers solutions to fluid flow dilemmas encountered in common engineering applications. The new edition contains completely reworked line drawings, revised problems, and extended end-of-chapter questions for clarification and expansion of key concepts. Includes appendices summarizing vectors, tensors, complex variables, and governing equations in common coordinate systems Comprehensive in scope and breadth, the Third Edition of Fundamental Mechanics of Fluids discusses: Continuity, mass, momentum, and energy One-, two-, and three-dimensional flows Low Reynolds number solutions Buoyancy-driven flows Boundary layer theory Flow measurement Surface waves Shock waves

Download or read book Turbulent Flow written by Peter S. Bernard and published by John Wiley & Sons. This book was released on 2002-08-19 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides unique coverage of the prediction and experimentation necessary for making predictions. * Covers computational fluid dynamics and its relationship to direct numerical simulation used throughout the industry. * Covers vortex methods developed to calculate and evaluate turbulent flows. * Includes chapters on the state-of-the-art applications of research such as control of turbulence.

Download or read book Analysis of Turbulent Flows with Computer Programs written by Tuncer Cebeci and published by Butterworth-Heinemann. This book was released on 2013-02-26 with total page 465 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analysis of Turbulent Flows is written by one of the most prolific authors in the field of CFD. Professor of Aerodynamics at SUPAERO and Director of DMAE at ONERA, Professor Tuncer Cebeci calls on both his academic and industrial experience when presenting this work. Each chapter has been specifically constructed to provide a comprehensive overview of turbulent flow and its measurement. Analysis of Turbulent Flows serves as an advanced textbook for PhD candidates working in the field of CFD and is essential reading for researchers, practitioners in industry and MSc and MEng students. The field of CFD is strongly represented by the following corporate organizations: Boeing, Airbus, Thales, United Technologies and General Electric. Government bodies and academic institutions also have a strong interest in this exciting field. An overview of the development and application of computational fluid dynamics (CFD), with real applications to industry Contains a unique section on short-cut methods – simple approaches to practical engineering problems

Download or read book Statistical Theory and Modeling for Turbulent Flows written by P. A. Durbin and published by Wiley. This book was released on 2001-02-27 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most natural and industrial flows are turbulent. The atmosphere and oceans, automobile and aircraft engines, all provide examples of this ubiquitous phenomenon. In recent years, turbulence has become a very lively area of scientific research and application, and this work offers a grounding in the subject of turbulence, developing both the physical insight and the mathematical framework needed to express the theory. Providing a solid foundation in the key topics in turbulence, this valuable reference resource enables the reader to become a knowledgeable developer of predictive tools. This central and broad ranging topic would be of interest to graduate students in a broad range of subjects, including aeronautical and mechanical engineering, applied mathematics and the physical sciences. The accompanying solutions manual to the text also makes this a valuable teaching tool for lecturers and for practising engineers and scientists in computational and experimental and experimental fluid dynamics.

Download or read book Atmospheric Modeling Data Assimilation and Predictability written by Eugenia Kalnay and published by Cambridge University Press. This book was released on 2003 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, first published in 2002, is a graduate-level text on numerical weather prediction, including atmospheric modeling, data assimilation and predictability.

Download or read book Data driven Approach for Turbulence Modeling in Rotating Flows and Stratified Flows written by Xinyi Huang and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Turbulence modeling, including wall models in large-eddy simulations (LESs) and RANS models in Reynolds-averaged Navier-Stokes (RANS) simulations, is usually not considered for non-canonical flows, including rotating flows and stratified flows. Modeling non-canonical flows encounters difficulties. Some of the main difficulties lie in the fact that these flows have multiple flow controlling parameters (FCPs), and thus, the flow behavior is hard to explore, let alone get accurate modeling. The data-driven approach is considered a possible solution to this. The increasing computational resources and shared turbulence data allow another way to utilize the data other than pure human analyses of the physics. However, pure data-driven methods are often criticized for their weak interpretability and generalizability. In this work, multiple data-driven techniques are applied to some persistent problems in turbulence modeling under the circumstances of rotating flows and stratified flows. The problems include not only the accurate modeling of the flow but also the efficient FCP space exploration, model selection, uncertainty quantification, etc. Both the dataset and existing knowledge of physics are utilized, and then data-driven approach shows the interpretability and generalizability. They show how these traditionally difficult problems can be tackled through physics-informed data-driven approach, which significantly saves human labor. To be more specific, data-driven approach to wall modeling is compared to physics-based approach for a spanwise rotating channel, and it shows a more accurate yet still generalizable behavior. When modeling is extended to an arbitrarily directional rotating channel, a surrogate model is efficiently developed through the utilization of Bayesian optimization, when such behavior is never understood in the existing literature. Data-driven approach is also applied to RANS modeling. The diverse modeling makes model selection awkward for a newbie, and we train a recommender system to provide guidelines. Modeling itself for non-canonical cases, e.g., stratified flows, is also troublesome, because the multi-stage behavior of the flow requires automated switching of modeling between different models as the flow develops. A linear logistic regression is developed for automating the classification. The models can then be evaluated through a global epistemic uncertainty quantification (UQ) method, which allows the exploration of dominating terms in a RANS model and determining a priori if a calibration can generalize to other flow conditions. In general, data-driven approach has been used for multiple applications in turbulence modeling, and they show their capability and interpretability.

Download or read book Data driven and Physics constrained Uncertainty Quantification for Turbulence Models written by Jan Felix Heyse and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Numerical simulations are an important tool for prediction of turbulent flows. Today, most simulations in real-world applications are Reynolds-averaged Navier-Stokes (RANS) simulations, which average the governing equations to solve for the mean flow quantities. RANS simulations require modeling of an unknown quantity, the Reynolds stress tensor, using turbulence models. These models are limited in their accuracy for many complex flows, such as those involving strong stream-line curvature or adverse pressure gradients, making RANS predictions less reliable for design decisions. For RANS predictions to be useful in engineering design practice, it is therefore important to quantify the uncertainty in the predictions. More specifically, in this dissertation the focus is on quantifying the model-form uncertainty associated with the turbulence model. A data-free eigenperturbation framework introduced in the past few years, allows to make quantitative uncertainty estimates for all quantities of interest. It relies on a linear mapping from the eigenvalues of the Reynolds stress into the barycentric domain. In this framework, perturbations are added to the eigenvalues in that barycentric domain by perturbing them towards limiting states of 1 component, 2 component, and 3 component turbulence. Eigenvectors are permuted to find the extreme states of the turbulence kinetic energy production term. These eigenperturbations allow to explore a range of shapes and alignments of the Reynolds stress tensor within constraints of physical realizability of the resulting Reynolds stresses. However, this framework is limited by the introduction of a uniform amount of perturbation throughout the domain and by the need to specify a parameter governing the amount of perturbation. Data-driven eigenvalue perturbations are therefore introduced in this work to address those limitations. They are built on the eigenperturbation framework, but use a data-driven approach to determine how much perturbation to impose locally at every cell. The target amount of perturbation is the expected distance between the RANS prediction and the true solution in the barycentric domain. A general set of features is introduced, computed from the RANS mean flow quantities. The periodic flow over a wavy wall (for which also a detailed high-fidelity simulation dataset is available) serves as training case. A random forest machine learning model is trained to predict the target distance from the features. A hyperparameter study is carried out to find the most appropriate hyperparameters for the random forest. Random forest feature importance estimates confirm general expectations from physical intuition. The framework is applied to two test cases, the flow over a backward-facing step and the flow in an asymmetric diffuser. Both test cases and the training case exhibit a flow separation where the cross sectional area increases. The distribution of key features is studied for these cases and compared against the one from the training case. It is found that the random forest is not extrapolating. The results on the two test cases show uncertainty estimates that are characteristic of the true error in the predictions and give more representative bounds than the data-free framework does. The sets of eigenvectors from the RANS prediction and the true solution can be connected through a rotation. The idea of data-driven eigenvector rotations as a data-driven extension to the eigenvectors is studied. However, continuousness of the prediction targets is not generally achievable because of the ambiguity of the eigenvector direction. The lack of smoothness prevents the machine learning models from learning the relationship between the features and the targets, making data-driven eigenvector rotations in the discussed setup not practical. The last chapter of this dissertation introduces a data-driven baseline simulation, which corresponds to the expected value in the data-driven eigenvalue perturbation framework. The Reynolds stress is a weighted sum of the Reynolds stresses from the extreme states. A random classification forest trained to predict which extreme state is closest to the true Reynolds stress is used to compute these weights. It does so by giving a probabilistic meaning to the raw predictions of the constituent decision trees. On the test cases, the data-driven baseline predictions are similar but not equal to the data-free baseline. They complement the uncertainty estimates from the data-driven eigenvalue perturbations.

Download or read book Modeling Complex Turbulent Flows written by Manuel D. Salas and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Turbulence modeling both addresses a fundamental problem in physics, 'the last great unsolved problem of classical physics,' and has far-reaching importance in the solution of difficult practical problems from aeronautical engineering to dynamic meteorology. However, the growth of supercom puter facilities has recently caused an apparent shift in the focus of tur bulence research from modeling to direct numerical simulation (DNS) and large eddy simulation (LES). This shift in emphasis comes at a time when claims are being made in the world around us that scientific analysis itself will shortly be transformed or replaced by a more powerful 'paradigm' based on massive computations and sophisticated visualization. Although this viewpoint has not lacked ar ticulate and influential advocates, these claims can at best only be judged premature. After all, as one computational researcher lamented, 'the com puter only does what I tell it to do, and not what I want it to do. ' In turbulence research, the initial speculation that computational meth ods would replace not only model-based computations but even experimen tal measurements, have not come close to fulfillment. It is becoming clear that computational methods and model development are equal partners in turbulence research: DNS and LES remain valuable tools for suggesting and validating models, while turbulence models continue to be the preferred tool for practical computations. We believed that a symposium which would reaffirm the practical and scientific importance of turbulence modeling was both necessary and timely.

Download or read book Model Reduction of Parametrized Systems written by Peter Benner and published by Springer. This book was released on 2017-09-05 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: The special volume offers a global guide to new concepts and approaches concerning the following topics: reduced basis methods, proper orthogonal decomposition, proper generalized decomposition, approximation theory related to model reduction, learning theory and compressed sensing, stochastic and high-dimensional problems, system-theoretic methods, nonlinear model reduction, reduction of coupled problems/multiphysics, optimization and optimal control, state estimation and control, reduced order models and domain decomposition methods, Krylov-subspace and interpolatory methods, and applications to real industrial and complex problems. The book represents the state of the art in the development of reduced order methods. It contains contributions from internationally respected experts, guaranteeing a wide range of expertise and topics. Further, it reflects an important effor t, carried out over the last 12 years, to build a growing research community in this field. Though not a textbook, some of the chapters can be used as reference materials or lecture notes for classes and tutorials (doctoral schools, master classes).