Download or read book Data driven and Nonlocal Approaches in Modeling Analysis and Simulation of Turbulent Mixing Phenomena written by Ali Akhavan Safaei and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The overreaching goal of this study is utilizing data-driven methods and sophisticated mathematical tools for modeling and simulation of turbulent transport of passive scalars. We focus on embedding the intrinsic nonlocal nature of the turbulence into our models. We study the nonlocal dynamics in the context of (i) subgrid-scale (SGS) modeling for largeeddy simulation (LES), and (ii) the turbulent cascade under large-scale anisotropic sources. Moreover, we implement stochastic modeling methodologies to systematically investigate the contributing mechanisms leading a high-speed hydrodynamic transport system into instability and chaos, as well as discovering the anomalies in the featured characteristics of the transport.First, we present a computational-statistical framework to obtain high-fidelity data for homogeneous isotropic turbulent (HIT) flow and passive scalar transport. A parallel implementation of the well-known pseudo-spectral method in addition to the comprehensive record of the statistical and small-scale quantities of the turbulent transport are offered for executing on distributed memory CPU-based supercomputers.Afterwards, we investigate the inherent nonlocal behavior of the SGS passive scalar flux through studying its two-point statistics obtained from the filtered direct numerical simulation (DNS) data for passive scalar transport in HIT flow. We propose a statistical model for microscopic SGS motions by considering the filtered Boltzmann transport equation (FBTE) for passive scalar. In FBTE, we approximate the filtered equilibrium distribution with an Îł-stable Levy distribution that incorporates a power-law behavior to resemble the observed nonlocal statistics of SGS scalar flux. Through generic ensemble-averaging of FBTE, we formulate a continuum-level closure model for the SGS scalar flux appearing in terms of a fractional-order Laplacian that is a nonlocal operator.Moreover, we revisit the spectral transfer model for the turbulent intensity in the passive scalar transport (under large-scale anisotropic forcing), and a subsequent modification to the scaling of scalar variance cascade is presented. Accordingly, we obtain a revised scalar transport model using fractional-order Laplacian operator that facilitates the robust inclusion of the nonlocal effects originated from large-scale anisotropy transferred across the multitude of scales in the turbulent cascade. We provide an a priori estimate for the nonlocal model, and examine the model through a new DNS. We conduct a detailed analysis on the evolution of the scalar variance, high-order statistics of scalar gradient, and two-point statistical metrics of the turbulent transport to compare the developed nonlocal model and its standard version.In another study, a deep learning surrogate model in the form of fully connected feedforward neural networks is developed to predict the SGS scalar flux in the context of large eddy simulation of turbulent transport. The deep neural network (DNN) model is trained and validated using filtered DNS dataset at P eλ = 240, Sc = 1 that includes the filtered scalar and velocity gradients as input features. Using the transfer learning concept, we generalize the performance of this trained model to turbulent scalar transport regimes with higher P eλ and Sc numbers with a relatively low amount of data and computations.Finally, in stochastic modeling of hydrodynamic transport, we study the flow dynamics inside a high-speed rotating cylinder after introducing strong symmetry-breaking disturbance factors at cylinder wall motion. We perform a statistical analysis on the fluctuating fields characterizing the fingerprints and measures of intense and rapidly evolving non-Gaussian behavior through space and time. Such non-Gaussian statistics essentially emerge and evolve due to an intensified presence of coherent vortical motions initially triggered by the flow instability due to symmetry-breaking rotation of the cylinder. We show that this mechanism causes significant memory effects in the flow so that noticeable anomaly in the time-scaling of enstrophy record is observed in the long run apart from the onset of instability.

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 Turbulence Modelling Approaches written by Konstantin Volkov and published by BoD – Books on Demand. This book was released on 2017-07-26 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurate prediction of turbulent flows remains a challenging task despite considerable work in this area and the acceptance of CFD as a design tool. The quality of the CFD calculations of the flows in engineering applications strongly depends on the proper prediction of turbulence phenomena. Investigations of flow instability, heat transfer, skin friction, secondary flows, flow separation, and reattachment effects demand a reliable modelling and simulation of the turbulence, reliable methods, accurate programming, and robust working practices. The current scientific status of simulation of turbulent flows as well as some advances in computational techniques and practical applications of turbulence research is reviewed and considered in the book.

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 Coarse Grained Simulation and Turbulent Mixing written by Fernando F. Grinstein and published by Cambridge University Press. This book was released on 2016-06-30 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: Small-scale turbulent flow dynamics is traditionally viewed as universal and as enslaved to that of larger scales. In coarse grained simulation (CGS), large energy-containing structures are resolved, smaller structures are spatially filtered out, and unresolved subgrid scale (SGS) effects are modeled. Coarse Grained Simulation and Turbulent Mixing reviews our understanding of CGS. Beginning with an introduction to the fundamental theory the discussion then moves to the crucial challenges of predictability. Next, it addresses verification and validation, the primary means of assessing accuracy and reliability of numerical simulation. The final part reports on the progress made in addressing difficult non-equilibrium applications of timely current interest involving variable density turbulent mixing. The book will be of fundamental interest to graduate students, research scientists, and professionals involved in the design and analysis of complex turbulent flows.

Download or read book Modeling and Simulation of Turbulent Mixing and Reaction written by Daniel Livescu and published by Springer Nature. This book was released on 2020-02-19 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights recent research advances in the area of turbulent flows from both industry and academia for applications in the area of Aerospace and Mechanical engineering. Contributions include modeling, simulations and experiments meant for researchers, professionals and students in the area.

Download or read book Modeling Approaches and Computational Methods for Particle laden Turbulent Flows written by Shankar Subramaniam and published by Academic Press. This book was released on 2022-10-20 with total page 588 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modelling Approaches and Computational Methods for Particle-laden Turbulent Flows introduces the principal phenomena observed in applications where turbulence in particle-laden flow is encountered while also analyzing the main methods for analyzing numerically. The book takes a practical approach, providing advice on how to select and apply the correct model or tool by drawing on the latest research. Sections provide scales of particle-laden turbulence and the principal analytical frameworks and computational approaches used to simulate particles in turbulent flow. Each chapter opens with a section on fundamental concepts and theory before describing the applications of the modelling approach or numerical method. Featuring explanations of key concepts, definitions, and fundamental physics and equations, as well as recent research advances and detailed simulation methods, this book is the ideal starting point for students new to this subject, as well as an essential reference for experienced researchers. Provides a comprehensive introduction to the phenomena of particle laden turbulent flow Explains a wide range of numerical methods, including Eulerian-Eulerian, Eulerian-Lagrange, and volume-filtered computation Describes a wide range of innovative applications of these models

Download or read book Modeling and Simulation of Turbulent Flows written by Roland Schiestel and published by John Wiley & Sons. This book was released on 2010-01-05 with total page 751 pages. Available in PDF, EPUB and Kindle. Book excerpt: This title provides the fundamental bases for developing turbulence models on rational grounds. The main different methods of approach are considered, ranging from statistical modelling at various degrees of complexity to numerical simulations of turbulence. Each of these various methods has its own specific performances and limitations, which appear to be complementary rather than competitive. After a discussion of the basic concepts, mathematical tools and methods for closure, the book considers second order closure models. Emphasis is placed upon this approach because it embodies potentials for clarifying numerous problems in turbulent shear flows. Simpler, generally older models are then presented as simplified versions of the more general second order models. The influence of extra physical parameters is also considered. Finally, the book concludes by examining large Eddy numerical simulations methods. Given the book’s comprehensive coverage, those involved in the theoretical or practical study of turbulence problems in fluids will find this a useful and informative read.

Download or read book Simulation and Modeling of Compressible Turbulent Mixing Layer written by Seyed Navid Samadi Vaghefi and published by . This book was released on 2014 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: Direct numerical simulations (DNS) of compressible turbulent mixing layer are performed for subsonic to supersonic Mach numbers. Each simulation achieves the self-similar state and it is shown that the turbulent statistics during this state agree well with previous numerical and experimental works. The DNS data is used to extract the physics of compressible turbulence and to perform a priori analysis for subgrid scale (SGS) closures. The flow dynamics in proximity of the turbulent/non-turbulent interface (TNTI), separating the turbulent and the irrotational regions, is analyzed using the DNS data. This interface is detected by using a certain threshold for the vorticity norm. The conditional flow statistics based on the normal distance from the TNTI are compared for different convective Mach numbers. It is shown that the thickness of the interface layer is approximately one Taylor length scale for both incompressible and compressible mixing layers, and the flow dynamics in this layer differs from deep inside the turbulent region. Various terms in the transport equations for total kinetic energy, turbulent kinetic energy, and vorticity are examined in order to better understand the transport mechanisms across the TNTI in compressible flows. The DNS data is also employed to analyze the local flow topology in compressible mixing layers using the invariants of the velocity gradient tensor. The topological and dissipating behaviors of the flow are analyzed in two different regions: near the TNTI, and inside the turbulent region. It is found that the distribution of various flow topologies in regions close to the TNTI differs from inside the turbulent region, and in these regions the most probable topologies are non-focal. The occurrence probability of different flow topologies conditioned by the dilatation level is presented and it is shown that the structures in the locally compressed regions tend to have stable topologies while in locally expanded regions the unstable topologies are prevalent. In order to better understand the behavior of different flow topologies, the probability distributions of vorticity norm, dissipation, and rate of stretching are analyzed in incompressible, compressed and expanded regions. The DNS data is also used to perform a priori analysis for subgrid scale (SGS) viscous and scalar closures. Several models for each closure are tested and effects of filter width, compressibility level, and Schmidt number on their performance are studied. A new model for SGS viscous dissipation is proposed based on the scaling of SGS kinetic energy. The proposed model yields the best prediction of SGS viscous dissipation among the considered models for filter widths corresponding to the inertial range. For the range of Mach numbers and Schmidt numbers studied in this work, the SGS scalar dissipation model based on proportionality of turbulent time scale and scalar mixing time scale produces the best results in the filter widths corresponding to the inertial subrange. For both viscous and scalar SGS dissipation models, two dynamic approaches are used to compute the model coefficient. It is shown that if the dynamic procedure based on global equilibrium of dissipation and production is employed, more accurate results are generated compared to the conventional dynamic method based on test-filtering.

Download or read book Simulation of Turbulent Flows with and without Combustion with Emphasis on the Impact of Coherent Structures on the Turbulent Mixing written by Cunha Galeazzo, Flavio Cesar and published by KIT Scientific Publishing. This book was released on 2016-10-14 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: The analysis of turbulent mixing in complex turbulent flows is a challenging task. The effective mixing of entrained fluids to a molecular level is a vital part of the dynamics of turbulent flows, especially when combustion is involved. The work has shown the limitations of the steady-state simulations and acknowledged the need of applying high-fidelity unsteady methods for the calculation of flows with pronounced unsteadiness promoted by large-scale coherent structures or other sources.

Download or read book Turbulent Mixing in Nonreactive and Reactive Flows written by S. Murthy and published by Springer. This book was released on 2012-07-08 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: Turbulence, mixing and the mutual interaction of turbulence and chemistry continue to remain perplexing and impregnable in the fron tiers of fluid mechanics. The past ten years have brought enormous advances in computers and computational techniques on the one hand and in measurements and data processing on the other. The impact of such capabilities has led to a revolution both in the understanding of the structure of turbulence as well as in the predictive methods for application in technology. The early ideas on turbulence being an array of complicated phenomena and having some form of reasonably strong coherent struc ture have become well substantiated in recent experimental work. We are still at the very beginning of understanding all of the aspects of such coherence and of the possibilities of incorporating such structure into the analytical models for even those cases where the thin shear layer approximation may be valid. Nevertheless a distinguished body of "eddy chasers" has come into existence. The structure of mixing layers which has been studied for some years in terms of correlations and spectral analysis is also getting better understood. Both probability concepts such as intermittency and conditional sampling as well as the concept of large scale structure and the associated strain seem to indicate possibilities of distinguishing and synthesizing 'engulfment' and molecular mixing.

Download or read book Approximate Deconvolution Models of Turbulence written by William J. Layton and published by Springer. This book was released on 2012-01-29 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents a mathematical development of a recent approach to the modeling and simulation of turbulent flows based on methods for the approximate solution of inverse problems. The resulting Approximate Deconvolution Models or ADMs have some advantages over more commonly used turbulence models – as well as some disadvantages. Our goal in this book is to provide a clear and complete mathematical development of ADMs, while pointing out the difficulties that remain. In order to do so, we present the analytical theory of ADMs, along with its connections, motivations and complements in the phenomenology of and algorithms for ADMs.

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 Uncertainty Quantification for Turbulent Mixing Simulations written by and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We have achieved validation in the form of simulation-experiment agreement for Rayleigh-Taylor turbulent mixing rates (known as?) over the past decade. The problem was first posed sixty years ago. Recent improvements in our simulation technology allow sufficient precision to distinguish between mixing rates for different experiments. We explain the sensitivity and non-universality of the mixing rate. These playa role in the difficulties experienced by many others in efforts to compare experiment with simulation. We analyze the role of initial conditions, which were not recorded for the classical experiments of Youngs et al. Reconstructed initial conditions with error bars are given. The time evolution of the long and short wave length portions of the instability are analyzed. We show that long wave length perturbations are strong at t = 0, but are quickly overcome by the rapidly growing short wave length perturbations. These conclusions, based solely on experimental data analysis, are in agreement with results from theoretical bubble merger models and numerical simulation studies but disagree with models based on superposition of modes.

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 Investigating and Modeling Turbulence Using Numerical Simulations written by Prakash Mohan (Ph. D.) and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Turbulence is a complex fluid phenomenon that is present in high Reynolds number flows. It has a profound effect on the flows in which it occurs, and it is therefore important to understand and model its effects. It occurs in multiple domains from flows inside our bodies to ocean currents and atmospheric winds. The difficulty in modeling and simulating turbulence arises from the fact that it is comprised of a wide range of scales that interact with each other non-linearly. The field of turbulence still has many open problems — from fundamental questions about the underlying physics to enabling tractable engineering models. The Navier-Stokes equations are a reliable representation of turbulent flows and solving them with sufficient accuracy gives us the detailed turbulent flow field. These are called Direct Numerical Simulations (DNS) and are an invaluable tool to study the turbulence phenomenon. In this work, we first consider how DNS of forced isotropic turbulence can be used to study time predictability of turbulence using Lyapunov exponents. Further analysis of the DNS field shows that flow instabilities act on the smallest eddies, and that at any time, there are many sites of local instabilities. DNS, however, is generally too expensive for simulating practical flows. Alternatively, Large Eddy Simulations (LES), in which only the largest scales of turbulent motion are simulated, is more promising as an engineering tool. However, in the near-wall region the large, dynamically important eddies are on the order of viscous scales, which makes resolving them very expensive. It is therefore desirable to formulate an approach in which the near-wall region is modeled, leading to the so-called wall-modeled LES. Spectral analysis of DNS data indicates that thin-film type asymptotics is a promising approach to model the interactions between the near-wall layer and the outer flow. For this approach an asymptotic analysis of the filtered Navier-Stokes equations is pursued in the limit in which the horizontal filter scale is large compared to the thickness of the wall layer. In the second part of this work, we present a new wall model formulated using the asymptotic analysis and insights from DNS data

Download or read book Eigenfunction Analysis of Turbulent Mixing Phenomena written by M. Winter and published by . This book was released on 1991 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: