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Quantification Of Structural Uncertainties In Rans Turbulence Models
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Book Synopsis Quantification of Structural Uncertainties in RANS Turbulence Models by : Eric Alexander Dow
Download or read book Quantification of Structural Uncertainties in RANS Turbulence Models written by Eric Alexander Dow and published by . This book was released on 2011 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents an approach for building a statistical model for the structural uncertainties in Reynolds averaged Navier-Stokes (RANS) turbulence models. This approach solves an inference problem by comparing the results of RANS calculations to direct numerical simulation. The adjoint method is used to efficiently solve an inverse problem to determine the RANS turbulent viscosity field that most accurately reproduces the mean flow field computed by direct numerical simulation. The discrepancy between the inferred turbulent viscosity and the turbulent viscosity predicted by RANS is modeled as a Gaussian random field. Finally, the uncertainty in the turbulent viscosity field is propagated to the quantities of interest. Results are first presented for turbulent flow through a straight channel. To model the uncertainty in more complex flows, the procedure is repeated for a collection of flows through randomly generated geometries.
Book Synopsis Physics-based Uncertainty Quantification of Reynolds-averaged-navier-stokes Models for Turbulent Flows and Scalar Transport by : Zengrong Hao
Download or read book Physics-based Uncertainty Quantification of Reynolds-averaged-navier-stokes Models for Turbulent Flows and Scalar Transport written by Zengrong Hao and published by . This book was released on 2020 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: Numerical simulations for turbulent flows and scalar (e.g. temperature, concentration and humidity) transport is one of the most challenging topics in urban wind engineering. For the design and optimization of configurations in cities, the Reynolds-averaged-Navier-Stokes (RANS) method for turbulence modeling has evident superiority over the turbulence-resolving methods (e.g. directly-numerical-simulation (DNS), large-eddy-simulation (LES), or RANS-LES hybrid approaches) in terms of efficiency and robustness. However, because "all models are wrong" (Box (1976)), the predictions of a RANS simulation always have uncertainties that originate in the inherent inadequacies of various physical hypotheses in the RANS models. To quantify these model uncertainties is not only significant for improving the practicability of RANS method in wind engineering, but also potentially help us understand the physics of turbulence in a broader sense. The objective of this thesis is to develop physics-based, data-free methods for RANS model uncertainty quantification (UQ) in engineering turbulent flows and scalar transport. These UQ methods are expected to estimate the appropriate bounds of quantities of interest (QoIs) at the cost of O(10) or fewer individual steady RANS simulations without any a priori data. The development of each method generally follows two principles: i) relaxing a well-established baseline model to address some inherent inadequacies in its physical assumptions; and ii) perturbing the released degrees-of-freedom (DOFs) based on some conceptual "limiting conditions" in physics. The studies of UQ methodologies in this thesis are divided into four separate parts as follows, of which Parts I and II are on the models for Reynolds stress, and Parts III and IV on the models for scalar flux. Part I addresses the uncertainty in the linear-eddy-viscosity (LEV) assumption that results in incorrect shape and orientation of Reynolds stress. This part directly applies the method previously proposed by Emory et al. (2013) and Gorle et al. (2012), named Reynolds-stress-shape-perturbation (RSSP), to examine its bounding behaviors for QoIs in complex problems. The investigation reveals that the RSSP method's incapability in bounding the turbulence-related QoIs in separation and backflow regions essentially does not originates in the LEV assumption but in the dissipation determination. Part II proposes the double-scale double-LEV (DSDL) model to address the uncertainty in the energy dissipation determination, which specifically overpredicts the dissipation rates in the turbulence with vortex shedding behind bluff bodies. The model uncertainty is represented by one or two uncertain parameters that roughly indicate the intensity of the interaction between coherent structures and stochastic turbulence. The applications of the DSDL model in several problems show promising performance in terms of bounding the turbulent energies behind bluff bodies and meanwhile maintaining appropriate mean-flow predictions. Part III proposes the one-equation (OE) method to quantify the uncertainty in scalar flux models. The method is designed from the perspective of ordinary vector field, aiming at optimizing the local productions of scalar flux magnitudes. It shows some favorable bounding behaviors for scalar-related QoIs, although the ignorance of uncertainty in the modeled pressure-scrambling effect limits its performance to some extent. Alternative to OE, Part IV proposes the pressure-scrambling-perturbation (PSP) method for scalar flux model UQ by addressing the uncertainty in the pressure-scrambling effect in scalar flux dynamics. It is based on two conceptual "limits" for the pressure-scrambling directions indicated by two classical phenomenological theories. The PSP method exhibits superior bounding behaviors over the OE method for the cases in this thesis. The works in this thesis are expected to contribute to the physical foundations of both the data-free and data-driven approaches for RANS model UQ.
Book Synopsis Quantification of Modelling Uncertainties in Turbulent Flow Simulations by : Wouter Nico Edeling
Download or read book Quantification of Modelling Uncertainties in Turbulent Flow Simulations written by Wouter Nico Edeling and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of this thesis is to make predictive simulations with Reynolds-Averaged Navier-Stokes (RANS) turbulence models, i.e. simulations with a systematic treatment of model and data uncertainties and their propagation through a computational model to produce predictions of quantities of interest with quantified uncertainty. To do so, we make use of the robust Bayesian statistical framework.The first step toward our goal concerned obtaining estimates for the error in RANS simulations based on the Launder-Sharma k-e turbulence closure model, for a limited class of flows. In particular we searched for estimates grounded in uncertainties in the space of model closure coefficients, for wall-bounded flows at a variety of favourable and adverse pressure gradients. In order to estimate the spread of closure coefficients which reproduces these flows accurately, we performed 13 separate Bayesian calibrations. Each calibration was at a different pressure gradient, using measured boundary-layer velocity profiles, and a statistical model containing a multiplicative model inadequacy term in the solution space. The results are 13 joint posterior distributions over coefficients and hyper-parameters. To summarize this information we compute Highest Posterior-Density (HPD) intervals, and subsequently represent the total solution uncertainty with a probability box (p-box). This p-box represents both parameter variability across flows, and epistemic uncertainty within each calibration. A prediction of a new boundary-layer flow is made with uncertainty bars generated from this uncertainty information, and the resulting error estimate is shown to be consistent with measurement data.However, although consistent with the data, the obtained error estimates were very large. This is due to the fact that a p-box constitutes a unweighted prediction. To improve upon this, we developed another approach still based on variability in model closure coefficients across multiple flow scenarios, but also across multiple closure models. The variability is again estimated using Bayesian calibration against experimental data for each scenario, but now Bayesian Model-Scenario Averaging (BMSA) is used to collate the resulting posteriors in an unmeasured (prediction) scenario. Unlike the p-boxes, this is a weighted approach involving turbulence model probabilities which are determined from the calibration data. The methodology was applied to the class of turbulent boundary-layers subject to various pressure gradients. For all considered prediction scenarios the standard-deviation of the stochastic estimate is consistent with the measurement ground truth.The BMSA approach results in reasonable error bars, which can also be decomposed into separate contributions. However, to apply it to more complex topologies outside the class of boundary-layer flows, surrogate modelling techniques must be applied. The Simplex-Stochastic Collocation (SSC) method is a robust surrogate modelling technique used to propagate uncertain input distributions through a computer code. However, its use of the Delaunay triangulation can become prohibitively expensive for problems with dimensions higher than 5. We therefore investigated means to improve upon this bad scalability. In order to do so, we first proposed an alternative interpolation stencil technique based upon the Set-Covering problem, which resulted in a significant speed up when sampling the full-dimensional stochastic space. Secondly, we integrated the SSC method into the High-Dimensional Model-Reduction framework in order to avoid sampling high-dimensional spaces all together.Finally, with the use of our efficient surrogate modelling technique, we applied the BMSA framework to the transonic flow over an airfoil. With this we are able to make predictive simulations of computationally expensive flow problems with quantified uncertainty due to various imperfections in the turbulence models.
Book Synopsis High Performance Computing by : Heike Jagode
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.
Book Synopsis Uncertainty Quantification In Computational Science: Theory And Application In Fluids And Structural Mechanics by : Sunetra Sarkar
Download or read book Uncertainty Quantification In Computational Science: Theory And Application In Fluids And Structural Mechanics written by Sunetra Sarkar and published by World Scientific. This book was released on 2016-08-18 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the last decade, research in Uncertainty Quantification (UC) has received a tremendous boost, in fluid engineering and coupled structural-fluids systems. New algorithms and adaptive variants have also emerged.This timely compendium overviews in detail the current state of the art of the field, including advances in structural engineering, along with the recent focus on fluids and coupled systems. Such a strong compilation of these vibrant research areas will certainly be an inspirational reference material for the scientific community.
Book Synopsis Data-driven and Physics-constrained Uncertainty Quantification for Turbulence Models by : Jan Felix Heyse
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.
Book Synopsis Shock Wave-Boundary-Layer Interactions by : Holger Babinsky
Download or read book Shock Wave-Boundary-Layer Interactions written by Holger Babinsky and published by Cambridge University Press. This book was released on 2011-09-12 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: Shock wave-boundary-layer interaction (SBLI) is a fundamental phenomenon in gas dynamics that is observed in many practical situations, ranging from transonic aircraft wings to hypersonic vehicles and engines. SBLIs have the potential to pose serious problems in a flowfield; hence they often prove to be a critical - or even design limiting - issue for many aerospace applications. This is the first book devoted solely to a comprehensive, state-of-the-art explanation of this phenomenon. It includes a description of the basic fluid mechanics of SBLIs plus contributions from leading international experts who share their insight into their physics and the impact they have in practical flow situations. This book is for practitioners and graduate students in aerodynamics who wish to familiarize themselves with all aspects of SBLI flows. It is a valuable resource for specialists because it compiles experimental, computational and theoretical knowledge in one place.
Book Synopsis Uncertainty Quantification of Turbulence Model Closure Coefficients for Transonic Wall-bounded Flows by : John Anthony Schaefer
Download or read book Uncertainty Quantification of Turbulence Model Closure Coefficients for Transonic Wall-bounded Flows written by John Anthony Schaefer and published by . This book was released on 2015 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The goal of this work was to quantify the uncertainty and sensitivity of commonly used turbulence models in Reynolds-Averaged Navier-Stokes codes due to uncertainty in the values of closure coefficients for transonic, wall-bounded flows and to rank the contribution of each coefficient to uncertainty in various output flow quantities of interest. Specifically, uncertainty quantification of turbulence model closure coefficients was performed for transonic flow over an axisymmetric bump at zero degrees angle of attack and the RAE 2822 transonic airfoil at a lift coefficient of 0.744. Three turbulence models were considered: the Spalart-Allmaras Model, Wilcox (2006) [kappa]-[omega] Model, and the Menter Shear-Stress Transport Model. The FUN3D code developed by NASA Langley Research Center and the BCFD code developed by The Boeing Company were used as the flow solvers. The uncertainty quantification analysis employed stochastic expansions based on non-intrusive polynomial chaos as an efficient means of uncertainty propagation. Several integrated and point-quantities are considered as uncertain outputs for both CFD problems. All closure coefficients were treated as epistemic uncertain variables represented with intervals. Sobol indices were used to rank the relative contributions of each closure coefficient to the total uncertainty in the output quantities of interest. Two studies were performed in this work. The main study identified a number of closure coefficients for each turbulence model for which more information will reduce the amount of uncertainty in the output significantly for transonic, wall-bounded flows. A case study demonstrated that the RAE 2822 sensitivity results of the main study are independent of the flow solver and of the computational grid topology and resolution"--Abstract, page iii.
Book Synopsis Multiscale and Multiresolution Approaches in Turbulence by : Pierre Sagaut
Download or read book Multiscale and Multiresolution Approaches in Turbulence written by Pierre Sagaut and published by World Scientific. This book was released on 2013 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book aims to provide the reader with an updated general presentation of multiscale/multiresolution approaches in turbulent flow simulations. All modern approaches (LES, hybrid RANS/LES, DES, SAS) are discussed and recast in a global comprehensive framework. Both theoretical features and practical implementation details are addressed. Some full scale applications are described, to provide the reader with relevant guidelines to facilitate a future use of these methods.
Book Synopsis Quantification of Spalart-Allmaras Turbulence Modeling Uncertainties for Hypersonic Flows Utilizing Output-based Grid Adaptation by : Carter John Waligura
Download or read book Quantification of Spalart-Allmaras Turbulence Modeling Uncertainties for Hypersonic Flows Utilizing Output-based Grid Adaptation written by Carter John Waligura and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, uncertainty in the Spalart-Allmaras (SA) turbulence model with the compressible Reynolds-averaged Navier-Stokes (RANS) equations is quantified for steady non-reacting hypersonic flows using a coarse-grained uncertainty metric. Output-based adaptation is utilized to guarantee negligible numerical error with complex flow features, such as shock wave-boundary-layer interactions (SBLI). The adapted meshes are generated using MIT Solution Adaptive Numerical Simulator (SANS) software, which is able to adapt high order unstructured meshes using a modified Continuous Galerkin (CG) finite element method (FEM) discretization. The meshes are iteratively adapted by minimizing the error estimate of a given output functional, such as integrated drag or heat flux, over a boundary. The goal of the study is to quantify the expected uncertainty bounds when using the SA model with modifications to the key assumptions of a linear eddy viscosity constitutive relation and incompressible flow. The uncertainty comparison is made between specific areas of hypersonic geometries such as the pre-compression flat plate region and the post-compression shocked-wedge region of a compression corner. Ultimately, this study improves the determination of uncertainty bounds in engineering design involving turbulent flow, provides more insight into exemplary meshing practices for high-speed flow involving SBLI, and highlights where additional work is needed for the development of turbulence models in the hypersonic regime.
Book Synopsis Numerical Prediction of Flow, Heat Transfer, Turbulence and Combustion by : D. Brian Spalding
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.
Book Synopsis Uncertainty Quantification of Turbulence Model Closure Coefficients in OpenFOAM and Fluent for Mildly Separated Flows by : Isaac Russell Witte
Download or read book Uncertainty Quantification of Turbulence Model Closure Coefficients in OpenFOAM and Fluent for Mildly Separated Flows written by Isaac Russell Witte and published by . This book was released on 2017 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, detailed uncertainty quantification studies focusing on the closure coefficients of eddy-viscosity turbulence models for several flows using two CFD solvers have been performed. Three eddy viscosity turbulence models considered are: the one-equation Spalart-Allmaras (SA) model, the two-equation Shear Stress Transport (SST) k-[omega] model, and the one-equation Wray-Agarwal (WA) model. OpenFOAM and ANSYS Fluent are used as flow solvers. Uncertainty quantification analyses are performed for subsonic flow over a flat plate, subsonic flow over a backward-facing step, and transonic flow past an axisymmetric bump. In the case of flat plate, coefficients of pressure, lift, drag, and skin friction are considered to be the output quantities of interest. In case of the backward-facing step, these quantities are considered along with the separation bubble size. In case of an axisymmetric transonic bump, the drag coefficient, lift coefficient, separation point and reattachment point are considered. In addition to these four quantities, global uncertainty is employed on every node in the flow for Reynolds shear stress to determine which areas of the flow the closure coefficients contribute most to the uncertainty. Uncertainty quantification is conducted using DAKOTA developed by Sandia National Laboratories using stochastic expansions based on non-intrusive polynomial chaos. All closure xii coefficients are treated as epistemic uncertain variables, each defined by a specified range. The influence of the closure coefficients on output quantities is assessed using the global sensitivity analysis based on variance decomposition. This yields Sobol indices which are used to rank the contributions of each constant. A comparison of the Sobol indices between the turbulence models, flow cases, and flow solvers is conducted. This research identifies closure coefficients for each turbulence model that contribute significantly to uncertainty in the model predictions; this information can then be used to improve the prediction capability of the models in separated flow region by a more judicious choice of the closure coefficients.
Book Synopsis Multiscale and Multiresolution Approaches in Turbulence by : Pierre Sagaut
Download or read book Multiscale and Multiresolution Approaches in Turbulence written by Pierre Sagaut and published by Imperial College Press. This book was released on 2006 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unique book gives a general unified presentation of the use of the multiscale/multiresolution approaches in the field of turbulence. The coverage ranges from statistical models developed for engineering purposes to multiresolution algorithms for the direct computation of turbulence. It provides the only available up-to-date reviews dealing with the latest and most advanced turbulence models (including LES, VLES, hybrid RANS/LES, DES) and numerical strategies. The book aims at providing the reader with a comprehensive description of modern strategies for turbulent flow simulation, ranging from turbulence modeling to the most advanced multilevel numerical methods. Sample Chapter(s). Chapter 1: A Brief Introduction to Turbulence (4,125 KB). Contents: A Brief Introduction to Turbulence; Turbulence Simulation and Scale Separation; Statistical Multiscale Modeling; Multiscale Subgrid Models: Self-Adaptivity; Structural Multiscale Subgrid Models: Small Scale Estimations; Unsteady Turbulence Simulation on Self-Adaptive Grids; Global Hybrid RANS/LES Methods; Zonal RANS/LES Methods. Readership: Researchers and engineers in academia and industry in aerospace, automotive and other aerodynamics-oriented fields; masters-level students in fluid mechanics, computational fluid dynamics and applied mathematics.
Book Synopsis Statistical Theory and Modeling for Turbulent Flows by : P. A. Durbin
Download or read book Statistical Theory and Modeling for Turbulent Flows written by P. A. Durbin and published by John Wiley & Sons. This book was released on 2011-06-28 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: Providing a comprehensive grounding in the subject of turbulence, Statistical Theory and Modeling for Turbulent Flows develops both the physical insight and the mathematical framework needed to understand turbulent flow. Its scope enables the reader to become a knowledgeable user of turbulence models; it develops analytical tools for developers of predictive tools. Thoroughly revised and updated, this second edition includes a new fourth section covering DNS (direct numerical simulation), LES (large eddy simulation), DES (detached eddy simulation) and numerical aspects of eddy resolving simulation. In addition to its role as a guide for students, Statistical Theory and Modeling for Turbulent Flows also is a valuable reference for practicing engineers and scientists in computational and experimental fluid dynamics, who would like to broaden their understanding of fundamental issues in turbulence and how they relate to turbulence model implementation. Provides an excellent foundation to the fundamental theoretical concepts in turbulence. Features new and heavily revised material, including an entire new section on eddy resolving simulation. Includes new material on modeling laminar to turbulent transition. Written for students and practitioners in aeronautical and mechanical engineering, applied mathematics and the physical sciences. Accompanied by a website housing solutions to the problems within the book.
Book Synopsis Management and Minimisation of Uncertainties and Errors in Numerical Aerodynamics by : Bernhard Eisfeld
Download or read book Management and Minimisation of Uncertainties and Errors in Numerical Aerodynamics written by Bernhard Eisfeld and published by Springer Science & Business Media. This book was released on 2013-02-11 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume reports results from the German research initiative MUNA (Management and Minimization of Errors and Uncertainties in Numerical Aerodynamics), which combined development activities of the German Aerospace Center (DLR), German universities and German aircraft industry. The main objective of this five year project was the development of methods and procedures aiming at reducing various types of uncertainties that are typical of numerical flow simulations. The activities were focused on methods for grid manipulation, techniques for increasing the simulation accuracy, sensors for turbulence modelling, methods for handling uncertainties of the geometry and grid deformation as well as stochastic methods for quantifying aleatoric uncertainties.
Book Synopsis A Methodology for the Uncertainty Quantification and Sensitivity Analysis of Turbulence Model Coefficients by : Matthew C. Dunn
Download or read book A Methodology for the Uncertainty Quantification and Sensitivity Analysis of Turbulence Model Coefficients written by Matthew C. Dunn and published by . This book was released on 2011 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Recent Numerical Advances in Fluid Mechanics by : Omer San
Download or read book Recent Numerical Advances in Fluid Mechanics written by Omer San and published by MDPI. This book was released on 2020-07-03 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent decades, the field of computational fluid dynamics has made significant advances in enabling advanced computing architectures to understand many phenomena in biological, geophysical, and engineering fluid flows. Almost all research areas in fluids use numerical methods at various complexities: from molecular to continuum descriptions; from laminar to turbulent regimes; from low speed to hypersonic, from stencil-based computations to meshless approaches; from local basis functions to global expansions, as well as from first-order approximation to high-order with spectral accuracy. Many successful efforts have been put forth in dynamic adaptation strategies, e.g., adaptive mesh refinement and multiresolution representation approaches. Furthermore, with recent advances in artificial intelligence and heterogeneous computing, the broader fluids community has gained the momentum to revisit and investigate such practices. This Special Issue, containing a collection of 13 papers, brings together researchers to address recent numerical advances in fluid mechanics.
