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 Coarse Grained Simulation and Turbulent Mixing written by Fenando 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: Reviews our current understanding of the subject. For graduate students and researchers in computational fluid dynamics and turbulence.

Download or read book Uncertainty Quantification and Grid based Geometrical Computations for Turbulent Fluid Mixing written by and published by . This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Turbulent Mixing and Solid Impact Studies in Multiscale Modeling written by and published by . This book was released on 2001 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The principal directions of the research reported were fluid interface instabilities, multiphase flow, solid dynamics, flow in porous media, uncertainty quantification and photonics. Our Front Tracking code, FronTier, has been extended to three dimensions, and is now functioning robustly for the simulation of three-dimensional complex fluid mixing flows. We have made a major effort in the study of multiphase flow, including a proposed model of averaged multiphase flow equations which seem to avoid most of the well known pitfalls for such equations. Validation studies for the Front Tracking code, FronTier Solid have been performed. Photonics, a new project for this work, is conducted in collaboration with C. Bowden of Redstone Arsenel, and a group of his collaborators. We have developed a parallelized FDTD code to allow simulations in complex 3D geometries for photonic crystals and other photonic devices.

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 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 Uncertainty Quantification in Scientific Computing written by Andrew Dienstfrey and published by Springer. This book was released on 2012-08-11 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed post-proceedings of the 10th IFIP WG 2.5 Working Conference on Uncertainty Quantification in Scientific Computing, WoCoUQ 2011, held in Boulder, CO, USA, in August 2011. The 24 revised papers were carefully reviewed and selected from numerous submissions. They are organized in the following topical sections: UQ need: risk, policy, and decision making, UQ theory, UQ tools, UQ practice, and hot topics. The papers are followed by the records of the discussions between the participants and the speaker.

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 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.

Download or read book Model Calibration and Forward Uncertainty Quantification for Large Eddy Simulation of Turbulent Flows written by and published by . This book was released on 2014 with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Modeling and Simulation of Turbulent Mixing and Reaction written by Daniel Livescu and published by Springer. This book was released on 2021-02-20 with total page 263 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 Numerical Methods for Hyperbolic Equations written by Elena Vázquez-Cendón and published by CRC Press. This book was released on 2012-11-05 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: Numerical Methods for Hyperbolic Equations is a collection of 49 articles presented at the International Conference on Numerical Methods for Hyperbolic Equations: Theory and Applications (Santiago de Compostela, Spain, 4-8 July 2011). The conference was organized to honour Professor Eleuterio Toro in the month of his 65th birthday. The topics cover

Download or read book Assessing the Applicability of the ASME V V20 Standard for Uncertainty Quantification of CFD in Nuclear Systems Fluid Modeling written by Andres Felipe Alvarez (S.B.) and published by . This book was released on 2017 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced modelIng and sImulatIon (M&S) of nuclear systems could offer a key contrIbutIon In enhancIng the competItIveness and safety performance of nuclear power plants. Large multI-organIzatIonal InItIatIves such as the ConsortIum for Advanced SImulatIon of LIght Water Reactors (CASL) and the Nuclear Energy Advanced ModelIng and SImulatIon (NEAMS) emphasIzes the Importance of M&S research to the U.S. nuclear Industry. UncertaInty QuantIfIcatIon (UQ) represents a fundamental area of research necessary to expand the applIcatIon of M&S Into nuclear Industry, but the fIeld Is stIll not mature, and no general consensus exIsts on current UQ methods. In thIs study, the ASME V&V20 I a proposed methodology for UQ of CFD -- Is applIed to a benchmark nuclear system turbulent mIxIng case In an effort to assess the applIcabIlIty and lImItatIons of the standard.

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.

Download or read book Annual Research Briefs written by Center for Turbulence Research (U.S.) and published by . This book was released on 2009 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Summary Review on the Application of Computational Fluid Dynamics in Nuclear Power Plant Design written by IAEA and published by International Atomic Energy Agency. This book was released on 2022-03-28 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: This publication documents the results of an IAEA coordinated research project (CRP)on the application of computational fluid dynamics (CFD) codes for nuclear power plant design. The main objective was to benchmark CFD codes, model options and methods against CFD experimental data under single phase flow conditions. This publication summarizes the current capabilities and applications of CFD codes, and their present qualification level, with respect to nuclear power plant design requirements. It is not intended to be comprehensive, focusing instead on international experience in the practical application of these tools in designing nuclear power plant components and systems. The guidance in this publication is based on inputs provided by international nuclear industry experts directly involved in nuclear power plant design issues, CFD applications, and in related experimentation and validation highlighted during the CRP.

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.