Uncertainty Quantification for Turbulent Mixing Simulations PDF Download
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Languages : en
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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.
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Languages : en
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Book Description
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.
Author: Fenando F. Grinstein
Publisher: Cambridge University Press
ISBN: 1107137047
Category : Science
Languages : en
Pages : 481
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Reviews our current understanding of the subject. For graduate students and researchers in computational fluid dynamics and turbulence.
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Languages : en
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Languages : en
Pages : 0
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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.
Author: Fernando F. Grinstein
Publisher: Cambridge University Press
ISBN: 1316571742
Category : Technology & Engineering
Languages : en
Pages : 481
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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.
Author: Jan Felix Heyse
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Languages : en
Pages :
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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.
Author: Daniel Livescu
Publisher: Springer Nature
ISBN: 9811526435
Category : Technology & Engineering
Languages : en
Pages : 273
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Book Description
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.
Author: Andrew Dienstfrey
Publisher: Springer
ISBN: 3642326773
Category : Computers
Languages : en
Pages : 335
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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.
Author: Zengrong Hao
Publisher:
ISBN:
Category : Fluid dynamics
Languages : en
Pages : 107
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Book Description
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.
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Languages : en
Pages : 41
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