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The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Tue, 23 Jul 2024 14:49:13 GMT2024-07-23T14:49:13ZBayesian estimates of parameter variability in the k − ε turbulence model
http://hdl.handle.net/10985/10077
Bayesian estimates of parameter variability in the k − ε turbulence model
EDELING, Wouter Nico; CINNELLA, Paola; DWIGHT, Richard P.; BIJL, H.
In this paper we are concerned with obtaining estimates for the error in Reynolds-Averaged Navier-Stokes (RANS) simulations based on the Launder-Sharma k−ε turbulence closure model, for a limited class of flows. In particular we search for estimates grounded in uncertainties in the space of model closure coeffi-cients, for wall-bounded flows at a variety of favourable and adverse pressure gradients. In order to estimate the spread of closure coefficients which repro-duces these flows accurately, we perform 13 separate Bayesian calibrations – each 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 to-tal 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 uncer-tainty bars generated from this uncertainty information, and the resulting error estimate is shown to be consistent with measurement data.
Wed, 01 Jan 2014 00:00:00 GMThttp://hdl.handle.net/10985/100772014-01-01T00:00:00ZEDELING, Wouter NicoCINNELLA, PaolaDWIGHT, Richard P.BIJL, H.In this paper we are concerned with obtaining estimates for the error in Reynolds-Averaged Navier-Stokes (RANS) simulations based on the Launder-Sharma k−ε turbulence closure model, for a limited class of flows. In particular we search for estimates grounded in uncertainties in the space of model closure coeffi-cients, for wall-bounded flows at a variety of favourable and adverse pressure gradients. In order to estimate the spread of closure coefficients which repro-duces these flows accurately, we perform 13 separate Bayesian calibrations – each 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 to-tal 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 uncer-tainty bars generated from this uncertainty information, and the resulting error estimate is shown to be consistent with measurement data.Simplex-stochastic collocation method with improved scalability
http://hdl.handle.net/10985/15517
Simplex-stochastic collocation method with improved scalability
EDELING, Wouter Nico; DWIGHT, Richard P.; CINNELLA, Paola
The Simplex-Stochastic Collocation (SSC) method is a robust tool used to propagate uncertain input distributions through a computer code. However, it becomes prohibitively expensive for problems with dimensions higher than 5. The main purpose of this paper is to identify bottlenecks, and to improve upon this bad scalability. In order to do so, we propose an alternative interpolation stencil technique based upon the Set-Covering problem, and we integrate the SSC method in the High-Dimensional Model-Reduction framework. In addition, we address the issue of ill-conditioned sample matrices, and we present an analytical map to facilitate uniformly-distributed simplex sampling.
Fri, 01 Jan 2016 00:00:00 GMThttp://hdl.handle.net/10985/155172016-01-01T00:00:00ZEDELING, Wouter NicoDWIGHT, Richard P.CINNELLA, PaolaThe Simplex-Stochastic Collocation (SSC) method is a robust tool used to propagate uncertain input distributions through a computer code. However, it becomes prohibitively expensive for problems with dimensions higher than 5. The main purpose of this paper is to identify bottlenecks, and to improve upon this bad scalability. In order to do so, we propose an alternative interpolation stencil technique based upon the Set-Covering problem, and we integrate the SSC method in the High-Dimensional Model-Reduction framework. In addition, we address the issue of ill-conditioned sample matrices, and we present an analytical map to facilitate uniformly-distributed simplex sampling.Estimation of Model Error Using Bayesian Model-Scenario Averaging with Maximum a Posterori-Estimates
http://hdl.handle.net/10985/15499
Estimation of Model Error Using Bayesian Model-Scenario Averaging with Maximum a Posterori-Estimates
SCHMELZER, Martin; DWIGHT, Richard P.; EDELING, Wouter Nico; CINNELLA, Paola
Mon, 01 Jul 2019 00:00:00 GMThttp://hdl.handle.net/10985/154992019-07-01T00:00:00ZSCHMELZER, MartinDWIGHT, Richard P.EDELING, Wouter NicoCINNELLA, PaolaData-Driven Deterministic Symbolic Regression of Nonlinear Stress-Strain Relation for RANS Turbulence Modelling
http://hdl.handle.net/10985/15556
Data-Driven Deterministic Symbolic Regression of Nonlinear Stress-Strain Relation for RANS Turbulence Modelling
SCHMELZER, Martin; DWIGHT, Richard P.; CINNELLA, Paola
This work presents developments towards a deterministic symbolic regression method to derive algebraic Reynolds-stress models for the Reynolds-Averaged Navier-Stokes (RANS) equations. The models are written as tensor polynomials, for which optimal coe cients are found using Bayesian inversion. These coe cient fields are the targets for the symbolic regression. A method is presented based on a regularisation strategy in order to promote sparsity of the inferred models and is applied to high-fidelity data. By being data-driven the method reduces the assumptions commonly made in the process of model development in order to increase the predictive fidelity of algebraic models.
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/10985/155562018-01-01T00:00:00ZSCHMELZER, MartinDWIGHT, Richard P.CINNELLA, PaolaThis work presents developments towards a deterministic symbolic regression method to derive algebraic Reynolds-stress models for the Reynolds-Averaged Navier-Stokes (RANS) equations. The models are written as tensor polynomials, for which optimal coe cients are found using Bayesian inversion. These coe cient fields are the targets for the symbolic regression. A method is presented based on a regularisation strategy in order to promote sparsity of the inferred models and is applied to high-fidelity data. By being data-driven the method reduces the assumptions commonly made in the process of model development in order to increase the predictive fidelity of algebraic models.