Fixed inducing points online Bayesian calibration for computer models with an application to a scale-resolving CFD simulation

Duan, Yu; Eaton, M.D.; Bluck, Michael J.

doi:10.1016/j.jcp.2021.110243

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…Samples w.r.t 𝐵𝑒𝑡𝑎(5,1.5) are concentrated around 0.9, which is consistent with the observation in our previous study. It is found in (Duan et al, 2021) that the peak of the posterior PDF of 𝐶 𝑠 , w.r.t the velocity observation at 𝑥/𝑎 = 0.375, tends towards 0.9. The means of 𝑈(0,1), 𝑁(0.5, 0.1 2 ) and 𝐵𝑒𝑡𝑎(2,2) are the same (i.e.…”

Section: Quantify the Uncertainty Due To 𝐶 𝑠mentioning

confidence: 93%

“…GP and it variants has been successfully applied to many subjects, i.e. studying the performance of a structural health monitoring system (Ghiasi et al, 2018;Olalusi and Spyridis, 2020); calibrating the model constants (Cox et al, 2001;Duan et al, 2021;Kajero et al, 2017); scrutinising CFD models (Duan et al, 2019); optimising engineering designs (Bernardini et al, 2015;Ding and Kareem, 2018), and global sensitivity analysis (Chen et al, 2005), as well as material informatics (Hoang et al, 2016;Duan, 2021a, 2021b). Therefore, the GP method is chosen as the analysis approach in this study.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quantification of the uncertainty within a SAS-SST simulation caused by the unknown high-wavenumber damping factor

Duan

Ahn

Eaton

et al. 2021

Nuclear Engineering and Design

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Section: Quantify the Uncertainty Due To 𝐶 𝑠mentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Quantification of the uncertainty within a SAS-SST simulation caused by the unknown high-wavenumber damping factor

Duan

Ahn

Eaton

et al. 2021

Nuclear Engineering and Design

Self Cite

View full text Add to dashboard Cite

Knowledge-informed generative adversarial network for functional calibration of computer models

Atamturktur

2023

Knowledge-Based Systems

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Data-driven Reynolds-averaged turbulence modeling with generalizable non-linear correction and uncertainty quantification using Bayesian deep learning

Tang

Wang

et al. 2023

Physics of Fluids

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The past few years have witnessed a renewed blossoming of data-driven turbulence models. Quantification of the concomitant modeling uncertainty, however, has mostly been omitted, and the generalization performance of the data-driven models is still facing great challenges when predicting complex flows with different flow physics not seen during training. A robust data-driven Reynolds-averaged turbulence model with uncertainty quantification and non-linear correction is proposed in this work with the Bayesian deep neural network. In this model, the Reynolds stress tensor is decomposed into linear and non-linear parts. The linear part is taken as the usual linear eddy viscosity model while the non-linear counterpart is learned by a Bayesian deep neural network. Independent tensor bases of invariants and tensors constituted by mean strain rate tensor and rotation rate tensor are embedded into the neural network to effectively consider key turbulence features in different flows. The proposed model is well validated through numerical simulations of four canonical flows that significantly deviate in geometrical configurations and/or Reynolds numbers from those in the training data. With the non-linear corrections of embedded invariants and tensors representing key features of turbulence, the proposed model not only improves the predictive capabilities of Reynolds-averaged turbulence models on the same mesh but also has better generalization performance when simulating complex turbulent flows with large scale separation. In addition, this model allows us to quantitatively demonstrate the confidence interval of the predicted flow quantities that are originated from the model itself.

show abstract

Fixed inducing points online Bayesian calibration for computer models with an application to a scale-resolving CFD simulation

Cited by 3 publications

References 35 publications

Quantification of the uncertainty within a SAS-SST simulation caused by the unknown high-wavenumber damping factor

Quantification of the uncertainty within a SAS-SST simulation caused by the unknown high-wavenumber damping factor

Knowledge-informed generative adversarial network for functional calibration of computer models

Data-driven Reynolds-averaged turbulence modeling with generalizable non-linear correction and uncertainty quantification using Bayesian deep learning

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