A hybrid approach combining DNS and RANS simulations to quantify uncertainties in turbulence modelling

Voet, Laurens J.A.; Ahlfeld, Richard; Gaymann, Audrey; Laizet, Sylvain; Montomoli, Francesco

doi:10.1016/j.apm.2020.07.056

Cited by 14 publications

(25 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The numerical solution of the flow field was obtained by the CFD method based on the fluid's generalized continuity equation and momentum equation. The equations were solved based on the three-dimensional incompressible implicit unsteady model, Reynolds average Navier-Stokes equation [21,22], and turbulence model [23].…”

Section: Governing Equations and Turbulence Modelmentioning

confidence: 99%

Influence of Flanks on Resistance Performance of High-Speed Amphibious Vehicle

Pan

Liu

2021

JMSE

View full text Add to dashboard Cite

In order to reduce the additional resistance of high-speed amphibious vehicles, Flanks are designed on the concave grooves. As a new drag reduction attachment, the principle of Flanks is analyzed and discussed in detail. In this paper, the HSAV model and Flanks coupling resistance tests are performed based on the Reynolds-averaged Navier–Stokes method and SST k−ω model. The accuracy of the numerical approach is verified by a series of towing tests. Results show that with a fixed installation angle and invariable characteristic parameters, Flanks can significantly reduce the total resistance at high speed, with a maximum drag reduction of 16%. In the meantime, Flanks also affect the attitude and flow field of the vehicle, consequently affecting the resistance composition and the sailing condition. A vehicle model self-propulsion test is designed and carried out, and it qualitatively verifies the drag reduction effect of the Flanks at high speed.

show abstract

Section: Governing Equations and Turbulence Modelmentioning

confidence: 99%

Influence of Flanks on Resistance Performance of High-Speed Amphibious Vehicle

Pan

Liu

2021

JMSE

View full text Add to dashboard Cite

show abstract

“…Recently, Voet et al. (2021) compared inverse weighted distance-, PCE- and co-kriging-based MFMs using the data of RANS and DNS for the turbulent flow over a periodic hill, and concluded that the co-kriging model outperforms the others in terms of accuracy. This is the first (and to our knowledge only) study where MFMs have been applied to engineering-relevant RANS and DNS data for the purpose of uncertainty propagation.…”

Section: Introductionmentioning

confidence: 99%

“…Voet et al. (2021) also found that the performance of the co-kriging can deteriorate when there is no significant correlation between the RANS and DNS data and at the same time there is a significant deviation between them. Motivated by this deficiency, we adapt and use the HC-MFM where the discrepancy between the data (and not their correlation) over the space of design parameters is learned using independent Gaussian processes.…”

Section: Introductionmentioning

confidence: 99%

Efficient prediction of turbulent flow quantities using a Bayesian hierarchical multifidelity model

2023

View full text Add to dashboard Cite

Multifidelity models (MFMs) can be used to construct predictive models for flow quantities of interest (QoIs) over the space of uncertain/design parameters, with the purpose of uncertainty quantification, data fusion and optimization. For numerical simulation of turbulence, there is a hierarchy of methodologies ranked by accuracy and cost, where each methodology may have several numerical/modelling parameters that control the predictive accuracy and robustness of its resulting outputs. Compatible with these specifications, the present hierarchical MFM strategy allows for simultaneous calibration of the fidelity-specific parameters in a Bayesian framework as developed by Goh et al. (Technometrics, vol. 55, no. 4, 2013, pp. 501–512). The purpose of the MFM is to provide an improved prediction, mainly interpolation over the range covered by training data, by combining lower- and higher-fidelity data in an optimal way for any number of fidelity levels; even providing confidence intervals for the resulting QoI. The capabilities of the MFM are first demonstrated on an illustrative toy problem, and it is then applied to three realistic cases relevant to engineering turbulent flows. The latter include the prediction of friction at different Reynolds numbers in turbulent channel flow, the prediction of aerodynamic coefficients for a range of angles of attack of a standard airfoil and the uncertainty propagation and sensitivity analysis of the separation bubble in the turbulent flow over periodic hills subject to geometrical uncertainties. In all cases, based on only a few high-fidelity data samples, the MFM leads to accurate predictions of the QoIs. The result of the uncertainty quantification and sensitivity analyses are also found to be accurate compared with the ground truth in each case.

show abstract

“…The drawback to the method is that it is very computationally expensive, with each simulation typically taking a number of days to run 17 . A number of works in the literature leverage a small set of DNS results with Reynolds-Averaged Navier-Stokes (RANS) simulations, which are less accurate due to simplifications in the turbulence closure but also cheaper to evaluate 18 – 20 . An alternative approach is to use the same model with meshes of varying coarseness in order to create low-fidelity surrogates.…”

Section: Introductionmentioning

confidence: 99%

Local bi-fidelity field approximation with Knowledge Based Neural Networks for Computational Fluid Dynamics

Pepper

Gaymann

Sharma

et al. 2021

Sci Rep

Self Cite

View full text Add to dashboard Cite

This work presents a machine learning based method for bi-fidelity modelling. The method, a Knowledge Based Neural Network (KBaNN), performs a local, additive correction to the outputs of a coarse computational model and can be used to emulate either experimental data or the output of a more accurate, but expensive, computational model. An advantage of the method is that it can scale easily with the number of input and output features. This allows bi-fidelity modelling approaches to be applied to a wide variety of problems, for instance in the bi-fidelity modelling of fields. We demonstrate this aspect in this work through an application to Computational Fluid Dynamics, in which local corrections to a velocity field are performed by the KBaNN to account for mesh effects. KBaNNs were trained to make corrections to the free-stream velocity field and the boundary layer. They were trained on a limited data-set consisting of simple two-dimensional flows. The KBaNNs were then tested on a flow over a more complex geometry, a NACA 2412 airfoil. It was demonstrated that the KBaNNs were still able to provide a local correction to the velocity field which improved its accuracy. The ability of the KBaNNs to generalise to flows around new geometries that share similar physics is encouraging. Through knowledge based neural networks it may be possible to develop a system for bi-fidelity, computer based design which uses data from past simulations to inform its predictions.

show abstract

A hybrid approach combining DNS and RANS simulations to quantify uncertainties in turbulence modelling

Cited by 14 publications

References 51 publications

Influence of Flanks on Resistance Performance of High-Speed Amphibious Vehicle

Influence of Flanks on Resistance Performance of High-Speed Amphibious Vehicle

Efficient prediction of turbulent flow quantities using a Bayesian hierarchical multifidelity model

Local bi-fidelity field approximation with Knowledge Based Neural Networks for Computational Fluid Dynamics

Contact Info

Product

Resources

About