Poster: Can the X-wing survive the reentry to Dagobah?

Ling, Yuenong; Williams, E. P.; Arranz, Gonzalo; Lozano-Durán, Adrián

doi:10.1103/aps.dfd.2021.gfm.p0017

Cited by 1 publication

(3 citation statements)

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“…However, work remains to be done on the SGS modelling front. In a recent work by our group, Ling et al (2022) extended the building-block flow methodology to the SGS model and showed that the prediction of the lift, drag and pitching moment coefficients in the CRM-HL are greatly improved compared to the BFWM combined with traditional SGS models. There is also a data science component to the problem, such as the need for efficient and reliable machine learning techniques for data classification and regression.…”

Section: Discussionmentioning

confidence: 99%

“…This hints at the necessity for a unified SGS/wall model approach where the building-block flow model is also used to devise a numerically consistent SGS model (referred to as a building-block flow model, BFM). Steps in that direction are already been undertaken by our group, and Ling et al (2022) have shown that the prediction of the lift, drag and pitching moment coefficients for the CRM-HL in § 4.7 are greatly improved in the first version of the BFM. (5) The prediction and classification tasks in the BFWM rely on ANNs.…”

Section: Model Limitationsmentioning

confidence: 99%

“…Other approaches include deriving SGS terms from optimal estimator theory (Vollant, Balarac & Corre 2017), deconvolution operators (e.g. Hickel et al 2004;Maulik & San 2017;Fukami, Fukagata & Taira 2019) or optimised SGS tensor accounting for numerical errors (Ling et al 2022).…”

Section: Introductionmentioning

confidence: 99%

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Machine learning building-block-flow wall model for large-eddy simulation

Lozano-Durán

Bae

2023

J. Fluid Mech.

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A wall model for large-eddy simulation (LES) is proposed by devising the flow as a combination of building blocks. The core assumption of the model is that a finite set of simple canonical flows contains the essential physics to predict the wall shear stress in more complex scenarios. The model is constructed to predict zero/favourable/adverse mean pressure gradient wall turbulence, separation, statistically unsteady turbulence with mean flow three-dimensionality, and laminar flow. The approach is implemented using two types of artificial neural networks: a classifier, which identifies the contribution of each building block in the flow, and a predictor, which estimates the wall shear stress via a combination of the building-block flows. The training data are obtained directly from wall-modelled LES (WMLES) optimised to reproduce the correct mean quantities. This approach guarantees the consistency of the training data with the numerical discretisation and the gridding strategy of the flow solver. The output of the model is accompanied by a confidence score in the prediction that aids the detection of regions where the model underperforms. The model is validated in canonical flows (e.g. laminar/turbulent boundary layers, turbulent channels, turbulent Poiseuille–Couette flow, turbulent pipe) and two realistic aircraft configurations: the NASA Common Research Model High-lift and NASA Juncture Flow experiment. It is shown that the building-block-flow wall model outperforms (or matches) the predictions by an equilibrium wall model. It is also concluded that further improvements in WMLES should incorporate advances in subgrid-scale modelling to minimise error propagation to the wall model.

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Section: Discussionmentioning

confidence: 99%

Section: Model Limitationsmentioning

confidence: 99%