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RANS predictions for high-speed flows using enveloping models

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arxiv 1704.01699 v1 pith:HEHMUYGX submitted 2017-04-06 physics.flu-dyn

classification physics.flu-dyn
keywords methodologyuncertaintyransboundsenvelopingflowmodelsable
verification ladder T0 review T1 audit T2 compute T3 formal
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In this investigation, we outline an enveloping models methodology for estimating structural uncertainty bounds on RANS closures. This methodology incorporates both eigenvalue and eigenvector perturbations in the spectral representation of the Reynolds stress tensor. The underlying rationale of this enveloping models methodology are explicated in detail and the mathematical nuances are outlined. This methodology is validated via its application to a canonical case of separated turbulent flow, while contrasted against numerical data. It is exhibited that this procedure is able to provide prudent estimates on the uncertainty of quantities of interest. Furthermore, uncertainty bounds of engineering utility can be engendered using just two specific RANS simulations, minimizing the computational overheads associated with uncertainty estimation. Thence, this methodology is applied to a supersonic axisymmetric "submerged" jet flow. The epistemic uncertainty envelopes are contrasted against the aleatoric uncertainty bounds predicated upon the predictions of the RANS closure. The methodology is able to provide satisfactory results for this high-speed flow.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification

    physics.flu-dyn 2025-09 reject novelty 4.0 of 10

    A 1D CNN learned to correct RANS turbulent kinetic energy profiles on an airfoil and periodic hills, but the proposed use for modulating EPM perturbation magnitudes to calibrate uncertainty bounds is never tested.

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