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• W2955482016 abstract "With the advent of improved computational resources, alternate design approaches that explicitly account for uncertainty in predictions, such as robust- and reliability-based design, are superseding deterministic design approaches in aerospace applications. In this context, accounting for the structural uncertainties in turbulence models has been identified as the greatest challenge toward simulation based design. At present, the primary methodology to estimate the structural uncertainty in turbulence models is based on tensor perturbations applied to the modeled Reynolds stress tensor. This methodology has been applied with success to a variety of problems in engineering analysis and design under uncertainty. However, the modeling rationale of this perturbation approach is still not unearthed. While we know that the procedure works in generating uncertainty estimates that account for the discrepancy in turbulence simulations, we do not know why it works or even how exactly it works. This may lead to its application to cases of turbulent flows or under conditions where it should not perform well. This could potentially lead to analyses that are misleading, or even designs that are hazardous. In this article, we outline the underlying modeling structure represented by this tensor perturbation procedure. The exact limitations addressed by each step of the perturbation methodology are isolated and explicated. This analysis enables us to identify the limitations of this procedure and outline the specific phenomena and types of turbulence model uncertainty where its application would be equivocal. Additionally, we outline how this enables us to derive quasirealizability conditions on the perturbations." @default.
• W2955482016 created "2019-07-12" @default.
• W2955482016 creator A5035364643 @default.
• W2955482016 creator A5047377841 @default.
• W2955482016 date "2019-07-01" @default.
• W2955482016 modified "2023-10-16" @default.
• W2955482016 title "Theoretical analysis of tensor perturbations for uncertainty quantification of Reynolds averaged and subgrid scale closures" @default.
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• W2955482016 doi "https://doi.org/10.1063/1.5099176" @default.
• W2955482016 hasPublicationYear "2019" @default.
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