A Novel Framework for Uncertainty Propagation in Multidisciplinary Design Life Cycle for Shock-Cell Noise Research

Granados-Ortiz, Francisco J.; Chen, Lai

doi:10.2514/6.2016-2731

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…9 and 10. The required number of collocation points was tested in [36], computing the convergence of statistical moments with Stochastic Collocation Method, so that level of accuracy of the sparse grid was intended here for gPC.…”

Section: Comparison and Discussion Of Uncertainty Quantification Resultsmentioning

confidence: 99%

On the influence of uncertainty in computational simulations of a high-speed jet flow from an aircraft exhaust

et al. 2019

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A classic approach to computational fluid dynamics is to perform simulations with a fixed set of variables in order to account for parameters and boundary conditions. However, experiments and real-life performance are subject to variability in their conditions. In recent years, the interest of performing simulations under uncertainty is increasing, but this is not yet a common rule, and simulations with lack of information are still taking place. This procedure could be missing details such as whether sources of uncertainty affect dramatic parts in the simulation of the flow. One of the reasons of avoiding to quantify uncertainties is that they usually require to run an unaffordable number of CFD simulations to develop the study. To face this problem, Non-Intrusive Uncertainty Quantification (UQ) has been applied to 3D Reynolds-Averaged Navier-Stokes simulations of an under-expanded jet from an aircraft exhaust with the Spalart-Allmaras turbulent model, in order to assess the impact of inaccuracies and quality in the simulation. To save a large number of computations, sparse grids are used to compute the integrals and built surrogates for UQ. Results show that some regions of the jet plume can be more sensitive than others to variance in both physical and turbulence model parameters. The Spalart-Allmaras turbulent model is demonstrated to have an accurate performance with respect to other turbulent models in RANS, LES and experimental data, and the contribution of a large variance in its parameter is analysed. This investigation explicitly outlines, exhibits and proves the details of the relationship between diverse sources of input uncertainty, the sensitivity of different quantities of interest to said uncertainties and the spatial distribution arising due to their propagation in the simulation of the high-speed jet flow. This analysis represents first numerical study that provides evidence for this heuristic observation.

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Section: Comparison and Discussion Of Uncertainty Quantification Resultsmentioning

confidence: 99%

On the influence of uncertainty in computational simulations of a high-speed jet flow from an aircraft exhaust

et al. 2019

Self Cite

View full text Add to dashboard Cite

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Uncertainty Quantification and Sensitivity Analysis applied to an under-expanded single jet

Granados-Ortiz

Chen

Arroyo

et al. 2016

46th AIAA Fluid Dynamics Conference

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A Novel Framework for Uncertainty Propagation in Multidisciplinary Design Life Cycle for Shock-Cell Noise Research

Cited by 2 publications

References 12 publications

On the influence of uncertainty in computational simulations of a high-speed jet flow from an aircraft exhaust

On the influence of uncertainty in computational simulations of a high-speed jet flow from an aircraft exhaust

Uncertainty Quantification and Sensitivity Analysis applied to an under-expanded single jet

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