[HTML][HTML] Certain trends in uncertainty and sensitivity analysis: An overview of software tools and techniques

D Douglas-Smith, T Iwanaga, BFW Croke… - … Modelling & Software, 2020 - Elsevier
Uncertainty and sensitivity analysis (UA/SA) aid in assessing whether model complexity is
warranted and under what conditions. To support these analyses a variety of software tools …

Sparse Bayesian learning of explicit algebraic Reynolds-stress models for turbulent separated flows

S Cherroud, X Merle, P Cinnella, X Gloerfelt - International Journal of Heat …, 2022 - Elsevier
Abstract A novel Sparse Bayesian Learning (SBL) framework is introduced for generating
stochastic Explicit Algebraic Reynolds Stress (EARSM) closures for the Reynolds-Averaged …

[PDF][PDF] Space-dependent aggregation of data-driven turbulence models

S Cherroud, X Merle, P Cinnella… - arXiv preprint arXiv …, 2023 - academia.edu
A machine-learning approach for data-driven Reynolds-Averaged Navier–Stokes (RANS)
predictions of turbulent flows including estimates of turbulence modelling uncertainties is …

Effectively subsampled quadratures for least squares polynomial approximations

P Seshadri, A Narayan, S Mahadevan - SIAM/ASA Journal on Uncertainty …, 2017 - SIAM
This paper proposes a new deterministic sampling strategy for constructing polynomial
chaos approximations for expensive physics simulation models. The proposed approach …

Sensitivity analysis of a coupled hydrodynamic-vegetation model using the effectively subsampled quadratures method (ESQM v5. 2)

TS Kalra, A Aretxabaleta, P Seshadri… - Geoscientific Model …, 2017 - gmd.copernicus.org
Coastal hydrodynamics can be greatly affected by the presence of submerged aquatic
vegetation. The effect of vegetation has been incorporated into the Coupled Ocean …

Blade envelopes Part I: Concept and methodology

CY Wong, P Seshadri, A Scillitoe… - Journal of …, 2022 - asmedigitalcollection.asme.org
Blades manufactured through flank and point milling will likely exhibit geometric variability.
Gauging the aerodynamic repercussions of such variability, prior to manufacturing a …

Programming with equadratures: an open-source package for uncertainty quantification, dimension reduction, and optimisation

P Seshadri, CY Wong, AD Scillitoe, BN Ubald… - AIAA SCITECH 2022 …, 2022 - arc.aiaa.org
View Video Presentation: https://doi. org/10.2514/6.2022-2108. vid This paper presents an
overview of the open-source code equadratures. While originally developed to replicate …

Design space exploration of stagnation temperature probes via dimension reduction

AD Scillitoe, B Ubald… - … Expo: Power for …, 2020 - asmedigitalcollection.asme.org
The measurement of stagnation temperature is important for turbomachinery applications as
it is used in the calculation of component efficiency and engine specific fuel consumption …

[PDF][PDF] Space-dependent Aggregation of Stochastic Data-driven Turbulence Models

XM Cherroud, P Cinnella… - arXiv preprint arXiv …, 2023 - researchgate.net
A stochastic Machine-Learning approach is developed for data-driven Reynolds-Averaged
Navier-Stokes (RANS) predictions of turbulent flows, with quantified model uncertainty. This …

Least squares approximation-based polynomial chaos expansion for uncertainty quantification and robust optimization in aeronautics

R Mura, T Ghisu, S Shahpar - AIAA AVIATION 2020 FORUM, 2020 - arc.aiaa.org
For many engineering problems, reliability and robustness are far more important than the
nominal performance when it comes to the choice of a design over another. Availability of …