Optimization under turbulence model uncertainty for aerospace design

The computational economy of Reynolds-Averaged Navier-Stokes solvers encourages their widespread use in the optimization of aerospace designs. Unfortunately, the real-world performance of the resulting optimized designs may have shortcomings. A common contributor to this shortfall is a lack of adequ...

Full description

Saved in:
Bibliographic Details
Published in:Physics of fluids (1994) 2019-10, Vol.31 (10)
Main Authors: Cook, L. W., Mishra, A. A., Jarrett, J. P., Willcox, K. E., Iaccarino, G.
Format: Article
Language:English
Subjects:
Aerodynamics
Aerospace engineering
Computational fluid dynamics
Design optimization
Diffusers
Fluid dynamics
Optimization
Perturbation methods
Physics
Reynolds averaged Navier-Stokes method
Simulation
Solvers
Turbulence models
Uncertainty
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The computational economy of Reynolds-Averaged Navier-Stokes solvers encourages their widespread use in the optimization of aerospace designs. Unfortunately, the real-world performance of the resulting optimized designs may have shortcomings. A common contributor to this shortfall is a lack of adequately accounting for the uncertainty introduced by the structure of the turbulence model. We investigate whether including measures of turbulence-based uncertainty, as predicted by the eigenspace perturbation method, in an optimization under uncertainty framework can result in designs that are more robust with respect to turbulence model-form uncertainty. In an asymmetric diffuser design problem and a transonic airfoil design problem, our optimization formulation taking account of turbulence-based uncertainty obtained designs that were more robust to turbulence model uncertainty than optimal designs obtained via deterministic approaches.
ISSN:1070-6631
1089-7666
DOI:10.1063/1.5118785