Bi-fidelity approximation for uncertainty quantification and sensitivity analysis of irradiated particle-laden turbulence

JOURNAL OF COMPUTATIONAL PHYSICS

卷:402

Article

Fairbanks, Hillary R.^1,2  Jofre, Lluis³  Geraci, Gianluca⁴  Iaccarino, Gianluca³  Doostan, Alireza⁵ 

[1] Univ Colorado, Appl Math & Stat, Boulder, CO 80309 USA

[2] Lawrence Livermore Natl Lab, Ctr Appl Sci Comp, Livermore, CA 94550 USA

[3] Stanford Univ, Ctr Turbulence Res, Stanford, CA 94305 USA

[4] Sandia Natl Labs, Ctr Comp Res, Albuquerque, NM 87185 USA

[5] Univ Colorado, Smead Aerosp Engn Sci, Boulder, CO 80309 USA

关键词: Bi-fidelity approximation;    Irradiated particle-laden turbulence;    Low-rank approximation;    Non-intrusive;    Predictive computational science;    Uncertainty quantification;   

DOI  :  10.1016/j.jcp.2019.108996

来源: Elsevier

【 摘 要 】

Particle-laden turbulent flows subject to radiative heating are relevant in many applications, for example concentrated solar power receivers. Efficient and accurate simulations provide valuable insights and enable optimization of such systems. However, as there are many uncertainties inherent in such flows, uncertainty quantification is fundamental to improve the predictive capabilities of the numerical simulations. For large-scale, multiphysics problems exhibiting high-dimensional uncertainty, characterizing the stochastic solution presents a significant computational challenge as most strategies require a large number of high-fidelity solves. This requirement might result in an infeasible number of simulations when a typical converged high-fidelity simulation requires intensive computational resources. To reduce the cost of quantifying high-dimensional uncertainties, we investigate the application of a non-intrusive, bi-fidelity approximation to estimate statistics of quantities of interest associated with an irradiated particle-laden turbulent flow. This method exploits the low-rank structure of the solution to accelerate the stochastic sampling and approximation processes by means of cheaper-to-run, lower fidelity representations. The application of this bi-fidelity approximation results in accurate estimates of the quantities of interest statistics, while requiring a small number of highfidelity model evaluations. (C) 2019 Elsevier Inc. All rights reserved.

【 授权许可】

Free