Evaluation of kriging based surrogate models constructed from mesoscale computations of shock interaction with particles

JOURNAL OF COMPUTATIONAL PHYSICS

卷:336

Article

Sen, Oishik¹  Gaul, Nicholas J.²  Choi, K. K.¹  Jacobs, Gustaaf³  Udaykumar, H. S.¹ 

[1] Univ Iowa, Mech & Ind Engn, Iowa City, IA 52242 USA

[2] LLC, RAMDO Solut, Iowa City, IA 52240 USA

[3] San Diego State Univ, Aerosp Engn, San Diego, CA 92115 USA

关键词: Surrogate models;    Metamodel;    Dynamic kriging;    Bayesian kriging;    Multiscale;    Particle-laden flows;    Shocks;    Drag;   

DOI  :  10.1016/j.jcp.2017.01.046

来源: Elsevier

【 摘 要 】

Macro-scale computations of shocked particulate flows require closure laws that model the exchange of momentum energy between the fluid and particle phases. Closure laws are constructed in this work in the form of surrogate models derived from highly resolved mesoscale computations of shock-particle interactions. The mesoscale computations are performed to calculate the drag force on a cluster of particles for different values of Mach Number and particle volume fraction. Two Kriging-based methods, viz. the Dynamic Kriging Method (DKG) and the Modified Bayesian Kriging Method (MBKG) are evaluated for their ability to construct surrogate models with sparse data; i.e. using the least number of mesoscale simulations. It is shown that if the input data is noise-free, the DKG method converges monotonically; convergence is less robust in the presence of noise. The MBKG method converges monotonically even with noisy input data and is therefore more suitable for surrogate model construction from numerical experiments. This work is the first step towards a full multiscale modeling of interaction of shocked particle laden flows. (C) 2017 Elsevier Inc. All rights reserved.

【 授权许可】

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