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Coupling of Micro-Scale and Macro-Scale Eulerian-Lagrangian Models for the Computation of Shocked Particle-Laden Flows

[+] Author Affiliations
Sean Davis, Gustaaf Jacobs

San Diego State University, San Diego, CA

Oishik Sen, H. S. Udaykumar

The University of Iowa, Iowa City, IA

Paper No. IMECE2013-62521, pp. V07AT08A011; 9 pages
  • ASME 2013 International Mechanical Engineering Congress and Exposition
  • Volume 7A: Fluids Engineering Systems and Technologies
  • San Diego, California, USA, November 15–21, 2013
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-5631-4
  • Copyright © 2013 by ASME


The accuracy and efficiency of several algorithms that couple output from full resolution micro-scale Direct Numerical Simulation computations to input for macro-scale Eulerian-Lagrangian (EL) methods for the computation of high-speed, particle-laden flow are assessed. A Stochastic Collocation method, a Gaussian Radial Basis Function (RBF) Artificial Neural Network (ANN), and an improved RBF-ANN are compared for the fitting of an analytical drag coefficient formula that depends on Mach number and Reynolds number. The improved RBF-ANN uses a clustering algorithm to enhance conditioning of interpolation matrices. The fitted drag coefficient mantle, used to trace point particles in macro-scale computations, is in excellent agreement with the analytical drag formula. The SC method requires fewer micro-scale realizations to obtain comparable accuracy of the drag coefficient. The Gaussian RBF does not converge monotonically, while the improved RBF-ANN converges algebraically and has the potential to provide error estimates.

Copyright © 2013 by ASME



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