Multi-Scale Modeling of Particle-Laden Blast Waves. Machine Learning of Closure Laws with Inter-Scale Coupling and Uncertainty Quantification

Abstract

Multi-scale methods were developed that learn closure terms using high-resolution meso-scale simulations of shocked particulate flows. Advances were made in data-driving learning of closure terms, (multidfidelity) surrogate modeling, stochastic macro-modeling of Eulerian-Lagrangian models, uncertainty quantification and verification and validation of the multi-scale framework.

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Document Details

Document Type
Technical Report
Publication Date
Jan 04, 2019
Accession Number
AD1085371

Entities

People

  • Gustaaf B. Jacobs
  • H. S. Udaykumar

Organizations

  • University of California, San Diego

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Blast
  • Blast Waves
  • Chemical Reactions
  • Computational Fluid Dynamics
  • Computational Science
  • Couplings
  • Department Of Defense
  • Equations
  • Fluid Dynamics
  • Fluid Mechanics
  • Heat Transfer
  • High Resolution
  • Learning
  • Machine Learning
  • Multiphase Flow
  • Multiscale Modeling
  • Particles
  • Reliability
  • Scale Models
  • Scientific Research
  • Shock Waves
  • Simulations
  • Three Dimensional
  • Two Dimensional
  • Uncertainty

Fields of Study

  • Environmental science

Readers

  • Computational Fluid Dynamics (CFD)
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms