Some examples

High-fidelity Multiphase Flow Simulation

Authors: S. H. Bryngelson, T. Colonius, V. Coralic, H. Le Berre, K. Maeda, J. Meng, A. Radhakrishnan, M. Rodriguez, K. Schmidmayer, J.-S. Spratt, and more! (alpha by last name)

MFC is an open source parallel simulation software for multi-component, multi-phase, and bubbly flows. MFC runs on entirely on either GPUs or CPUs, dealer’s choice. It weak scales ideally to 95% of OLCF Summit (14,000 V100 GPUs) at 48% of the compute roof-line. Its efficient simulation algorithms are capable of solving flows like droplet atomization, bubble cavitation, and their interactions with strong shocks. The simulation method consists of:

  • 5- and 6-equation diffuse-interface models
  • High-order-accurate WENO interface-capturing methods
  • HLL-type Riemann solvers
  • Sub-grid physics models, mostly based on moment methods
  • Soft material models
  • TVD time-integration schemes
  • Characteristic-based boundary conditions

QBMMlib: Moment Methods for Fully-coupled Flows

Authors: S. H. Bryngelson, E. Cisneros, and Q. Wang (alpha by last name)

QBMMlib is an open source Mathematica package for solving populating balance equations with quadrature-based moment methods (QBMMs). QBMMs are used for fully-coupled disperse flow and combustion problems. However, formulating and closing the corresponding governing equations can be complex. QBMMlib makes using these methods simple and accessible:

  • Symbolic and automatic formulation of moment transport equations for a population balance equation and dynamical system
  • Moment inversion trades moment sets for quadrature points
    • Algorithms: QMOM, HyQMOM, CQMOM, and more
  • Quadratures closes the moment transport and governing flow equations
  • Embedded Runge–Kutta algorithms for realizable time integration

The algorithm initialization and solution can span just 13 lines of code. Example notebooks demonstrate QBMMlib on bubble dynamics problems.

PyQBMMlib: With Esteban Cisneros we developed a Python version of QBMMlib that leverages JIT compiling for significantly improved performance.