A component-based parallel infrastructure for the simulation of fluid–structure interaction

The Uintah computational framework is a component-based infrastructure, designed for highly parallel simulations of complex fluid–structure interaction problems. Uintah utilizes an abstract representation of parallel computation and communication to express data dependencies between multiple physics components. These features allow parallelism to be integrated between multiple components while maintaining overall scalability. Uintah provides mechanisms for load-balancing, data communication, data I/O, and checkpoint/restart. The underlying infrastructure is designed to accommodate a range of PDE solution methods. The primary techniques described here, are the material point method (MPM) for structural mechanics and a multi-material fluid mechanics capability. MPM employs a particle-based representation of solid materials that interact through a semi-structured background grid. We describe a scalable infrastructure for problems with large deformation, high strain rates, and complex material behavior. Uintah is a product of the University of Utah Center for Accidental Fires and Explosions (C-SAFE), a DOE-funded Center of Excellence. This approach has been used to simulate numerous complex problems, including the response of energetic devices subject to harsh environments such as hydrocarbon pool fires. This scenario involves a wide range of length and time scales including a relatively slow heating phase punctuated by pressurization and rupture of the device.

[1]  James E. Guilkey,et al.  Implicit time integration for the material point method: Quantitative and algorithmic comparisons with the finite element method , 2003 .

[2]  D. Sulsky Erratum: Application of a particle-in-cell method to solid mechanics , 1995 .

[3]  J. Brackbill,et al.  FLIP: A method for adaptively zoned, particle-in-cell calculations of fluid flows in two dimensions , 1986 .

[4]  B. A. Kashiwa,et al.  A multimaterial formalism , 1994 .

[5]  A. A. Amsden,et al.  Numerical calculation of almost incompressible flow , 1968 .

[6]  J. E. Guilkey,et al.  An Eulerian-Lagrangian Approach For Large Deformation Fluid StructureInteraction Problems, Part 1 : Algorithm Development , 2003 .

[7]  James B Hoying,et al.  Computational modeling of multicellular constructs with the material point method. , 2006, Journal of biomechanics.

[8]  Biswajit Banerjee,et al.  The Mechanical Threshold Stress model for various tempers of AISI 4340 steel , 2005, cond-mat/0510330.

[9]  David C. Cann,et al.  A Report on the Sisal Language Project , 1990, J. Parallel Distributed Comput..

[10]  John A. Nairn,et al.  Calculation of J-Integral and Stress Intensity Factors using the Material Point Method , 2004 .

[11]  Thomas C. Henderson,et al.  Simulating accidental fires and explosions , 2000, Comput. Sci. Eng..

[12]  D. L. Shirer Basic: the little language that wouldn't die , 2000 .

[13]  Allen D. Malony,et al.  SMARTS: exploiting temporal locality and parallelism through vertical execution , 1999, ICS '99.

[14]  W. VanderHeyden,et al.  A cell-centered ICE method for multiphase flow simulations , 1993 .

[15]  Gerald T. Seidler,et al.  Simulation of the densification of real open-celled foam microstructures , 2005 .

[16]  D. Sulsky,et al.  A particle method for history-dependent materials , 1993 .

[17]  J. Brackbill,et al.  Flip: A low-dissipation, particle-in-cell method for fluid flow , 1988 .

[18]  Philip J. Smith,et al.  Numerical modeling of radiative heat transfer in pool fire simulations , 2005 .

[19]  T. Harman,et al.  An Eulerian-Lagrangian Approach For Large Deformation Fluid StructureInteraction Problems, Part 2: Multi-physics Simulations Within A ModemComputational Framework , 2003 .

[20]  James E. Guilkey,et al.  An Improved Contact Algorithm for the Material Point Method and Application to Stress Propagation in Granular Material , 2001 .

[21]  P. Colella,et al.  Local adaptive mesh refinement for shock hydrodynamics , 1989 .