Enabling HPC simulation workflows for complex industrial flow problems

The use of simulation based engineering taking advantage of massively parallel computing methods by industry is limited due to the costs associated with developing and using high performance computing software and systems. To address industries ability to effectively include large-scale parallel simulations in daily production use, two key areas need to be addressed. The first is access to large-scale parallel computing systems that are cost effective to use. The second is support for complete simulation workflow execution on these systems by industrial users. This paper presents an approach, and set of associated software components, that can support industrial users on large-scale parallel computing systems available at various national laboratories, universities, or on clouds.

[1]  Rao V. Garimella,et al.  Mesh data structure selection for mesh generation and FEA applications , 2002 .

[2]  Onkar Sahni,et al.  Controlling Unstructured Mesh Partitions for Massively Parallel Simulations , 2010, SIAM J. Sci. Comput..

[3]  Marlon E. Pierce,et al.  The Apache Airavata Application Programming Interface: Overview and Evaluation with the UltraScan Science Gateway , 2014, 2014 9th Gateway Computing Environments Workshop.

[4]  Mark S. Shephard,et al.  a General Topology-Based Mesh Data Structure , 1997 .

[5]  Mark S. Shephard,et al.  Moving curved mesh adaptation for higher-order finite element simulations , 2010, Engineering with Computers.

[6]  CAMERON W. SMITH,et al.  APPLICATION SPECIFIC MESH PARTITION IMPROVEMENT∗ , 2015 .

[7]  Onkar Sahni,et al.  Cardiovascular flow simulation at extreme scale , 2010 .

[8]  Joe Walsh,et al.  A comparison of techniques for geometry access related to mesh generation , 2004, Engineering with Computers.

[9]  Mark S. Shephard,et al.  Parallel refinement and coarsening of tetrahedral meshes , 1999 .

[10]  Mark S. Shephard,et al.  HPC Simulation Workflows for Engineering Innovation , 2014, XSEDE '14.

[11]  Mark S. Shephard,et al.  The M3D-C1 approach to simulating 3D 2-fluid magnetohydrodynamics in magnetic fusion experiments , 2008 .

[12]  Mark S. Shephard,et al.  Bringing HPC to Engineering Innovation , 2013, Computing in Science & Engineering.

[13]  Glen Hansen,et al.  Development of a used fuel cladding damage model incorporating circumferential and radial hydride responses , 2013 .

[14]  Onkar Sahni,et al.  Dynamic Stall Alleviation for an SC1095 Airfoil using Synthetic Jets , 2015 .

[15]  Mark S. Shephard,et al.  Efficient distributed mesh data structure for parallel automated adaptive analysis , 2006, Engineering with Computers.

[16]  Onkar Sahni,et al.  Tools to support mesh adaptation on massively parallel computers , 2011, Engineering with Computers.

[17]  Timothy J. Tautges,et al.  An Interoperable, Data-Structure-Neutral Component for Mesh Query and Manipulation , 2010, ACM Trans. Math. Softw..

[18]  Onkar Sahni,et al.  Anisotropic Adaptation for Transonic Flows with Turbulent Boundary Layers , 2015 .

[19]  M. Shephard,et al.  Parallel volume meshing using face removals and hierarchical repartitioning , 1999 .

[20]  Ümit V. Çatalyürek,et al.  A repartitioning hypergraph model for dynamic load balancing , 2009, J. Parallel Distributed Comput..

[21]  References , 1971 .

[22]  Benjamin A. Matthews,et al.  Scalable fully implicit finite element flow solver with application to high-fidelity flow control simulations on a realistic wing design , 2014 .

[23]  Robert Haimes,et al.  Unified Geometry Access for Analysis and Design , 2003, IMR.