High performance computing methods for nonlinear Bayesian uncertainty quantification

2018 ◽  
Vol 143 (3) ◽  
pp. 1925-1925
Author(s):  
Jan Dettmer ◽  
Stan E. Dosso ◽  
Charles W. Holland
Keyword(s):  
Uncertainty Quantification ◽  
High Performance Computing ◽  
High Performance ◽  
Computing Methods ◽  
Performance Computing

Uncertainty Quantification of Molten Hafnium Infusion Into a B4C Packed-Bed

2019 ◽  
Author(s):  
Arturo Schiaffino ◽  
V. M. Krushnarao Kotteda ◽  
Vinod Kumar ◽  
Arturo Bronson ◽  
Sanjay Shantha-Kumar
Keyword(s):  
Uncertainty Quantification ◽  
High Performance Computing ◽  
Molten Metal ◽  
High Performance ◽  
Control Volume ◽  
Packed Bed ◽  
Network Simulator ◽  
Matrix Composites ◽  
Formidable Challenge ◽  
Performance Computing

Abstract In the manufacturing of metal matrix composites (MMC), liquid-metal reactive infusion with a solid mesh or particles composed of ceramic or metal may be used. The objective of this study is to determine the uncertainty quantification of the modeling of liquid hafnium infusion to expedite the processing and improve properties of MMCs ultimately. Uncertainty quantification (UQ) characterized the uncertainty scientifically especially for high-performance computing with observed physics and/or chemistry of the phenomena and predicted from estimated parameters. In this work, molten hafnium infusing through a boron carbide packed bed is modeled to optimize the manufacturing of components used for a hypersonic vehicle. The creation of molten matrix composites by the infiltration of molten metal represents a formidable challenge to be accurately modeled. First, the structural randomness associated with porous mediums complicates the prediction of the flow passing through it. Secondly, the properties of the molten metal could vary inside our control volume, since the temperature inside the control volume is not constant. Also, there are several chemical reactions and solidification rates occurring in during the impregnation. Given the recent advances in high-performance computing, an in-house pore network simulator are implemented along with Dakota, an open-source, exascale software, to determine the optimal parameters (e.g., porosity and temperature) and uncertainty quantification for the modeling.

scholarly journals galapagos-c: analysis of galaxy morphologies using high-performance computing methods

2014 ◽  
Vol 444 (4) ◽  
pp. 3089-3117 ◽  
Author(s):  
Andreas Hiemer ◽  
Marco Barden ◽  
Lee S. Kelvin ◽  
Boris Häußler ◽  
Sabine Schindler
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Computing Methods ◽  
Performance Computing

Comparison of Parallel Computing Methods for Fast Cone-Beam Reconstruction with Similar Optimization Strategies

2014 ◽  
Vol 519-520 ◽  
pp. 85-89
Author(s):  
Xiang Zhang ◽  
Bin Yan ◽  
Lei Li ◽  
Feng Zhang ◽  
Xiao Qi Xi ◽  
...  
Keyword(s):  
Parallel Computing ◽  
High Performance Computing ◽  
High Performance ◽  
Computing Methods ◽  
Experimental Results ◽  
Cone Beam ◽  
Cone Beam Reconstruction ◽  
Fdk Algorithm ◽  
Performance Computing

To investigate the performance of acceleration technologies for FDK algorithm, two of the most common high-performance computing hardware, multi-core CPU and GPU, are involved in our experiment. Both runtime and accuracy are regarded as the standards to evaluate the performance of four different programming methods: OpenMP, GLSL, CUDA and OpenCL. All the methods are estimated with comparable optimization strategies. The experimental results show that GPU has higher efficiency than multi-core CPU for fast cone-beam reconstruction, meanwhile CUDA is the best choice for programming on the multi-processor featured GPU.

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