High Performance Computing Saudi Arabia (HPC Saudi) 2017
http://hdl.handle.net/10754/623334
Poster session submissions2021-07-25T06:38:28ZSimulation of Cycle-to-Cycle Variation in Dual-Fuel Engines
http://hdl.handle.net/10754/623336
Simulation of Cycle-to-Cycle Variation in Dual-Fuel Engines
Jaasim, Mohammed; Pasunurthi, Shyamsundar; Jupudi, Ravichandra S.; Gubba, Sreenivasa Rao; Primus, Roy; Klingbeil, Adam; Wijeyakulasuriya, Sameera; Im, Hong G.
Standard practices of internal combustion (IC) engine experiments are to conduct the measurements of quantities averaged over a large number of cycles. Depending on the operating conditions, the cycle-to-cycle variation (CCV) of quantities, such as the indicated mean effective pressure (IMEP) are observed at different levels. Accurate prediction of CCV in IC engines is an important but challenging task. Computational fluid dynamics (CFD) simulations using high performance computing (HPC) can be used effectively to visualize such 3D spatial distributions. In the present study, a dual fuel large engine is considered, with natural gas injected into the manifold accompanied with direct injection of diesel pilot fuel to trigger ignition. Multiple engine cycles in 3D are simulated in series as in the experiments to investigate the potential of HPC based high fidelity simulations to accurately capture the cycle to cycle variation in dual fuel engines. Open cycle simulations are conducted to predict the combined effect of the stratification of fuel-air mixture, temperature and turbulence on the CCV of pressure. The predicted coefficient of variation (COV) of pressure compared to the results from closed cycle simulations and the experiments.
2017-03-13T00:00:00ZBatched Triangular DLA for Very Small Matrices on GPUs
http://hdl.handle.net/10754/623339
Batched Triangular DLA for Very Small Matrices on GPUs
Charara, Ali; Keyes, David E.; Ltaief, Hatem
In several scientific applications, like tensor contractions in deep learning computation or data compression in hierarchical low rank matrix approximation, the bulk of computation typically resides in performing thousands of independent dense linear algebra operations on very small matrix sizes (usually less than 100). Batched dense linear algebra kernels are becoming ubiquitous for such scientific computations. Within a single API call, these kernels are capable of simultaneously launching a large number of similar matrix computations, removing the expensive overhead of multiple API calls while increasing the utilization of the underlying hardware.
2017-03-13T00:00:00ZPerformance Results using ANSYS HPC
http://hdl.handle.net/10754/623335
Performance Results using ANSYS HPC
Karim, Abbass; Ramon, Jose
2017-03-13T00:00:00ZEnhanced Software Management Environment based on Linux Environment Modules aimed to enforce scientific integrity in complex software environments
http://hdl.handle.net/10754/623337
Enhanced Software Management Environment based on Linux Environment Modules aimed to enforce scientific integrity in complex software environments
Naranjo, Jorge; Marchand, Benoit; Al-Barwani, Muataz
2017-03-13T00:00:00Z