Transforming Combustion Science and Technology with Exascale Simulations.
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This AI for Energy report further details grand challenges that provide significant opportunities for energy applications across nuclear energy, the power grid, carbon management, energy storage, and energy materials over the next decade. The main conclusions and opportunities from this study are available in the Key Findings section of this report.
We were given a charge to assess the necessity for new or upgraded facilities to ensure the Office of Science (SC) remains at the forefront of scientific discovery. This effort included evaluating five specific ASCR facilities for their potential to contribute to this goal and to rate the readiness for construction of each.
Combustion of hydrocarbon fuels has been a very challenging scientific and engineering problem due to the complexity of turbulent flows and hydrocarbon reaction kinetics. There is an urgent need to develop an efficient modeling capability to accurately predict the combustion of complex fuels. Detailed chemical kinetic models for the surrogates of fuels such as gasoline, diesel and JP-8 consist of thousands of chemical species and Arrhenius reaction steps. Oxygenated fuels such as bio-fuels and heavier hydrocarbons, such as from newer fossil fuel sources, are expected to have a much more complex chemistry requiring increasingly larger chemical kinetic models. Such models are beyond current computational capability, except for homogeneous or partially stirred reactor type calculations. The advent of highly parallel multi-core processors and graphical processing units (GPUs) promises a steep increase in computational performance in the coming years. This paper will present a software framework that translates the detailed chemical kinetic models to high-performance code targeted for GPU accelerators.
Combustion simulations of H2-O2 ignition are presented here, with a self-consistent plasma fluid model for ignition initiation. The plasma fluid equations for a nanosecond pulsed discharge are solved and coupled with the governing equations of combustion. The discharge operates with the propagation of cathode directed streamer, with radical species produced at streamer heads. These radical species play an important role in the ignition process. The streamer propagation speeds and radical production rates were found to be sensitive to gas temperature and fuel-oxidizer equivalence ratio. The oxygen radical production rates strongly depend on equivalence ratio and subsequently results in faster ignition of leaner mixtures.
This paper summarizes the outcomes from the Turbulent Flow Simulation at the Exascale: Opportunities and Challenges Workshop, which was held 4-5 August 2015, and was sponsored by the U.S. Department of Energy Office of Advanced Scientific Computing Research. The workshop objective was to define and describe the challenges and opportunities that computing at the exascale will bring to turbulent-flow simulations in applied science and technology. The need for accurate simulation of turbulent flows is evident across the U.S. Department of Energy applied-science and engineering portfolios, including combustion, plasma physics, nuclear-reactor physics, wind energy, and atmospheric science. The workshop brought together experts in turbulent-flow simulation, computational mathematics, and high-performance computing. Building upon previous ASCR workshops on exascale computing, participants defined a research agenda and path forward that will enable scientists and engineers to continually leverage, engage, and direct advances in computational systems on the path to exascale computing.
This work investigates novel algorithm designs and optimization techniques for restructuring chemistry integrators in zero and multidimensional combustion solvers, which can then be effectively used on the emerging generation of Intel's Many Integrated Core/Xeon Phi processors. These processors offer increased computing performance via large number of lightweight cores at relatively lower clock speeds compared to traditional processors (e.g. Intel Sandybridge/Ivybridge) used in current supercomputers. This style of processor can be productively used for chemistry integrators that form a costly part of computational combustion codes, in spite of their relatively lower clock speeds. Performance commensurate with traditional processors is achieved here through the combination of careful memory layout, exposing multiple levels of fine grain parallelism and through extensive use of vendor supported libraries (Cilk Plus and Math Kernel Libraries). Important optimization techniques for efficient memory usage and vectorization have been identified and quantified. These optimizations resulted in a factor of ~ 3 speed-up using Intel 2013 compiler and ~ 1.5 using Intel 2017 compiler for large chemical mechanisms compared to the unoptimized version on the Intel Xeon Phi. The strategies, especially with respect to memory usage and vectorization, should also be beneficial for general purpose computational fluid dynamics codes.
Coupled Computational Fluid Dynamics(CFD) - Discrete Element Method (DEM) models provide an accurate description of multiphase physical systems where a granular phase exists in an underlying continuous medium. The time integration of the granular phase in these simulations are typically handled using an explicit time integration scheme with a global time step that is much smaller than fluid time scales, in order to resolve inter-particle collisions. An adapative time integration technique that avoids this global constraint for the granular phase is presented in this work. The algorithm involves the use of a local time stepping approach to resolve collisional time scales for only a subset of particles that are in close proximity to potential collision partners, thereby resulting in substantial reduction of computational cost. This approach is observed to be 2-3X faster than traditional explicit methods for problems that involve both dense and dilute regions, while maintaining the same level of accuracy.
A laminar slot jet of supercritical CO2 entering a supercritical CO2 ambient environment is simulated with a computational fluid dynamics package using incorporating a thermodynamically consistent equation of state and high-fidelity transport coefficient models. For a jet at 8 MPa, at a temperature varying from 600 to 1000 K, entering an ambient at 8 MPa and a temperature of 1000 K, the flow structure is sensitive to the difference in operating temperature. Because there is very little heat transfer to the core of the jet, these results may be caused by both the density difference in the fluids, and momentum and heat transfer at the edge of the jet.
Accelerated bearing life tests were conducted to evaluate the effects of ODC-free bearing cleaning solvents as a replacement to CFC-113. Three chemically different, ODC-free cleaning solvents were tested against the CFC-113 baseline. Test bearings were representative of the type that would be used for a space scanner bearing application. The bearings were lubricated with two space flight oils. Lives with the replacement solvents exceeded those obtained with CFC-113 baseline. Pennzane-lubricated bearings enjoy a 2 to 6X life advantage over those lubricated with Bray 815Z oil.
Combustion of hydrocarbon fuels has been a very challenging scientific and engineering problem due to the complexity of turbulent flows and hydrocarbon reaction kinetics. There is an urgent need to develop an efficient modeling capability to accurately predict the combustion of complex fuels. Detailed chemical kinetic models for the surrogates of fuels such as gasoline, diesel and JP-8 consist of thousands of chemical species and Arrhenius reaction steps. Oxygenated fuels such as bio-fuels and heavier hydrocarbons, such as from newer fossil fuel sources, are expected to have a much more complex chemistry requiring increasingly larger chemical kinetic models. Such models are beyond current computational capability, except for homogeneous or partially stirred reactor type calculations. The advent of highly parallel multi-core processors and graphical processing units (GPUs) promises a steep increase in computational performance in the coming years. This paper will present a software framework that translates the detailed chemical kinetic models to high- performance code targeted for GPU accelerators.
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