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Experts at Argonne are developing new engine combustion models that incorporate accurate descriptions of two-phase flows, chemistry, transport phenomena and device geometries to provide predictive simulations of engine and fuel performance.

Caterpillar joins Argonne's VERIFI team to undertake cooperative virtual engine design

Caterpillar has entered into a cooperative research and development agreement (CRADA) with the DOE's Argonne National Laboratory and its Virtual Engine Research Institute and Fuels Initiative (VERIFI), where researchers are developing new engine combustion models that incorporate "accurate descriptions of two-phase flows, chemistry, transport phenomena, and device geometries to provide predictive simulations of engine and fuel performance." This is the first such CRADA undertaken by VERIFI since its inception this past spring and also includes Convergent Science.

Cat has high hopes for the project and anticipates that simulations developed by VERIFI’s researchers will minimally reduce the time and cost of the design cycle for new engines, allow the rapid adaptation of fuels from new sources, and lead to substantial increases in fuel economy while meeting future emissions standards.

One of the benefits of working with Argonne researchers is that it will provide Cat engineers access to high-performance computing facilities and expertise in spray and combustion modeling to improve its own simulation practices.

Engines have many parameters that must be included in any reliable simulation. From fuel injectors to combustion chemistry, every part of the process of combustion has parameters with some level of uncertainty associated with them. These uncertainties are often extremely difficult for scientists to reduce. The field of uncertainty analysis provides the tools to investigate how parameter uncertainty influences simulation outcomes. Argonne uses a specific form of uncertainty analysis called global sensitivity analysis (GSA), which breaks down the uncertainty into constitute parts.

“There are lots of unknowns that are involved,” said Sibendu Som, a mechanical engineer at Argonne. “We’re using sensitivity analysis to understand how they all affect overall uncertainty.”

As Argonne sees it, VERIFI is "the first and only source in the world for high-fidelity, three-dimensional, end-to-end combustion engine simulation/visualization and simultaneous powertrain and fuel simulation, with uncertainty analysis."

The parameters being investigated include the relationships between the diameter of the nozzle in the fuel injector, the dynamics of the fuel spray, the proportion of fuel to air in the combustion chamber, and the exhaust products. By gaining a better understanding of how these parameter uncertainties affect outcomes, the VERIFI researchers seek to help industry create cleaner and more efficient engines.

Overall, Som and Argonne mechanical engineer Yuanjiang Pei and chemist Michael Davis have investigated 32 different parameters simultaneously, trying to establish how the uncertainties vary under different conditions. “If we can find a way to understand how uncertainty effects our simulations, we can take a step toward developing a more predictive simulation,” Som said.

Building on several decades of work by chemists, statisticians, and applied mathematicians, Argonne chemists have developed the tools to apply GSA to large chemical models in collaboration with other researchers at the University of Colorado and the University of Leeds.

These techniques were further refined in the last two years to allow their efficient application to engine simulations, leading to the present study, which involves a collaboration with the University of Connecticut. These new methods demonstrate the benefits of close collaboration between basic and applied research.

“This is the first time we’ve applied these methods in such a complicated system,” said Argonne mechanical engineer Doug Longman. “We have demonstrated that GSA can be used in a systematic way for something as complex as an engine simulation.”

In particular, VERIFI researchers are taking an iterative approach in which data gathered from the simulations can be fed back to both engine modelers and combustion chemists to further reduce uncertainty and create more predictive engine simulations.

“What’s unique about VERIFI is the way we’ve refined the tools to create engine simulations that are more reliable and applied high-performance computing resources to run simulations faster and more intensively than ever before,” Som said.

By taking advantage of the advances in computational power available today, the VERIFI team can identify the most important engine and fuel parameters and develop unique engine simulations and analyses to enable optimized engine combustion in the presence of uncertainty at any operating condition. In the near future, the VERIFI team plans to run diesel engine simulations of unprecedented scale on Mira, Argonne’s 10-petaflop IBM Blue Gene/Q supercomputer.

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