Super simulations fast-track development of cleaner engine components

The auto industry is devising more low- and zero-emissions technologies, but the transition to cleaner vehicles won’t happen overnight. The combustion engine likely will stick around for decades, especially for commercial fleets and large, heavy-duty diesel vehicles like those used in construction and mining. 

A research and development partnership between Deerfield, Illinois-based Caterpillar and Argonne National Laboratory in Lemont, Illinois, is advancing improved vehicle fuel efficiency and reduced emissions with a better engine piston design.

The research

The team used supercomputers at Argonne to simulate better piston designs. The CONVERGE tool blends heat transfer and combustion data with environmental data on soot and nitrogen oxide production. They ran hundreds of simulations to identify promising piston bowl designs. 

These supercomputer simulations significantly speed product development, which contributes to lower product development costs.

“By leveraging the supercomputing resources available at Argonne, we ran very detailed simulations and also got the results much more quickly, reducing the simulation time from months to weeks,” Chao Xu, an Argonne postdoctoral appointee leading the simulation efforts, said in a news release.

Caterpillar created prototypes of the best designs using 3D printing techniques.

“Our work with Argonne on this project enabled the exploration of a massive design space,” said Jon Anders, principal investigator and senior engineering specialist in Caterpillar’s Integrated Components and Solutions division.

“We were able to optimize and test a piston on a timeline that was far shorter than would have otherwise been possible.”

The results

One design proved especially promising. It improves the fuel and air mixing process that occurs in the piston bowl. The research team estimates it could reduce fuel consumption by almost 1% and reduce soot by up to 20%.

The project also produced an industry-friendly approach to optimizing engine designs by using in-house computers for predictive modeling. The researchers will publish their methodology so other companies can use it as a guide for designing their own piston bowls.

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