Diesel fuels still account for nearly ten percent of all energy-related U.S. carbon emissions – most of them from heavy-duty vehicles like trucks and construction equipment. Energy efficiency is key to these machines, which struggle to operate via alternative fuel sources. Recently, a team of researchers from Argonne National Laboratory worked with Caterpillar – one of the largest manufacturers of construction vehicles and mining equipment – to advance Caterpillar’s products’ efficiency with the help of supercomputing.

The Argonne team utilized CONVERGE, a 3D computational fluid dynamics tool provided by Convergent Science, to build a framework for combustion engine optimization. Combining that model’s outputs – such as heat transfer data – and external soot and nitrogen oxide data, they used Argonne supercomputing power to run hundreds of simulations with one independent variable: various design iterations of piston bowls, the chambers in diesel engines that house the combustion process.

“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,” said Chao Xu, a postdoctoral researcher at Argonne who led the simulations, in an interview with Argonne’s Liz Thompson. Specifically, the researchers used the Mira supercomputer, an IBM Blue Gene/Q system that contained nearly 50,000 nodes and delivered 8.59 Linpack petaflops before it was retired on the last day of 2019. The team also made use of Argonne’s Laboratory Computing Resource Center.

The result: several promising new designs for piston bowls, including one that improved the fuel-air mixing process so effectively that it reduced fuel consumption by almost one percent and soot by up to 20 percent.

“By working together and leveraging simulation expertise and computing resources from Argonne with manufacturing and testing expertise at Caterpillar, we were able to optimize and test a piston on a timeline that was far shorter than would have otherwise been possible,” said Jon Anders, the senior engineering specialist in Caterpillar’s Integrated Components and Solutions division who served as the principal investigator for the project.

Argonne also developed a simplified model based on the hundreds of simulations that allowed industry partners to run their own internal simulations at much lower (up to 40 percent less) computational costs.

“The workflow we developed will benefit everyone,” said Sibendu Som, supervisor of the Argonne side of the partnership and manager of the Computational Multi-Physics Research Section in Argonne’s Energy Systems division. “We are publishing our methodology so companies can use it to design new piston bowls for themselves.”

To read the reporting from Argonne’s Liz Thompson, click here.