Argonne helps optimize piston bowl design for Caterpillar heavy-duty engines using high-performance computing
Argonne National Laboratory is helping Caterpillar improve efficiency and reduce emissions in their heavy-duty diesel engines. Heavy-duty diesel engines still power most large vehicles used in the construction, mining and transportation industries in the United States. Engineers are working to improve the fuel efficiency of these engines while minimizing pollution to reduce energy consumption and ensure the sustainability of these industries in the future.
Taking advantage of Argonne’s high-performance computing resources, researchers developed a potential piston design for Caterpillar’s engines that could improve fuel efficiency while reducing emissions.
Argonne researchers used supercomputers to optimize the design of piston bowls in heavy-duty engines for Caterpillar Inc. The top designs reduced fuel consumption and soot formation, and others had potential to reduce NOx. (Image by Chao Xu/Argonne National Laboratory.)
The team first created a framework to optimize combustion system design using a 3D computational fluid dynamics tool called CONVERGE, developed by Convergent Science, Inc. Merging heat transfer and combustion data derived from CONVERGE models with environmental data on soot and nitrogen oxide (NOx) production, they then ran hundreds of high-fidelity simulations to develop promising designs for piston bowls.
Using this method, they were able to identify several designs that had the potential to improve fuel efficiency while reducing emissions. Caterpillar created prototypes of the top-performing designs using additive manufacturing techniques to validate the model results.
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
One particularly promising piston bowl design improved the mixing process between fuel and air. Researchers found that it could reduce fuel consumption by nearly 1%, a measurable improvement, while reducing soot by up to 20%.
The workflow we developed will benefit everyone. We are publishing our methodology so companies can use it to design new piston bowls for themselves.—Sibendu Som, manager of the Computational Multi-Physics Research Section in Argonne’s Energy Systems division
In addition to the project’s simulation innovations, one of the team’s key contributions was its development of an industry-friendly approach, which allows companies to optimize their engine designs using their own in-house computer systems. This simplified model, based on the results of hundreds of the complex simulations, provides a similar level of accuracy while reducing the computational requirements by as much as 40%.
It actually reduces the testing costs if we have a predictive model and optimize designs on a supercomputer. It also reduces the time industry needs to develop a product—a great benefit.—Prithwish Kundu, a research scientist who is managing the project at Argonne
Our work with Argonne on this project enabled the exploration of a massive design space. 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.—Jon Anders, principal investigator and senior engineering specialist in Caterpillar’s Integrated Components and Solutions division
This research was funded by DOE’s Advanced Manufacturing Office and Vehicle Technologies Office in the Office of Energy Efficiency and Renewable Energy, under the High Performance Computing for Manufacturing (HPC4Mfg) Program umbrella. The team used Argonne’s Laboratory Computing Resource Center, as well as the Mira supercomputer (now retired), operated by the Argonne Leadership Computing Facility, a DOE Office of Science User Facility.