Contact: Peter Senecal
University of Wisconsin-Madison

MADISON – Could Charles Darwin’s rules of evolution help engineers design high-performance engines of the future?

Computer models developed at the University of Wisconsin-Madison are doing just that, by using genetic algorithms to simultaneously increase fuel efficiency and reduce pollution.

Peter Senecal, a post-doctorate engineer at UW-Madison, created the computer models to help sort through literally billions of combinations of factors that determine engine performance – a task too enormous for conventional computer simulations.

Senecal says the most important advance is in improving pollution emissions without sacrificing fuel efficiency, and vice versa. Normally, engine designers who concentrate on solving one problem end up with major tradeoffs in the other.

The results to date have been dramatic. Using a Silicon Graphics supercomputer at UW-Madison’s Engine Research Center, Senecal created a diesel engine design that reduces nitric oxide emissions by three-fold and soot emissions by 50 percent over the best available technology. At the same time, the model reduced fuel consumption by 15 percent.

Six engine performance measures were studied, including fuel injection timing, injection pressure, and amount of exhaust recirculation. The simulation was then reproduced experimentally in a real diesel engine housed at the ERC. “We found that the agreement was excellent between what was measured in the lab engine and what the computer predicted,” Senecal says.

Senecal’s research will be published in an upcoming issue of the International Journal of Engine Research. He will also give an invited presentation Wednesday, June 21, to the Society of Automotive Engineers international meeting in Paris.

His work also is turning heads in the engine manufacturing industry, which faces major new federal pollution control mandates by the year 2002. Caterpillar Inc., a Peoria-based manufacturer of diesel engines for trucks and heavy equipment, is funding Senecal’s post-doctorate work that will focus on improving the geometry of engines.

Senecal says genetic algorithms have been developed in recent years for other engineering challenges, such as designing bridges and airplane wings. “I kind of stumbled onto this in the literature, and wasn’t sure if it would work for something as complex as engine design,” he says.

Here’s how it works: Senecal begins with five “individuals,” which are defined as one distinct set of the six engine parameters. Four of the individuals are randomly selected and the fifth is the baseline, or best known set of parameters.

Next, a computer model is used to weed out the best parameters of the first group. The two fittest “parents” are then allowed to “reproduce” and a new generation is formed, complete with “mutations” that represent marked improvements over the previous generation. The process is continued through successive generations until the computer identifies the most “fit” member of the group.

Senecal says this process narrows the field of potentially one billion calculations on the computer down to 200 to 250 of the best possibilities. The computer can accomplish in weeks what would otherwise take decades to run.

Mechanical engineering Professor Rolf Reitz, Senecal’s Ph.D. thesis advisor, says the computer model is extremely versatile and could be used for all types of engines. While curent work focuses on questions like fuel injection and air intake, studies of engine hardware are just beginning.

Reitz says the typical engine piston, for example, has not been fundamentally improved upon for decades. Yet engineers have no idea whether a different geometry could produce much better engines.

If engine manufacturers want a more powerful engine, or a more durable engine, one can program the genetic model to find those traits, too. “If you want your children to be long jumpers, high jumpers or sprinters, you can specify these attributes with this program,” Reitz says.

The diesel engine industry faces a U.S. Environmental Protection Agency mandate to cut nitric oxide emissions in half by 2002. Wisconsin’s small engine industry, also facing pollution-control deadlines, also has initiated a research program at UW-Madison using the genetic model.


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