This article was prepared by staff writers in collaboration with outside contributors.
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A Wisconsin engineer is making use of computation
methods that mimic natural selectionor, in this case, maybe eugenicsto
improve the performance of diesel engines.
Peter Senecal, a partner with Convergent Thinking in Madison, Wis., is
using computational fluid dynamics, advanced visualization, and a selection
method using genetic algorithms aimed at reducing engine emissions and
enhancing fuel efficiency. He is looking ahead to 2007, when tighter vehicle
emissions requirements take effect.
Genetic algorithms are mathematical operations modeled on the biological
principle of gene selection. The idea is to identify desirable traits
in different computer models and then combine them. Senecal applied the
method to engines when he was earning his Ph.D. in mechanical engineering
at the University of Wisconsin in Madison.
Now Senecal and two of his partners, Keith Richards and Eric Pomraning,
are continuing the research into cleaner and more efficient engines. A
fourth partner in Convergent Thinking, David Schmidt, who is also a professor
at the University of Massachusetts in Amherst, is developing techniques
to simulate diesel fuel injection more accurately.
Convergent
Thinking models interaction between fuel spray and combustion chamber
geometrycritical to diesel engine performance and emissions. Right:
the temperature field of combustion in a conventional piston bowl with
a six-hole fuel injector.
Early research looked at changes in fuel injection velocity and timing.
Significant improvements have been found, for example, by increasing the
operating pressures of the fuel injectors. In its latest phase, the research
factors in changes to engine geometry, as well as adjustments in fuel
injection. Past results suggest that traditional engine geometry doesn't
take full advantage of new injection systems, Senecal said.
The team uses KIVA CFD software, developed at the Los Alamos National
Laboratory. The version Senecal uses was first adapted by the University
of Wisconsin and further refined in-house at Convergent Thinking. Researchers
view results by using EnSight visualization software from CEI of Apex,
N.C.
The researchers use genetic algorithms to sort complex information of
fluid dynamics in different combustion chambers. They run the algorithms
through a series of engine simulations, each with a slightly altered design.
After running the scenarios, the genetic algorithms select the best performer
from a group of trials and combine characteristics from that engine with
those of other high performers. The engines with the best "genes"
are simulated, using the same CFD and visualization process as in past
studies. Senecal then rates the engines on their fuel efficiency and the
amount of soot and NOx they generate.
Findings have helped to optimize engine design for increased efficiency
and lower emissionstwo areas of great importance to engine manufacturers
and environmentalists.
"We can now indicate to designers the variables that are most important
or ones that might have been overlooked had the computer not identified
them," Senecal said. The computational studies, for example, have
highlighted the importance of injecting fuel in short bursts instead of
a single stream, for a cleaner and more efficient burn.
It usually takes Senecal and his colleagues two weeks to run a series
of tests that can identify engine characteristics that produce significantly
lower emissions without sacrificing fuel economy. At the University of
Wisconsin, his engine optimization tool resulted in a design that consumed
15 percent less fuel than a standard diesel engine while producing one-third
the amount of nitrogen oxide and half the soot. The design, which did
not involve changes in the shape of the combustion chamber, was tested
and confirmed in a university laboratory.
Senecal said he plans a similar test of a design that will involve altered
engine geometry. He said he can't say much about it because it's propieitary.
He did say that he expects even better numbers than those in the earlier
lab test.
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© 2002 by The American Society of Mechanical Engineers
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