Browse Publications Technical Papers 2007-01-0168
2007-04-16

Computational Optimization of a Split Injection System with EGR and Boost Pressure/Compression Ratio Variations in a Diesel Engine 2007-01-0168

A previously developed CFD-based optimization tool is utilized to find optimal engine operating conditions with respect to fuel consumption and emissions. The optimization algorithm employed is based on the steepest descent method where an adaptive cost function is minimized along each line search using an effective backtracking strategy. The adaptive cost function is based on the penalty method, where the penalty coefficient is increased after every line search. The parameter space is normalized and, thus, the optimization occurs over the unit cube in higher-dimensional space.
The application of this optimization tool is demonstrated for the Sulzer S20, a central-injection, non-road DI diesel engine. The optimization parameters are the start of injection of the two pulses of a split injection system, the duration of each pulse, the exhaust gas recirculation rate, the boost pressure and the compression ratio. A zero-dimensional engine code is used to simulate the exhaust and intake strokes to predict the conditions at the closure of the inlet valves. These data are then used as initial values for the three-dimensional CFD simulation which, in turn, computes the emissions and specific fuel consumption.
Simulations were performed for two different cost functions which varied on their emphasis on the emissions targets. The best case showed that the nitric oxide and the particulates could be reduced by over 21% and almost 27%, respectively, below the EPA mandates, while the specific fuel consumption was reduces by approximately 1% over an experimentally verified operating point.
The effect of using boost pressure and the compression ratio as optimization parameters was also investigated and it was found that optimizing either parameter resulted in the nearly same optimal path. Further, it was found that the final emissions and fuel consumption values predicted by optimizing either parameter are practically the same.

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