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Neural networks are useful tools for optimization studies since they are very fast, so that while capturing the accuracy of multi-dimensional CFD calculations or experimental data, they can be run numerous times as required by many optimization techniques. This paper describes how a set of neural networks trained on a multi-dimensional CFD code to predict pressure, temperature, heat flux, torque and emissions, have been used by a genetic algorithm in combination with a hill-climbing type algorithm to optimize operating parameters of a diesel engine over the entire speed-torque map of the engine. The optimized parameters are mass of fuel injected per cycle, shape of the injection profile for dual split injection, start of injection, EGR level and boost pressure. These have been optimized for minimum emissions. Another set of neural networks have been trained to predict the optimized parameters, based on the speed-torque point of the engine.

DPF regenerations involve a trade-off between fuel economy and DPF durability. High temperature regenerations of DPFs have fewer fuel penalties but simultaneously tend to give higher substrate temperatures, which can reduce thermal reliability. In order to weaken the trade-off, the integrated system-level model [1,2,3,4] is used to conduct optimization studies and explore novel regeneration strategies for DPF aftertreatment devices. The integrated model developed in the Engine Research Center (ERC) includes sub-models for engines, emissions, aftertreatment devices and controllers. Based on the engine and regeneration fuel economy, multiple and single cycle regeneration tests are performed and analyzed. The optimal soot loadings to initiate and terminate regenerations are discussed. A pulsed regeneration strategy, which is characterized by injecting multiple pulses of fuel (upstream of a DOC) during regenerations, is investigated.