ANN Applied to Engine Optimization and Modeling

Neural networks have been successfully applied to diesel engine related areas other than emissions prediction. Delagrammatikas et al. developed a neural network to simulate a heavy duty diesel engine with the objective of reducing in-vehicle engine design time. In this work the ability of a neural network to model a heavy duty diesel engine for optimization purposes was compared to commonly used high-fidelity models. It was determined that the neural network possessed greater stability than the current high-fidelity model, and produced results with less than six percent error compared to the high-fidelity baseline model. Through this research it was

30 determined that neural networks are applicable in engine optimization and modeling situations and are comparable in accuracy and computational time to current high fidelity models [49]. Researchers have also worked on determining the viability of employing genetic algorithms and neural networks to aide in the optimization of diesel engine operations. Diesel engines were modeled and simulated with artificial neural networks, which could predict engine emissions and fuel consumption based on input engine operation characteristics [37]. The specific exhaust constituents that were modeled were HC, CO, PM, and NOx. The fuel consumption was evaluated on a brake-specific scale. A secondary objective was to arrive at an optimal combination of engine input parameters that would reduce the fuel consumption, while still complying with emission standards. Attempts at optimizing engine parameters via numerical modeling and techniques have been widely documented and have been determined to be applicable in limited scenarios. Numerical optimization algorithms can be affected by local extrema, and discontinuities in the models functions. This work examined artificial neural networks for optimization of the engine parameters, due to their relative immunity to functional discontinuities such as non-linear behavior and local minima and maxima. Table 2.4.6.1 shows each of the input variables associated with the artificial neural network (ANN). These parameters were varied independently in order to construct 440 test cases that were evaluated by the ANN. This study determined that ANN and genetic algorithms can be effectively employed to model engine operations and emissions, and be used to optimize engine operations to meet emissions and fuel consumption targets [37].

Table 2.4.6.1: Engine Operating Parameters Used as Inputs to Genetic Algorithm [37]. Operating Parameters Variable

Engine Speed N Fuel Mass Injected Mf

Air Mass Ma

Exhaust Gas Recirculation EGR Injection Pressure IP Start of Pilot Injection SOIP Start of Main Injection SOIM

Intake Temperature Tint Water Temperature Tw

31 Tutuncu et al. also determined through research that artificial neural networks were applicable to diesel and gasoline engine modeling. The objective of this work was to model exhaust emissions and performance of gasoline and diesel fueled internal combustion engines via an artificial neural network. Since the diesel engine model is most relevant to the current research topic, only it will be addressed here. The ANN model required five inputs to produce six outputs. R- Squared values for each of the modeled characteristics were over 0.99 when the artificial neural network results were compared to experimental data [41].

Other research has been directed towards simulating the rate of combustion in diesel engines during transient operation with an empirical model. An artificial neural network was selected for the model due to its speed of computation and application to nonlinear phenomena. To verify the results of the model, the model outputs were compared to experimentally obtained results. The neural network modeled the rate of heat released from a turbocharged diesel engine during transient conditions, and required the following inputs: in-cylinder pressure, air and fuel mass flow, EGR rate, boost pressure, exhaust manifold pressure, and intake and exhaust gas temperatures. The output of the model was the rate of heat released based on the time segment that the valves were closed and the heat lost through the engine walls. In the training of the artificial neural network, the input and output data was normalized so that the neural network learned about differences and not actual values. This normalization helped the neural network learn to predict more accurately. The learning method employed for the neural network was back propagation, and the model was structured in a multilayer perceptron manner. The multilayer peceptron structure consisted of one hidden layer and between one and eight neurons. After comparing the outputs of the neural network model to experimental results it was determined that neural networks are applicable to any engine transient operation situation [45]. An artificial neural network has also been designed to predict specific fuel consumption and exhaust temperature associated with a heavy duty diesel engine. The neural network developed for this task consisted of three inputs and two outputs. The inputs employed by the network were engine speed, brake mean effective pressure, and injection timing. The outputs were the desired objectives discussed above, brake specific fuel consumption, and exhaust temperature. When the neural network outputs were compared to experimental data, a difference of less than two

32 percent was achieved. This work shows that with few inputs, neural networks can accurately predict exhaust temperature and fuel consumption [48].

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