Performance maps of a diesel engine

This paper suggests a mechanism for determining the constant specific-fuel consumption curves of a diesel engine using artificial neural-networks (ANNs). In addition, fuel–air equivalence ratio and exhaust temperature values have been predicted with the ANN. To train the ANN, experimental results have been used, performed for three cooling-water temperatures 70, 80, 90, and 100 °C for the engine powers ranging from 1000 to 2300 – for six different powers of 75–450 kW with incremental steps of 75 kW. In the network, the back-propagation learning algorithm with two different variants, single hidden-layer, and logistic sigmoid transfer function have been used. Cooling water-temperature, engine speed and engine power have been used as the input layer, while the exhaust temperature, break specific-fuel consumption (BSFC, g/kWh) and fuel–air equivalence ratio (FAR) have also been used separately as the output layer. It is shown that R^2 values are about 0.99 for the training and test data; RMS values are smaller than 0.03; and mean errors are smaller than 5.5% for the test data.