Air mass flow estimation of diesel engines using neural network - Uzun, Abdullah (Fuel (Guildford), 2014)
•We examine changes of air intake in diesel engine variety of engine speed.•Experimental studies are not enough all speeds of engine.•Neural network (NN) is able to detect the amount of air required all engine speeds.•NN based model can be used as an alternative method for estimating the effects of diesel engine’s air intake mass flow. Air mass management is one of major factors affecting the performance of diesel engines, where experimental studies play a significant role in the performance studies. However, the experimental studies are quite expensive and time consuming. Neural network’s (NN) have been used increasingly in a variety of engineering researches. NN based model are generally developed from experimental data. The objective of the study is to investigate the adequacy of neural networks (NN) as a quicker, more secure and more robust method to determine the effects of intercooling process on performance charged diesel engine’s air intake mass flow. In this study, a NN model has been developed configured tested for this purpose. The training and test data is obtained from real experimental work delivered earlier. Further details of development of NN are also demonstrated.