Air mass flow estimation of diesel engines using neural network

•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...

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Veröffentlicht in:	Fuel (Guildford) Jg. 117; S. 833 - 838
1. Verfasser:	Uzun, Abdullah
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier Ltd 01.01.2014 Elsevier
Schlagworte:	Adequacy Air intakes Air mass flow Air masses Applied sciences Charging Diesel Diesel engine Diesel engines Energy Energy. Thermal use of fuels Engineering research Engines and turbines Equipments for energy generation and conversion: thermal, electrical, mechanical energy, etc Exact sciences and technology Fuels Intercooling Neural networks Scaled Conjugate Gradient Algorithm Scaled Conjugate Gradient Algorithm Diesel engine Neural networks Air mass flow Intercooling Air flow Neural network Algorithm
ISSN:	0016-2361, 1873-7153
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Abstract	•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.
AbstractList	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. 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 networkas (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 engineas 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. •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.
Author	Uzun, Abdullah
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Keywords	Scaled Conjugate Gradient Algorithm Diesel engine Neural networks Air mass flow Intercooling Air flow Neural network Algorithm
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Snippet	•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... Air mass management is one of major factors affecting the performance of diesel engines, where experimental studies play a significant role in the performance...
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SubjectTerms	Adequacy Air intakes Air mass flow Air masses Applied sciences Charging Diesel Diesel engine Diesel engines Energy Energy. Thermal use of fuels Engineering research Engines and turbines Equipments for energy generation and conversion: thermal, electrical, mechanical energy, etc Exact sciences and technology Fuels Intercooling Neural networks Scaled Conjugate Gradient Algorithm
Title	Air mass flow estimation of diesel engines using neural network
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