Artificial Neural Networks for Internal Combustion Engine Performance and Emission Analysis
作者： Anant BhaskarGarg , Parag Diwan , Mukesh Saxena
DOI: 10.5120/15212-3705
关键词: Reduction (complexity) 、 Internal combustion engine 、 Computer science 、 Experimental research 、 Fuel efficiency 、 Range (aeronautics) 、 Automotive engineering 、 Operations research 、 Artificial neural network
摘要: This paper presents an analytical work for better design system that contributes to the reduction of fuel consumption and emission vehicle performance. The main technological issue on engines today is comply with standards cost-effective measures in order keep engine price still attractive customer. experimental research performance are time consuming quite expensive. purpose this optimize using artificial neural networks (ANN). Back propagation network was used prediction model analyzed data from various tests which different operating parameters measured. highlights framework suitable ANN several engine. optimization includes a range engine-operating conditions, specified limits emissions. General Terms Artificial Neural Networks, Engine Operation, approaches management operations, algorithms, architecture
ijcaonline.org参考文章(10)
Y He, C J Rutland, Application of artificial neural networks in engine modelling International Journal of Engine Research. ,vol. 5, pp. 281- 296 ,(2004) , 10.1243/146808704323224204
Barat Ghobadian, Hadi Rahimi, AM Nikbakht, Gholamhassan Najafi, TF Yusaf, None, Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network Renewable Energy. ,vol. 34, pp. 976- 982 ,(2009) , 10.1016/J.RENENE.2008.08.008
Gholamhassan NAJAFI, Barat GHOBADIAN, Talal F YUSAF, Hadi RAHIMI, Combustion Analysis of a CI Engine Performance Using Waste Cooking Biodiesel Fuel with an Artificial Neural Network Aid American Journal of Applied Sciences. ,vol. 4, pp. 759- 767 ,(2007) , 10.3844/AJASSP.2007.759.767
Kemal Tütüncü, Novruz Allahverdi, Modeling the performance and emission characteristics of diesel engine and petrol-driven engine by ANN computer systems and technologies. pp. 58- ,(2009) , 10.1145/1731740.1731803
Eric Rask, Mark Sellnau, Simulation-Based Engine Calibration: Tools, Techniques, and Applications SAE Technical Paper Series. ,vol. 113, pp. 821- 832 ,(2004) , 10.4271/2004-01-1264
Simon Haykin, Neural Networks: A Comprehensive Foundation ,(1998)
Jos M. Alonso, Fernando Alvarruiz, Jos M. Desantes, Leonor Hernndez, Vicente Hernndez, Germn Molt, Combining Neural Networks and Genetic Algorithms to Predict and Reduce Diesel Engine Emissions IEEE Transactions on Evolutionary Computation. ,vol. 11, pp. 46- 55 ,(2007) , 10.1109/TEVC.2006.876364
Yahya H. Zweiri, Lakmal, D. Seneviratne, Diesel Engine Indicated Torque Estimation Based on Artificial Neural Networks acs/ieee international conference on computer systems and applications. pp. 791- 798 ,(2007) , 10.1109/AICCSA.2007.370723
José M Desantes, Jose J Lopez, Jose M Garcia, Leonor Hernández, None, Application of Neural Networks for Prediction and Optimization of Exhaust Emissions in a H.D. Diesel Engine SAE 2002 World Congress & Exhibition. ,(2002) , 10.4271/2002-01-1144
Yakup Sekmen, Mustafa Gölcü, Perihan Erduranlı, Yaşar Pancar, Prediction of Performance and Smoke Emission Using Artificial Neural Network in a Diesel Engine Mathematical & Computational Applications. ,vol. 11, pp. 205- 214 ,(2006) , 10.3390/MCA11020205