Version 1
: Received: 21 December 2023 / Approved: 22 December 2023 / Online: 26 December 2023 (04:09:25 CET)
How to cite:
Doddi, S.; Hosamani, B.R.; Kusur, C.S.; Puthani, P.; Mangond, K. Analysis of Combustion Characteristics’ of CI DI VCR Engine using Blends of Mixture of two Biodiesel and Diesel with Artificial Neural Network. Preprints2023, 2023121883. https://doi.org/10.20944/preprints202312.1883.v1
Doddi, S.; Hosamani, B.R.; Kusur, C.S.; Puthani, P.; Mangond, K. Analysis of Combustion Characteristics’ of CI DI VCR Engine using Blends of Mixture of two Biodiesel and Diesel with Artificial Neural Network. Preprints 2023, 2023121883. https://doi.org/10.20944/preprints202312.1883.v1
Doddi, S.; Hosamani, B.R.; Kusur, C.S.; Puthani, P.; Mangond, K. Analysis of Combustion Characteristics’ of CI DI VCR Engine using Blends of Mixture of two Biodiesel and Diesel with Artificial Neural Network. Preprints2023, 2023121883. https://doi.org/10.20944/preprints202312.1883.v1
APA Style
Doddi, S., Hosamani, B.R., Kusur, C.S., Puthani, P., & Mangond, K. (2023). Analysis of Combustion Characteristics’ of CI DI VCR Engine using Blends of Mixture of two Biodiesel and Diesel with Artificial Neural Network. Preprints. https://doi.org/10.20944/preprints202312.1883.v1
Chicago/Turabian Style
Doddi, S., Prashant Puthani and Keerti Mangond. 2023 "Analysis of Combustion Characteristics’ of CI DI VCR Engine using Blends of Mixture of two Biodiesel and Diesel with Artificial Neural Network" Preprints. https://doi.org/10.20944/preprints202312.1883.v1
Abstract
Combustion parameters are the important aspects in the development and research of an internal combustion engines. Hence present work is an attempt to develop an artificial neural network (ANN) model for anticipating the combustion characteristics of computerized CI DI VCR, four strokes, single cylinder, and water cooled engine using mixture of two biodiesel and diesel blends as a fuel. The two biodiesel selected are Jatropha and Simarouba and mixed in the ratio of 75:25. Two biodiesel mixtures is used for the preparation of various blends with diesel. Experiments are carried out at different compression ratio, at different % load and blends B20, B40, B60and neat diesel (B00) fuel to accumulate the assorted combustion data. The input variables are % load, compression ratio, blends and crank angle. The outputs well thought-out are cylinder pressure (CP), net heat release rate (NHRR), cumulative heat release (CHR), rate of pressure rise (RPR), mass fraction burned (MFB) and maximum cylinder pressure. The assessment is focused on developing artificial neural network for the prediction and verifying the combustion characteristics of engine. MATLAB software is used for analysis of different combustion aspects of the engine. Multilayer neural network perceptions are used with Feed-Forward Back Propagation algorithm with performance function for mapping the input and output parameters. The experimental outcomes are compared with ANN estimated results for the analysis. The outcome from the study authenticate that experimental results are in match with ANN results. The accuracy of the ANN model shows the developed model can effectively predict the engine combustion characteristics with excellent regression coefficients ‘R’ and its values are close to unity and the mean square errors are extremely less. ANN model approach is competent towards predicting the combustion aspect of diesel engine with good accuracy
Keywords
combustion; cylinder pressure; rate of pressure rise; net heat release rate; rate of pressure rise; mass fraction burned; artificial neural network
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
