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Original Article
Balaji Ganesh
RV College of Engineering
Corresponding Author
ORCiD: https://orcid.org/0000-0003-1103-7506
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This work is licensed under a CC BY 4.0 License. Read Full License
This work provides a sequential approach to improve efficiency of Combustion Ignition engines which involves both performance and emissions by using Artificial Neural Network. In recent years continuous work is going on in improving the output and to reduce the emissions especially for Combustion Ignition engines which are mostly used for transportation purposes. In view of the above, the experimental data of a four stroke Combustion Ignition engine is taken as reference. However, the experimental data is split into three categories as input data, target data and output data in neural networks. All these data is trained using neural network toolbar in MATLAB with ten hidden layers by which error deviation are calculated, in order to reduce error deviation between neural network and experimental values, design of inlet manifolds is varied and performance parameters along with emissions is calculated and compared with neural network values. The results showed minimum error over the emission and performance parameters of CI engines from the manifold designs and ANN model. These results provide a sequential approach to improve efficiency of Combustion Ignition engines with the help of neural networks.
Keywords
Neural Networks, MATLAB, Emissions, C.I Engines, Inlet Manifolds
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Original Article
Balaji Ganesh
RV College of Engineering
Corresponding Author
ORCiD: https://orcid.org/0000-0003-1103-7506
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
The most recent version of this article is available here.
No community comments so far
Version 1
Posted 16 Dec, 2020
No comments provided
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Mechanical Engineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
The most recent version of this article is available here.
This work provides a sequential approach to improve efficiency of Combustion Ignition engines which involves both performance and emissions by using Artificial Neural Network. In recent years continuous work is going on in improving the output and to reduce the emissions especially for Combustion Ignition engines which are mostly used for transportation purposes. In view of the above, the experimental data of a four stroke Combustion Ignition engine is taken as reference. However, the experimental data is split into three categories as input data, target data and output data in neural networks. All these data is trained using neural network toolbar in MATLAB with ten hidden layers by which error deviation are calculated, in order to reduce error deviation between neural network and experimental values, design of inlet manifolds is varied and performance parameters along with emissions is calculated and compared with neural network values. The results showed minimum error over the emission and performance parameters of CI engines from the manifold designs and ANN model. These results provide a sequential approach to improve efficiency of Combustion Ignition engines with the help of neural networks.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
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