dc.date.accessioned |
2021-06-03T11:02:22Z |
|
dc.date.available |
2021-06-03T11:02:22Z |
|
dc.date.issued |
2021 |
|
dc.identifier.issn |
1000-9345 |
|
dc.identifier.uri |
https://www.doi.org/10.1186/s10033-020-00525-4 |
|
dc.identifier.uri |
https://hdl.handle.net/20.500.12619/95473 |
|
dc.description |
The experimental data were obtained under the European Commission Marie Curie Transfer of Knowledge Scheme (FP6) pursuant to Contract MTKI-CT-4022004-509777 and was performed within a framework of a research and technological development program with the title SUSTAINABLE FUELUBE. |
|
dc.description |
Bu yayın 06.11.1981 tarihli ve 17506 sayılı Resmî Gazete’de yayımlanan 2547 sayılı Yükseköğretim Kanunu’nun 4/c, 12/c, 42/c ve 42/d maddelerine dayalı 12/12/2019 tarih, 543 sayılı ve 05 numaralı Üniversite Senato Kararı ile hazırlanan Sakarya Üniversitesi Açık Bilim ve Açık Akademik Arşiv Yönergesi gereğince açık akademik arşiv sistemine açık erişim olarak yüklenmiştir. |
|
dc.description |
Bu yayın 06.11.1981 tarihli ve 17506 sayılı Resmî Gazete’de yayımlanan 2547 sayılı Yükseköğretim Kanunu’nun 4/c, 12/c, 42/c ve 42/d maddelerine dayalı 12/12/2019 tarih, 543 sayılı ve 05 numaralı Üniversite Senato Kararı ile hazırlanan Sakarya Üniversitesi Açık Bilim ve Açık Akademik Arşiv Yönergesi gereğince açık akademik arşiv sistemine açık erişim olarak yüklenmiştir. |
|
dc.description.abstract |
Engine tests are both costly and time consuming in developing a new internal combustion engine. Therefore, it is of great importance to predict engine characteristics with high accuracy using artificial intelligence. Thus, it is possible to reduce engine testing costs and speed up the engine development process. Deep Learning is an effective artificial intelligence method that shows high performance in many research areas through its ability to learn high-level hidden features in data samples. The present paper describes a method to predict the cylinder pressure of a Homogeneous Charge Compression Ignition (HCCI) engine for various excess air coefficients by using Deep Neural Network, which is one of the Deep Learning methods and is based on the Artificial Neural Network (ANN). The Deep Learning results were compared with the ANN and experimental results. The results show that the difference between experimental and the Deep Neural Network (DNN) results were less than 1%. The best results were obtained by Deep Learning method. The cylinder pressure was predicted with a maximum accuracy of 97.83% of the experimental value by using ANN. On the other hand, the accuracy value was increased up to 99.84% using DNN. These results show that the DNN method can be used effectively to predict cylinder pressures of internal combustion engines. |
|
dc.description.sponsorship |
European CommissionEuropean CommissionEuropean Commission Joint Research Centre [MTKI-CT-4022004-509777] |
|
dc.language |
English |
|
dc.language.iso |
eng |
|
dc.publisher |
SPRINGER |
|
dc.relation.isversionof |
10.1186/s10033-020-00525-4 |
|
dc.rights |
info:eu-repo/semantics/openAccess |
|
dc.subject |
Artificial neural network |
|
dc.subject |
Deep neural network |
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dc.subject |
HCCI engine |
|
dc.subject |
Cylinder pressure |
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dc.subject |
Excess air coefficient |
|
dc.title |
Cylinder Pressure Prediction of An HCCI Engine Using Deep Learning |
|
dc.type |
Article |
|
dc.identifier.volume |
34 |
|
dc.relation.journal |
CHINESE JOURNAL OF MECHANICAL ENGINEERING |
|
dc.identifier.issue |
1 |
|
dc.identifier.wos |
WOS:000607880700004 |
|
dc.identifier.doi |
10.1186/s10033-020-00525-4 |
|
dc.identifier.eissn |
2192-8258 |
|
dc.contributor.author |
Yasar, Halit |
|
dc.contributor.author |
Cagil, Gultekin |
|
dc.contributor.author |
Torkul, Orhan |
|
dc.contributor.author |
Sisci, Merve |
|
dc.relation.publicationcategory |
Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı |
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dc.rights.openaccessdesignations |
DOAJ Gold |
|