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Tytuł artykułu

Improving Diesel Engine Reliability Using an Optimal Prognostic Model to Predict Diesel Engine Emissions and Performance Using Pure Diesel and Hydrogenated Vegetable Oil

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The reliability of internal combustion engines becomes an important aspect when traditional fuels with biofuels. Therefore, the development of prognostic models becomes very important for evaluating and predicting the replacement of traditional fuels with biofuels in internal combustion engines. The models have been made to model AVL 5402 engine emission, vibration, and sound pressure parameters using a three-stage statistical regression models. The fifteen parameters might be accurately predicted by a single statistic presented here. Both fuel type (diesel fuel and HVO) and engine parameters that can be adjusted were considered, since this analysis followed the symmetry of the methods. The data analysis process included three distinct steps and symmetric statistical regression testing was performed. The algorithm examined the effectiveness of various engine settings. Finally, the optimal fixed engine parameter and the optimal statistic were used to construct an ANCOVA model. The ANCOVA model improved the accuracy of prediction for all fifteen missing parameters.
Rocznik
Strony
art. no. 174358
Opis fizyczny
Bibliogr. 47 poz., rys., tab., wykr.
Twórcy
  • Institute of Data Science and Digital Technologies, Vilnius University, Lithuania
autor
  • Faculty of Mechanical Engineering, Lublin University of Technology, Poland
  • Institute of Mechanical science, Faculty of Mechanical Engineering, Vilnius Gediminas Technical University, Lithuania
  • Department of Automobile Engineering, Vilnius Gediminas Technical University, Lithuania
  • Institute of Mechanical science, Faculty of Mechanical Engineering, Vilnius Gediminas Technical University, Lithuania
  • Faculty of Mechanical Engineering, Lublin University of Technology, Poland
Bibliografia
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-47e68705-9fd4-4074-afb0-d49092778725
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