A polynomial regression model based educational software tool to interpret the internal combustion engine characteristics
Navaneetha Krishnan Balakrishnan 1 , Jennifer Philip 2 , Hasan Amin 3 , Prince Brahma 4 , Aaron Borges 4 , Vrishin Chari 4 , C Prabhu 4 *
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1 Everild Scientific, Coimbatore, Tamil Nadu, INDIA2 Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, NETHERLANDS3 Ford Motors, Chennai, Tamil Nadu, INDIA4 Department of Automobile Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Chengalpattu, Tamil Nadu, INDIA* Corresponding Author

Abstract

Technical education requires regular upgrades in pedagogical methodologies to keep up student’s skill on par with ever demanding job market. This paves the way for creating newer e-learning concepts for classroom to replace or supplement established teaching protocols. In line with this motive, this study deals with the development of an educational software tool to understand the traits of an internal combustion engine. The core of this software tool consists of polynomial regression equations, which in turn was arrived from statistical models using real world experimental data. A MATLAB-based GUI allows the operator to effortlessly interact with the software tool. Upon installation, the software requires the user to define input variables for it to automatically compute data and represent the output data in both visual and tabulated form. The advantage of three-dimensional surface plots for visual representation allows for understating the interactive effect of multiple input parameters on any given output parameter. Overall, average relative error for the model is less than 6%, thus exhibiting a good statistical fit.

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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.

Article Type: Research Article

EUR J SUSTAIN DEV RES, 2024, Volume 8, Issue 2, Article No: em0252

https://doi.org/10.29333/ejosdr/14302

Publication date: 01 Apr 2024

Online publication date: 28 Feb 2024

Article Views: 1607

Article Downloads: 2846

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