Application of machine learning algorithms for predicting the engine characteristics of Hydrogen fuelled dual fuel engines

The global concern over emissions resulting from the combustion of fossil fuels has intensified over recent decades. Stringent emission regulations have compelled automobile manufacturers to prioritize research and development efforts towards exhaust emissions mitigation. This study aims to leverage machine learning models to assess the accuracy of numerical predictions for a dual-fuel compression ignition (CI) engine running on hydrogen and diesel. Various input parameters, including hydrogen concentration, engine load, diesel intake, speed, and equivalence ratio, will be incorporated to analyze emissions such as oxides of nitrogen (NOx), carbon dioxide (CO2), hydrocarbons (HC), and smoke.

How to Apply

Express your interest in this project by emailing Dr Maryam Ghodrat at m.ghodrat@unsw.edu.au with a copy of your CV and your academic transcript(s). 

If you are shortlisted, you will be asked to submit a formal application for admission to the PhD program.