Gas mixing detection in Solid Oxide Fuel Cells using Machine Learning approach

点击翻译按钮获取中文摘要

Solid Oxide Fuel Cells (SOFCs) are electrochemical devices that converts chemical energy (of hydrogen or hydrocarbons with oxidants) into electrical energy. SOFCs are suitable for powering heavy duty automotive vehicles or for stationary power generation. SOFCs operate at 650 degreesC or above. The high temperature operating environment causes stress and can lead to cracks on the brittle fuel cell. The cracks allow gas mixing and uncontrolled combustion that could eventually destroy the fuel cell. This project addresses the timely detection of these cracks that is paramount for the safe operation of SOFCs. The project has two planned research outputs. The first study builds a model that analyse the heat distribution of a SOFC stack based on the gas flow and heat release of crack scenarios. The second study builds machine learning classifiers to detect the crack faults.