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

使用机器学习方法检测固体氧化物燃料电池中的气体混合

• 批准号: 2710691
• 金额: --
• 依托单位: Loughborough University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2710691
• 关键词: Gas; mixing; detection; Solid; Oxide

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.

固体氧化物燃料电池（SOFC）是将化学能（氢或碳氢化合物与氧化剂）转化为电能的电化学装置。SOFC适用于为重型机动车辆提供动力或用于固定发电。固体氧化物燃料电池在650摄氏度或更高的温度下工作。高温操作环境引起应力，并且可能导致脆性燃料电池上的裂纹。裂缝允许气体混合和不受控制的燃烧，最终可能破坏燃料电池。该项目旨在及时检测这些裂缝，这对SOFC的安全运行至关重要。该项目有两项计划的研究成果。第一项研究建立了一个模型，分析了基于气体流动和热释放的裂纹的情况下，SOFC堆的热分布。第二个研究建立机器学习分类器来检测裂纹故障。