堆栈(抽象数据类型)
固体氧化物燃料电池
热的
热电联产
材料科学
机械工程
核工程
工艺工程
余热
陶瓷
灵活性(工程)
发电
工程类
计算机科学
复合材料
功率(物理)
热交换器
热力学
物理
化学
统计
数学
物理化学
电极
阳极
程序设计语言
作者
Khalil Khanafer,Ali Al-Masri,Kambiz Vafai,Preethi Preethichandra
标识
DOI:10.1016/j.seta.2022.102159
摘要
Because of its high efficiency, fuel flexibility, and high-quality waste heat for cogeneration requirements, the solid oxide fuel cell (SOFC) is a potential fuel cell type for power generation in a variety of applications. High working temperatures provide these benefits, but they also come with drawbacks, such as restrictions on the operating environment, problems with thermal management, long on/off times, and the issue of selecting appropriate materials to assure compatibility of the physical material properties of the fuel cell stack components. However, the elevated process temperatures of the SOFC system result in technical challenges. The heat-up stage is a critical issue for the SOFC stack, since the transient process conditions lead to thermal gradients which are combined with material gradients. The result is a mismatch in the thermal–mechanical material behavior inducing high thermal stresses, which in turn affect the functionality of the fuel cell stack. In the SOFC stack under consideration, a glass ceramic joint is used to ensure reliable sealing between cells. This region is identified to be at risk, due to high thermal stresses. In this study the computational modeling approach is applied to predict the thermal-structural response of the stack components to different heat-up strategies and investigate the system with respect to fuel cell temperature, thermal gradients, and induced stresses. Computational thermal and structural results for time dependent heat-up speeds are presented and compared. Essentially, running the process with a constant speed would not provide an optimized solution. Instead, the obtained results show that the heat-up speed should be adjusted to achieve the desired state of fuel cell temperature, reduced thermal gradients and stresses. This target can be met by applying mathematical modeling approach, since experimental analysis are time and cost consuming.
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