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Keyhanpour, Mahdi, Ghassemi, Majid. (1403). Investigating the performance of tubular direct ammonia IT-SOFC with temkin- pyzhev kinetic model using machine learning and CFD. فناوری آموزش, (), -. doi: 10.22061/jcarme.2025.11195.2470
Mahdi Keyhanpour; Majid Ghassemi. "Investigating the performance of tubular direct ammonia IT-SOFC with temkin- pyzhev kinetic model using machine learning and CFD". فناوری آموزش, , , 1403, -. doi: 10.22061/jcarme.2025.11195.2470
Keyhanpour, Mahdi, Ghassemi, Majid. (1403). 'Investigating the performance of tubular direct ammonia IT-SOFC with temkin- pyzhev kinetic model using machine learning and CFD', فناوری آموزش, (), pp. -. doi: 10.22061/jcarme.2025.11195.2470
Keyhanpour, Mahdi, Ghassemi, Majid. Investigating the performance of tubular direct ammonia IT-SOFC with temkin- pyzhev kinetic model using machine learning and CFD. فناوری آموزش, 1403; (): -. doi: 10.22061/jcarme.2025.11195.2470

Investigating the performance of tubular direct ammonia IT-SOFC with temkin- pyzhev kinetic model using machine learning and CFD

Journal of Computational & Applied Research in Mechanical Engineering (JCARME)
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 30 بهمن 1403    اصل مقاله (1.12 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22061/jcarme.2025.11195.2470
نویسندگان
Mahdi Keyhanpour؛ Majid Ghassemi*
Department of Mechanical Engineering, Khaje Nasir Toosi University of Technology, Tehran, Tehran, 1999143344, Iran
تاریخ دریافت: 02 شهریور 1403،  تاریخ بازنگری: 27 بهمن 1403،  تاریخ پذیرش: 30 بهمن 1403 
چکیده
Researchers encounter difficulties in producing clean energy and addressing environmental issues. Solid oxide fuel cells (SOFCs) present a promising prospect to the growing demand for clean and efficient electricity due to their capacity to convert chemically stored energy into electrical energy directly. In enhancing this technology, ammonia is employed as a cost-effective and carbon-free fuel with convenient transport capabilities. Efficiently predicting the performance of a system in relation to its operating environment has the potential to expedite the identification of the optimal operating conditions across a broad spectrum of parameters. For this purpose, the performance of intermediate temperature solid oxide fuel cell (IT-SOFC) with inlet ammonia fuel is predicted utilizing machine learning, which is efficient in time and cost. Initially, the system is simulated with computational fluid dynamics finite element code to generate data for training machine learning algorithms (DNN, RFM and LASSO regression), followed by an evaluation of the predictive accuracy of these algorithms. The analysis demonstrates that the three examined algorithms exhibit sufficient accuracy in predicting the performance of the introduced solid oxide fuel cell (SOFC) system, all surpassing a 95 percent threshold. The RFM and DNN exhibit the most accurate predictions for the maximum temperature and power density of fuel cells, respectively.
کلیدواژه‌ها
Solid oxide fuel cell؛ CFD؛ Machine learning؛ Ammonia؛ Temkin-phyzhev
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