Researchers from Korea have now developed an artificial intelligence (AI)-guided optimization framework that dramatically reduces the computational effort needed to optimize solid oxide electrolysis cell (SOEC) operation. By combining high-fidelity computational fluid dynamics (CFD) simulations with an AI-driven active learning framework, the researchers rapidly identified operating conditions that improve hydrogen production efficiency while maintaining the thermal stability required for long-term operation. This paper was made available online on 25 June 2026 and has been published in Volume 303, Part 1, of the journal Applied Thermal Engineering on August 1 2026.
Instead of evaluating every possible operating condition, the AI framework learns from each completed simulation and predicts which operating conditions are most likely to provide valuable new information. This allows researchers to focus computational resources on the most promising operating regions, replacing exhaustive trial-and-error searches with a faster, more data-efficient optimization strategy.
Unlike conventional approaches that seek a single optimum, the framework identifies a Pareto-optimal operating region that balances two competing objectives: maximizing electrochemical performance while minimizing temperature differences that can accelerate material degradation and reduce device lifespan. This gives engineers the flexibility to select operating conditions based on practical priorities, whether maximizing efficiency, improving durability, or achieving the best compromise between the two.
The framework improved the electrochemical performance index (EPI) by 14% while reducing in-plane temperature differences by 80% compared with the baseline operating condition. Compared with conventional random sampling using the same computational budget, the AI-guided approach achieved 2.5% higher final EPI and a 90.5% lower final temperature difference.
Most significantly, the framework achieved these results using only 17 high-fidelity CFD simulations. An exhaustive search across the same operating space would have required 6,561 simulations, equivalent to approximately 22,963.5 computational hours. The AI-guided framework achieved comparable optimization performance in just 60 hours, demonstrating its potential to shorten engineering design cycles and accelerate the development and commercialization of efficient green hydrogen technologies.
"Optimizing advanced hydrogen technologies has traditionally required enormous computational resources because engineers often need to evaluate thousands of possible operating conditions," said Mingi Choi, Assistant Professor in the Department of Future Energy Convergence at Seoul National University of Science and Technology. "By learning which simulations are most informative, our framework dramatically reduces the computational effort needed to find promising operating conditions. We believe this approach can accelerate the development of green hydrogen technologies and support faster innovation across a wide range of engineering applications."
Beyond SOECs, the researchers believe the AI-guided framework could accelerate the design of fuel cells, batteries, catalytic systems, and other energy technologies that rely on computationally expensive simulations. By reducing computational cost while maintaining optimization quality, the AI-guided framework could increasingly support scientific discovery by shortening development cycles and helping bring clean-energy technologies to market more quickly.
Reference
Title of original paper: Multi-objective optimization of solid oxide electrolysis cell efficiency and thermal gradient using an active-learning–CFD hybrid framework
Journal: Applied Thermal Engineering
DOI: https://doi.org/10.1016/j.applthermaleng.2026.132115
About the institute Seoul National University of Science and Technology (SEOULTECH)
Seoul National University of Science and Technology, commonly known as 'SEOULTECH,' is a national university located in Nowon-gu, Seoul, South Korea. Founded in April 1910, around the time of the establishment of the Republic of Korea, SEOULTECH has grown into a large and comprehensive university with a campus size of 504,922 m2.
It comprises 10 undergraduate schools, 35 departments, 6 graduate schools, and has an enrollment of approximately 14,595 students.
Website: https://en.seoultech.ac.kr/
About Assistant Professor Mingi Choi
Mingi Choi is an Assistant Professor in the Department of Future Energy Convergence at Seoul National University of Science and Technology. His research group investigates electrochemical mechanisms, characterization, and computational modeling of solid oxide fuel cells to optimize operating conditions and process technologies. He received his Ph.D. in Engineering from Sungkyunkwan University.
About Dr. Yonggyun Bae
Dr. Yonggyun Bae specializes in SOFC/SOEC interconnect and stack technologies, CFD-based electrochemical and thermo-fluid optimization, reforming catalyst kinetics, and single-cell and stack testing. He received his Ph.D. in Engineering from Yonsei University.
About Mr. Hoseob Lee
Hoseob Lee is an integrated M.S.–Ph.D. student in the Department of Future Energy Convergence at Seoul National University of Science and Technology, focusing on SOFC/SOEC CFD modeling and AI-based optimization.
Applied Thermal Engineering
10.1016/j.applthermaleng.2026.132115
Computational simulation/modeling
Not applicable
Multi-objective optimization of solid oxide electrolysis cell efficiency and thermal gradient using an active-learning–CFD hybrid framework
1-Aug-2026
The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.