Systematic and predictive trends to chromium poisoning in solid oxide fuel cell cathodes
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Engineering topics
Publications and source records attributed to Mason, Jerry H..
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Neural networks and computer vision techniques are used to rapidly predict solid oxide fuel and electrolysis cell performance from low-resolution tomographic data.
47th International Conference and Expo on Advanced Ceramics and Composites (ICACC 2023), Daytona Beach, FL, January 22-27, 2023
23rd Annual Solid Oxide Fuel Cell (SOFC) Project Review Meeting, Pittsburgh PA, October 25-27, 2022
Ni redistribution in the hydrogen electrodes of solid oxide cells is an important degradation mechanism. Its driving force is still under debate. This work focuses on the interplay between Ni(OH) 2 diffusion and Ni-YSZ wettability change. With a Ni(OH) 2 diffusion model developed in our previous work, we further employ three models for the Ni-YSZ contact angle to quantify the Ni-YSZ wettability change. The microstructure evolutions in a reconstructed Ni-YSZ electrode are simulated under selected experimental operating conditions. It is shown that the phenomenological model can capture the driving force of Ni spreading/detachment on YSZ surface and the Ni migration reported in experiments can be qualitatively reproduced through the competition between Ni(OH) 2 diffusion and Ni-YSZ wettability change. It is also found that the initial microstructure of the active layer and the microstructure characteristics in the support layer can strongly affect the distribution of steam partial pressure and overpotential, hence the Ni redistribution, in the active layer, which may explain the inconsistencies in experiments. Further, our results show that there are two missing pieces in current theory of Ni redistribution: a mechanism of fast Ni diffusion under humid atmosphere and the physical reason behind the Ni spreading/detachment.
Computer vision techniques are combined with NETL’s machine learning based model trained on results from NETL's long-term SOFC/SOEC performance prediction tool, in order to produce a rapid assessment tool for evaluation and improvement of SOC electrodes from more easily collected microstructural data.
TMS 2022, Virtual, February 27–March 3, 2022
17th International Symposium on Solid Oxide Fuel Cells (SOFC-XVII), Virtual, July 18-23, 2021
17th International Symposium on Solid Oxide Fuel Cells (SOFC-XVII), Virtual, July 18-23, 2021
Temperature gradients resulting from local electrochemical reactions, current distribution and geometry of gas flow channels in solid oxide fuel cells (SOFCs) create thermal stresses, localized thermophysical property gradients and uneven property evolution, contributing to SOFC degradation. This paper presents a new method to perform temperature measurements (up to 800°C) at high spatial resolutions to monitor the operation of SOFCs. Using femtosecond laser irradiation, distributed fiber sensors were hardened for high temperature environment applications. Distributed fiber sensors were embedded in interconnected plates using an additive manufacturing method to perform temperature measurements with 4-mm spatial resolution during the operation of a planar fuel cell. The measurement revealed the impact of various H 2 fuel concentrations and current loads have on temperature profiles of the SOFC tested. Temperature variation on the anode side was found to be less than 5°C, and 3°C on the cathode side. The measurements were compared to results from a multiphysics fuel cell performance model simulating similar conditions. These simulations predicted similar temperature gradients, indicating the experimental data obtained is reasonable. The model also predicts that the effect of the embedded sensor has on the local temperature will be minimal and that the gradient of temperature in the gas channels will be captured despite the separation between the sensor and the gas flow. Finally, the high spatial resolution data harnessed by these distributed fiber sensors provides experimental support for model-based design and optimization to improve the operational efficiency and longevity of solid oxide fuel cells and fuel cell assemblies.