Development of a SOFC Performance Model to Analyze the Powder to Power Performance of Electrode Microstructures
Bibliographic record
Abstract
While the development of new solid oxide fuel cell (SOFC) electrodes must fundamentally occur within a lab setting, computational models which aid in the initial characterization and selection process can help reduce the overall financial cost and development time. A computational performance model, which both evaluates the structural properties and predicts the performance (current production) within electrode microstructures, generated from either experimental or numerical techniques, is presented. The application of the model to investigate the effects of different initial powder manufacturing parameters on the electrode performance is also demonstrated. The first part of this work presents the aforementioned performance model, which was developed in a modified version of OpenFOAM, MicroFOAM (1). The model begins by evaluating the total TPB length and the normalized effective species transport of all three phases within the electrode microstructure. The model then predicts the current production within the electrode by coupling the three percolating transport regions (electron, ion and pore) at the electrochemically active triple phase boundary (TPB), with a Butler-Volmer type expression. The identification of the microstructure properties and performance will enable relationships between these properties to be established. A particle-based numerical reconstruction model (2) is employed in this study to generate the synthetic electrode microstructures. The generated microstructures consist of a random distribution of overlapping spherical particles placed using a drop-and-roll algorithm. The packing algorithm allows for user specification of the initial starting parameters and is ideal for studying microstructure with a wide range of properties. Among the modifiable initial parameters include; the solid volume faction, porosity, and particle size distribution. In the second part of the study the performance model, developed in the first section, and the packing algorithm, mentioned above, are used to study the effects of different manufacturing parameters on the structural properties and performance of a cermet anode active layer. Initially the structural properties and performance of anodes generated with different initial electron-ion phase volume fractions (solid volume fraction) and porosities will individually be examined to identify the microstructure settings which maximizes the current production. Once identified, the two optimal individual settings will be combined and varied again to explore what optimal balance maximizes current production. Once identified, conclusions about the microstructure properties and manufacturing settings will be reviewed and discussed. Further manufacturing parameters including the particle size, particle size distribution, and thickness may also be explore in this work. References Choi, H.-W., Berson, A., Pharoah, J.G., Beale, S.B. Proc. IMechE. J. Power & Energy, 225(2): 183-197 (2011). Kenney, B., Vadmanis, M., Baker, C., Pharoah, J.G., Karen, K. J. Power Sources, 189: 1051-1059 (2009).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".