Direct Simulation of Transport Properties from Three-Dimensional (3D) Reconstructed Solid-Oxide Fuel-Cell (SOFC) Electrode Microstructures
Bibliographic record
Abstract
A well-known approach to develop a high efficiency solid-oxide fuel-cell (SOFC) consists of extracting the microstructure and transport properties such as volume fractions, internal surface area, geometric connectivity, effective gas diffusivity, effective electronic conductivity and geometric tortuosities from three-dimensional (3D) microstructure of the SOFC electrodes; thereafter, performing the SOFC efficiency calculations using previously mentioned quantities. In the present work, dual-beam focused ion beam - scanning electron microscopy (FIB-SEM) is applied on one of the SOFC cathodes, a lanthanum strontium manganite (LSM) electrode, to estimate the aforementioned properties. A framework for calculating transport properties is presented in this work. 3D microstructures of LSM electrode are reconstructed from a series of two-dimensional (2D) cross-sectional FIB-SEM images. Volume percentages of connected, isolated and dead-ends networks of pore and LSM phases are estimated. Different networks of pore and LSM phases are discretized with tetrahedral elements. Finally, the finite element method (FEM) is applied to calculate effective gas diffusivity and electronic conductivity of pore and LSM phases, respectively. Geometric tortuosities are estimated from the porosity and effective transport properties. The results obtained using FEM are compared with the finite volume method (FVM) results obtained by Gunda et al. [J. Power Sources, 196(7), 35929(2011)] and other numerical results obtained on randomly generated porous medium. Effect of consideration of dead-ends and isolated-ends networks on calculation of effective transport properties is studied.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".