Mathematical Modeling of Solid Oxide Fuel Cells
Bibliographic record
Abstract
In this thesis, an integrated microstructural–electrochemical modeling framework for Solid Oxide Fuel Cells (SOFCs) is presented. At the microscale, the model numerically reconstructs the microstructure of the electrodes, which are random porous composite media wherein the electrochemical reactions occur. The effective properties of the electrodes are evaluated in the reconstructed microstructures and used, as input parameters, in physically–based electrochemical models, consisting of mass and charge balances written in continuum approach, which describe the transport and reaction phenomena at the mesoscale within the cell. Therefore, the strong coupling between microstructural characteristics and electrochemical processes can be conveniently taken into account by the integrated model. The presented modeling framework represents a tool to fulfill a from–powder–to–power approach: it is able to reproduce and predict the SOFC macroscopic response, such as the current–voltage relationship, knowing only the powder characteristics and the operating conditions, which are the same measurable and controllable parameters available in reality. As a consequence, empirical, fitted or adjustable parameters are not required, feature which makes the model fully predictive and widely applicable in a broad range of conditions and fuel cell configurations as an interpretative tool of experimental data and as a design tool to optimize the system performance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".