Development of an open source software library for solid oxide fuel cells
Bibliographic record
Abstract
This report details the development of a Multi-Scale integrated fuel cell suite of software developed at NRC and Queens/RMC Fuel Cell Centre in conjunction with Forschungszentrum Jülich GmbH. Following the history of the project, some mathematical details of the cell/small stack level models are provided, together with brief details on other scale models. The model is developed for application to solid oxide fuel cells, though it may readily be applied to polymer electrolyte fuel cells. The implementation of the model into the C++ class library, OpenFoam, is then explained together with details of how to download and run the code from the repository where it resides. Some examples of practical applications, considered as validation and verification exercises of the code, together with discussion highlighting the advantages and disadvantages associated with the open source implementation, are provided. Finally, general conclusions from the project are drawn and suggestions for future work are proposed. ***Note: The openFuelCell code was migrated to a new GIT repository on SourceForge, see http://openfuelcell.sourceforge.net/. To obtain an account to download the code, contact Ron Jerome (ron.jerome@nrc-cnrc.gc.ca).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.030 |
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".