Continuum scale modelling and complementary experimentation of solid oxide cells
Bibliographic record
Abstract
Solid oxide cells are an exciting technology for energy conversion. Fuel cells, based on solid oxide technology, convert hydrogen or hydrogen-rich fuels into electrical energy, with potential applications in stationary power generation. Conversely, solid oxide electrolysers convert electricity into chemical energy, thereby offering the potential to store energy from transient resources, such as wind turbines and other renewable technologies. For solid oxide cells to displace conventional energy conversion devices in the marketplace, reliability must be improved, product lifecycles extended, and unit costs reduced. Mathematical models can provide qualitative and quantitative insight into physical phenomena and performance, over a range of length and time scales. The purpose of this paper is to provide the reader with a summary of the state-of-the art of solid oxide cell models. These range from: simple methods based on lumped parameters with little or no kinetics to detailed, time-dependent, three-dimensional solutions for electric field potentials, complex chemical kinetics and fully-comprehensive equations of motion based on effective transport properties. Many mathematical models have, in the past, been based on inaccurate property values obtained from the literature, as well as over-simplistic schemes to compute effective values. It is important to be aware of the underlying experimental methods available to parameterise mathematical models, as well as validate results. In this article, state-of-the-art techniques for measuring kinetic, electric and transport properties are also described. Methods such as electrochemical impedance spectroscopy allow for fundamental physicochemical parameters to be obtained. In addition, effective properties may be obtained using micro-scale computer simulations based on digital reconstruction obtained from X-ray tomography/focussed ion beam scanning electron microscopy, as well as percolation theory. The cornerstone of model validation, namely the polarisation or current-voltage diagram, provides necessary, but insufficient information to substantiate the reliability of detailed model calculations. The results of physical experiments which precisely mimic the details of model conditions are scarce, and it is fair to say there is a gap between the two activities. The purpose of this review is to introduce the reader to the current state-of-the art of solid oxide analysis techniques, in a tutorial fashion, not only numerical and but also experimental, and to emphasise the cross-linkages between techniques.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".