Computational modelling of explosions caused by failed Li‐ion batteries
Bibliographic record
Abstract
Abstract The implementation rate of renewable energy sources such as lithium‐ion batteries has grown over the last decade. Consequently, the number of explosion occurrences associated with these batteries has also increased. Such events are due to a process called thermal runaway (TR). The flamelet combustion approach has been widely used to model premixed combustion. However, its applicability for modelling accidental explosions from lithium‐ion batteries remains limited. Moreover, the effects and contributions from stress, strain, and wrinkling on the flame front in gas mixtures from Li‐ion batteries are not fully understood. As far as computational modelling is concerned, the same effects require further investigation. The current research investigates the performance of the flamelet approach for modelling premixed combustion scenarios caused by the gases ejected by a fully charged lithium‐ion‐phosphate (LFP) battery. A new laminar burning velocity correlation is proposed based on experimental data to calculate overpressure, flame position, and flame velocity in a semi‐confined geometry. Promising results are presented resorted by good agreement with experimental data.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".