Sorption time based sizing of a solid-state hydrogen storage bed and thermal management system
Bibliographic record
Abstract
Solid-state hydrogen (H2) storage is a promising technology for transitioning to a carbon neutral H2 economy. However, it is limited by the slow exothermic/endothermic reactions that occur during charging/discharging owing to the poor thermal conductivity of most solid-state H2 storage materials. Although many researchers have addressed this challenge using various thermal management systems (TMSs), there is a lack of design tools available for sizing the reaction bed and TMS. This study aims to develop a multi-level model to size the solid-state storage system consisting of the reactor and the corresponding TMS. The sizing models are based on the sorption-time, an indicator that is crucial to the solid-state H2 storage technology. In addition, the proposed sizing procedure contains an inner loop and outer loop that apply the algebraic model (AM) and a combined lumped parameter model/computational fluid dynamics (LPM/CFD) model, respectively, resulting in marked reduction in solution time. Validations are conducted through comparison of AM predicted results with those of the experiments on solid-state H2 storage involving both internal and external TMSs. As the computational cost for the AM is negligible, the developed multi-level model facilitates the sizing of industrial-scale solid-state H2 storage systems with large or complex reactor beds and sophisticated TMSs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".