Enhancing hydrogen storage efficiency in metal hydride tanks through conical heat exchangers and phase change material integration
Bibliographic record
Abstract
This study proposes an innovative approach to enhance the thermal management of metal hydride (MH) hydrogen storage systems by integrating a conical heat exchanger and phase change material (PCM) jacket. Magnesium-based MH reactors offer high hydrogen storage capacities, but their performance is limited by poor thermal conductivity, which hinders charging and discharging processes. The conical coil heat exchanger design improves heat transfer, reducing hydrogen charging time by 30% compared to conventional helical coils. Additionally, incorporating a PCM jacket optimizes the thermal performance, achieving up to a 69% reduction in charging time. The PCM absorbs excess heat during hydrogen charging and releases it during discharging, further improving the reactor's charging/discharging rate. Sensitivity analyses on hydrogen inlet pressure, heat transfer fluid temperature, and Reynolds number are performed, identifying optimal operating conditions. The proposed design is validated using experimental data, confirming its effectiveness in reducing charging and discharging times. To address the long-term thermal behavior of the PCM, future studies are recommended to experimentally validate PCM thermal stability over multiple hydrogen charging/discharging cycles. This integrated system offers a promising solution for enhancing hydrogen storage in clean energy applications, contributing to the development of more efficient and sustainable hydrogen fuel technologies. • Conical heat exchanger reduces hydrogen charging time by 30% in MH tanks. • Phase change material integration cuts charging time by up to 69%. • Sensitivity analysis identifies optimal hydrogen inlet pressure and thermal conditions. • Novel design improves thermal management for efficient hydrogen storage.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".