Numerical study of thermal effects in cryo-adsorptive hydrogen storage tank
Bibliographic record
Abstract
Hydrogen storage is an important issue in the practical application of hydrogen technology. Among various hydrogen storage technologies, the cryo-adsorptive hydrogen storage appears to have huge potential for a further research because of its high storage capacity at low pressure. In this study, a computational fluid dynamics model and a lumped parameter model are developed to simulate the cryo-adsorptive hydrogen storage processes. These two models are implemented on the fluentTM platform and matlab/simulinkTM environment, respectively. The thermodynamic behavior and thermal effect during the cryo-adsorptive hydrogen storage processes in a cryo-adsorption storage system are analyzed. Two adsorbents, activated carbon (Norit R0.8) and metal-organic-framework (Cu-BTC), have been studied. The pressure increases quickly at early stage and then keeps steady during the slow filling process. The temperature has larger gradient in the radial and smaller gradient in the axis. During the fast filling process, the release of adsorption heat leads to the temperature increasing in a short time when there is not enough time for efficient heat transfer; during the slow filling process, heat transfer becomes the main factor of temperature change. The effect of mass flow rate on temperature is more significant at the location near tank wall than the center location of the tank. A better external heat transfer condition and higher bed thermal conductivity lead to lower temperature level which will increase the adsorption capacity.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".