Thermal effect simulation of hydrogen cryo-adsorption storage system
Bibliographic record
Abstract
One of the primary challenges to introduce hydrogen on the energy market is to improve onboard hydrogen storage and research more efficient distribution technologies to increase the amount of stored gas while lessening the storage pressure. The aim of this paper is to study hydrogen storage in an on-board hydrogen adsorption storage tank under low temperature and moderate pressures through the finite element analysis software Comsol MultiphysicsTM. The experimental study is carried out in a cylindrical tank with granular adsorbents in which the bed temperature is measured at various positions. The adsorbents we used in the experiment are activated carbon (NORIT R0.8) and metal-organic framework (Cu-BTC). Compared with the experiment results, the simulated pressure and temperature of the activated carbon (NORIT R0.8) have better agreement with experimental results than the metal-organic framework (Cu-BTC). The material properties of Cu-BTC are necessary to be identified accurately. Owing to the mass flow rate controlled comparatively accurate, the simulated mass balance have a good accordance with the experiment results. The effective thermal conductivity of the adsorbent bed and the heat transfer coefficient of the tank wall with liquid nitrogen affect significantly the heat transport during the adsorption process. The simulation results are very sensitive with the boundary type of inlet and other outer boundaries of the tank, which is valuable for further study.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".