Implementation for Model of Adsoptive Hydrogen Storage Using UDF in Fluent
Bibliographic record
Abstract
This paper builds an axisymmetrical geometry model and simulates the charging, domancy, discharging and domancy processes of hydrogen storage tank based on activated carbon bed in a steel container at room temperature (302K) and medium storage pressure (10 MPa). The CFD model is based on the mass, momentum and energy conservation equations of the hydrogen storage system formed of gaseous and adsorbed hydrogen, activated carbon bed and steel tank wall. The adsorption model is based on Dubinin-Astakov adsorption isotherms. The simulation is implemented using a finite volume method through the computational fluid dynamics commercial software Fluent. User defined functions (UDFs) hooked in Fluent software are given to set the boundary conditions or modify the mass and energy conservation equations.The simulating results have good agreement with experimental results. Results show that the temperature of central region is higher than that near the wall during the charging process,while the temperature of central region is lower than that near the wall during the discharging process.The amount of adsorbed hydrogen is greater than that of the compressed gaseous hydrogen. Hydrogen storage by adsorption on high surface area activated carbon has obvious advantages.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".