Heat Transfer Analysis Methodology for Compression Hydrogen Storage Tank during Charge–Discharge Cycle
Bibliographic record
Abstract
Heat transfer analysis for the compression hydrogen storage tank (CHST) during the charge–discharge cycle is necessary to ensure quick and safe refueling for fuel cell vehicles. In this paper, a dual‐zone dual‐temperature (DZDT) model for a CHST during the charge–discharge cycle process is established. The constant/variable mass flow rates and heat transfer coefficients (HTCs) are combined to form three methods. Method 1 uses constant mass flow rate and constant HTC. Method 2 uses variable mass flow rate and variable HTC calculated through the energy conservation equation. Method 3 uses variable mass flow rate and variable HTC calculated through the empirical equation. Then, these methods are applied to the DZDT model for heat transfer analysis in three cases. Research shows that for the charging process, the simulated hydrogen temperatures by Method 2 agree well with experiment data for three CHSTs. Method 1 has a maximum error of about 20°C for 19 L CHST, 15°C for 29 L CHST, and 25°C for 40 L CHST. The error of Method 3 is between Methods 1 and 2. The simulated hydrogen pressures by Methods 2 and 3 agree well with the experimental data, while Method 1 has a maximum error of about 5 MPa for 19 L CHST, 10 MPa for 29 L CHST, and 3 MPa for 40 L CHST. For the discharge process, the simulated hydrogen temperatures by Methods 2 and 3 have a relatively slight difference with the experimental data, while Method 1 has relatively significant differences for three CHSTs. Only slight differences exist between the simulated hydrogen pressures by Methods 1, 2, and 3 with the experimental data for three CHSTs. In short, Method 2 can simulate the hydrogen temperature and pressure well during the charge–discharge process. Method 3 can simulate the approximate hydrogen temperature and precise hydrogen pressure during the charge–discharge process. Method 1 can only simulate the hydrogen pressure during the discharging process. The conclusions of this article can inform researchers which analysis methods are more reasonable to choose in future hydrogen‐filling studies.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".