Temperature Distribution within a Compressed Gas Cylinder during Fast Filling
Bibliographic record
Abstract
Currently, pressurized gas is the leading technology for vehicular on-board hydrogen storage. During refueling, the hydrogen is expanded from the high-pressure fueling station cylinders, into the “empty” vehicle cylinder. The mass of the gas inside a cylinder can be calculated from the knowledge of the pressure and average gas temperature. However, during the fill process, the compression of the gas inside the cylinder leads to a rapid increase in temperature, this phenomenon along with the continuous introduction of cooler gas creates an evolving spatial distribution of gas temperature within the cylinder. In order to determine a correlation between the massaveraged gas temperature and local measurement of gas temperature, this study presents a CFD model of the filling of a hydrogen compressed gas cylinder. The model developed in this study is 2D and axi-symmetric, and solves the governing equations for compressible, unsteady, viscous turbulent flow. The model incorporates real gas effects, convective heat transfer from the gas to the cylinder walls and conduction through the cylinder walls to ambient. The results of the model show a large spatial variation of gas temperature within the cylinder during filling. The modeling results also help to identify the optimum location for the onboard gas temperature sensor such that the local measurement best represents the mass-averaged temperature of the gas within the cylinder. Hence allowing for the calculation of the mass of gas within the cylinder without using an expensive flow meter.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".