Correlations for prediction of hydrogen gas viscosity and density for production, transportation, storage, and utilization applications
Bibliographic record
Abstract
Accurate determination of hydrogen transport properties is essential for hydrogen production , transportation, storage, and utilization. Different Equations of State (EoS) are commonly used to calculate hydrogen thermophysical properties . However, EoS approaches are usually implicit and require numerous calculations and could be very time consuming. Therefore, the end users often desire the predictions of thermophysical properties using simple empirical correlations, which can be considered a practical solution to reduce the computational burden of EoS calculations. This study presents new explicit empirical correlations using symbolic regression analysis of available experimental data to calculate hydrogen viscosity and density. The developed viscosity correlation provides accurate predictions over the temperature range of 100–2130 K for dilute gas and 14 (H 2 triple point) −1000 K for hydrogen gas up to 220 MPa. The results show an average absolute deviation (AAD) of 1.06% in the predicted gas viscosity , with the largest deviation in the vicinity of the critical point . The dilute gas viscosity was also predicted with an AAD of 0.467%. The density correlation represents a high prediction accuracy with an AAD of 0.26% over the temperature and pressure ranges of 150–423 K and 0.1–220 MPa, respectively. The developed correlations offer a higher prediction accuracy and find applications in hydrogen production , transportation, storage, and utilization value chain.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".