Towards accurate modeling of transport properties of supercritical hydrogen using entropy scaling theory and improved volume-translated cubic equations of state
Bibliographic record
Abstract
Hydrogen is an important energy source due to its renewability, high energy density, and clean combustion. Accurate prediction of transport properties of hydrogen is crucial for the design and optimization of various hydrogen-based energy systems. In this study, we combine the entropy scaling theory with four cubic equations of state (CEOSs) (including the original Soave-Redlich-Kwong EOS (SRK EOS) and Peng-Robinson EOS (PR EOS), as well as the volume-translated SRK EOS (VT-SRK EOS) and volume-translated PR EOS (VT-PR EOS) proposed in our previous work), leading to the development of various entropy-scaling-theory-based transport property models. Compared to the conventional models, the entropy scaling theory integrated with our previously proposed models is capable of more accurately predicting the transport properties of hydrogen at pressures from triple-point pressure (i.e., 0.01 MPa) to 300 MPa and temperatures from critical temperature (i.e., 33.15 K) to 600 K. More specifically, the models coupled with the VT-SRK EOS yield %AADs of 3.09, 5.03, and 3.19 in predicting viscosity, thermal conductivity, and self-diffusion coefficient, respectively. The proposed models combined with the VT-PR EOS also perform well in predicting these three transport properties, achieving %AADs of 2.96, 4.94, and 2.43, respectively. It is worthwhile mentioning that the VT-SRK EOS and VT-PR EOS can completely reproduce the true critical volume of hydrogen. We believe the good accuracy and broad applicability of the models proposed in this work can make it a powerful tool for modeling the transport properties of hydrogen under diverse conditions encountered in practical hydrogen-based energy applications.
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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".