Towards More Accurate Prediction of Transport Properties of Hydrogen Over Wide Pressure/Temperature Conditions Using Entropy Scaling Theory and Volume-Translated Cubic Equations of State
Bibliographic record
Abstract
Abstract Hydrogen is an important energy source due to its renewability, high energy density per unit mass, and clean combustion. Accurate prediction of transport properties (i.e., viscosity, thermal conductivity, and self-diffusion coefficient) of hydrogen plays a crucial role in the design, operation, and optimization of various hydrogen-based industrial systems. In this study, we combine the entropy scaling theory with the volume-translated cubic equations of state (i.e., volume-translated Soave-Redlich-Kwong equation of state and volume-translated Peng-Robinson equation of state) proposed in our previous study, leading to the development of entropy-scaling-based transport property models. Compared to the conventional models, these improved models are 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 new models coupled with the volume-translated Soave-Redlich-Kwong equation of state (i.e., 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 coupled with the volume-translated Peng-Robinson equation of state (i.e., VT-PR EOS) also perform well in predicting these three transport properties, with %AADs of 2.96, 4.94, and 2.43, respectively. It is also worthwhile mentioning that the VT-SRK EOS and VT-PR EOS can completely reproduce the critical volume of hydrogen.
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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".