Study of a low Mach model for two-phase flows with phase transition II: tabulated equation of state
Bibliographic record
Abstract
In order to model the water flow in a nuclear reactor core, the authors carried out several studies coupling a low Mach model - named Low Mach Nuclear Core (LMNC) model - to the stiffened gas law for the equation of state. The LMNC model is derived from the compressible Navier-Stokes equations through an asymptotic expansion with respect to the Mach number commonly assumed to be small in this domain of application. This simplified system of equations provides qualitative results worth of interest under the stiffened gas hypothesis such as analytical solutions in dimension 1 and enables an easier numerical treatment in any dimension compared to the parent compressible model solved in the low Mach regime. Moreover, in the temperature and pressure regime of interest (namely high temperature and pressure situations), the stiffened gas law turns out to be inaccurate, which requires a new modelling of the equation of state. This is why this paper is devoted to the coupling of the LMNC model to an equation of state tuned by means of experimental values (NIST) for thermodynamic variables. The very point in this study consists in presenting an easy-to-implement procedure to fit tabulated values and derivatives satisfying positivity and monotonicity constraints for pure liquid and vapour phases. Modifications of previously published numerical schemes designed for a stiffened gas law are detailed in dimensions 1 and 2 to allow the use of a general equation of state. In the regime of interest and when the coolant is water, numerical results highlight the difference of tabulated equation of state with the stiffened gas law and also show that thermal conduction effects can be ignored.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".