Prediction of the thermophysical properties of molten salt fast reactor fuel from first-principles
Why this work is in the frame
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Bibliographic record
Abstract
Molten fluorides are known to show favourable thermophysical properties which make them good candidate coolants for nuclear fission reactors. Here we investigate the special case of mixtures of lithium fluoride and thorium fluoride, which act both as coolant and as fuel in the molten salt fast reactor concept. By using ab initio parameterised polarisable force fields, we show that it is possible to calculate the whole set of properties (density, thermal expansion, heat capacity, viscosity and thermal conductivity) which are necessary for assessing the heat transfer performance of the melt over the whole range of compositions and temperatures. We then deduce from our calculations several figures of merit which are important in helping the optimisation of the design of molten salt fast reactors.
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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 it