Flow visualization and heat transfer measurements of molten salt natural convection
Bibliographic record
Abstract
Natural convection is an important mode of heat transfer in the design of molten salt energy technologies. While heat transfer correlations for natural convection in water have been widely studied, there is a lack of equivalent experimental data for molten salts due to material compatibility and instrumentation challenges posed by high-temperature experiments in a corrosive molten salt environment. An experimental investigation of convective heat transfer in a differentially-heated cavity using a binary nitrate salt mixture, NaNO 3 -KNO 3 (60-40 wt % ), is presented with the goal of obtaining empirical heat transfer correlations which can be compared to existing correlations. Particle Image Velocimetry (PIV) measurements are implemented to study changes in the large-scale flow structures with varying Rayleigh number. A triple-paned window design is introduced to address heat losses through the optical window and minimize deviations from ideal differentially-heated cavity boundary conditions. Transitions between Nusselt number scaling regimes are identified through heat transfer measurements and the mean flow field of large-scale circulation cells observed via PIV measurements. Good agreement with existing correlations for water is found in the range of 2 × 1 0 7 < Ra < 2 × 1 0 8 , while the observed heat transfer rates are found to have significantly stronger scaling exponents in the range of 2 × 1 0 8 < Ra < 2 × 1 0 9 .
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".