Salt Composition Selection for Molten Salt Reactors: Required Pumping Power, Heat Exchanger Size, and Other Considerations
Bibliographic record
Abstract
A consequential design parameter of all molten salt reactor (MSR) designs is the composition of the primary fuel/coolant salt. Given the wide range of proposed salt compositions, understanding the tradeoffs associated with each composition is beneficial. Correspondingly, the primary objective of this foundational study is to conduct analyses and provide information to support the selection of salt compositions for MSRs. Neutron activation and cost and supply considerations are explored, and the absorption cross section is provided for the candidate fluoride salt components. The required pumping power and physical size of a molten salt heat exchanger are examined for several candidate fluoride salt compositions. Both clean (i.e. without fissile material) coolant salts, and fuel salts are analyzed. The required pumping power and heat transfer surface area were found to differ substantially among the candidate clean salts, but not for salts with a high fraction (22% mol fraction) of UF4.Highlights1. Optimization of circulation velocity considering pumping requirements for, and the physical size of, a molten salt heat exchanger.2. Salt composition selection considerations for MSRs: parasitic absorption, cost, activation, heat transport.3. Screening process identified Li, Na, K, Be, Zr, and F as feasible constituent components.4. Candidate coolant salts exhibited substantial differences in thermophysical performance while the differences between uranium-bearing salts were comparatively minor.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".