Convection driven by a nonuniform radiative internal heat source in a cavity: Example of medical isotope production in liquid targets
Bibliographic record
Abstract
In the present study we use two-dimensional direct numerical simulations (DNS) to understand the coupled heat transfer to fluid flow in a liquid target for the production of nuclear medicines. Fluid motion is driven by buoyancy created by heat generated by a proton beam. The internal heat source has a gaussian distribution in the vertical direction and a rapidly growing intensity in the horizontal direction until it reaches a range at the Bragg peak where the heating drops to zero. The structure of the heating imposes two convective cells, separated at the location of the range. We solve the governing fluid flow and energy equations in a square cavity subject to highly nonuniform internal heating generated by the energy deposition of a proton beam. While most studies of convection driven by an internal heat source in a fluid layer have been focused on a uniform heating of the fluid, our study shows that the nonuniformity in the heat source has important implications for the temperature and flow fields, the boundary heat fluxes, and the growth of convective instabilities in the flow. Interestingly, the scalings of the maximum and averaged temperatures with the Rayleigh number compare similarly to previously found power laws for uniformly heated fluid layers. At higher power levels, the layer of fluid near the top cold boundary becomes convectively unstable via Rayleigh–Taylor instabilities. By comparing the rate of growth of these instabilities to their rate of advection to the boundaries of the cavity, a model is developed that predicts the instability of the convective cells for different values of the range of the beam and the Rayleigh number. Crucially, we demonstrate that the disturbances in the production of isotopes due to convective instabilities and the design of the cooling system is dependent on the location of the Bragg peak and must be considered in design of future generation of this class of target.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".