Design of a Hybrid Computational Fluid Dynamics–Monte Carlo Radiation Transport Methodology for Radioactive Particulate Resuspension Studies
Bibliographic record
Abstract
There are numerous scenarios where radioactive particulates can be displaced by external forces. For example, the detonation of a radiological dispersal device in an urban environment will result in the release of radioactive particulates that in turn can be resuspended into the breathing space by external forces such as wind flow in the vicinity of the detonation. A need exists to quantify the internal (due to inhalation) and external radiation doses that are delivered to bystanders; however, current state-of-the-art codes are unable to calculate accurately radiation doses that arise from the resuspension of radioactive particulates in complex topographies. To address this gap, a coupled computational fluid dynamics and Monte Carlo radiation transport approach has been developed. With the aid of particulate injections, the computational fluid dynamics simulation models characterize the resuspension of particulates in a complex urban geometry due to air-flow. The spatial and temporal distributions of these particulates are then used by the Monte Carlo radiation transport simulation to calculate the radiation doses delivered to various points within the simulated domain. A particular resuspension scenario has been modeled using this coupled framework, and the calculated internal (due to inhalation) and external radiation doses have been deemed reasonable. GAMBIT and FLUENT comprise the software suite used to perform the Computational Fluid Dynamics simulations, and Monte Carlo N-Particle eXtended is used to perform the Monte Carlo Radiation Transport simulations.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".