Neutron Transport Characteristics of a Nuclear Reactor Based Dynamic Neutron Imaging System
Bibliographic record
Abstract
An advanced dynamic neutron imaging system has been constructed in the McMaster Nuclear Reactor (MNR) for nondestructive testing and multi-phase flow studies in energy and environmental applications. A high quality neutron beam is required with a thermal neutron flux greater than 5.0×106 n/cm2-s and a collimation ratio of 120 at image plane to promote high-speed neutron imaging up to 2000 frames per second. Neutron source strength and neutron transport have been experimentally and numerically investigated. Neutron source strength at the beam tube entrance was evaluated experimentally by measuring the thermal and fast neutron fluxes, and simple analytical neutron transport calculations were performed based upon these measured neutron fluxes to predict facility components in accordance with high-speed dynamic neutron imaging and operation safety requirements. Monte-Carlo simulations (using MCNP-4B code) with multiple neutron energy groups have also been used to validate neutron beam parameters and to ensure shielding capabilities of facility shutter and cave walls. Neutron flux distributions at the image plane and the neutron beam characteristics were experimentally measured by irradiating a two-dimensional array of Copper foils and using a real-time neutron radiography system. The neutron image characteristics — such as neutron flux, image size, beam quality — measured experimentally and predicted numerically for beam tube, beam shutter and radiography cave are compared and discussed in detail in this paper. The experimental results show that thermal neutron flux at image plane is nearly uniform over an imaging area of 20.0-cm diameter and its magnitude ranges from 8.0×106 – 1.0×107 n/cm2-sec while the neutron-to-gamma ratio is 6.0×105 n/cm2-μSv.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".