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Record W4414860728 · doi:10.1139/cjp-2025-0096

Numerical investigation on thermal performance of rotating target for CANS

2025· article· en· W4414860728 on OpenAlexvenueno aff
Pulin Bai, Xuewu Wang, Zhe Wang

Bibliographic record

VenueCanadian Journal of Physics · 2025
Typearticle
Languageen
FieldEngineering
TopicSpacecraft and Cryogenic Technologies
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsThermalDissipationRotation (mathematics)Neutron temperatureBeam (structure)Thermal engineeringPower (physics)Neutron

Abstract

fetched live from OpenAlex

Compact accelerator-driven neutron sources (CANSs) demonstrate significant potential for applications in both scientific and industrial fields. A critical challenge for further expanding CANS’s applications is to improve the neutron yield, which requires a highly efficient thermal dissipation ability for neutron targets to bear the high power of incident beam. By employing the rotating target, the bearable thermal load of neutron target can be significantly enhanced. Therefore, a quantitative evaluation of the thermal dissipation capacity of rotating target is in need. With the finite volume method, we conduct a systematic numerical investigation on the thermal performance of the rotating target. Particularly, we study the effects of the number of target piece, the rotation speed, the flow rate of coolant, and the incident beam power with different beam spot distributions on the temperature of the neutron-producing layer of the target. These results allow us to quantify the thermal performance of rotating target while meeting stringent engineering criteria. Our results provide a robust foundation for the application of rotating targets in CANS.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.712
Threshold uncertainty score0.210

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.198
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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