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The Fusion Fuel Cycle Simulator — towards integrated dynamic process simulation of fusion fuel cycles

2025· article· en· W4410438655 on OpenAlexaffabout
Tim Teichmann, J. Schwenzer, Christian Day, Y. Matsunaga, John McGrady, Yoshifumi Kume, C. Baus, Satoshi Konishi, Amir Motamed Dashliborun

Bibliographic record

VenueFusion Engineering and Design · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsCanadian Nuclear LaboratoriesAtomic Energy (Canada)
Fundersnot available
KeywordsFusionProcess (computing)Fusion powerComputer scienceFuel cycleDynamic simulationNuclear engineeringSimulationAutomotive engineeringEnvironmental sciencePlasmaEngineeringNuclear physicsPhysicsOperating system

Abstract

fetched live from OpenAlex

Kyoto Fusioneering in collaboration with Canadian Nuclear Laboratories, under the auspice of Fusion Fuel Cycles Inc. is developing the Fusion Fuel Cycle Simulator (FFC Sim ), a modular and flexible fusion fuel cycle design and simulation tool that can be used to provide a dynamic, physics-based and closed-loop simulation of holistic fusion fuel cycles. The FFC Sim aims to enable accurate quantitative prediction of individual unit performances, support dynamic and steady state simulation, as well as the integration of process chains up to full fuel cycle scale. This paper will introduce the high-level concepts of the tool, its modeling basis and implementation, as well as select verification cases.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.009
GPT teacher head0.266
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations3
Published2025
Admission routes2
Has abstractyes

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