Detailed transient modeling and FPGA-based real-time digital-twin development for sodium-cooled fast reactor
Bibliographic record
Abstract
The important role of small modular reactors (SMRs) in global energy production is highlighted by their substantial contribution towards clean, consistent, and high-capacity electrical power. These reactors are instrumental in the shift towards energy sources that mitigate carbon emissions and support sustainable development goals . Among the various nuclear reactor technologies, Liquid Metal-cooled Reactors (LMRs) stand out for their outstanding efficiency, enhanced safety features, and their capacity to utilize long-lived radioactive waste more effectively, thereby contributing to a more sustainable fuel cycle. This paper introduces a novel real-time digital-twin (RTDT) for the classical sodium-cooled fast reactor (SFR): the Experimental Breeder Reactor (EBR-II). To the best of our knowledge, this is the first RTDT developed specifically for SFRs, addressing a critical gap in current nuclear reactor simulation technologies. The RTDT is based on a 5 1 s t -order nonlinear transient model of the EBR-II system, which contains 3 subsystems at the top level and 6 subsystems at the component level. The entire EBR-II system was validated offline first in Simulink and in C programming language. The RTDT was then implemented on the Xilinx® VCU 118 field-programmable gate array (FPGA) based emulation platform. The results show the performance of the developed RTDT in comparison to the offline simulation and experimental results. The proposed RTDT achieved a significant improvement in computational speed, with 79.5% acceleration over real-time execution. The real-time capability enabled the emulation of various operational scenarios, including steady-state operation and transient conditions, providing invaluable insights into reactor performance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.004 | 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".