Detailed transient modeling and FPGA-based real-time digital-twin development for sodium-cooled fast reactor
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it