Real-Time Digital-Twin for Synergistic Interaction of SMRs and Sustainable Power Systems
Bibliographic record
Abstract
Amid the challenges of digitalization, coordination complexities, and uncertainty in integrating diverse energy sources, digital-twin technology and small modular reactors (SMRs) are increasingly emerging as cutting-edge tools and technical pathways to address these issues. Against this backdrop, this article proposes a modular, scalable real-time digital-twin (RTDT) and surrogate physical-twin (SPT) collaborative hardware framework that predicts and responds to anomalies six times faster-than-real-time, while also addressing simulation interface issues across multiple physics modalities. The proposed system, deployed on a multiembedded field-programmable gate arrays platform, facilitates communication via the Aurora 64b/66b protocol and gigabit Ethernet based on IEEE 802.3, evaluating the impact of latency and data integrity on real-time performance. The SPT case study explores interactions among SMRs, the IEEE 39-bus system, and the CIGRÉ B4 DC grid with wind farms. The RTDT-SPT platform highlights the advantages of integrating next-generation advanced nuclear technology, renewable energy, and digital management systems by enhancing the coordination of SMRs in primary and secondary frequency control, reactor-leading mode, and turbine-leading modes, effectively smoothing the volatility of wind power generation.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".