Розробка інформаційної моделі цифрового двійника технологічного процесу енергоблоку атомної електростанції
Bibliographic record
Abstract
This study's object is the technological process that occurs at the power unit of a nuclear power plant, which is considered as a complex technical system with a multi-level hierarchical structure of functional subsystems. This paper addresses the task to improve the efficiency of modeling, monitoring, and controlling the technological process at a nuclear power plant as a complex technical system with a multi-level hierarchical structure. A new approach to modeling the modes of a nuclear power plant based on system-cluster theory has been proposed. A cluster structure with key subclusters has been built: power control, protection, heat carrier adjustment, and emergency shutdown. Mathematical models have been constructed that take into account physical processes and logical-dynamic behavior of the monitoring and control system. A feature of the devised approach is the use of the value of fractal dimensionality as a quantitative indicator of the self-similar scaled structure of functional subclusters. An algorithm for calculating fractal dimensionality has been proposed, which allows for real-time analysis of dynamic changes in the external and internal structure of the power unit process control. Threshold values of the fractal dimensionality of subclusters have been determined for comparison with current parameters under normal and emergency modes. It was established that the loss of one control level in a subcluster leads to a decrease in fractal dimensionality from 1.83 to 1.60, signaling a possible degradation of the SCADA level. A model of a digital twin of the power unit process has been built based on a system-cluster approach, which allows for the implementation of visualization, simulation, monitoring, and diagnostics functions
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".