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KPI Science News
ISSN 2617-5509 · e‑ISSN 2663-7472 Open Access · CC BY 4.0
Vol. 143 · Issue 2 · 2026 Jun 29, 2026 article

SIMULATION MODEL OF A DIGITAL TWIN FOR AN NPP POWER UNIT TECHNOLOGICAL PROCESS

KB
Kostiantyn Brovko Corresponding department of electrical engineering and electric power industry, V. N. Karazin Kharkiv National University, Kharkiv, Ukraine, Ukraine Ukraine
PB
Pavlo Budanov department of electrical engineering and electric power industry, V. N. Karazin Kharkiv National University, Kharkiv, Ukraine Ukraine
OV
Oleh Velykohorskyi department of electrical engineering and electric power industry, V. N. Karazin Kharkiv National University, Kharkiv, Ukraine Ukraine
YO
Yuliia Oliinyk department of electrical engineering and electric power industry, V. N. Karazin Kharkiv National University, Kharkiv, Ukraine Ukraine
NV
Nataliia Vynokurova department of electrical engineering and electric power industry, V. N. Karazin Kharkiv National University, Kharkiv, Ukraine Ukraine
SV
Serhii Voitenko department of electrical engineering and electric power industry, V. N. Karazin Kharkiv National University, Kharkiv, Ukraine Ukraine
PublishedJun 29, 2026
PDF (Ukrainian)
KPISN 2 VOL 143 · 2
VOL 143 · ISS 2 · 2026 View issue

Abstract

Background. Modern nuclear power plant (NPP) units are characterised by high structural complexity, multi-level hierarchical Information and Control (I&C) systems, and a vast number of interconnected technological parameters. Existing digital twin models primarily focus on individual physical processes – such as neutron kinetics, thermal hydraulics, or protection logic – often overlooking the comprehensive structure of control systems. The lack of a formalised approach for real-time assessment of subsystems’ hierarchical integrity limits the early detection of pre-accident states and proactive safety management. Objective. The paper aims to develop a simulation model of a digital twin for the NPP technological process based on a system-cluster approach. This method enables the assessment of the hierarchical, self-similar scaled structure of subsystems using fractal dimension as a quantitative metric. Methods. The study utilises a system-cluster approach, decomposing the power unit into functional sub-clusters: power control, protection system, coolant parameter regulation, and emergency shutdown. The mathematical framework integrates neutron kinetics equations, thermal-hydraulic relations, and logical-dynamic algorithms. To quantify structural complexity, the information space coverage method was employed to calculate fractal dimensions. The digital twin was implemented as a hardware-and-software complex integrated with the I&C, incorporating modules for data acquisition, structural identification, and predictive analytics. Results. Fractal dimension values were obtained for key subclusters of the power unit, reflecting their hierarchical complexity (notably, the maximum of 1.83 for the coolant control system and the minimum of 1.0 for the protection system). Normalised ranges of fractal dimension were determined as indicators of normal operation. Furthermore, a monitoring algorithm was developed to detect the loss of structural levels or subsystem degradation before functional failures manifest. Practical implementation demonstrated the feasibility of real-time structural diagnostics and adaptive response. Conclusions. The proposed fractal-cluster approach ensures comprehensive modelling of the NPP technological process by accounting for its hierarchical structure. Utilising fractal dimension as a structural diagnostic parameter expands the capabilities of traditional control methods and establishes the necessary framework for proactive safety management strategies based on digital twin technology.

Keywords

#digital twin #power unit #fractal dimension #system-cluster approach #information and management system

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