This paper introduces a conceptual framework for the development and implementation of a digital twin specifically designed for the Wały Śląskie Hydroelectric Power Plant. The primary focus is on defining the digital twin concept, addressing critical challenges encountered during its conceptualization, such as incomplete plant documentation, limited real-time data availability, and complexities in system integration. The digital twin architecture developed to address these issues features modular components, including data acquisition, data processing, visualization, and integration layers, emphasizing scalability, adaptability, and secure interoperability. Computational Fluid Dynamics (CFD) simulations and AI-based modeling techniques are integrated into the proposed framework as essential tools, providing critical data inputs for efficiency modeling and operational analysis. The proposed digital twin concept ensures accurate and reliable digital representation, facilitating informed decision-making and efficient operational management within hydropower plants.

Developing digital twins for legacy hydropower units is difficult when turbine documentation, calibrated performance data, and integrated measurements are incomplete. This study presents a Computational Fluid Dynamics (CFD)-assisted reconstruction workflow for a Kaplan turbine at the Wały Śląskie Hydropower Plant and evaluates its use as a physics-informed foundation for a digital twin. The flow passage was reconstructed from archival documentation, direct measurements, and optical 3D scanning of the runner. A steady-state Reynolds-averaged Navier–Stokes model was then prepared in OpenFOAM v2506 for selected head levels, guide-vane openings, and runner-blade angles. The simulations determined hydraulic performance, flow-field structures, and combinatory characteristics of the double-regulated turbine. The computed hydraulic efficiency reached approximately 85% in the nominal-head range, and the highest-efficiency region formed a broad plateau rather than a sharp optimum. CFD-derived and measurement-derived combinatory trends were consistent, although absolute values remain limited by relative field measurements and uncalibrated Winter–Kennedy flow estimation, a differential-pressure-based method. The CFD results were reduced to compact response surfaces and integrated with reconstructed geometry into an advisory digital twin for operating-point assessment, visualization, documentation, and training. This study establishes a robust workflow for this specific Kaplan turbine case where reverse engineering, integrated with CFD analysis, generates high-fidelity surrogate models for hydropower digital twins, effectively addressing the challenge of incomplete legacy documentation.