Development and Implementation of a Digital Twin Solution

The objective of this project is to design, develop, and implement a comprehensive Digital Twin solution for the National Research Council of Canada’s Ocean, Coastal and River Engineering (NRC OCRE) facilities, specifically the M32 Building and the Large Wave Current Flume (LWCF). The Digital Twin will create a high fidelity, data driven virtual representation of NRC OCRE’s physical assets to improve operational efficiency, reduce maintenance costs, lower energy and fuel consumption, and enhance forecasting capabilities for research and facility operations. The solution will deliver a high‑fidelity digital representation of the targeted physical assets, integrating real‑time and historical data from environmental sensors, physical modelling instrumentation, electrical load monitors, operational equipment status, weather feeds, and simulation outputs derived from both physics-based models (e.g., Computational Fluid Dynamics [CFD]) and AI/ML predictive models. The digital twin will support advanced analytics, physics‑based modelling, and machine learning to enable predictive maintenance, scenario testing, and operational optimization. The contractor will work collaboratively with NRC‑OCRE subject matter experts to define scope, use cases, and system specifications, ensuring alignment with scientific, operational, and engineering
priorities. The Contractor is expected to provide the data architecture component of this project required for data storage and management infrastructure. This capability must be secure, scalable, and compliant with Government of Canada cybersecurity, data governance. The data architecture must support the acquisition, processing, harmonization, and long‑term management of diverse data types — including sensor data, simulation outputs, environmental data, and multimedia content — while maintaining interoperability with NRC’s existing digital systems and infrastructure. This component must support the acquisition, processing, harmonization, and long‑term management of diverse data types—sensor data, simulation outputs, environmental data, and intuitive visualization interfaces (dashboards, 2D/3D, AR/VR), while maintaining interoperability with NRC’s existing digital systems. The end result will be a
sustainable, expandable digital twin platform that aligns with NRC’s long‑term research, operational, and digitalization goals.