This research explores tidal energy as a reliable renewable source using digital twin technology. By simulating tidal farms in the Long Island Sound, it evaluates performance and environmental impacts before construction. The approach enables efficient, fish-friendly energy design, offering a scalable solution for sustainable ocean-based power generation worldwide.

This research develops Smart Twin PM, a six-layer digital twin system for predictive maintenance in manufacturing. By combining real-time data analytics, physics-based validation, cybersecurity checks, and smart scheduling, it reduces unexpected failures by 15% and false alarms by 20%, enabling proactive, trustworthy, and efficient machine maintenance.

This research explores next-generation digital twins—virtual representations of real-world systems that support decision-making through simulation and AI. By combining decentralization, privacy-preserving architectures, explainable AI, and scenario analysis, the work aims to help individuals and organizations evaluate alternative futures, make informed decisions, and build more transparent intelligent systems.