AI-Driven Horizons: The Future of Biofouling Detection in Tidal Stream Turbines

This webinar will present a comprehensive machine learning-driven roadmap for the future of biofouling detection and estimation in tidal stream turbines. The talk will begin with an introduction to the phenomenon of biofouling in marine environments and its detrimental effects on turbine performance, hydrodynamic efficiency, structural integrity, and long-term maintenance costs. It will then provide a critical overview of existing detection and estimation approaches, identifying their respective strengths, technical limitations, and practical deployment challenges in highly dynamic underwater conditions. Building on this foundation, the webinar will examine recent scientific and technological advances, particularly those using data-driven models, remote sensing strategies, and hybrid physics–AI frameworks. The discussion will emphasize how machine learning can enhance early-stage fouling detection, improve estimation accuracy under incomplete or noisy datasets, and support predictive maintenance strategies. Finally, the presentation will consider emerging research directions and opportunities for innovation, outlining the key enablers needed to transition from concept to real-world implementation. By charting this trajectory, the webinar aims to highlight how intelligent monitoring and decision-support tools can ultimately enable more efficient, resilient, and sustainable biofouling management practices across the marine renewable energy sector.