ABSTRACT: One of the main sources of renewable energy is wind, which generates tremendous power while also reducing the need for greenhouse gas-emitting traditional power sources such as hydrocarbons and coal. However, many wind turbines installed in the early 2000's are nearing the end of their lifespan, and the problem remains of how to maintain, reduce, or decommission these aging turbines in a cost efficient way. In this talk we describe a digital twin for damage detection and maintenance scheduling of wind turbines which can track the condition of a wind turbine under different operating conditions. A key concern for wind energy that contributes to power production losses and high maintenance costs is deterioration of the turbine blades over time from environmental stressors such as lighting strikes, icing, and accumulation of airborne particles which can result in leading edge erosion of the blades. We will discuss surrogate modeling of the turbines and classification of levels of leading edge erosion via machine learning.

BIO: Sue Minkoff is the Chair of Applied Mathematics at Brookhaven National Laboratory. From 2012-2024 she was a Professor of Mathematical Sciences and an Affiliated Professor in the Departments of Sustainable Earth Systems Sciences and Science and Mathematics Education at the University of Texas at Dallas. From 2000-2012 she served on the faculty in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County. Minkoff's research interests include uncertainty quantification, digital twins modeling, Earth science, and photonics. She received her doctorate in Computational and Applied Mathematics from Rice University. From 1995-1997 she was a National Science Foundation-Industrial postdoc joint with the University of Texas at Austin and British Petroleum, and from 1997-2000 she held the von Neumann Fellowship in the Mathematics Department at Sandia National Labs. In 2000 Minkoff was promoted to Senior Member of the Technical Staff in Sandia's Geophysics Department.