KBH Graduate Fellow Presents Wind Energy Research at International Conferences

Written by Benjamin Zastrow, PhD Candidate in Aerospace Engineering and Kay Bailey Hutchison Computational Energy Fellow

I recently had the opportunity to present research titled “Accelerating Wind Farm Uncertainty Quantification with Multifidelity Monte Carlo” at two conferences: NAWEA, North American Wind Energy Academy/WindTech at Portland State University and MATHIAS Days, organized by TotalEnergies in Paris.

Across both events, it was clear that computational science is helping engineers address the unique challenges of each energy source while providing shared tools for problems like uncertainty quantification that appear across the industry.

At WindTech, discussions included assessments of wind droughts in Texas and how wind resource uncertainties affect estimates of wind farm annual energy production. Similar topics came up at MATHIAS Days in presentations about quantifying uncertainty in 24-hour wind power forecasts using machine learning, history-matching of oil fields with Markov chain Monte Carlo, and stochastic optimization for fuel supply management. These varied applications demonstrate how methods developed in one area can often be adapted to solve new problems across disciplines.

Thank you to my advisors at The University of Texas at Austin, Karen Willcox and Anirban Chaudhuri, and to our collaborators at TotalEnergies, Rami Nammour, Wenchao YU, and Florian Euzenat, for your many ideas and suggestions. Thanks also to TotalEnergies, the Kay Bailey Hutchison Energy Center, and the Oden Institute for Computational Engineering and Sciences for providing the resources to make this kind of conference travel and collaboration possible for early-career researchers like me.