Sustainable Offshore Wind-Farm Siting in the Changing Climate

Interdisciplinary Areas: Power, Energy, and the Environment

Project Description

Offshore wind resources have been recognized as clean, renewable solutions to fulfill coastal communities' electrical needs. However, they are affected by the changes in climate and extreme weather patterns. This project studies and predicts sustainable sites for offshore wind farms under climate change, with an intense focus on the Caribbean, where in-situ observations are scarce. The research approaches are three-pronged, with remote-sensing and reanalysis data, high-resolution and climate models, and data analytics including assimilation and machine learning. The ocean surface winds obtained by assimilating the Global Navigation Satellite System Reflectometry (GNSS-R) delay-Doppler map will make up for the lack of in-situ data in the Caribbean. High-resolution modeling will then be used to downscale a present-day CMIP6 climate model to the region. Assuming the physical processes remain unchanged into the near future, with the detailed meteorological information, we build machine learning models with uncertainty quantification for downscaling mid-21st-century climate projections. A GIS-based multifactorial approach including natural, economic, and infrastructure factors will be utilized for site decisions. Optimal wind turbines will be investigated; the optimal farm designs will also be examined using Jensen's far wake kinematic model. The approach in this study is transferrable to other world regions covered by GNSS-R.

Start Date

06/01/2022

Postdoctoral Qualifications

A qualified candidate is equipped with strong physics, mathematics, and computing background. Experiences with numerical methods, computational fluid dynamics, and data analytics including machine learning and neural nets are highly desirable. Domain knowledge in environmental fluids, weather, and climate will help immensely. The project is computationally intensive; we strongly encourage tenacious and inquisitive individuals to apply.

Co-Advisors

James L. Garrison, jgarriso@purdue.edu, Professor of Aeronautics and Astronautics
Professor of Electrical and Computer Engineering (by courtesy), webpage: https://engineering.purdue.edu/AAE/people/ptProfile?resource_id=1422

Wen-wen Tung, wwtung@purdue.edu, Associate Professor of Earth, Atmospheric, and Planetary Sciences, webpage: https://www.eaps.purdue.edu/tung/members.html 

External Collaborators

Sue Ellen Haupt, Senior Scientist/Deputy Directory, Research Applications Laboratory at National Center for Atmospheric Research, webpage: https://staff.ucar.edu/users/haupt

Bibliography

1. Huang, F, Garrison, JL, Mark Leidner, S, et al. Assimilation of GNSS reflectometry delay-Doppler maps with a two-dimensional variational analysis of global ocean surface winds. QJR Meteorol Soc. 2021; 147: 2469– 2489. https://doi-org.ezproxy.lib.purdue.edu/10.1002/qj.4034
2. Ruf, C., Asharaf, S., Balasubramaniam, R., Gleason, S., Lang, T., McKague, D., Twigg, D., & Waliser, D. (2019). In-Orbit Performance of the Constellation of CYGNSS Hurricane Satellites, Bulletin of the American Meteorological Society, 100(10), 2009-2023. Retrieved Jul 14, 2021, from https://journals.ametsoc.org/view/journals/bams/100/10/bams-d-18-0337.1.xml
3. Zhang, C., Tung, W. & Cleveland, W. S. In Search of the Optimal Atmospheric River Index for US Precipitation: A Multifactorial Analysis. J. Geophys. Res. Atmos. 126, 1–18 (2021).
4. Tung, W. et al. Divide and recombine (D&R) data science projects for deep analysis of big data and high computational complexity. Japanese J. Stat. Data Sci. 1, 139–156 (2018).
5. Haupt, S.E., B. Kosovic, J.A. Lee, and P. Jimenez, 2019: Mesoscale Modeling of the Atmosphere, in Modeling and Simulation in Wind Plant Design and Analysis, P. Veers, Ed., IET Press, Volume 1, pp 65-116. Book DOI: 10.1049/PBPO125F, Chapter DOI: 10.1049/PBPO125F_ch3, e-ISBN: 9781785615221