Research

My main research interest is in fundamental fluid dynamics. I use computational fluid dynamics (e.g. RANS) and data science (e.g. machine learning) techniques to better understand and model fluid flows in engineering systems. I am currently working under Dr. Heng Xiao at Virginia Tech getting my Ph.D. on Data Assimilation in Turbulence Modeling.

My other research interest is in renewable energy. Prior to coming to Virginia Tech, I worked on marine renewable energy at Sandia National Laboratories for 3.5 years. There I was part of the team that developed and released the widely-used open-source code WEC-Sim. I also worked on a project for predicting extreme loads on wave energy converters using a variety of statistical tools.

Publications

[1] Wu, Jinlong, et al. Physics-Informed Covariance Kernel for Model-Form Uncertainty Quantification with Application to Turbulent Flows. Computers and Fluids 193 (2019) 104292

[2] Coe, Ryan G., et al. Full Long-Term Design Response Analysis of a Wave Energy Converter. Renewable Energy 116 (Feb. 2018), pp. 356–366.

[3] Michelén Ströfer, Carlos, et al. Data-Driven, Physics-Based Feature Extraction from Fluid Flow Fields using Convolutional Neural Networks“.  Communications in Computational Physics, Vol. 25, No. 3, pp. 625-650.

[4] Ratanak So et al. Statistical Analysis of a 1:7 Scale Field Test Wave Energy Converter Using WEC-Sim. IEEE Transactions on Sustainable Energy 8.3 (Jan. 2017), pp. 1118–1126.