Research Videos & Multimedia

[M1] POSYDON – Balancing the Short- and Long-Term Needs of Offshore Wind Energy Operation: 

The below video is one of the outputs from POSYDON — a stochastic-programming-based optimization model which jointly determines the optimal production and maintenance decisions for a fleet of offshore wind turbines. The video shows the evolution of the Dynamic Maintenance Cost (DMC) function, as well as the maintenance schedule for 5 offshore wind turbines. For more details on the formulation of POSYDON and the definition of DMC, please refer to our paper:

Papadopoulos, Petros, Farnaz Fallahi, Murat Yildirim, and Ahmed Aziz Ezzat. “Joint Optimization of Maintenance and Production in Offshore Wind Farms: Balancing the Short-and Long-Term Needs of Wind Energy Operation.” arXiv preprint arXiv:2303.06174 (2023).

 

[M2] AIRU-WRF: A Physics-Guided Spatio-Temporal Wind Forecasting Model and its Application to the U.S. Mid-Atlantic Offshore Wind Energy Areas

The below videos are related to our  offshore wind forecasting model, AIRU-WRF (the AI-powered Rutgers University Weather Research & Forecasting Model), which generates short-term, high-resolution wind resource and power forecasts in the U.S. Mid/North Atlantic that are highly relevant to the operation of the offshore wind projects that are soon to be installed in this geographical region. The first video shows the evolution of the geopotential height response surface over time, which is needed to estimate the geostrophic winds–one of the input features to our forecasting model. The second video shows AIRU-WRF’s wind field forecast maps, wherein red polygons roughly depict the OSW energy lease areas, which have generated $4.21 in bid auction in March of 2022. For more details on the formulation of POSYDON and the definition of DMC, please refer to our paper:

Ye, Feng, Joseph Brodie, Travis Miles, and Ahmed Aziz Ezzat. “AIRU-WRF: A Physics-Guided Spatio-Temporal Wind Forecasting Model and its Application to the US Mid-Atlantic Offshore Wind Energy Areas.” arXiv preprint arXiv:2303.02246 (2023).

 

[M3] Machine Learning for Revealing Spatial Dependence among Nanoparticles: Understanding Catalyst Film Dewetting via Gibbs Point Process Models

The below videos shows simulation results for the time evolution of simulated nanoparticle locations and areal density heat maps using our ML-based surrogate model for Catalyst Film Dewetting. For more details, please refer to our paper:

Machine Learning for Revealing Spatial Dependence among Nanoparticles: Understanding Catalyst Film Dewetting via Gibbs Point Process Models, Ahmed Aziz Ezzat and Mostafa Bedewy, The Journal of Physical Chemistry C 2020 124 (50), 27479-27494 DOI: 10.1021/acs.jpcc.0c07765