Research 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
[M4] Probabilistic Simulation of Vessel Operations to Support The Blue Economy
The below video is one of the outputs from our probabilistic vessel simulation framework developed in collaboration with Blue Ocean Transfers (BOT). The data-driven simulation model integrates environmental and operational information to produce probabilistic characterizations of key vessel operational metrics, including serviceability, fuel consumption, emissions, and costs. The video shows an animation of multiple simulation runs in the U.S. Mid-Atlantic Region, where the position of the vessel is animated along its planned route for each time-step. As the vessel moves along the route, the met-ocean scenarios are sampled using a bootstrapped time series model. Subsequently, the met-ocean scenarios are fed into fuel consumption models, which estimate the fuel consumption for various propulsion systems. The time series animations show the hourly and cumulative diesel consumption for a Tier III Diesel Vessel.
For details on our simulation model, please refer to our paper: Cory Petersen, Jiaxiang Ji, Feng Ye, Josh Kohut, Ahmed Aziz Ezzat, David Saginaw, Avril Montanti, and Jack Cammarota, “Probabilistic Simulation of Vessel Operations to Support The Blue Economy,” Under Review (2025).