The Ocean Energy Safety Institute (OESI) is pleased to announce it will fund six proposals for innovation and technology development to improve the safety and environmental sustainability of Wind Energy development. The OESI received 45 project proposals for four distinct pathways in response to the Wind Energy Request for Proposals (RFP). Total funding for the six projects selected will reach $2,689,928 pending successful contract negotiations.
Organized under an agreement between the Bureau of Safety and Environmental Enforcement (BSEE), Department of Energy (DOE), and Texas A&M Engineering Experiment Station (TEES), the OESI is a consortium of industry, national labs, NGOs, and academia created to support the development of critical safety and environmental improvements for all offshore energy activities, including renewable and traditional energy. In June this year, the OESI announced ten awards in the Oil and Gas application area, with a project start date of October 1 and a total value of $3,885,057. Four awards in the Marine Energy application area were announced in September, which are currently in the contracting phase and have a total value of $1,099,672.
Funding will be awarded to the following project titles and lead organizations:
| Project Title | Lead Organization | Requested Amount |
|---|---|---|
| W-T1-P1.1 Enhanced site condition understanding, resulting in proper design basis (metocean, geotechnical and seismic) and safe operational windows | ||
| Pacific Region Seismic Soil Investigation and Dynamic Soil-Structure Behavior Modeling | Triton Anchor | $500,000 |
| Hindcasting and forecasting geotechnical operational windows of seabed mobility and scour based on a global windfarm GIS, CFD modelling and ML/AI | University of Florida | $500,000 |
| W-T1-P1.4 Modeling of dynamic loading (ice, wind, wave, current, seismic and rain) on turbine systems and components | ||
| Development of variable-fidelity FOWT (floating offshore wind turbine) digital twins for system/component safety in extreme sea environments | Texas A&M Engineering Experiment Station | $355,931 |
| W-T2-P2.1 Reducing human exposure to hazardous conditions | ||
| Remote Smart Monitoring of Offshore Wind Plant Components | The University of Texas at Dallas | $499,649 |
| Metahuman Models for Reducing Human Exposure to Hazardous Conditions During Offshore Wind Turbine Operations and Maintenance | Texas A&M Engineering Experiment Station | $499,356 |
| W-T2-P2.3 Improved understanding and risk analysis to reduce personnel transport and transfer risk | ||
| Risk assessment of human transfer from service vessels to wind turbines, and development of an optimal transfer system using two-body simulation tools | University of Massachusetts Amherst | $334,992 |
| TOTAL | $2,689,928 | |
Project Synopsis
Pacific Region Seismic Soil Investigation and Dynamic Soil-Structure Behavior Modeling
PI: Nathan Krohn, Triton Anchor
Safety concerns for floating offshore wind in the Pacific region include issues with subsea mooring infrastructure, such as seismic-related mooring loss, marine life hazards, fishing industry and vessel traffic interference, seafloor and habitat destruction, and oversized anchor handling. These concerns can be addressed with specialized high-capacity anchors. Still, the primary challenge lies in the lack of understanding of the region’s geotechnical and seismic conditions and their impact on mooring foundations, as most wind turbine platforms rely on a limited number of anchors, which compromises redundancy and safety. To address this, this project proposes a detailed soil investigation to assess the Pacific Ocean’s dynamic seismic soil conditions. This project call will provide the opportunity for innovative research along the US West Coast to investigate and characterize site-specific soils and seismic environments and ultimately provide a better understanding of the seismic effects on plate and caisson technologies.
The main project goals are: 1. Investigate a minimum of 20 locations across the Pacific coast’s current wind lease areas and develop strength profiles for at least 10 meters of soil data at each location, 2. Determine the relationship between recorded seismic events and seafloor/slope soil stability and calibrate a general correlation between magnitude, frequency, and soil behavior, 3. Provide a Pacific region soil characterization assessment with site-specific seismic properties to influence the geotechnical communities’ anchor design basis and anchor certification processes for safer and more resilient systems.
Hindcasting and forecasting geotechnical operational windows of seabed mobility and scour based on a global windfarm GIS, CFD modelling and ML/AI
PI: Sivaramakrishnan Balachandar, University of Florida
Innovations in marine technology for the oil and gas industry focus on minimizing marine infrastructure while emphasizing operational safety due to significant environmental and safety risks. Conversely, marine wind farms (offshore wind farms, offshore wind farm (OWF)) rely on larger-scale infrastructure in the marine environment to achieve cost-efficiency. Ensuring safe and consistent operations in this context involves addressing design and economic constraints. The success of OWFs depends on understanding geohazards and implementing proper scour protection to prevent expensive cable failures, as sediment dynamics in structures like sandbanks and channels are influenced by complex interactions with the marine environment (morphodynamics) across various scales, from seasonal weather patterns to extreme events like hurricanes. The US is relatively new to the offshore wind farm (OWF) market and is adopting various aspects of turbine technology and OWF development. However, a crucial consideration is the geotechnical characterization of the US seabed, particularly as wind turbines become larger, wind farms expand in size, and floating OWFs are positioned farther from the shore, facing extreme metocean conditions. Accurate knowledge of the seabed’s geotechnical properties is vital for determining anchor stability under cyclical loading. Given the rapid growth in this sector, there’s a need to develop advanced ground models that can predict shallow seabed geotechnical conditions before the collection of core borings and high-resolution geophysical data. This study aims to create a new generation of seabed models that incorporate morphodynamics and possess both predictive and inverse capabilities to support offshore wind expansion. To translate these learnings to the US wind market, this project plan to simultaneously compile a geotechnical GIS database documenting active seabed hydrodynamic and morphodynamic processes and regimes on US continental shelves in diverse settings. The database will target different stages of the lease process (BOEM) and be compatible with the NOAA Digital Coast Database. The impact of different shelf controls and metocean forcing will vary in different morphodynamic regions.
Development of variable-fidelity FOWT (floating offshore wind turbine) digital twins for system/component safety in extreme sea environments
PI: MooHyun Kim, Texas A&M Engineering Experiment Station
The project focuses on the development and validation of digital twin models to simulate the dynamic responses of Floating Offshore Wind Turbines (FOWTs) under extreme environmental conditions such as hurricanes, submarine earthquakes, tsunamis, and ice loadings. Texas A&M University (TAMU) and Technip Energies (T.EN) will work on both high-fidelity Computational Fluid Dynamics (CFD) and mid-fidelity potential-flow models. These models will simulate the coupled dynamics of floater-turbine-control-mooring systems, incorporating complex interactions between wind, wave, and current forces. TAMU’s CHARM3D-FAST and NREL’s OpenFAST will be employed for mid-fidelity simulations, while T.EN’s high-fidelity Wind Turbine Numerical Wave Basin (WTNWB), using StarCCM+, will handle more detailed simulations. The project will conduct extensive simulations for 5MW and 15MW turbines, comparing results from both models and validating them against experimental data, such as those from DeepCWind model tests. The high-fidelity model will address highly nonlinear phenomena, like freak waves and solitary waves, and the mid-fidelity model will account for nonlinear wave-current interactions and second-order effects using Morison drag formula. The findings will be used to improve the design load cases and guidelines, particularly for extreme conditions like earthquakes and tsunamis, and the American Bureau of Shipping (ABS) will evaluate and potentially revise their requirements based on the project’s outcomes.
Remote Smart Monitoring of Offshore Wind Plant Components
PI: Mario Rotea, The University of Texas at Dallas
The focus of this work is on remote monitoring for the early detection of incipient faults and damage in mooring lines, anchors, high-voltage underwater cables, hulls, and turbine/hull interfaces. Early detection of these events is expected to reduce human intervention time for maintenance and repair. Two different technologies for remote monitoring will be investigated. Smart Remote Monitoring System using Motion Sensing, a technology developed by TAMU, uses motion sensors to monitor the hull, mooring lines, anchors, and power cables of floating offshore wind turbines. The data is fed to an onboard computer running ANNs to detect faults and damage, which can then be communicated to a control room. It can also estimate incoming 3D waves and currents.
Smart Remote Monitoring System using Distributed Fiber Optic Sensing – the NEC laboratory uses Distributed Fiber Optic Sensing (DFOS) to create a sensor network for offshore wind turbines. The DFOS technology will provide real-time data on temperature, strain, acoustics, and vibration, allowing for early fault detection. The system will be developed for fixed-bottom and floating turbines, focusing on mooring lines/anchors, collision incidents, and high-voltage underwater cables.
Metahuman Models for Reducing Human Exposure to Hazardous Conditions During Offshore Wind Turbine Operations and Maintenance
PI: Jian Tao, Texas A&M University
Personnel working in the offshore wind energy industry face significant risks during the installation, operation, maintenance, and decommissioning of wind systems. Despite existing safety standards and practices, the inherent dangers of working in challenging environmental conditions can lead to serious injuries or even fatalities. Current methods for predicting and mitigating these hazards could benefit from more advanced, integrated technology solutions.
The primary goal of this project is to develop a digital platform that combines advanced visualization, biomechanical simulation, and data science methods to enhance the safety and well-being of workers in the offshore wind energy industry. The platform aims to provide a detailed, accurate, and real-time analysis of potential hazards, which can be used to develop safer work practices, redesign tasks or equipment, and enhance training programs.
Risk assessment of human transfer from service vessels to wind turbines, and development of an optimal transfer system using two-body simulation tools
PI: Krish Sharman, University of Massachusetts Amherst
