Akselos SA and a consortium of partners led by Principle Power Inc. announced today that it was awarded $3.6 million in funding from the US Department of Energy’s Advanced Research Projects Agency-Energy (ARPA-E). The funding will be used to develop, validate, and operate the world’s first digital twin software on the WindFloat Atlantic (WFA) Project, which will be one of the world’s only existing floating offshore wind farms.
The Department of Energy invested a total of $26 million across 13 projects as part of a program called ATLANTIS. The consortium of partners which also includes the American Bureau of Shipping (ABS) and EDP Renewables, received the largest share of the overall grant.
“This is an important step in making floating offshore wind feasible all over the world, as we work towards achieving a low carbon economy. Floating offshore wind will play a critical role in scaling up the energy transition, and this ground breaking project will ultimately lower the levelized cost of energy for consumers. We are humbled to be working with ARPA-E and all the forward thinking partners on this visionary project,” commented Thomas Leurent, CEO of Akselos.
Akselos brings next generation simulation technology to the market, the first of its kind to be powerful enough to create holistic, real-time digital twins or large, complex assets. The sensor-enabled digital twin responds in real time to reflect the asset’s current condition, enabling active control of asset integrity and operations. Used throughout the asset lifecycle, Akselos technology enables a digital thread, a data-driven architecture that links information generated from across the asset lifecycle enabling a step-change in design and operations.
The partners received this competitive award from ARPA-E’s Aerodynamic Turbines Lighter and Afloat with Nautical Technologies and Integrated Servo-control (ATLANTIS) program, which seeks to develop radically new floating offshore wind turbines (FOWTs) by maximizing their rotor-area-to-total-weight ratio while maintaining or ideally increasing turbine generation efficiency; build a new generation of computer tools to facilitate FOWT design; and collect real data from full and lab-scale experiments to validate the FOWT designs and computer tools.