As a result of our work, our client has an accurate and time-efficient predictive model at its disposal to analyze various design scenarios. The execution of more scenarios speeds up the installation phase of the monopiles resulting in significant economic benefits. 

"We were really satisfied with the collaboration with PropheSea. The

entire project went smooth, from project initiation to deployment." 

Pieter Vaes, Lead Engineer, DEME

DEME is a world leader and unrivalled expert in the offshore energy market with a proven track record in, amongst others, the transportation and installation of offshore monopiles for wind turbines.                             
                             
During the offshore installation, these large structures are subjected to wave loads. These need to be predicted as accurate as possible to ensure a safe and smooth installation at the highest possible environmental limits as well as an economically viable design of the monopile structures. The more scenarios taken into consideration, the greater the impact on the installation limits, reducing the overall installation planning.

Time-consuming computational model limits possibilities.  

The limited number of scenarios was one of the main obstacles of a Computational Fluid Dynamics Model DEME used. The calculations needed are not only complex, they are also extremely time consuming when using a computational model. Therefore, evaluating a large set of scenarios was not feasible in terms of computational costs and labor time.

"Calculating a time series of 10 minutes can lead up to more than 

1 day of computational time, even on a high-performance cluster." 

Brecht Devolder, CFD Engineer, DEME

DEME called upon our in-depth expertise in custom predictive software. We thoroughly analyzed their challenges, objectives, and desired outcome. We decided to create a surrogate model of the Computational Fluid Dynamics model.                             
                             

Less computational time, reduced costs, more scenarios, improved workability.   

In just 3 months, we turned a complex and time-consuming computational model into a data-driven artificial intelligence model, tailored to DEME’s business needs. Our AI model could predict accurately the necessary parameters to calculate the wave loads.
Next to the positive impact on the design phase, our AI model also leads to a significant cost reduction in labor force as well as in CPU cost.                         

"The surrogate model truly provided a big added value. Thanks to the AI solution,

we were able to execute a broad range of extra simulations which would 

otherwise be computationally unfeasible." 

Brecht Devolder, CFD Engineer, DEME

This project was right up our street. Our predictive models can be data-driven, based on machine learning technology, or by solving mathematical equations. Therefore, we were perfectly positioned to help DEME optimize the processes controlling their design phase.                                                  

"This project was a perfect match for PropheSea as we have a thorough

knowledge of both data-driven and mathematical based 

(solving PDEs) modelling approaches." 

Tomas Van Oyen, PropheSea