The rapid expansion of offshore wind energy has led to the deployment of increasingly large and complex offshore wind farms (OWFs) in dynamic marine environments. With an expected lifetime of several decades, understanding and predicting the long-term morphodynamic evolution of the seabed is necessary to ensure the structural stability, minimize maintenance costs, and mitigate environmental impacts. Traditional process-based 3D morphodynamic models offer detailed insights into sediment transport and seabed evolution, but their high computational costs and decreasing predictive accuracy in time make them impractical for simulating multi-decadal timescales. Simplified or idealised modelling approaches, while computationally efficient, often fail to capture the complexity of real-world processes. Thus, hybrid methodologies that integrate 3D process-based simulations in data-driven modeling approaches allow developing efficient and robust strategies for long-term morphodynamic forecasting in OWF environments. The primary objective of this internship is to propose a framework for predicting long-term (50 years) seabed sediment transport processes and morphological changes in offshore wind farm areas, with a focus on shallow shelf seas (shallow to intermediate water depths), which are also common locations for marine renewable energy projects.

Specific goals include:

Collect the site-specific metocean datasets (tides, waves, wind), bathymetric surveys, and sediments characteristics for the selected study site.
Calibrate and validate the process-based 3D morphodynamic model openTELEMAC (hydrodynamics module TELEMAC-3D, coupled with wave propagation module TOMAWAC and sediment transport/morphodynamics module GAIA) for the selected study site.
Characterize the bathymetry and representative historical hydro-meteorological conditions driving the system using statistical approaches.
Generate a library of test cases including historical and newly created synthetic hydro-meterological cases to cover a wide range of parameter (tides, waves, winds) values.
Complete process-based simulations of the selected subset of representative bathymetric and hydro-meteorological conditions.
Propose a hybrid modelling framework (metamodel) that balances computational efficiency with predictive accuracy over decadal timescales, and validate the general applicability of the approach.
 

At the end of the internship, the new proposed framework will enable future studies using climate scenarios to make projections of hydro-meterological drivers and thus the probabilistic evaluation of long-term morphological evolution.

-        Level: 3rd year engineering student or Master 2 on numerical modeling, applied mathematics, civil/hydraulic engineering

-        Start Date: Flexible, but should begin no later than March 2026

-        Duration: 6 months

-        Compensation: To be confirmed

-        Location: EDF Lab Chatou / LNHE-Saint-Venant Hydraulic Laboratory, Chatou