STAGE: assessment of LT Sediment Transport Morphodynamic Impacts of Offshore Wind Turbine Found[...]

EDF; Chatou; Sur place; EUR 40 000 - 60 000; Plein temps

Résumé du poste

A leading renewable energy company in France is offering an internship focused on predicting long-term seabed sediment transport processes in offshore wind farm areas. The role involves collecting and analyzing metocean data, calibrating a 3D morphodynamic model, and developing a hybrid modeling framework to enhance predictive accuracy. Ideal candidates will be pursuing a Master's degree in a relevant field and have strong analytical and modeling skills. This is an exciting opportunity to contribute to sustainable energy solutions.

Qualifications

• Familiarity with hydrodynamic and sediment transport modeling tools.
• Strong analytical skills and ability to interpret complex datasets.
• Experience with programming or data analysis software is a plus.

Responsabilités

• Collect and analyze site-specific metocean datasets.
• Calibrate and validate 3D morphodynamic model for the study site.
• Characterize bathymetry and hydro-meteorological conditions.
• Generate libraries of test cases for simulations.
• Develop a hybrid modeling framework for predictive accuracy.

Connaissances

Statistical analysis

Hydrodynamic modeling

Data-driven modeling

3D modeling

Formation

Master's degree in Environmental Science, Marine Engineering, or related field

Outils

TELEMAC

Statistical software

Description du poste

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.