Floating solar structures are becoming more popular as an important source of energy production offshore. However, the reliability of these nascent marine platforms needs scheduled maintenance based on proper monitoring. Many structural elements,  joints, mooring connections, panels and floating platforms are in an array, making it difficult to trace the defects. Synchronised measurement of acceleration responses with environmental conditions (waves, wind) with ML (Machine Learning) will be used in cognisance of fundamental physics to estimate fatigue life of the components and structural elements of floating solar arrays. Acceleration responses are easier to measure than strain and stress in floating solar platforms. The project involves wave and wind tank testing, hydrodynamic assessment and numerical simulations.

Objectives (Ob):

Ob1. Numerical models for arrays will calculate fatigue damage and develop a correlation matrix of fatigue and accelerations in different components.

Ob2. ML will train the framework, having the accelerations and knowing the correlation of the accelerations with fatigue.

Ob3. Live, location-specific fatigue estimates for several floating solar array configurations will be developed, considering metocean data.

Ob4. The idea will be validated by wave tank testing.

Expected Results (R):

Overall: Novel ML-driven framework for safe and serviceable life derisking floating solar farms.

R1. Correlation of fatigue with monitored acceleration responses via hydrodynamic models

R2. A robust ML framework based on correlation of measured variables and feature engineering.

R3. Real-time fatigue estimates of floating solar arrays from wave/wind and acceleration measurements.

R4. Verification & validation repository and tank testing protocols.

References

Emami, A., & Karimirad, M. (2025). Further development of offshore floating solar and its design requirements. Marine Structures, 100, Article 103730. https://doi.org/10.1016/j.marstruc.2024.103730

Campbell, M., Karimirad, M., Minh Nhat, N., Van, M., & Pakrashi, V. Predicting floating photovoltaic platform multi-variable dynamic responses with neural network learning methods. In Proceedings of the ASME 2025 44th International Conference on Ocean, Offshore and Arctic Engineering OMAE2025 (Proceedings of the International Conference on Ocean, Offshore and Arctic Engineering - OMAE ). American Society of Mechanical Engineers (ASME).

Baruah, G., Karimirad, M., Abbasnia, A., MacKinnon, P., Friel, D., & Sarmah, N. (2024). Nonlinear hydrodynamic assessment of a floating solar double-hull substructure using viscous numerical wave tank. Ocean Engineering, 312(Part 2), Article 119045. https://doi.org/10.1016/j.oceaneng.2024.119045

Essential Background of Candidate

Minimum of a strong upper second class (2.1) honours degree (completed or in the final stages of completion) in 

Civil Engineering, Environmental Engineering, Mechanical Engineering, Ocean, Coastal and Marine Engineering or Offshore Renewable Energy

Research Proposal

Please note that applicants are not required to upload a research proposal as part of the application. Instead, interested candidates should upload a copy of their CV and a covering letter outlining their motivation to undertake a PhD on this theme, and describing any relevant experience in:

Wave and wind tank testing, marine and offshore structures, dynamic analysis, floating solar, machine learning, Monitoring Responses of marine structures, fatigue Life estimation

Application Procedure

• To apply, visit https://go.qub.ac.uk/pgapply (link to the QUB Direct Application Portal)
• Apply for Degree of Doctor of Philosophy in “Civil Engineering” at Queen's University Belfast, School of Natural and Built Environment.
• State name of lead supervisor on application form ‘Dr Madjid Karimirad”.
• State the intended SOURCE OF FUNDING on your application as 'DIAMOND European MSCA DN’ (please specify).
• Include your CV and a covering letter.