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Dr. M. Sergio Campobasso, PhD

NEW: 1 November 2025: We have a 3.5-year fully-funded PhD Scholarship (Stipend and tuition fees) to carry out the research project entitled

HIGH-FIDELITY EXASCALE-ENABLED INFRASTRUCTURE FOR ANALYSING THE IMPACT OF WIND FARM WAKES ON WIND/SEA INTERACTIONS for which we are seeking a talented and enthusiastic candidate. This multi-disciplinary project spans high-performance computing, wind farm aerodynamics and environmental science. Deadline to apply is 9 January 2026. If you are interested in learning more about this opportunity and applying, please find more information here.

I am Associate Professor in Renewable Energy Systems and Computational Fluid Dynamics (CFD) at the School of Engineering of Lancaster University, UK. My career started in the aircraft engine industry with BMW Rolls-Royce before moving to academia. My present research focuses on outstanding challenges in the wind energy sector, including the analysis and the optimization of the interactions of offshore and onshore wind turbines and wind farms with the environment in which they operate. Some of his recent achievements are in the highly multi-disciplinary area of leading edge erosion of wind turbine blades, presently a major challenge in the wind energy sector. Blade erosion reduces turbine annual energy production and increases O&M costs. This problem is particularly severe offshore due to the harsh environment accelerating erosion and requiring costly O&M intervention. Using holistic turbine models combining low- and high-fidelity simulation codes, applied meteorology and machine learning,  we investigate the impact of climatic conditions on blade erosion and the dependence of energy yield and maintenance frequency on the installation site climate. I am also engaged with the development of digital twins and novel turbine control technologies to optimize wind turbine and farm productivity and maintenance frequency.

I am interested in developing novel analysis technologies using simulation and machine learning to assess and mitigate the environmental impact of the forthcoming very large scale deployment of wind energy power plants, e.g. the alterations of the natural heat flux at sea/atmosphere interface due to the wakes of large wind farm clusters. I am also interested in demonstrating the capability of these technologies to improve wind farm design with the aim of extending their life and improve their productivity. To accomplish this, we plan to investigate and exploit novel and more powerful high-performance computing (HPC) technologies, e.g. Graphics Processing Units (GPUs), to enable higher resolution in the simulation of these problems, and advanced machine learning methods to enable faster exploitation of the developed knowledge.

Specific problems I and my group have addressed include unsteady aerodynamics of horizontal and vertical axis wind turbines, investigation of oscillating wings to harvest tidal and estuarine energy, robust design optimization of wind turbines accounting for uncertainty of the environmental conditions and engineering manufacturing processes. My group uses both high- and low-fidelity simulation codes, and I have developed large CFD codes supported by distributed and shared-memory parallel computing, including linear and nonlinear frequency-domain, and adjoint CFD codes.

My research benefits from longstanding collaborations with national and international partners, including Strathclyde University, EPCC at the University of Edinburgh, Sapienza University of Rome and Technical University of Denmark, and international organizations, including the International Energy Agency (IEA). 

We make every effort to keep these pages up-to-date, but some pages are still under construction. Should you be interested in further information, please drop me an email.

M. Sergio Campobasso

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