Research Interests

Dr. Campobasso is Senior Lecturer in Renewable Energy Systems and Computational Fluid Dynamics. His career started in the aircraft engine industry with BMW Rolls-Royce before moving to academia. His present research focuses on outstanding challenges in the wind energy sector, including the analysis and the optimisation 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 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, Dr. Campobasso and his group investigate the impact of climatic conditions on blade erosion and the dependence of energy yield and maintenance frequency on the installation site climate. He is also engaged with the development of digital twins and novel turbine control technologies to optimise wind turbine and farm productivity and maintenance.

He is interested in developing novel analysis technologies to assess and mitigate the present and long-term detrimental impact of wind turbine and wind farm wakes on both wind farm efficiency and durability, and the environment, e.g. the alterations of heat flux at sea/atmosphere interface due to the wakes of large wind turbine clusters. To accomplish this, he plans to deploy more HPC technologies (e.g. Graphics Processing Units) 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 Dr. Campobasso and his group addressed include unsteady aerodynamics of horizontal and vertical axis wind turbines, investigation of oscillating winds to harvest tidal and estuarine energy, robust design optimisation of horizontal axis turbines accounting for uncertainty of the environmental conditions and engineering manufacturing processes. His group uses both high- and low-fidelity simulation codes, and he has developed large CFD codes supported by distributed and shared-memory parallel computing, including linear, nonlinear frequency-domain and adjoint CFD codes.

His research benefits with longstanding collaborations with national and international partners, including Strathclyde University, Sapienza University of Rome and Technical University of Denmark.