Dr. M. Sergio Campobasso, PhD
NEW: 17 July 2020: A consortium of North West Universities led by Lancaster Uni won a £14M bid for collaborative R&D projects with indusrial partners in the area of low carbon emission technology, with emphasis on PhD and Masters by Research scholarships. Topics include wind and marine energy. More info for companies and applicants at webinar 21 July https://lnkd.in/ddbaWdm or by contacting me at m.s.campobasso_at_lancaster.ac.uk.
NEW: 13 June 2020: A bursary of up to 7 months is now available to carry out new Research and Development work in the area of wind turbine blade erosion with emphasis on image processing, advanced CAD and Computational Fluid Dynamics. The research is part of the ALPS project aiming to assess the impact of wind turbine blade erosion on the energy production of offshore wind farms. Details on the research and how to apply are available here.
NEW: 4 June 2020: We are coordinating and editing a Special Issue in the open source journal Sustainability entitled Development and Application of Computational Fluid Dynamics in Offshore Renewable Energy. The deadline for submitting original research and review articles has been extended to 30 October 2020. A dowloadable flyer for free dissemination is available here.
My interest in Engineering started at the age of six, with the Japanese Manga Goldrake. Since then, I have moved to more realistic problems. In the past 20 years I have developed and/or used novel high- and low-fidelity simulation technologies for rotary machine analysis and design, including linear, nonlinear and adjoint Navier-Stokes (NS) Computational Fluid Dynamics (CFD) systems for industrial and academic research and development. For more than 10 years, my research work has focused on Renewable Energy Systems. In this framework, one significant achievement of my team has been the development of a novel harmonic balance (HB) compressible NS CFD code (COSA), successfully validated against real turbine data (wind turbine in yawed flow), that we have shown to reduce by up to 50x the run-time of high-fidelity NS CFD based on the conventional time-domain CFD, an accomplishmen that makes CFD more amenable to industrial application. Recently we also extended this technology to tidal stream turbines by developing the ARCTIC incompressible NS CFF code.
New research ideas sometimes arise fortuitously. For example, novel findings on the hydrodynamics of oscillating wings for tidal and estuarine stream power generation (guidelines for the maximization of the hydrodynamic efficiency in real installations and wing end design) were achieved with the COSA code, and published in the International Journal of Marine Energy in 2016. The original impetus for this work arose during a conversation on CFD code validation at the Gala dinner of the Conference on Modelling Fluid Flows 2009 in Budapest, with a researcher who had presented his laminar two-dimensional simulations of an ideal oscillating wing.
The CFD codes we develop and/or use run efficiently both on office desktops and large distributed- and shared-memory computer cluster, such as ARCHER (UK national supercomputing service) using tens of thousands of cores in parallel. Parallelizing our CFD codes in close collaboration with the experts at the Edinburgh Parallel Computing Centre (EPCC), home to ARCHER, we have developed a highly efficient distributed-memory (MPI) infrastructure of our CFD applications, and a novel hybrid parallelization method, based on combining MPI and OpenMP, resulting in an efficient time-parallelization of harmonic balance NS codes.
But it is not all about NS CFD and supercomputing. In collaboration with researchers at the University of Strathclyde, we have produced one of the first demonstrations of a probabilistic multi-disciplinary design optimization technology obtained by integrating state-of-the-art industrial engineering codes for wind turbine development, applying novel uncertainty analysis methods, and steering the robust design process with evolution-based optimization. We have used this technology to minimize the mean and minimize the standard deviation of wind cost of energy accounting for engineering uncertainty (e.g. turbine blade manufacturing and assembly tolerances), and environmental uncertainty (e.g. variability of wind characteristics with installation site).
Our new research projects include a) the analysis of the surface wave-induced unsteady loads on tidal stream current such as those in the MayGen tidal array in the Pentland Firth of the coast of Scotland, b) the analysis of tidal stream turbine wakes and the assessment of power losses due to turbine/wake interactions in the array, c) the investigation of unsteady rotor aerodynamics of utility-scale floating offshore wind turbines like those of the HyWind pilot project in the North Sea (the first utility-scale floating wind farm in the world to date), and d) the development of a wind turbine Annual Energy Prediction Loss Prediction System (ALPS) to assess wind turbine and wind wind farms enery yield losses due to blade leading edge erosion. All of these projects aim at reducing the cost of offshore and marine renewable energy, and are carried out in collaboration with major industrial stake-holders and the UK Engineering and Physical Sciences Research Council.
These pages provide more detail on my research for anyone interested in computing and renewable energy, from prospective postgraduate students to academic and industry colleagues interested in collaborating and exploring the solution to new Renewable Energy challenges to help preserve our planet and its natural environment for present and future generations. 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