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Dr Ben Howey

Dr Ben Howey

Earth Observation Data Analyst

beh11/29/2023 8:12:23    |     +44 (0)1752 633100 (switchboard)
"I have a passion for problem-solving in the digital and physical domain."

Ben is a data analyst within the Digital Innovation and Marine Autonomy group. Since joining the team in 2019, Ben has played a key role in the Copernicus Marine Environment Monitoring Service(CMEMS). His responsibilities span the maintenance and enhancement of this service, ensuring seamless compliance with service agreements. This covers all aspects of documentation, data and algorithm validations, meeting data delivery needs on a daily, monthly, and annual basis. Collaborating with scientists, he implements and validates state-of-the-art algorithms, transforming research into a practical operational service. Other projects have included classical machine learning implementations, database management, code efficiency improvements and benchmarking code energy consumption, aligning with a sustainable solutions approach.
With an academic and industry background, Ben holds a PhD in Mechanical Engineering from the University of Plymouth, specializing in the physical and numerical modeling of interconnected wave energy converters. He also holds Masters in Offshore Renewable Energy (Engineering pathway) and an undergraduate degree in Mechanical Engineering and CAD, as well as experience from time spent as a manufacturing engineer in the pharmaceutical industry.

  • Howey, B. in review. Compact floating wave energy converter arrays: inter-device mooring connectivity and performance. Applied Ocean Research.
  • Brown, SA; Ransley, EJ; Zheng, S; Xie, N; Howey, B.; Greaves, DM. 2020. Development of a fully nonlinear, coupled numerical model for assessment of floating tidal stream concepts. Ocean Engineering, 218, 108253. doi: 10.1016/j.oceaneng.2020.108253
  • Gomes, RPF; Gato, LMC; Henriques, JCC; Portillo, JCC; Howey, BD; Collins, KM; Hann, MR; Greaves, DM. 2020. Compact floating wave energy converters arrays: Mooring loads and survivability through scale physical modelling. Applied Energy, 280, 115982. doi: 10.1016/j.apenergy.2020.115982