Overview
Renewable Energy is one of the fastest growing sectors addressing the most important challenges of our age. Offshore renewables, energy distribution, and the environmental impacts of constructing and decommissioning the infrastructure are some one of the most pressing research themes faced by the UK and beyond. The Net Zero Maritime Energy Solutions Centre (N0MES) for Doctoral Training is creating the future specialist workforce needed by our industrial partners through PhD projects finding solutions to real-life industrial needs.
About this opportunity
The successful PhD student will be co-supervised and work alongside our external partner [Dstl, https://www.gov.uk/government/organisations/defence-science-and-technology-laboratory].
Sound generated by anthropogenic activities associated with the construction and maintenance of marine renewable energy platforms, such as piling and geoacoustic surveys, has the potential to adversely affect the health and wellbeing of marine mammals. Understanding how sound propagates in marine environments is critical to ensuring the responsible deployment of marine platforms. The use of machine learning algorithms to determine physical quantities of a complex ocean environment via acoustic data is relatively unexplored. Traditionally, research in underwater acoustics (the field of sound wave generation, propagation, scattering and reception in water) focuses on the use of sound wave navigation and ranging (sonar) systems for communication, sensing, marine wildlife monitoring, target detection, and exploration. The maritime energy sector uses sonar for environmental monitoring, to assess the impact of renewable energy platforms on marine life and the seabed, for example the FLOWBEC project at the European Marine Energy Centre in Orkney, and the Menter Môn led Marine Characterisation & Research Project (MCRP) in the Morlais Demonstration Zone off Holy Island, Anglesey.
The operational use of sonar systems is strongly dependent on an accurate acoustic description of the marine environment. Unfortunately, direct measurement of these acoustic properties is difficult and expensive, and acoustic models are limited by the extent of data and information that is available. Ocean environment information is equally important for our understanding of complex ocean processes and sustainable use of the oceans. The process of extracting information indirectly from acoustic data is inversion. Since an acoustic measurement results from an acoustic signal propagated through the environment, it contains acoustic information about the ocean environment that can be derived using appropriate models and methods. The aim of this project is to use data-driven machine learning models to improve the description of the ocean environment resulting from inversion, to represent a broader range of ocean properties relevant to underwater acoustics, and to support our understanding of the impact of maritime energy platforms on the ocean environment. The focus of the project will be the development of machine learning models which can be used for acoustic data collected from in-situ and remote sensors, historical data, and synthetic data obtained from simulation and modelling. The ultimate goal is to obtain an up-to-date and accurate representation of the acoustic environment for any sonar deployment.
N0MES offers 4-year PhD studentships for exceptional researchers. With the support of the University of Liverpool (UoL), Liverpool John Moores University (LJMU) and over 30 maritime energy sector partners, N0MES postgraduate researchers will pursue new, engineering-centred, interdisciplinary research.