Centre for Marine and Renewable Energy (MaREI) - Doctoral Theses
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Item Nurturing blue growth: enabling sustainable development of emerging marine sectors(University College Cork, 2024) Giannoumis, Jessica; Wheeler, Andrew; Dooley, Lawrence; Cummins, ValerieCurrent marine resource exploitation practices and management are unsustainable as resource degradation is ongoing and coastal regions struggle to realise sustainable development of marine resources. The key topic of this research is expanding knowledge on the reconciliation of environmental and economic models regarding the sustainable development of marine resources through the EU-introduced concept of blue growth. In the context of this research, blue growth refers to the sustainable development of marine resources, generating livelihoods, and securing well-being from innovation in emerging marine sectors. Blue growth development attracted interest across Europe and beyond, as the utilisation of marine resources is viewed as an opportunity to meet climate change obligations, enable a transition away from finite resources, and creating employment opportunities, thereby enabling long-term regional economic development. Blue growth development initially focused on the development of five emerging marine sectors with economic growth potential including coastal tourism, aquaculture, ocean renewable energy including offshore wind development, seabed mining, and marine biotechnology. Yet, EU coastal regions struggle with the realisation of blue growth as they received limited guidance from the European Commission on what blue growth is and what successful blue growth development looks like. This highlights a need to investigate what nurtures blue growth to enable coastal regions to realise their blue growth potential. This qualitative and interdisciplinary research focuses on the potential of blue growth in coastal regions focusing on the development of emerging marine sectors. In the context of this research, a region refers to coastal regions with common economic activities and characteristics, such as access to regionally specific marine resources, and common administrative characteristics such as specific political and governmental functions, e.g., regional economic development policies. Within the scope of this research, emerging sectors refer to rapidly growing industries utilising innovative technologies to enable sustainable development of regions, job creation, and technological advancement. This research investigates the manifestation and effectiveness of an EU intervention, the ProtoAtlantic project which includes regions of Orkney (SCT), Cork (IRE), Brest (FR), Porto (PT), and Las Palmas (SP) and two in-depth cases in Norway and Scotland. ProtoAtlantic was a Interreg Atlantic Area project, initially funded from November 2017 to October 2020, due to Covid-19, the project was extended to October 2021. The study harnessed an opportunity to engage with a wide range of multiple stakeholders representing stakeholders from government, industry, and academia. Data collection from the ProtoAtlantic cases included extensive desktop research and policy analysis of marine and generic development strategies in each case, analysis of regional blue growth stakeholder workshops which were carried out in each region, as well as analysis of additional material provided through the ProtoAtlantic project such as the outcomes of the ProtoAtlantic accelerator programme, and semi-structured interviews with nine regional stakeholders. The two deep dive cases included the offshore wind sector development around the DeepWind cluster in Scotland and the Norwegian aquaculture sector. Data collection from the in-depth cases included extensive desktop research and policy analysis of marine development strategies with particular focus on offshore wind development in Scotland and aquaculture development in Norway, in addition to 32 semi-structured interviews. To date, limited scientific attention has been paid to blue growth realisation from a marine governance perspective. Even less research has been undertaken to understand blue growth development from a business perspective. The research aim was to expand on how economic opportunities can catalyse sustainable development in a marine context. By achieving economic sustainability, coastal communities may consequently be in a better position to achieve environmental and social sustainability. The findings of this research address this research gap and provide practical contributions on how decisionmakers in coastal regions can nurture and realise their regional blue growth potential. In-depth analysis found that blue growth requires a systems approach which enables the integration of blue growth antecedents, this has been lacking from current marine management approaches. Furthermore, the study found that economic development approaches to marine resource management can secure well-being of coastal communities and ensure sustainable practices to marine resource utilisation. This research offers a modification of Ostrom’s Social-Ecological Systems framework, the expansions of the framework provide insight into collective action, the role of technology development, and the need for bespoke regional approaches to identify and realise blue growth. This research examines the role of regional stakeholders, the need for entrepreneurial activity and clustering activities in driving blue growth development and offers recommendations for policymakers and decisionmakers in coastal regions to nurture blue growth adoption and development. This research also presents a Practitioner’s Guide to Blue Growth which offers relevant questions to enable practitioners and intermediaries in the identification and realisation of their regional blue growth potential.Item A multi-method approach to understanding the ecology of harbour porpoise in Irish waters(University College Cork, 2023) Todd, Nicole R. E.; Jessopp, Mark John; Rogan, Emer; Kavanagh, Ailbhe; Irish Research CouncilSmall coastal cetaceans are often some of the most threatened species by anthropogenic and climate change impacts. Distribution and behavioural patterns can be difficult to determine for these wide-ranging, cryptic species that spend a limited amount of time at the surface, making direct observation difficult. Harbour porpoise (Phocoena phocoena phocoena, Linnaeus, 1758) is protected across European waters, listed under Annex II of the EU Habitats Directive, requiring Special Areas of Conservation (SAC) for their conservation. Despite its protected status, harbour porpoise are a relatively understudied species. It is therefore important to determine long-term habitat use patterns to ensure effective conservation is put in place. This research uses long-term passive acoustic monitoring (PAM) to increase our understanding of harbour porpoise habitat use in Irish waters. Firstly, feeding buzzes and spatial-orientation echolocation clicks of harbour porpoise were differentiated within a 9-year PAM dataset from northwest Ireland (Chapter 2). The spatio-temporal distribution of foraging behaviour was investigated using Generalized Additive Models (GAMs), at multiple temporal scales. The research identified clear interannual and seasonal variation, with peak foraging buzzes detected in autumn, as well as highlighting a negative impact of construction related activities in the area. A new PAM monitoring network was also established in an SAC designated for harbour porpoise in southwest Ireland, over a 3-year period. GAMs were used to examine harbour porpoise occurrence and foraging behaviour in relation to intra-site differences in habitat use and environmental variables (Chapter 5). Harbour porpoise were detected year-round within the SAC, with seasonal trends in occurrence and foraging behaviour observed, with peak detections in the late autumn and winter reflecting similar trends from Broadhaven Bay (Chapter 2). Clear preferences in habitat use were identified, with porpoise occurrence and foraging varying across small spatial scales, as well as across diel, tidal, and lunar cycles. This research also noted an overall decline in acoustic detections across the monitoring period, reflecting wider population trends in Irish waters that bears further investigation. An in-field comparison of a widely used PAM tool, the C-POD (Cetacean POrpoise Detector) with its recently developed successor the F-POD (Full waveform capture POD) was conducted, providing timely insights into the integration of this new equipment into acoustic monitoring programmes (Chapter 3). The F-POD recorded twice the amount of harbour porpoise detections compared to a co-deployed C-POD. GAMs highlighted similar patterns of harbour porpoise occurrence, however, in contrast to the F-POD, the C-POD failed to detect sufficient foraging rates to identify temporal trends in foraging behaviour. This work suggests that the switch to F-PODs will likely have minimal effect on our understanding of seasonal patterns of occurrence but may improve our understanding of foraging. Following on from this finding, an in-field playback experiment was conducted to determine the detection probability and effective detection radius/area (EDR/EDA) of three commonly used PAM devices, the C-POD, the F-POD, along with a continuous recording hydrophone (SoundTrap) (Chapter 4). The SoundTrap detected porpoise playbacks at the greatest distance, followed by the F-POD. The C-POD detection range was considerably less than the other two PAM devices. The type of harbour porpoise echolocation (spatial orientation clicks versus buzzes) was also found to influence the detection range, with clicks detected at a closer range across all devices. Understanding how this range of PAM devices compare provides valuable information to enable robust comparison of studies and inform appropriate planning of acoustic monitoring programmes. Collectively, the research has significantly enhanced our knowledge of acoustic monitoring methodologies and identified key harbour porpoise habitat use patterns. The findings can inform more effective conservation management of harbour porpoise at a national and international level. Additionally, this research contributes valuable insights to inform the designation of protected areas to cover important feeding habitats, and ensure targeted protections are put in place in the future.Item Applications of big data and machine learning in global energy system modelling(University College Cork, 2022) Joshi, Siddharth; O'Gallachoir, Brian; Holloway, Paul; Glynn, James; Science Foundation IrelandGlobal efforts to limit atmospheric warming well below 2 degree celcius above pre-industrial levels form the backbone of our response to mitigate the detrimental effects of climate change. The energy sector contributes circa 75% of global GHG emissions, amongst which the Electricity and Heat sectors each contribute ~40%, and the Transport sector contributes ~20% to the total global energy-related GHG emissions. The recent IPCC AR6 report finds that in nearly all possible emission scenarios considered, the world is heading towards a 1.5 degree celcius global temperature rise by the early 2030s. Pursuant to this, Energy Systems Models (ESMs) and Integrated Assessment Models (IAMs) are essential tools that provide energy system pathways to limit global warming below the temperature threshold. Thus, improving the accuracies of ESMs and IAMs will lead to measurable improvement in energy policy formulation and evaluation,thereby increasing the likelihood of meeting the commitments under the Paris Climate Agreement. This thesis develops and applies novel frameworks and methods that use a big data and machine learning driven strategy to improve the technology potential assessment of global decentralised solar PV technology and projection of transport energy service demand. The frameworks and methods developed in this thesis are presented in a format of methodological design principles followed by a case study using them. Specifically, on the supply side, the thesis investigates the global high-resolution spatiotemporal technical potential of rooftop solar PV for 2015 and further growth in the technical potentials from 2020-2050. For this assessment case study, the developed framework utilises a suite of GIS derived geospatial metrics in conjunction with a custom machine learning framework to calculate the global rooftop area at a high spatial resolution. Further using an IAM, the role of decentralised solar PV in global future energy transitions is explored. On the demand side, the thesis introduces a new machine learning model called ‘TrebuNet’ that is capable of high accuracy in estimating future energy service demand in the transport sector. The thesis thus provides the first development of machine learning and GIS based methods to improve the accuracy of global ESMs and IAMs. Particular attention is also paid towards the reproduction and transparency of the methods and the frameworks developed in this thesis for cross- disciplinary research. The thesis contributes to the important task of climate change mitigation by providing a bridge between mature IAM and ESM modelling and emerging machine learning-big data-driven tools. In doing so, this thesis demonstrates how the emerging methods in conjunction with large geospatial open source data, can aid in improving the technology representation of variable renewable energy technology in energy systems. The thesis also lays the foundation for providing solutions to energy system related tasks that are currently limited by high computational costs and data. The datasets and analysis generated by this thesis are presently assisting in unlocking the global role of decentralised renewable energy technologies in future energy systems and are also encouraging shifts in national decarbonisation pathways.Item Sediment transport modelling and geomorphological assessments related to offshore renewable energy developments in the Irish Sea(University College Cork, 2022) Creane, Shauna; Murphy, Jimmy; O'Shea, Michael; Coughlan, Mark; Irish Research Council for Science, Engineering and TechnologyA combination of in-situ geophysical, geological and oceanographic datasets, and advanced numerical modelling tools are used to: improve the understanding of hydrodynamics and morphodynamics in the Irish Sea, develop new methods and approaches to investigate hydrodynamics and seabed morphodynamics in an offshore setting, collect and produce novel datasets that will contribute to this scientific field, and facilitate the sustainable growth of anthropogenic activities in the Irish Sea. These new methods and approaches include, using process-based indicators to understand sediment wave development and distribution, utilising ADCP-based suspended solids concentration as a numerical model calibration tool, and the application of a ‘sediment budget’ to an offshore sand bank to understand external influences on the stability of its morphodynamic system. Results provide hydrodynamic proof underpinning the presence of the bed load parting (BLP) in central Irish Sea and associated divergent sediment transport pathways driving sediment dispersal across this tidally-dominated continental shelf sea. Analysis of tidal propagation through the Irish Sea Basin concludes that the origin of the BLP is mainly attributed to the intersection of the north and south tidal fronts at an inclined angle due to Coriolis Forcing and coastline interactions. Minor factors impacting the shape and location of the BLP are the change in tidal character at (a) abrupt bathymetry changes, (b) headlands and intricate coastline topography, and (c) large-scale constrictions. These outcomes set the basis of understanding for the thesis. Building upon this knowledge, analysis of targeted, high resolution, time-lapse bathymetry datasets in the south-western Irish Sea reveals sediment waves in a range of sizes (height = 0.1 to 25.7 m, and wavelength = 17 to 983 m), occurring in water depths of 8.2 to 83 mLAT, and migrating at a rate of 1.1 to 79 m/yr. Combined with numerical modelling outputs, a strong divergence of sediment transport pathways from the previously understood predominantly southward flow in the south Irish Sea is revealed. Furthermore, a new source and sink mechanism are defined for offshore independent sediment wave assemblages, whereby each sediment wave field is supported by circulatory residual current cells originating from offshore sand banks. Reliable sediment transport modelling is required to investigate these physical processes further, therefore, the need for cost-effective sediment validation datasets for 2D sediment transport models is addressed, utilising ADCP-based datasets. A robust spatial timeseries of ADCP-based suspended solids concentration was successfully calculated in an offshore, tidally-dominated setting. Three new validation techniques are deemed advantageous in developing an accurate 2D suspended sediment transport, including i) validation of 2D modelled suspended sediment concentration using water sample-based suspended solids concentration, ii) validation of the flood-ebb characteristics of 2D modelled suspended load transport and suspended sediment concentration using ADCP-based datasets and iii) validation of the 2D modelled peak suspended sediment concentration over a spring-neap cycle using the ADCP-based suspended solids concentration. The robust coupled hydrodynamic and sediment transport model produced from this research is used as a tool of investigation in subsequent chapters. The complex hydrodynamic processes controlling upper slope mobility and long-term base stability of Arklow Bank are determined. Results reveal a flood and ebb tidal current dominance on the west and east side of the bank respectively, ultimately generating a large anticlockwise residual current eddy encompassing the entire bank. The positioning of multiple off-bank anticlockwise residual current eddies on the edge of this cell is shown to both facilitate and inhibit east-west fluctuations of the upper slopes of the bank and control long-term bank base stability. Within Arklow Bank’s morphological cell, eight morphodynamically and hydrodynamically unique bank sections or ‘sub-cells’ are identified, whereby a complex morphodynamic-hydrodynamic feedback loop is present. The local east-west fluctuation of the upper slopes of the bank is driven by migratory on-bank stationary and transient clockwise residual eddies and the development of ‘narrow’ residual current cross-flow zones. Together these processes drive upper slope mobility but maintain long term bank base stability. A sediment budget was successfully estimated for an offshore linear sand bank, Arklow Bank, whereby seven source and nine sink pathways are identified. The restriction of sediment sources off the southern extent of Arklow Bank impact erosion and accretion patterns in the mid and northern sections of the bank after just one lunar month simulation. Where tidal current is the primary driver of sand bank morphodynamics, wind- and wave-induced flow is shown to alter sediment distribution patterns. This advanced body of work forms a robust scientific evidence-base to facilitate the sustainable growth of offshore renewable developments.Item Development and implementation of a framework to aid the transition to proactive maintenance approaches on air handling units in the industrial setting(University College Cork, 2023) Ahern, Michael; Bruton, Ken; O'Sullivan, Dominic; Science Foundation IrelandThis research explores the transition to proactive maintenance of HVAC equipment, specifically AHUs in industrial facilities, to make progress towards climate goals. HVAC systems account for 14% of global energy consumption, with the potential to increase efficiency by 20% by addressing energy-wasting faults according to Roth et al. However, these faults are difficult to detect due to their compensating control logic. This research highlights the potential of digitalisation techniques, particularly AI, to identify and rectify these faults, which would contribute to an approximate 2.8% global efficiency improvement. Notably, the study focuses on the nuances of industrial facilities, which have received limited attention compared to other building types. The research identifies several gaps in existing literature, including the knowledge gap between proactive data analysis and reactive engineering mind-sets, the data gap between high-quality experimental datasets and poor-quality industrial datasets, the operational gap between known baselines in experimental studies and unknown baselines in industrial settings, and the practice-theory gap between data-driven approaches in the literature and rule-based approaches in commercial tools. To address the knowledge gap, this thesis presents the IDAIC framework, a domain knowledge integration-type adaptation of the CRISP-DM process model. The implementation of the framework in an industrial facility to curate a dataset and develop a data assessment decision tree has contributed towards closing the data gap. Additionally, the study proposes extensions to the APAR ruleset and a practical data-driven fault detection method to address the operational gap. The deployment of the IDAIC framework as a tool leverages the UML modelling language to address practical considerations and demonstrate the approach's flexibility. Therefore, the main research outputs include the IDAIC framework, an industrial AHU dataset, a data assessment decision tree, an extension to the APAR ruleset, and a proactive maintenance decision support tool. Notably, this research unveils a fault in which the outside air damper is stuck in the fully open position, leading to estimated annual savings of €60,000. These findings validate the effectiveness of a human-centric, domain expertise-integrated approach that is resilient to industrial challenges, contributing to sustainable energy efficiency improvements and the achievement of climate targets using the best available solutions.