Energy Engineering - Masters by Research Theses

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    GEOBIM, BIM integrated geohazard monitoring of at risk slopes and historical retaining structures
    (University College Cork, 2022-06-30) Pantoja Porro, Roberto; O'Shea, Michael; Murphy, Jimmy; Geological Survey of Ireland
    Over time, structures such as slopes and retaining walls are increasingly deteriorating, resulting in a risk of collapse. Factors such as climate change, human activities, societal development, rapid growth of cities, increasing population and economy make geological disasters occur more frequently than usual. Geological hazards of nature, slope collapse, slope fractures or slope movements have become a problem to be solved by civil engineering. With the advent of low-cost sensors, optical topographic surveying and BIM (Building Information Modelling), such risk could be mitigated and, in some cases, eliminated. The main aim of this research was to use wireless sensors to monitor slopes that are potentially at risk and to incorporate all the information obtained in BIM (Building Information Modelling), in order to make a digitalized vision of the structures in real time. High precision and innovative tools, such as drone flights and slope scanners were utilized for a detailed analysis of the risk of change in the geohazards including soil slopes and historic retaining walls. Through the combination of data from sensors with point clouds generated from drone flights, an early warning system was developed. This early warning system was clearly able to display when there was surface changes therefore highlighting the areas of high risk of collapse. This thesis shows how continuous real-time surveillance of soil slopes and retaining walls can be achieved clearly and concisely, in a cost-effective manner.
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    High resolution wave and tidal energy resource assessment in the Irish and western UK waters
    (University College Cork, 2021-10-19) Furlong, Rebecca; O’Connell, Ross; Murphy, Jimmy; European Regional Development Fund
    As island countries, both Ireland and the UK have long and extensive coastlines, making offshore renewable energy easily accessible. Previous studies in the area have shown that there is resource availability for both wave and tidal energy in UK and Irish waters, with an abundant wave resource off the west coast of Ireland and a well-known tidal resource within the Irish and Celtic Seas. The Irish Climate Action Plan 2019 set out that 70% of electricity would come from renewable sources by 2030, meaning that research and development that is ongoing in the offshore industry is key to reaching that target. This study aims to create new, updated GIS layers showing both the wave and tidal energy resource as well as the parameters needed to compute them, including significant wave heights, wave energy periods and tidal current speeds. The Copernicus Marine Service recently updated two models that now contain wave spectrum data at high resolutions, both less than 5 kilometres, and long hindcasts of greater than 20 years. The accessibility of this data means that the wave energy resource can be modelled very accurately at high resolutions, a parameter that is hugely important for marine renewable energy developers to gain an understanding of potential deployment site characteristics. Data for tidal resource analysis is available through the Irish Marine Institute and is based on a ROMS model. This study has shown that the Copernicus models correlate very well with each other, and it is possible to create resource layers for use in GIS with the data. This information is imperative for marine renewable energy developers at a first stage so they can have a thorough understanding of the resource availability and conditions available at a proposed site. Having resource information available within a GIS tool can give developers a spatial overview of where the best resource is available, while the GIS can also provide valuable information such as the location of the closest grid connections and the nature of the underlying bedrock, all factors that can influence the location of a wave or tidal farm.
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    A case-study in the introduction of a digital-twin in a large-scale manufacturing facility
    (University College Cork, 2020-11-02) O'Sullivan, Jamie; O'Sullivan, Dominic; Bruton, Ken; Science Foundation Ireland
    The exponential increase in data produced in recent times has had a profound impact in all areas of society. In the field of industrial engineering, the knowledge produced by this newly obtained data is driving business forward. Automating the process of capturing data from industrial machines, analyzing it and using the knowledge gained to make better decisions for the machines is the crux of the digital twin. Digital twins uncover a wealth of knowledge about the physical asset they duplicate. Sensor technology, Internet of Things platforms, information and communication technology and smart analytics allow the digital twin to transform a physical asset into a connected smart item that is now part of a cyber physical system and that is far more valuable than when it existed in isolation. The digital twin can be adopted by the maintenance engineering industry to aid in the prediction of issues before they occur thus creating value for the business. This thesis discusses the introduction of a maintenance digital twin to a large-scale manufacturing facility. Issues that hamper such work are discovered and categorized to highlight the difficulty of the practical installation of this concept. The work here highlights the difficulties when working on digital systems in manufacturing facilities and how this isn’t discussed in journal articles and the disconnect between academia and industry on this topic. To aid in the installation, a digital twin framework is created that simplifies the digital twin development process into steps that can be completed independently. Work on implementing this framework is commenced and early successes highlight the benefit of sensoring critical assets. The payback of the initial practical work is immediate, and it presents a promising outlook for the iterative development of a maintenance digital twin using the framework. The thesis’ work highlights the benefit in reducing project scale and complexity and hence risk for digital systems in manufacturing facilities by following the framework developed. The later part of the thesis discusses machine learning and how this AI topic can be integrated into the digital twin to allow the digital asset to fulfill its potential.
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    Visual inspection and bridge management
    (University College Cork, 2020-10-29) Quirk, Lucy; Murphy, Jimmy; Pakrashi, Vikram
    This thesis estimates the impact of visual inspection prior to its implementation in a Bridge Management System (BMS) using Value of Information (VoI). Visual inspection is the principal assessment method for bridge structures, whereby a condition rating is assigned reflecting the structural condition of a bridge, based on the judgements of a trained inspector. The impact of data collected from visual inspection is contingent on its ability to guide towards optimal maintenance decisions throughout the lifecycle to maximise network performance. The VoI concept from Bayesian pre- posterior analysis is defined as the quantification of the reduction of uncertainty in a decision-making problem, after new information is received. This concept has seen multifaceted applications in the optimisation of Structural Health Monitoring techniques, typically focussing on the ability to monitor a specific parameter to determine the degradation rate and condition of a single asset. The merits of visual inspection data have been largely overlooked thus far. This work outlines and applies a framework to put a measure on the impact that visual inspection provides to infrastructure asset managers operating a BMS, and to illustrate how this is influenced by the underlying uncertainties of the model parameters.