Civil and Environmental Engineering - Masters by Research Theses

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    An experimental investigation into the most prominent sources of uncertainty in wave tank testing of floating offshore wind turbines
    (University College Cork, 2022-08-30) Lyden, Eoin; Murphy, Jimmy; Judge, Frances
    There is an urgent need to replace carbon-based energy sources with renewable energy sources, and floating offshore wind is seen as a critical component in the drive towards energy diversification. Floating offshore wind facilitates accessing a far vaster wind resource that exists in deeper waters, further offshore. Floating offshore wind platforms must undergo wave tank testing in the early stages of development to assess model responses to different wave and wind conditions. Wave tank testing, while highly beneficial, is liable to errors arising throughout the testing campaign. Errors can arise during wave tank setup, testing, and analysis of results. Some of the primary sources of error include errors in the model location within the tank, errors in model parameters like mass, inertia and CoG, and errors due to incorrect replication of mooring forces and aerodynamic forces from the turbine. Scaling wind turbine blade properties can be challenging; this is because aerodynamic forces are scaled using Reynolds scaling, but all hydrodynamic forces are scaled using Froude scaling. For this reason, wind emulation systems are used to replicate the aerodynamic forces from the turbine only. Testing was completed using two very different floating offshore wind concepts. A sensitivity analysis was completed by conducting variations to the wind emulation system used, the model inertia and centre of gravity, and the mooring stiffness of the model. The magnitudes of the variations to the inertia, centre of gravity and mooring stiffness were based on the uncertainty in the values of each of the parameters. Three wind emulation systems of varying complexity were used for this comparison, a simple weighted pulley system, a constant thruster and the software in the loop system developed by CENER. The comparison was conducted to assess the influence of wind emulation systems on the uncertainty of platform response It was found that the effects of each variation conducted were magnified at resonance, and the magnitude of platform response was affected to a greater extent than the period of resonance response. Of all the variations to the model properties conducted, the inertia about the y-axis and location of the centre of gravity along the x-axis affected pitch response to the greatest extent. A 7% change in the inertia about the y-axis coupled with an 8.57% resulted in a 10% change in the period of resonance response for pitch, Tr, and 52% decrease in the magnitude of resonance respsonse for pitch, Tr, mag. Changes in the wind emulation system affected the pitch response most significantly, while the period of resonance response Tr, was mostly unaffected , the magnitude of resonance response Tr, mag, was reduced by nearly 90% when a pulley system was used in lieu of a conventional thruster for a semi-submersible model. Changes in mooring stiffness did not influence the period of resonance response but did affect the magnitude of resonance response, particularly in surge. For a linear horizontal mooring system applied to a semi-submersible model, a 1% decrease in the spring stiffness resulted in a 9% decrease in the magnitude of resonance response for surge, Tr, mag.
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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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    A methodology and trial implementation for digitising information on a factory floor
    (University College Cork, 2019-10-04) Duffy, Annie; O'Sullivan, Dominic; Bruton, Ken; Science Foundation Ireland
    In recent years manufacturing industries have moved towards Smart Manufacturing, to achieve improved efficiency and production targets. Part of this innovation of current processes includes digitisation and improving access to machine information, usually through the introduction of new technology to assist with this transition. In order to maintain smooth processes and uninterrupted production, various information sources must be available on the factory floor. This project aims to provide a proof of concept for digitisation and access to necessary information during Pulse Walks. The methodology used to develop this tool is discussed. Observations during Pulse Walks were used to highlight the areas that this could be applied to, and a survey was used to determine the most useful information sources to include. Another aspect of this project is to introduce a method of digitally storing issues discussed during the Pulse Walk, to highlight recurring issues and problematic areas. This was developed to be used as part of the tool during Pulse Walks. This research will present a proof of concept for an app that will act as a digital information hub for accessing information and logging issues from the Pulse Walks. The use cases for this tool have been deliberated and the benefits clearly identified. This tool can assist with tracking recurring issues, using previously logged issues to create a historical database. The issue logging dashboard can be used for investigating reasons for machine downtime. This tool aims to improve production efficiency for a manufacturing line in a factory through issue tracking.