Civil and Environmental Engineering - Doctoral Theses
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Item Enhancing the alternative use and circularity potential of grass through an anaerobic digestion-based biorefinery approach(University College Cork, 2025-09-30) Shinde, Rajas Dattatray; Wall, David; O'Shea, Richard; Murphy, Jerry; Barth, Susanne; Thorne, Fiona; Henchion, Maeve; Teagasc; SFI Research Centre for Energy, Climate and MarineA predominantly livestock-based farming system makes agriculture the largest source of greenhouse gas emissions in Ireland. The sector contributes around 38% of total emissions, primarily attributed to methane produced by ruminants. Combined with increasing socio-economic pressures, this makes agriculture a particularly challenging sector for achieving Ireland’s climate-neutrality goals. There is an urgent need to develop farm diversification pathways that can enhance farm viability while reducing emissions. Over 90% of Ireland’s agricultural area is under grassland to support livestock, and given this abundant grass resource, there is growing interest in diversifying its use beyond forage. This thesis investigates the potential of an anaerobic digestion (AD)-based biorefinery approach to unlock alternative uses for grass. A two-phase AD system, combining a leach bed reactor (LBR) and an upflow anaerobic sludge blanket (UASB) reactor, was experimentally validated for grass biorefinery applications. Such systems enable the extraction of intermediates of the AD process, namely volatile fatty acids (VFAs), alongside biogas production. VFAs are high-value green chemicals used as building blocks in food, pharmaceutical, cosmetics, and biofuel industries. The LBR-UASB system yielded approximately 23 kg of VFAs and 75 kWh of biogas per tonne of silage. A novel demand-driven operational strategy was developed where biogas production was aligned with peak electricity demand hours, while VFAs were prioritised at other times. This dynamic AD-biorefinery approach represents a key innovation, demonstrating the potential to complement variable renewables and provide on-site demand management through dispatchable biogas, while enhancing revenue and resilience to evolving market conditions. Building on the experimental results, a techno-economic assessment was conducted for a conceptual biorefinery model, processing 35,000-tonne fresh weight of silage per year (20% dry matter), producing polyhydroxyalkanoate (PHA) biopolymer from VFAs, fibre insulation from residual silage, and utilising biogas produced on demand to meet internal energy needs during peak hours. The capital and annual operational costs were estimated at approximately €370 and €114 per tonne of silage, respectively. Recognising the central role of farmers in providing feedstock, a bio-economic model was integrated to evaluate the opportunity cost of diverting land from livestock to silage production for a biorefinery. The minimum silage price required to compensate farmers for both production costs and the foregone livestock income was estimated at €72/t fresh weight. The biorefinery achieved a 20-year net present value (NPV) of –€4 million, indicating it was not financially viable under current conditions. Sensitivity analysis identified fibre yield, silage dry matter content, and feedstock cost as key profitability drivers. Scenario analysis identified plausible pathways for improvement. A combination of 20% capital subsidy, 11% reduction in annual operational costs, and a 10% improvement in the silage dry matter content could achieve a 20-year NPV of €3.5 million and a discounted payback period of 8 years. A Delphi survey involving expert stakeholders across the Irish bioeconomy identified key enablers and barriers to developing grass biorefinery value chains. Stakeholders emphasised rapid deployment of basic AD infrastructure as a foundation for more complex cascading biorefineries such as the one proposed in this study. They also highlighted the need for comprehensive sustainability assessments, public engagement, and coherent policy frameworks to de-risk investment. Integrating experimental validation, techno-economic assessment, and stakeholder insights, this thesis provides a comprehensive evaluation of a dynamic AD-based biorefinery model for enhancing alternative uses of grass. The findings demonstrate that with modest policy support and continued process improvements, such systems could provide Irish farmers with a viable farm diversification pathway while contributing to Ireland’s climate and circular bioeconomy objectives. The integrated methodological framework developed offers a blueprint for the development of grass biorefineries in Ireland and beyond.Item Deep learning-based photogrammetry monitoring of large-scale underground infrastructure at CERN(University College Cork, 2025) Ouyang, Aohui; Li, Zili; Osborne, John Andrew; Science Foundation Ireland; CERN; Irish Centre for Applied Geoscience (iCRAG)In many countries worldwide, thousands of miles of existing underground tunnel infrastructure are facing consistent structural degradation. The structural deterioration of existing tunnels has been increasingly evidenced by crack growth, water leakages and other defects arising from lining material ageing, drainage blockage, etc. One representative tunnel network is the underground infrastructure at the European Organization for Nuclear Research (CERN), which spans over 83km, hosting the largest particle physics underground laboratory in the world. Built in the 1970s, CERN’S underground infrastructure network shows clear signs of structural degradation, combined with complex geological conditions that pose a risk to its serviceability. As integral part of Tunnel Asset Management (TAM) tasks, periodic inspections and the documentation of tunnel defects can provide insights for early-stage structural remediation prior to escalation into more severe damage. However, some challenges, such as the vast scale of infrastructure, the radioactive environment, and limited inspection windows, pose great challenges to traditional, manual visual inspection. This thesis develops a computer-vision-based inspection method for monitoring the tunnel defects at CERN, addressing various scales from millimetre-scale cracks (at the pixel level) to tens-of-centimetre water leakages. To conduct the pixel level crack monitoring in large-scale CERN underground infrastructure, a remote and automated system is developed based on the inhouse CERNBot, using robot-mounted imaging technology. This system can collect crack images remotely and stitch them together to create a panorama image of the tunnel surface. Employing transfer learning, the benchmark semantic segmentation model, the DeepLab V3plus, is finetuned to detect cracks automatically. A novel smooth blending prediction method is implemented on the panorama to present long-distance tunnel crack distribution, alleviating misclassification problems encountered in high-resolution image inference. In addition, transfer learning, tailored loss functions, and the regularization techniques have been developed based on the CERN tunnel crack database characteristics to maintain high performance and generalization of the proposed method. The proposed monitoring system was applied to two typical tunnel sections, creating long tunnel panoramas over 100 meters. The resultant crack density distribution allows identification of critical crack-damaged tunnel sections. On the other hand, the detected crack spatial distribution reveals the long-term mechanism of deformation for different tunnel sections at CERN: the transversal shearing mode is prevalent in the Large Hardon Collider (LHC) tunnel section, whereas the vertical elongation of the tunnel cross section is the dominant deformation mode in an inclined TT1 tunnel. Towards demarcation of water leakage defects in the LHC tunnel, a computer vision-based method is developed based upon the Tunnel Inspection Monorail (TIM) platform. Equipped with 360-degree camera, the customized TIM is deployed for the remote video data acquisition. A customized Omnidirectional Image (ODI) processing pipeline is proposed to transform the gathered omnidirectional video into two-dimensional tunnel mosaics with units of 10-metre length. Furthermore, an ensemble deep learning strategy leveraging transfer-learning is employed to generate a scaled demarcation map of tunnel leakage areas. The proposed method was employed to inspect a 3.45km-long tunnel alignment, spanning from LHC point 4 to point 3. The spotted water leakage distribution identifies critical leakage zones along the extended tunnel alignment and reveals spatial leakage patterns primarily attributed to accumulated water pressure behind the lining, caused by drainage system clogging due to calcite deposits. Using the proposed system, the spatial distribution of the detected drainage plates suggests that the construction joints are particularly vulnerable to tunnel waterproofing degradation, after decades of operation. To achieve accurate quantification and documentation of refined tunnel defects, especially cracks, a digitalization method tool is also developed on a detailed level, enabling 3D visualization and logging into structured data. The method reconstructs sparse point clouds with Structure from Motion (SfM) and cleans out the irrelevant tunnel facilities using a two-stage filtering method. The denoised 3D point clouds are then fitted with customised meshes and textured into 3D reconstruction models. The flat scaled orthomosaic is generated by the cylindrical unrolling. Deep learning methods are also employed for pixel-level crack detection in this high-resolution image, enabling precise extraction of crack locations and quantification. Applied to four different CERN tunnel sections, the method presents the spatial crack distributions in 3D space around the tunnel circumference and quantifies crack dimensions. Furthermore, the digitalization method provides a statistical tool to investigate the crack geometry characteristics in a whole cracked section. Statistical analysis shows that the crack occurrences decrease with increasing crack lengths, largely following a log-normal distribution. The corresponding fitting parameters can serve as quantification indicators of crack length characteristics All these proposed methods offer visualization tools for engineers to inspect the tunnel asset remotely, presenting the tunnel lining surface in consecutive panoramas. The developed image-based deep learning algorithms facilitate the automated tunnel defect visual inspection and digitalize the tunnel defects into structured data format. In field applications, the proposed approaches enhance tunnel asset management (TAM) by identifying severely defect-affected tunnel sections, revealing the spatial distribution patterns of defects over a larger Field of View (FoV), and generating digital representations of deteriorated tunnel areas. Furthermore, the application of the proposed approaches offers potentials to investigate tunnel defects from a statistical angle. The subsequent statistical analysis reveals inherent defect characteristics and provides engineering insights in long term tunnel ageing behaviour.Item Advanced experimental modelling of hydrodynamic and aerodynamic wakes in floating offshore wind turbines using particle image velocimetry(University College Cork, 2024) Belvasi, Navid; Murphy, Jimmy; Judge, Frances; Desmond, Cian; Horizon 2020Accurate evaluation of power generation and aerodynamic performance is essential for optimising energy production in floating wind farms. Understanding the influence of floating substructure motions on turbine aerodynamics is crucial for achieving this, as the two-way coupling between the turbine and substructure impacts aerodynamic performance. Meanwhile, these substructure motions are inherently tied to their hydrodynamics, creating a complex interplay that requires holistic analysis. A significant knowledge gap lies in understanding the impact of floating offshore wind turbine (FOWT) motions on the wake meandering process. This process is characterised by low-frequency, large-scale instability in the turbine’s wake, causing it to shift in and out of rotor planes. Such shifts lead to energy losses and a reduction in fatigue life for downstream turbines. As FOWT motions occur on a similar scale to wake dynamics and atmospheric turbulence, they are likely to play a role in influencing wake meandering. However, these interactions remain poorly understood. Additionally, uncertainties persist in estimating such FOWT motions using current potential theory (POT) tools, particularly in low-frequency ranges dominated by nonlinear hydrodynamic forces. While computational fluid dynamics (CFD) methods are employed to calibrate viscous damping coefficients in POT models to improve accuracy, their high computational cost limits practical use. The reliability of simplified CFD models for refining POT viscous damping remains unexplored, representing a clear knowledge gap. Furthermore, these simplified CFD models require detailed spatial and temporal data on turbulence behaviours in FOWT hydrodynamics for comparisons and cross-validation. Conventional wave basin instruments—such as wave probes, pitot tubes, acoustic and laser doppler velocimeters—are insufficient for capturing the flow velocity characteristics necessary to measure parameters like pressure distributions, Reynolds stresses, turbulent kinetic energy (TKE), and turbulent intensity (TI). This highlights the need to utilise advanced measurement techniques, such as underwater stereoscopic particle image velocimetry (SPIV). While underwater SPIV has shown significant promise in marine engineering wave basin studies, its application in FOWT campaigns remains largely unexplored. This interdisciplinary PhD research aims to investigate the relationships between FOWT turbine far wake meandering, substructure motions, and hydrodynamics. Its first objective was to study the influence of FOWT motions on turbine far-wake behaviour. The second objective was to analyse the impact of CFD model simplifications on the estimation of viscous damping coefficients to inform POT models. The third objective was to investigate underwater SPIV for 3-dimensional turbulence measurements of FOWT hydrodynamics. Using SPIV in a wind tunnel, the far-wake meandering of a FOWT model—based on the Floatgen demonstration—was measured at a longitudinal distance of 8.125 times the rotor diameter downstream of the turbine. Recent research in this area has mainly concentrated on the surge motion signature in the wake meandering of an FOWT. This study advances the state of the art by broadening the analysis to 3 degrees of freedom—surge, pitch, and heave—characterised by multi-frequency time series derived from a realistic prototype, as well as under its representative marine atmospheric boundary layer (ABL). The study found that in the presence of ABL, FOWT motions do not affect the turbine far-wake recovery. However, the spectral density analysis showed that FOWT motions frequency could introduce distinct peaks in the far wake frequency spectrum, causing shocks or resonance in downstream turbines if similar FOWT is used. The PhD research expanded its investigations to SPIV wave basin testing of FOWT to meet Objective 3. Prior to the wave basin experiments, a comprehensive literature review was conducted to examine SPIV applications and challenges in measuring FOWT hydrodynamics. This study was the first to investigate the use of underwater SPIV in addressing critical research gaps in FOWT hydrodynamics, reviewing its limitations, benefits, and disadvantages. Next, the SPIV method was applied in a wave basin to explore the relationship between FOWT motions and hydrodynamic behaviour. The FOWT model was tested under three configurations: fixed, floating, and floating with an operational turbine. Velocity contours, TKE, TI, and Reynolds stress tensors were examined. The analysis then discussed the turbulence characteristics of fluid flow behind these simplified test models. Using insights from wave basin SPIV tests, simplified CFD models were developed to calibrate viscous damping coefficients in FOWT POT models, aiming to address Objective 2. While existing literature primarily focuses on using fully complex CFD models to analyse FOWT behaviour, the novelty of this study lies in proposing and evaluating a streamlined approach that leverages simplified CFD models to inform the damping coefficients in POT tools for FOWT. The resulting damping coefficients from CFD test configurations—including fixed, floating, and full-system models, as well as decay tests and current flow setups—were analysed and compared. While drag coefficients varied significantly among the test configurations, simulations incorporating environmental conditions—including under fixed, floating, and full system configurations—produced damping values consistent with expected platform behaviour. Finally, a POT model of the FOWT was calibrated using damping coefficients from the CFD models, followed by a sensitivity analysis of response amplitude operators (RAOs). Comparisons of RAOs for surge, heave, and pitch motions revealed that uncorrected POT models tend to overestimate response times, particularly for surge and pitch, by up to double the actual values. However, calibrating the damping coefficients in the POT model using results from simplified CFD models improved precision, reducing RAO errors to within 10–20%. This PhD research contributes to and advances the development of FOWTs by reviewing the aerodynamic and hydrodynamic wake behaviour. Key contributions summarised as advancing floating wind hydrodynamic testing using underwater SPIV, evaluating simplified CFD models to inform viscous terms in POT models to enhance its motion estimations, and analysing FOWT motions effects on the wake meandering process utilising 3 Degree of freedom motion time series and in the presence of the atmospheric boundary layer.Item Technical and economic assessment of renewable hydrogen production and utilization for the transportation sector(University College Cork, 2024) Li, Yunfei; Murphy, Jerry; Lin, Richen; O'Shea, Richard; Wall, David; Science Foundation IrelandHydrogen energy is mooted to play a critical role in decarbonization of the transportation sector, due in part to its relatively high energy density (120 MJLHV/kg) and clean combustion product (the only combustion product is water). Renewable hydrogen can be generated from various renewable energy sources (such as solar, wind, tidal, and biomass) through technologies including water electrolysis, biomass gasification, microbial fermentation, and water thermochemical cycles. The produced renewable hydrogen can then be utilized in the transportation sector via hydrogen fuel cell vehicles or after conversion to other renewable fuel vectors (including but not limited to ammonia (NH3), methane (CH4), methanol (CH3OH) or Fischer-Tropsch (F-T) fuels). However, despite extensive research on renewable hydrogen production and application technologies in the current literature, there is no definitive specific technical roadmap for its production and application in the transportation sector. This is viewed as an overarching state-of-the-art research gap in this thesis. Therefore, this study aims to undertake a quantitative analysis and comparison of renewable hydrogen production technologies as well as utilization using techno-economic analysis (TEA) and multi-criteria decision analysis (MCDA) methods. For the renewable hydrogen production technologies, a literature review indicates that polymer electrolyte membrane (PEM) water electrolysis technology has quickly become a prominent method for renewable hydrogen production due to its high electrolyser stack efficiency (58%-75%), the high purity of the produced hydrogen (99.99%), rapid advancement in technology readiness level ((TRL) which is expected to reach 9 by 2030), quick system response time (milliseconds), compact design, and flexible scalability to meet varying production demands. Alternatively, biomass gasification technology is also suitable for current hydrogen production requirements due to its maturity (TRL = 8 based on 2024 scenario), wide applicability to different feedstocks (including but not limited to willow, coconut shell, rice husk, and forestry thinning), ability to mitigate the environmental impact of waste disposal, and capability to produce value-added byproducts such as syngas for chemical synthesis and biochar for soil enhancement. Thus, PEM water electrolysis and biomass gasification technologies are suggested as the most suitable renewable hydrogen production methods for short-term implementation by 2030. For renewable hydrogen utilization, the produced renewable hydrogen can be used directly or as a feedstock, in conjunction with a source of biogenic CO2 to produce synthetic hydrocarbons or combined with N2 to produce NH3. Therefore, this thesis introduces the concepts of power-to-x (PtX) and biomass-to-x (BtX) to produce renewable CH4, CH3OH, NH3, and F-T fuels. Following the numerical calculations of mass and energy flows for these technological pathways, a techno-economic analysis was performed for each pathway, providing a comprehensive assessment of the system performance of these renewable fuel production technologies. Technically, it is calculated that hydrogen production technologies exhibit the highest system energy efficiency (74% for power-to-hydrogen (PtH2) and 63% for biomass-to-hydrogen (BtH2)) among the five renewable fuels when excluding the efficiency associated with sourcing biogenic CO2. However, when biogenic CO2 is utilized to produce renewable hydrocarbons, the CO2 mass demand varies by nearly 100% depending on the renewable hydrocarbon pathway despite use of the same electrolyser. The sustainable supply of biogenic CO2 is a critical bottleneck for the development of renewable hydrocarbons. From an energy perspective, the CH3OH synthesis pathway shows superior system energy efficiency to other hydrocarbons (which include carbon and as such excludes hydrogen itself as a separate energy vector), achieving 59.8% in the PtX configuration (based on a 60 MW electrolyser capacity) and 61.0% in the BtX configuration (based on a 50 MW gasification capacity). This is followed by the CH4 synthesis pathway, with efficiencies of 55.3% for PtX and 52.5% for BtX. Economically, PtX technology is heavily reliant on the availability of abundant renewable electricity and water, which accounts for more than 90% of the annual operating expenditure. In contrast, the capital expenditure (CAPEX) for the BtX pathway is approximately 46%-49% higher than that of the PtX pathway, resulting in a levelised cost of energy (LCOE) that is 4%-40% higher than that of PtX fuels, making BtX technology less economically favorable. The TEA method demonstrates notable limitations when comparing technologies across multiple criteria. To address this limitation, this thesis introduces the MCDA method to facilitate the comparison of PtX and BtX pathways and assess the hierarchy ranking of these renewable fuel production technologies. This study employs the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), and Weighted Sum Method (WSM) methods for the MCDA process. Six technical and economic criteria were evaluated: system energy efficiency, land area requirements for feedstock cultivation, resource potential, capital expenditure, operating expenditure, and the levelized cost of renewable energy. The results indicate that "capital expenditure" receives the highest weight based on the objective weighting method Criteria Importance Through Intercriteria Correlation (CRITIC). Following in importance are the criteria "land area requirements for feedstock cultivation" and "resource potential". The MCDA results rank power-to-hydrogen (PtH2) highest, followed by power-to-ammonia (PtNH3) and power-to-methanol (PtCH3OH), with Biomass-to-Fischer-Tropsch (BtF-T) fuels lowest ranked. The subsequent sensitivity analysis confirms the stability of the top-ranked production technology despite changes in technical and economic criteria weights. This thesis provides strategic insights into the selection of renewable hydrogen production and utilization technologies by evaluating their technical and economic performance, equipping stakeholders with essential knowledge to facilitate the integration of renewable hydrogen into the decarbonization pathways of transportation sector.Item Energy efficient achievement of indoor air quality and thermal comfort using mechanical ventilation heat recovery and solar-energy pre-heating(University College Cork, 2025) Tohidi Moghadam, Talie; O'Sullivan, Dominic; Norton, Brian; SFI Research Centre for Energy, Climate and Marine; EPSRC Centre for Doctoral Training in Medical ImagingThis research explores the viability of integrating solar air heating technologies with a Mechanical Ventilation Heat Recovery (MVHR) system for pre-heating ventilation air to achieve energy efficiency and maintain Indoor Air Quality (IAQ) and thermal comfort in an institutional building. Key research gaps addressed include balancing IAQ with ventilation energy efficiency, IAQ control in high-occupancy educational buildings, the lack of experimental-based research on energy-efficient ventilation strategies, and the potential of solar thermal technologies in reducing heating loads. A critical systematic literature review was conducted to assess the impact of the COVID-19 pandemic on ventilation practices, IAQ, and energy efficiency. An experimental field study in a university lecture theatre considered mixed-mode ventilation strategies and their impact on IAQ and energy use. A subsequent experimental study in a smaller classroom investigated seasonal performance of Transpired Solar Collectors (TSCs) integrated with an MVHR system for pre-heating ventilation air. Experimental results validated “EnergyPlus” simulations for ventilation control strategies and “RETScreen” analysis for TSC solar air heating applications. Field measurements confirmed that low ventilation rates correlate with high CO₂ levels, while increased ventilation enhances IAQ but results in higher energy consumption, emphasizing the need for optimised ventilation control strategies. The integration of TSCs with an MVHR system improved ventilation preheating efficiency, reducing overall heating energy demand by approximately 20% while maintaining IAQ and thermal comfort across different seasons. The feasibility analysis further revealed that TSC solar air heating can reduce an entire building annual heating energy consumption by more than 30% and in the particular building, it reduced Greenhouse Gas (GHG) emissions from natural gas and electricity usage by 10.5 tonnes CO₂, with a simple payback period of 8.1 years and an internal rate of return of 15.2%, proving its economic viability. This research delivers several key contributions to the field of sustainable building design and ventilation. It presents the first systematic review addressing the intersection of COVID-19-driven ventilation requirements and associated energy challenges. The work also identifies limitations in current field-based IAQ assessment practices and offers Ireland’s first experimental evaluation and performance quantification of an integrated TSC and MVHR system. Additionally, it introduces the first techno-economic feasibility study of solar-assisted ventilation in the Irish context. These original contributions support progress toward Sustainable Development Goals (SDGs). The project further aligns with University College Cork’s sustainability agenda, contributing to ongoing efforts to improve energy efficiency, reduce emissions, and support the transition toward net-zero energy buildings.