Civil and Environmental Engineering - Doctoral Theses

Permanent URI for this collection

https://hdl.handle.net/10468/195

Browse

Now showing 1 - 20 of 54

Analysis of building performance data
(University College Cork, 2018) Hoerster, Stephan Carlo; Menzel, Karsten; Brown, Ken; Higher Education Authority; Bilfinger HSG FM
In recent years, the global trend for digitalisation has also reached buildings and facility management. Due to the roll out of smart meters and the retrofitting of buildings with meters and sensors, the amount of data available for a single building has increased significantly. In addition to data sets collected by measurement devices, Building Information Modelling has recently seen a strong incline. By maintaining a building model through the whole building life-cycle, the model becomes rich of information describing all major aspects of a building. This work aims to combine these data sources to gain further valuable information from data analysis. Better knowledge of the building’s behaviour due to high quality data available leads to more efficient building operations. Eventually, this may result in a reduction of energy use and therefore less operational costs. In this thesis a concept for holistic data acquisition from smart meters and a methodology for the integration of further meters in the measurement concept are introduced and validated. Secondly, this thesis presents a novel algorithm designed for cleansing and interpolation of faulty data. Descriptive data is extracted from an open meta data model for buildings which is utilised to further enrich the metered data. Additionally, this thesis presents a methodology for how to design and manage all information in a unified Data Warehouse schema. This Data Warehouse, which has been developed, maintains compatibility with an open meta data model by adopting the model’s specification into its data schema. It features the application of building specific Key Performance Indicators (KPI) to measure building performance. In addition a clustering algorithm, based on machine learning technology, is developed to identify behavioural patterns of buildings and their frequency of occurrence. All methodologies introduced in this work are evaluated through installations and data from three pilot buildings. The pilot buildings were selected to be of diverse types to prove the generic applicability of the above concepts. The outcome of this work successfully demonstrates that the combination of data sources available for buildings enable advanced data analysis. This largely increases the understanding of buildings and their behavioural patterns. A more efficient building operation and a reduction of energy usage can be achieved with this knowledge.
The application of computer modelling to develop a methodology for ‘existing building’ upgrades to move towards carbon-neutral buildings
(University College Cork, 2014) Murray, Sean Noel; O'Sullivan, Dominic
The retrofitting of existing buildings for decreased energy usage, through increased energy efficiency and for minimum carbon dioxide emissions throughout their remaining lifetime is a major area of research. This research area requires development to provide building professionals with more efficient building retrofit solution determination tools. The overarching objective of this research is to develop a tool for this purpose through the implementation of a prescribed methodology. This has been achieved in three distinct steps. Firstly, the concept of using the degree-days modelling method as an adequate means of basing retrofit decision upon was analysed and the results illustrated that the concept had merit. Secondly, the concept of combining the degree-days modelling method and the Genetic Algorithms optimisation method is investigated as a method of determining optimal thermal energy retrofit solutions. Thirdly, the combination of the degree-days modelling method and the Genetic Algorithms optimisation method were packaged into a building retrofit decision-support tool and named BRaSS (Building Retrofit Support Software). The results demonstrate clearly that, fundamental building information, simplified occupancy profiles and weather data used in a static simulation modelling method is a sufficient and adequate means to base retrofitting decisions upon. The results also show that basing retrofit decisions upon energy analysis results are the best means to guide a retrofit project and also to achieve results which are optimum for a particular building. The results also indicate that the building retrofit decision-support tool, BRaSS, is an effective method to determine optimum thermal energy retrofit solutions.
Biomethane production from co-digestion of grass silage and slurry
(University College Cork, 2017) Himanshu, Himanshu; Murphy, Jeremiah D.G.; O’Kiely, Padraig; Seventh Framework Programme
The core aim of this thesis was to quantify the effects of co-digesting forage silages with animal slurries on methane yields and to investigate if antagonistic or synergistic outcomes occur. In order to complete this assessment, the economic impacts of changing forage silage characteristics, of changing the mixing ratios of forage silage and cattle slurry in binary mixtures (and the presence of synergy or antagonism) and of changing the costs of providing these feedstocks for anaerobic digestion (AD) on the cost of methane production in an on-farm AD facility were accessed. An initial objective, however, was to define an optimal methodology for laboratory-scale anaerobic digestion, specifically to determine the impact of altering the headspace volume within incubation bottles and the overhead pressure measurement and release (OHPMR) frequency on methane yield using a manual manometric biochemical methane potential (mBMP) batch digestion method. Two anaerobic batch co-digestion experiments were conducted with forage silages and animal slurries. In the first experiment, oven-dried perennial ryegrass (harvested at two growth stages) or red clover (harvested at two growth stages) silages and cattle slurry were co-digested. Each binary mixture had synergistic effects which resulted in 2.8-7.5% higher methane yields than predicted from mono-digestion of individual substrates. In the second experiment, cattle slurry (two types) or pig slurry was co-digested with undried perennial ryegrass silages (harvested at two growth stages). Each silage and slurry mixture had antagonistic effects which resulted in methane yields 5.7-7.6% below those predicted from mono-digestion of individual substrates. In the initial experiment and in order to broaden the conditions under which the assessment was made, the biogas and methane yields of cellulose, barley grain, grass silage and cattle slurry were determined in response to three incubation bottle headspace volumes and four OHPMR frequencies. The methane yields of barley, silage and slurry were also compared with those from an automated volumetric method (i.e. AMPTS). Headspace volume and OHPMR frequency effects on biogas yield were mediated mainly through headspace pressure, with the latter having a negative effect on the biogas yield measured but relatively little effect on methane yield. Two mBMP treatments that produced methane yields equivalent to AMPTS were identified. Economic modelling results showed significant impacts of AD feedstock characteristics and their provision cost on the cost of methane production in an AD facility. The feedstock provision cost contributed about half of the total cost of methane production when the AD facility solely operated on grass silage. The total cost of methane produced from mono-digestion of cattle slurry that was supplied free of charge was more than double the cost of methane produced from grass silage. For co-digestion of grass silage and cattle slurry, the total cost of methane production progressively increased as the proportion of slurry in the co-digested mixture increased. Antagonistic and synergistic methanogenesis resulted in a corresponding 6% higher and 5% lower total cost of methane production during co-digestion of grass silage and cattle slurry (at a silage:slurry volatile solids ratio of 0.8:0.2) compared to the binary mixture without these effects.
Biomethane production from food waste and organic residues
(University College Cork, 2014) Browne, James D.; Murphy, Jeremiah D.G.; Bord Gáis Éireann; Irish Research Council for Science Engineering and Technology
Anaerobic digestion (AD) of biodegradable waste is an environmentally and economically sustainable solution which incorporates waste treatment and energy recovery. The organic fraction of municipal solid waste (OFMSW), which comprises mostly of food waste, is highly degradable under anaerobic conditions. Biogas produced from OFMSW, when upgraded to biomethane, is recognised as one of the most sustainable renewable biofuels and can also be one of the cheapest sources of biomethane if a gate fee is associated with the substrate. OFMSW is a complex and heterogeneous material which may have widely different characteristics depending on the source of origin and collection system used. The research presented in this thesis investigates the potential energy resource from a wide range of organic waste streams through field and laboratory research on real world samples. OFMSW samples collected from a range of sources generated methane yields ranging from 75 to 160 m3 per tonne. Higher methane yields are associated with source segregated food waste from commercial catering premises as opposed to domestic sources. The inclusion of garden waste reduces the specific methane yield from household organic waste. In continuous AD trials it was found that a conventional continuously stirred tank reactor (CSTR) gave the highest specific methane yields at a moderate organic loading rate of 2 kg volatile solids (VS) m-3 digester day-1 and a hydraulic retention time of 30 days. The average specific methane yield obtained at this loading rate in continuous digestion was 560 ± 29 L CH4 kg-1 VS which exceeded the biomethane potential test result by 5%. The low carbon to nitrogen ratio (C: N <14:1) associated with canteen food waste lead to increasing concentrations of volatile fatty acids in line with high concentrations of ammonia nitrogen at higher organic loading rates. At an organic loading rate of 4 kg VS m-3day-1 the specific methane yield dropped considerably (381 L CH4 kg-1 VS), the pH rose to 8.1 and free ammonia (NH3 ) concentrations reached toxicity levels towards the end of the trial (ca. 950 mg L-1). A novel two phase AD reactor configuration consisting of a series of sequentially fed leach bed reactors connected to an upflow anaerobic sludge blanket (UASB) demonstrated a high rate of organic matter decay but resulted in lower specific methane yields (384 L CH4 kg-1 VS) than the conventional CSTR system.
Biomethane production from macroalgae
(University College Cork, 2016) Tabassum, Muhammad Rizwan; Murphy, Jeremiah D.G.; Science Foundation Ireland; Gas Networks Ireland; Ervia, Ireland
Irish brown seaweeds have been identified as a potential bio-resource with potentially high specific methane yields. Anaerobic digestion is deemed the most feasible technology due to its commercial viability for handling such wet feedstock. However, the biomethane potential of seaweed is highly dependent on its chemical composition which can vary by species type, cultivation method, and time of harvest. This study aims to investigate and optimize the process for the production of biomethane from Irish brown seaweeds focusing on the key technology bottlenecks including for seaweed characterization, biomethane potential assessment, optimization of long-term anaerobic digestion and suitable pre-treatment technologies to enhance potential gas yields. Laminaria digitata and Ascophyllum nodosum were tested for seasonal variation. From the characterization and batch digestion of L. digitata, August was found to be the optimal month for harvest due to high organic matter content, low level of ash and ultimately highest biomethane yield. The specific methane yield of 53 m3 CH4 t-1 wwt in August was 4.5 times higher than the yield in December (12 m3 CH4 t-1 wwt), with ash content the key factor in seasonal variation. For A. nodosum, the optimal harvest month was October with polyphenol content found to be a more influential factor than ash. The gross energy yields from both species were evaluated in the range of 116-200 GJ ha-1 yr-1. Continuous digestion trials were subsequently designed for S. latissima and L. digitata to optimize the key digestion parameters. Results from mono-digestion and co-digestion with dairy slurry revealed that both seaweeds could be digested at maximum biomethane efficiency to a loading rate of 4 kg VS m-3 d-1. Accumulation of salt in the digesters was a concern for long term digestion and it was reasoned that suitable pretreatment may be required prior to digestion. Various pre-treatments were subsequently tested on L. digitata to enhance the gas yield. It was found that maceration after hot water washing yielded 25% more specific methane and up to 54% salt removal as compared to untreated L. digitata. The experiments undertaken aim to assist in providing a basic guideline for feasible design and operation of seaweed digesters in Ireland.
Bottom-up modelling of energy demand and technical energy savings potential in the Irish residential sector
(University College Cork, 2014) Dineen, Denis; Ó Gallachóir, Brian P.; Irish Research Council for Science Engineering and Technology
The International Energy Agency has repeatedly identified increased end-use energy efficiency as the quickest, least costly method of green house gas mitigation, most recently in the 2012 World Energy Outlook, and urges all governing bodies to increase efforts to promote energy efficiency policies and technologies. The residential sector is recognised as a major potential source of cost effective energy efficiency gains. Within the EU this relative importance can be seen from a review of the National Energy Efficiency Action Plans (NEEAP) submitted by member states, which in all cases place a large emphasis on the residential sector. This is particularly true for Ireland whose residential sector has historically had higher energy consumption and CO2 emissions than the EU average and whose first NEEAP targeted 44% of the energy savings to be achieved in 2020 from this sector. This thesis develops a bottom-up engineering archetype modelling approach to analyse the Irish residential sector and to estimate the technical energy savings potential of a number of policy measures. First, a model of space and water heating energy demand for new dwellings is built and used to estimate the technical energy savings potential due to the introduction of the 2008 and 2010 changes to part L of the building regulations governing energy efficiency in new dwellings. Next, the author makes use of a valuable new dataset of Building Energy Rating (BER) survey results to first characterise the highly heterogeneous stock of existing dwellings, and then to estimate the technical energy savings potential of an ambitious national retrofit programme targeting up to 1 million residential dwellings. This thesis also presents work carried out by the author as part of a collaboration to produce a bottom-up, multi-sector LEAP model for Ireland. Overall this work highlights the challenges faced in successfully implementing both sets of policy measures. It points to the wide potential range of final savings possible from particular policy measures and the resulting high degree of uncertainty as to whether particular targets will be met and identifies the key factors on which the success of these policies will depend. It makes recommendations on further modelling work and on the improvements necessary in the data available to researchers and policy makers alike in order to develop increasingly sophisticated residential energy demand models and better inform policy.
Bridge-vehicle interaction for structural health monitoring: potentials, applications, and limitations
(University College Cork, 2014) Jaksic, Vesna; Pakrashi, Vikram; Irish Research Council for Science Engineering and Technology
Structural Health Monitoring (SHM) is an integral part of infrastructure maintenance and management systems due to socio-economic, safety and security reasons. The behaviour of a structure under vibration depends on structure characteristics. The change of structure characteristics may suggest the change in system behaviour due to the presence of damage(s) within. Therefore the consistent, output signal guided, and system dependable markers would be convenient tool for the online monitoring, the maintenance, rehabilitation strategies, and optimized decision making policies as required by the engineers, owners, managers, and the users from both safety and serviceability aspects. SHM has a very significant advantage over traditional investigations where tangible and intangible costs of a very high degree are often incurred due to the disruption of service. Additionally, SHM through bridge-vehicle interaction opens up opportunities for continuous tracking of the condition of the structure. Research in this area is still in initial stage and is extremely promising. This PhD focuses on using bridge-vehicle interaction response for SHM of damaged or deteriorating bridges to monitor or assess them under operating conditions. In the present study, a number of damage detection markers have been investigated and proposed in order to identify the existence, location, and the extent of an open crack in the structure. The theoretical and experimental investigation has been conducted on Single Degree of Freedom linear system, simply supported beams. The novel Delay Vector Variance (DVV) methodology has been employed for characterization of structural behaviour by time-domain response analysis. Also, the analysis of responses of actual bridges using DVV method has been for the first time employed for this kind of investigation.
Building effectiveness communication ratios (BECs): an integrated ‘life-cycle’ methodology for mitigating energy-use in buildings
(University College Cork, 2006-08) Morrissey, Elmer D.; Keane, Marcus M.; Irish Research Council for Science Engineering and Technology; Sustainable Energy Authority of Ireland
Current building regulations are generally prescriptive in nature. It is widely accepted in Europe that this form of building regulation is stifling technological innovation and leading to inadequate energy efficiency in the building stock. This has increased the motivation to move design practices towards a more ‘performance-based’ model in order to mitigate inflated levels of energy-use consumed by the building stock. A performance based model assesses the interaction of all building elements and the resulting impact on holistic building energy-use. However, this is a nebulous task due to building energy-use being affected by a myriad of heterogeneous agents. Accordingly, it is imperative that appropriate methods, tools and technologies are employed for energy prediction, measurement and evaluation throughout the project’s life cycle. This research also considers that it is imperative that the data is universally accessible by all stakeholders. The use of a centrally based product model for exchange of building information is explored. This research describes the development and implementation of a new building energy-use performance assessment methodology. Termed the Building Effectiveness Communications ratios (BECs) methodology, this performance-based framework is capable of translating complex definitions of sustainability for energy efficiency and depicting universally understandable views at all stage of the Building Life Cycle (BLC) to the project’s stakeholders. The enabling yardsticks of building energy-use performance, termed Ir and Pr, provide continuous design and operations feedback in order to aid the building’s decision makers. Utilised effectively, the methodology is capable of delivering quality assurance throughout the BLC by providing project teams with quantitative measurement of energy efficiency. Armed with these superior enabling tools for project stakeholder communication, it is envisaged that project teams will be better placed to augment a knowledge base and generate more efficient additions to the building stock.
Characteristics of the wave energy resource at the Atlantic marine energy test site
(University College Cork, 2013) Cahill, Brendan; Lewis, Anthony W.; Irish Research Council for Science Engineering and Technology; Science Foundation Ireland
The wave energy industry is progressing towards an advanced stage of development, with consideration being given to the selection of suitable sites for the first commercial installations. An informed, and accurate, characterisation of the wave energy resource is an essential aspect of this process. Ireland is exposed to an energetic wave climate, however many features of this resource are not well understood. This thesis assesses and characterises the wave energy resource that has been measured and modelled at the Atlantic Marine Energy Test Site, a facility for conducting sea trials of floating wave energy converters that is being developed near Belmullet, on the west coast of Ireland. This characterisation process is undertaken through the analysis of metocean datasets that have previously been unavailable for exposed Irish sites. A number of commonly made assumptions in the calculation of wave power are contested, and the uncertainties resulting from their application are demonstrated. The relationship between commonly used wave period parameters is studied, and its importance in the calculation of wave power quantified, while it is also shown that a disconnect exists between the sea states which occur most frequently at the site and those that contribute most to the incident wave energy. Additionally, observations of the extreme wave conditions that have occurred at the site and estimates of future storms that devices will need to withstand are presented. The implications of these results for the design and operation of wave energy converters are discussed. The foremost contribution of this thesis is the development of an enhanced understanding of the fundamental nature of the wave energy resource at the Atlantic Marine Energy Test Site. The results presented here also have a wider relevance, and can be considered typical of other, similarly exposed, locations on Ireland’s west coast.
Data analytics for fault prediction and diagnosis in wind turbines
(University College Cork, 2018) Leahy, Kevin; O'Sullivan, Dominic; Science Foundation Ireland
As feed-in tariffs for wind energy are gradually being replaced by market driven auction-based systems, the need for cost savings at every stage of a wind energy project is more apparent than ever. A proven and effective way of reducing maintenance costs is through a condition-based maintenance (CBM) strategy. By using supervisory control and data acquisition (SCADA) system data instead of retrofitting a dedicated condition monitoring (CM) system, CM functionality can be gained at a fraction of the cost. This thesis investigates using SCADA system data for various levels of CM: fault detection, diagnosis and prediction. First, a case study is presented on using classification techniques for CM using SCADA data. Various methods for dealing with the massive class imbalance seen in fault data are evaluated. It was found that all three levels of CM are possible using classification techniques, though with a high number of false positives. Adding a class-weight to the minority class or undersampling the majority class were found to be the best ways of dealing with class imbalance. Sources of accurate failure data can be difficult to obtain for wind turbines. The second part of this thesis presents a novel way of building a historical failure database using alarm system and availability data. This was shown to produce an accurate database of unplanned stoppages related to assembly-level failures, scheduled maintenance, or grid, noise or shadow-related events. Next, common issues with some of the classification approaches present in the literature are addressed, as well as the lack of demonstration of how these approaches would perform in the field. A formalised framework with a prescribed list of steps following best practice guidelines is presented for performing CM using classification techniques on turbine SCADA data. A case study is performed which uses a sliding window metric to evaluate field performance, showing that such a system is effective at flagging faults in advance, but more data is needed to reduce the false positive rate. It is noted throughout the thesis that turbine alarm systems have some consistent shortcomings, and do not live up to their full potential. Hence, a novel methodology is presented which uses clustering techniques to identify similar sequences of alarms as they occurred during unplanned stoppages. A case study applying the methodology showed that just under half of the 456 stoppages could be sorted into one of fifteen distinct types of alarm sequence.
Data-driven analysis of reliability, accessibility and survivability in marine renewable energy projects
(University College Cork, 2019) Barker, Aaron; Murphy, James; Lewis, Tony; Science Foundation Ireland; Electricity Supply Board
Increased activity in the Marine Renewable Energy industry has driven the need for an improved understanding of the wave climate and wave energy resource, which are fundamental to the development of any marine energy project. This thesis assesses the characterisation of the wave energy resource available at the Killard Point site in Co. Clare, as part of a joint industry project on the Electricity Supply Board (ESB)’s’s WestWave project, Ireland’s first proposed commercial wave energy installation. This assessment is done with an eye on the newly formed International Electrotechnical Commission standards for metocean resource assessment, with a focus on producing a standardised analysis method which informs the extractable wave energy resource. Many existing practices are questioned, and their merits assessed. This thesis adds novel tools and advanced data analysis methods, which are implemented to develop new methodologies for enhancing our understanding of our wave resource, and which subsequently enable improved assessment of the impacts of reliability, accessibility and survivability of Marine Renewable Energy projects. The impact of spectral shape on device energy production is examined using both a theoretical and practical application, to show the disconnect between currently accepted practices and the level of certainty which will be required to drive commercial success. A new methodology for the assessment of extreme wave conditions is developed, while a large contribution of this thesis is in developing and applying machine learning techniques to enhance the accuracy and dependability of wave parameter relationships and the prediction of device energy production by improving the estimation of absent wave data. This approach has been shown to result in a reduction in power production error at Killard Point from 30% to just 3.5%. This novel Machine Learning method is integral in enabling the level of characterisation that will be necessary for the commercial success of Marine Renewable Energy projects. The major contribution of this thesis is the development of an enhanced understanding of the available wave resource at the Killard Point site; producing a numerical hindcast nearshore wave model which attempts to bring the project to the level required by IEC standards, while addressing technical issues which affect the standardisation, accuracy, usability and predictability of the data gathered. This work does not focus on the Marine Renewable Energy technology in use, nor will it explore in great detail the economic vagaries of MRE projects. Instead, it focusses on developing methods which will provide a large missing piece of the puzzle in MRE development, accurate and dependable metocean analysis. The results presented here have wider applicability, and indeed much of this research has taken place, or has been verified at, other sites along the west-coast of Ireland.
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 Ireland
This 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.
Early warning sign attributes for the safety of drinking water treatment works
(University College Cork, 2013) Feehan, John; McKeogh, Eamon J.; Dokas, Ioannis
A proactive risk management strategy seeks to prevent accidents from taking place and maintain the safety of a system. In this context, the task of identifying and disseminating early warning signs and signals is among the most important. The problem is that warning signs that are present before an accident takes place are often being overlooked and not picked up or identified as warning signs. If these warning signs were responded to, then an accident may be averted. Accidents occuring in the critical domain of a drinking water treatments works can have serious implications for the public health of consumers of the water supplied. Realising and comprehending early warning signs is a major challenge for the domain of systems safety and especially in the domain of a water treatment works. The approaches that are typically used to enhance the realisation, comprehension and dissemination of early warning signs in the water treatment domain in Ireland mainly involves the creation of accident scenarios, the use of monitoring data and procedures for the dissemination of warnings. While all of these approaches are all useful to inform the mental or process models of possible accident scenarios, nevertheless, accidents are still occurring in this domain. Therefore, a new approach to enhance the comprehension of and effective dissemination of early warning signs is required in order to improve safety and proactive risk management strategies. The contributions of this thesis is the provision of a set of attributes associated with the early warning sign concept that provides meaningful data on the early warning signs and allows recipients to better comprehend them. The values of these attributes were customised for application in the water treatment domain. This research proves that early warning signs at a water treatment works received with information on their attributes are comprehended and communicated more effectively and efficiently than the usual pragmatic approach and thereby improves the safety and proactive risk management strategies.
Effects of disparate information levels on bridge management and safety
(University College Cork, 2017) Hanley, Ciaran; Pakrashi, Vikram; Kelliher, Denis; Irish Research Council
Maintenance planning and life-cycle assessment methods for bridge networks have received large research interest for many years; with modern emphasis often based on probabilistic approaches, due to their ability to handle uncertainty. This allows for risk-based approaches to quantify structural safety, which is largely seen as a superior approach than the deterministic methods typically used in practice. Structural safety can broadly be defined as an acceptable level of chance/probability that the structure will not fail in its function; i.e. to resist the loads/actions to which it is subjected to. The structural reliability method provides for the computation of structural safety by accounting for probabilistically uncertain load models and uncertainties around the return period of extreme load events, as well as the uncertainty in the resistance capacity of the structural system. In addition to a single-point-in-time evaluation of structural safety, the life-cycle performance can be evaluated using physical models for future deterioration; again, constrained under uncertain information about future deterioration. However, being a probabilistic method, it can be somewhat subjective in nature, based on the availability of accurate data and the reliance on expert knowledge, and thus sensitive to the parameters of the input model which rely on the level of information available for the problem at hand. While structural safety is the apex of modern maintenance planning and lifecycle assessment, the most prevalent performance indicator for which future maintenance and intervention decisions are made come from visual inspection based condition ratings. These visual inspections are used to evaluate the extent of deterioration present and assign a condition rating based on a predefined scale of damage, after which bridge managers trigger further assessment or intervention actions based on acceptable damage levels. Again, in evaluating a single or small number of bridges, there is a degree of subjectivity and reliance on expert knowledge that is also seen with probabilistic assessment methods. Unlike structural reliability, which often suffers from a lack of available or accurate information, condition rating data for large bridge networks generate a large repository of data which provides an excellent opportunity to look at data on a larger scale than is currently implemented in practice. This results in disparate levels of information being available for bridge networks, with large amounts of lower level information and small amounts of detailed information. In this thesis, how disparate information levels affect these two assessment methods will be explored and efforts to mitigate against the uncertainty in the information will be suggested. It will be shown that: Reliability-based calibrations of bridges are possible through observed clustering of parametric importance and sensitivity measures, based on uncertainty in relation to the available information for probabilistic modelling (Hanley and Pakrashi 2016) Existing bridges assessed under code-defined traffic load are sensitive to safety reclassification due to evolving definitions, leading to misinterpretation of the actual state of the structure and, thus, a misallocation of resources (Hanley et al. 2017a) Bridges designed under modern, more conservative code-defined traffic load models and assessed under probabilistic load models can expect a longer projected service life before intervention is required, and that the initial construction cost of this conservatism is largely offset when lifecycle cost is considered (Hanley et al. 2016a) The use of multivariate analysis methods are applicable to modern bridge management systems that store large amounts of data, and that these methods can provide for clustering of bridges based on their structural forms and states of disrepair (Hanley et al. 2015) Large groups of specific bridge types have well-defined, consistent factor structures, whereby a bespoke linear combination of individual elemental condition ratings provide an accurate assessment of the bridge’s overall condition rating; improving on currently implemented decision tools in existing bridge management systems (Hanley et al. 2016b, 2017b,c) This work provides a basis for which further research can be undertaken into developing an information-driven probabilistic decision making framework, leading to the quantification of the value of disparate information levels. The potential future applications include incorporating the derived underlying factor structure of large data-sets of bridges directly into structural reliability methods through probabilistic graphical models, such as Bayesian Belief Networks; thus providing a more robust, information driven framework from which to make decisions under uncertainty.
Empirical analysis and improved modelling of natural gas demand in Ireland
(University College Cork, 2013) Rogan, Fionn; Ó Gallachóir, Brian P.; Murphy, Jeremiah D.G.; Bord Gáis Éireann
Countries across the world are being challenged to decarbonise their energy systems in response to diminishing fossil fuel reserves, rising GHG emissions and the dangerous threat of climate change. There has been a renewed interest in energy efficiency, renewable energy and low carbon energy as policy‐makers seek to identify and put in place the most robust sustainable energy system that can address this challenge. This thesis seeks to improve the evidence base underpinning energy policy decisions in Ireland with a particular focus on natural gas, which in 2011 grew to have a 30% share of Ireland’s TPER. Natural gas is used in all sectors of the Irish economy and is seen by many as a transition fuel to a low-carbon energy system; it is also a uniquely excellent source of data for many aspects of energy consumption. A detailed decomposition analysis of natural gas consumption in the residential sector quantifies many of the structural drives of change, with activity (R2 = 0.97) and intensity (R2 = 0.69) being the best explainers of changing gas demand. The 2002 residential building regulations are subject to an ex-post evaluation, which using empirical data finds a 44 ±9.5% shortfall in expected energy savings as well as a 13±1.6% level of non-compliance. A detailed energy demand model of the entire Irish energy system is presented together with scenario analysis of a large number of energy efficiency policies, which show an aggregate reduction in TFC of 8.9% compared to a reference scenario. The role for natural gas as a transition fuel over a long time horizon (2005-2050) is analysed using an energy systems model and a decomposition analysis, which shows the contribution of fuel switching to natural gas to be worth 12 percentage points of an overall 80% reduction in CO2 emissions. Finally, an analysis of the potential for CCS in Ireland finds gas CCS to be more robust than coal CCS for changes in fuel prices, capital costs and emissions reduction and the cost optimal location for a gas CCS plant in Ireland is found to be in Cork with sequestration in the depleted gas field of Kinsale.
A facilities maintenance management process based on degradation prediction using sensed data
(University College Cork, 2014) Tobin, Ena; Brown, Kenneth N.; Damien Fay; Science Foundation Ireland
Energy efficiency and user comfort have recently become priorities in the Facility Management (FM) sector. This has resulted in the use of innovative building components, such as thermal solar panels, heat pumps, etc., as they have potential to provide better performance, energy savings and increased user comfort. However, as the complexity of components increases, the requirement for maintenance management also increases. The standard routine for building maintenance is inspection which results in repairs or replacement when a fault is found. This routine leads to unnecessary inspections which have a cost with respect to downtime of a component and work hours. This research proposes an alternative routine: performing building maintenance at the point in time when the component is degrading and requires maintenance, thus reducing the frequency of unnecessary inspections. This thesis demonstrates that statistical techniques can be used as part of a maintenance management methodology to invoke maintenance before failure occurs. The proposed FM process is presented through a scenario utilising current Building Information Modelling (BIM) technology and innovative contractual and organisational models. This FM scenario supports a Degradation based Maintenance (DbM) scheduling methodology, implemented using two statistical techniques, Particle Filters (PFs) and Gaussian Processes (GPs). DbM consists of extracting and tracking a degradation metric for a component. Limits for the degradation metric are identified based on one of a number of proposed processes. These processes determine the limits based on the maturity of the historical information available. DbM is implemented for three case study components: a heat exchanger; a heat pump; and a set of bearings. The identified degradation points for each case study, from a PF, a GP and a hybrid (PF and GP combined) DbM implementation are assessed against known degradation points. The GP implementations are successful for all components. For the PF implementations, the results presented in this thesis find that the extracted metrics and limits identify degradation occurrences accurately for components which are in continuous operation. For components which have seasonal operational periods, the PF may wrongly identify degradation. The GP performs more robustly than the PF, but the PF, on average, results in fewer false positives. The hybrid implementations, which are a combination of GP and PF results, are successful for 2 of 3 case studies and are not affected by seasonal data. Overall, DbM is effectively applied for the three case study components. The accuracy of the implementations is dependant on the relationships modelled by the PF and GP, and on the type and quantity of data available. This novel maintenance process can improve equipment performance and reduce energy wastage from BSCs operation.
Feasibility of combined wind-wave energy platforms
(University College Cork, 2014) O'Sullivan, Keith Patrick; Murphy, James; European Commission
The European Union has set out an ambitious 20% target for renewable energy use by 2020. It is expected that this will be met mainly by wind energy. Looking towards 2050, reductions in greenhouse gas emissions of 80-95% are to be sought. Given the issues securing this target in the transport and agriculture sectors, it may only be possible to achieve this target if the power sector is carbon neutral well in advance of 2050. This has permitted the vast expansion of offshore renewables, wind, wave and tidal energy. Offshore wind has undergone rapid development in recent years however faces significant challenges up to 2020 to ensure commercial viability without the need for government subsidies. Wave energy is still in the very early stages of development so as yet there has been no commercial roll out. As both of these technologies are to face similar challenges in ensuring they are a viable alternative power generation method to fossil fuels, capitalising on the synergies is potentially a significant cost saving initiative. The advent of hybrid solutions in a variety of configurations is the subject of this thesis. A singular wind-wave energy platform embodies all the attributes of a hybrid system, including sharing space, transmission infrastructure, O&M activities and a platform/foundation. This configuration is the subject of this thesis, and it is found that an OWC Array platform with multi-MegaWatt wind turbines is a technically feasible, and potentially an economically feasible solution in the long term. Methods of design and analysis adopted in this thesis include numerical and physical modelling of power performance, structural analysis, fabrication cost modelling, simplified project economic modelling and time domain reliability modelling of a 210MW hybrid farm. The application of these design and analysis methods has resulted in a hybrid solution capable of producing energy at a cost between €0.22/kWh and €0.31/kWh depending on the source of funding for the project. Further optimisation through detailed design is expected to lower this further. This thesis develops new and existing methods of design and analysis of wind and wave energy devices. This streamlines the process of early stage development, while adhering to the widely adopted Concept Development Protocol, to develop a technically and economically feasible, combined wind-wave energy hybrid solution.
Floating wave energy converters: wave measurement & analysis techniques
(University College Cork, 2015) Barrett, Seán Noel; Lewis, Anthony; Marine Institute; Higher Education Authority
The wave energy industry is entering a new phase of pre-commercial and commercial deployments of full-scale devices, so better understanding of seaway variability is critical to the successful operation of devices. The response of Wave Energy Converters to incident waves govern their operational performance and for many devices, this is highly dependent on spectral shape due to their resonant properties. Various methods of wave measurement are presented, along with analysis techniques and empirical models. Resource assessments, device performance predictions and monitoring of operational devices will often be based on summary statistics and assume a standard spectral shape such as Pierson-Moskowitz or JONSWAP. Furthermore, these are typically derived from the closest available wave data, frequently separated from the site on scales in the order of 1km. Therefore, variability of seaways from standard spectral shapes and spatial inconsistency between the measurement point and the device site will cause inaccuracies in the performance assessment. This thesis categorises time and frequency domain analysis techniques that can be used to identify changes in a sea state from record to record. Device specific issues such as dimensional scaling of sea states and power output are discussed along with potential differences that arise in estimated and actual output power of a WEC due to spectral shape variation. This is investigated using measured data from various phases of device development.
Green grass: developing grass for sustainable gaseous biofuel
(University College Cork, 2011) Nizami, Abdul-Sattar; Murphy, Jeremiah D.G.; Department of Agriculture, Food and the Marine
Grass is ubiquitous in Ireland and temperate northern Europe. It is a low input perennial crop; farmers are well versed in its production and storage (ensiling). Anaerobic digestion is a well understood technology. Grass is a lignocellulosic feedstock which is fibrous; it can readily cause difficulties with moving parts (wrapping around mixers); it also has a tendency to float. This thesis has an ambition of establishing the ideal digester configuration for production of biogas from grass. After extensive analysis of the literature, two different digester systems were designed, fabricated, commissioned and operated. The first system was a two stage wet continuous system commonly referred to as a Continuously Stirred Tank Reactor (CSTR). The second was a two stage, two phase system employing Sequentially Fed Leach Beds complete with an Upflow Anaerobic Sludge Blanket (SLBR-UASB). These were operated on the same grass silage cut from the same field at the same time. Small biomethane potential (BMP) assays were also evaluated for the same grass silage. The results indicated that the CSTR system produced 451 L CH4 kg-1 VS added at a retention time of 50 days while effecting a 90% destruction in volatile dry solids. The SLBR-UASB produced 341 L CH4 kg-1 VS added effecting a 75% reduction in volatile solids at a retention time of 30 days. The BMP assays generated results in the range 350 to 493 L CH4 kg-1 VS added. This thesis concludes that a disparity exists in the BMP tests used in the industry. The CSTR when designed specifically for grass silage is shown to be extremely effective in methane production. The SLBR-UASB has significant potential to allow for lower retention times with good levels of methane production. This technology has more potential for research in enzymatic hydrolysis and for use of digestate in added value products.
Holistic sustainability assessment of biomethane systems
(University College Cork, 2017) Czyrnek-Delêtre, Magdalena M.; Murphy, Jeremiah D.G.; Leahy, Paul; Science Foundation Ireland
European states, including Ireland must ensure that an increasing portion of energy from renewable sources. This is a particular issue for transport, which in comparison to electricity and heat has very low levels of renewable penetration. Electric vehicles (EVs), liquid and gaseous biofuels are the most likely sources for future energy in transport. However, renewable does not automatically mean sustainable. For example the sustainability of biofuels sourced from food crops has been queried in the context of land use change emissions. This thesis has an ambition of assessing sustainable options for advanced biomethane production in Ireland, a country with a temperate oceanic climate, using various methodologies (life cycle assessment, energy system modelling and cost analysis). Biomethane is a versatile gaseous biofuel that is considered advanced when produced from second and third generation feedstocks such as wastes, residues, grasses, and seaweed, but a simplified and unified framework for biofuels LCA is required to compare different options. Under a low-level land use change emissions scenario, biomethane from grass could play a major role in the Irish energy system for transport in 2050, requiring only 5-11% of Ireland’s agricultural land. With high land use emissions, however, the model would suggest using hydrogen, residues-based biodiesel, and EVs. Biomethane from seaweed could be deemed unsustainable if the system is not optimised. However in an optimal configuration it could achieve 70% greenhouse gases (GHG) savings as compared to gasoline. Such reductions in GHG emissions can be achieved in an optimal system: integrating seaweed cultivation with fish farming; using innovative growing techniques; ensuring optimal seaweed composition; reusing digestate; and using renewable electricity to power plant operations. Biomethane from landfill gas was shown to require a subsidy to allow financial sustainability. Thus in conclusion, biomethane can be a sustainable transport biofuel, but requires system optimisation and state subsidies.

Browse

Browsing Civil and Environmental Engineering - Doctoral Theses by Title

Results Per Page

Sort Options