Civil and Environmental Engineering

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3D modelling of concrete tunnel segmental joints and the development of a new bolt-spring model
(Elsevier Ltd., 2021-01-21) Wang, Fei; Huang, Hongwei; Soga, Kenichi; Li, Zili; National Natural Science Foundation of China
In a segmental metro tunnel, it is widely observed that cracks, water leakage and other structural defects usually appear at segmental joint section of a smaller stiffness than main tunnel segment section. In this study, a 3D finite element analysis was conducted to simulate a typical concrete segmental joint explicitly using 3D continuum elements, and the performance of 3D continuum model was validated against laboratory tests available in literature. Since such continuum model may cost excessive computational time when a large-scale tunnel structure is analysed, a new bolt-spring model was developed to simplify the structural features of concrete segmental joint. In the bolt-spring model, the interaction between bolt and segment was modelled by a set of normal springs and shear springs: the stiffness of the normal springs is mainly determined by the bolt itself, whilst the shear springs take account of bolt-segment friction, interaction and shear resistance of the bolt. In the meanwhile, the interaction between two adjoining tunnel segments is explicitly modelled using contact elements. The proposed bolt-spring model is able to simulate the details of joint deformation and contact pressure between segments more realistically than previously available by conventional methods (e.g. continuous ring, beam-spring model (BSM)), where segment-segment interaction is not explicated modelled. Compared to the continuum model, the bolt-spring model saves up to 90% computational time without compromising numerical accuracy. Furthermore, this paper compared the mechanical behaviour of a concrete joint against that of a cast-iron one with particular emphasis on the development of different bolt-spring models.
Access to a floating wind turbine
(The Royal Institution of Naval Architects, 2017-03) Shanley, Matthew; Wright, Christopher S.; Otter, Aldert; Desmond, Cian J.; Murphy, Jimmy; The Royal Institution of Naval Architects; Lir National Ocean Test Facility, Ireland; Science Foundation Ireland
The offshore wind turbine service industry is now well established with a large number of turbines being successfully operated and maintained. A number of methods and technologies are available to allow the safe transfer of service crews to these primarily fixed monopile installations. The most common of these is the bow transfer method which uses a combination of a high friction fender and a large vessel thrust to minimise relative motion between the bow and the turbine foundation. An upcoming challenge for the offshore wind turbine service industry will be the increasing use of floating foundations in far offshore and deep water sites. A number of structures are currently being developed and the first commercial floating wind farm is expected to be commissioned in late 2017. The use of floating structures will make it more difficult to ensure crew safety and comfort during transfer operations as the interaction between two floating bodies needs to be considered. Thus, the bow transfer method used to access fixed foundations may not be suitable for accessing floating turbine platforms. This paper will use a combination of physical and numerical modelling to assess the ability of a wind farm service vessel to maintain contact with a floating offshore wind turbine structure by use of the bow transfer method.
Adding value to EU energy policy analysis using a multi-model approach with an EU-28 electricity dispatch model
(Elsevier Ltd., 2017-05-03) Collins, Seán; Deane, John Paul; Ó Gallachóir, Brian P.; Science Foundation Ireland
The European Council has agreed ambitious EU climate and energy targets for 2030, including a 40% reduction in greenhouse gas emissions compared to 1990 levels and a minimum share of 27% renewable energy consumption. This paper investigates the challenges faced by the European power systems as the EU transitions towards a low carbon energy system with increased amounts of variable renewable electricity generation. The research here adds value to, and complements the power systems results of the PRIMES energy systems model that is used to inform EU energy and climate policy. The methodology uses a soft-linking approach that scrutinizes the power system in high temporal and technical detail for a target year. This enables generation of additional results that provide new insights not possible using a single model approach. These results point to: 1) overestimation of variable renewable generation by 2.4% 2) curtailment in excess of 11% in isolated member states 3) EU interconnector congestion average of 24% 4) reduced wholesale electricity pricing and few run hours raising concerns for the financial remuneration of conventional generation 5) maintenance of sufficient levels of system inertia in member states becomes challenging with significant penetrations of variable renewable generation.
Advanced biohydrogen production using pretreated industrial waste: outlook and prospects
(Elsevier Ltd., 2018-08-16) Prabakar, Desika; Manimudi, Varshini T.; Subha, Suvetha K.; Sampath, Swetha; Mahapatra, Durga Madhab; Rajendran, Karthik; Pugazhendhi, Arivalagan
In order to address existing environmental concerns as a result of non-renewable energy sources and to meet future energy demands, biohydrogen offers a suitable alternative energy reserve. Discrete as well as integrative methods of biohydrogen production have been analyzed over time, optimized for achieving high yields. In addition, key process parameters such as temperature, pH, hydraulic retention time, substrate concentration etc., which influence the rate of production have been clarified. Several studies have exploited industrial waste as feed sources for the production of biohydrogen; however, lower yields from these add an additional requirement for suitable pretreatment methods. The present communication examines various pretreatment methods used to increase the accessibility of industrial wastewater/waste for biohydrogen production. Furthermore, a brief overview addresses challenges and constraints in creating a biohydrogen economy. The impacts of pretreating wastes on biohydrogen generation and the latest trends are also supplied. This study helps in the critical understanding of agro-industrial wastes for biohydrogen production, thereby encouraging future outcomes for a sustainable biohydrogen economy.
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.
Application of Marine Spatial Planning tools for tidal stream farm micro-siting
(Elsevier, 2022-02-11) Álvarez, M.; Ramos, V.; Carballo, R.; López, I.; Fouz, D. M.; Iglesias, Gregorio; Xunta de Galicia; Ministerio de Educación, Cultura y Deporte; Fundação para a Ciência e a Tecnologia
The operation of tidal stream energy farms may interfere with other uses of the marine space, especially in depth-limited areas (estuaries, rivers, etc.) which are typically subject to multiple demands of use. The Marine Spatial Planning Directive (MSP) was passed by the European Commission in 2014 to ensure a harmonic coexistence between different maritime activities and to protect the marine environment. In this context, the objective of this work is to present a methodology based on MSP tools for tidal-farm siting in depth-limited areas. The methodology is illustrated through a case study: Ria de Ribadeo, a shallow-water estuary in NW Spain. Having considered a number of uses (archaeological, biodiversity, fishing, aquaculture, recreational and navigation), two exploitable tidal farm sites (Areas A and C) with annual energy densities of 1 were found. The estuary is periodically dredged to maintain navigation. Dredging-related risks were analysed using a novel indicator, the Dredging Associated Risk (DAR), based on which Area C was discarded and Area A had its exploitable surface area reduced by 25%. In sum, the methodology proposed was proven to be effective for tidal stream farm planning.
Application of S transform in structural health monitoring
(2009) Pakrashi, Vikram; Ghosh, Bidisha
The successful detection of change in a data or in any of its derivatives in the presence of noise is a critical component of structural health monitoring and damage detection. This sudden change can be brought about by a sudden change in the strain or the stress field of the structural system under consideration. Two very typical examples of such sudden changes are the sudden change in stiffness of a vibrating single degree of freedom system in time and the local perturbation of stress and strain fields of a beamlike structure in space due to the presence of an open crack. New methods and analysis techniques have become popular in the field of structural health monitoring to detect and characterise such changes. Time – frequency techniques, like wavelet analysis are being more widely used in this regard in the recent times for the detection of presence, location and the calibration of the extent of these changes. This paper presents the application of S transform for the successful detection and calibration of damage in time and in space in the presence of additive Gaussian white noise. The performance of S transform based detection is compared with wavelet based and statistics based methodologies. The application and use of S transform in the field of structural health monitoring is observed to be extremely promising.
Are electrofuels a sustainable transport fuel? Analysis of the effect of controls on carbon, curtailment, and cost of hydrogen
(Elsevier Ltd., 2019-04-30) McDonagh, Shane; Deane, Paul; Rajendran, Karthik; Murphy, Jerry D.; Science Foundation Ireland; Gas Networks Ireland; Ervia, Ireland
Variable renewable electricity (VRE) decarbonises the electricity grid, but its intermittency leads to variations in price, carbon intensity, and curtailment over time. This has led to interest in utilising difficult to manage electricity to produce electrofuels (such as hydrogen via water electrolysis) for transport. The vast majority of the environmental impact of electrofuels is contained in the electricity they consume however, only consuming otherwise curtailed electricity (produced when supply exceeds demand) leads to prohibitively expensive hydrogen due to low run hours. Using a model which bids for wholesale electricity, two operational strategies (controls) aimed at increasing sustainability without requiring policy changes were tested in electricity system models of 40–60% renewable electricity penetration. (1) Bid price control set a maximum price the plant will pay for electricity. (2) Wind forecast control dictated that the plant may only run when a minimum forecast VRE production is met. It was shown that sourcing electricity at times of low cost or high forecast wind power can lead to more decarbonised hydrogen production (up to 56% more) at a lower cost (up to 57% less). When economically optimised (minimising levelised costs) the bid price control reduced the carbon intensity of the electrofuel produced by 5–25%, and the wind forecast control by 14–38%, compared to the grid average. Both controls demonstrated a high proclivity to utilising otherwise curtailed electricity and can be said to aid grid balancing. The bid price control also greatly reduced the average cost of electricity to the plant. The positive impacts increased with renewables penetration, and significant synergies between economic and environmentally conscious operation of the plants were noted. The operational strategies tested in this paper allow for transport fuels to be produced from grid electricity, without exacerbating the mismatch of supply and demand. Future decentralised quasi-storage using these operating strategies may economically produce transport fuel, and aid grid balancing.
Artificial neural network application in short-term prediction in an oscillating water column
(The International Society of Offshore and Polar Engineers (ISOPE), 2010-01) Sheng, Wanan; Lewis, Anthony; Science Foundation Ireland; Department of Communications, Energy and Natural Resources, Ireland
Oscillating Water Column (OWC) is one type of promising wave energy devices due to its obvious advantage over many other wave energy converters: no moving component in sea water. Two types of OWCs (bottom-fixed and floating) have been widely investigated, and the bottom-fixed OWCs have been very successful in several practical applications. Recently, the proposal of massive wave energy production and the availability of wave energy have pushed OWC applications from near-shore to deeper water regions where floating OWCs are a better choice. For an OWC under sea waves, the air flow driving air turbine to generate electricity is a random process. In such a working condition, single design/operation point is nonexistent. To improve energy extraction, and to optimise the performance of the device, a system capable of controlling the air turbine rotation speed is desirable. To achieve that, this paper presents a short-term prediction of the random, process by an artificial neural network (ANN), which can provide near-future information for the control system. In this research, ANN is explored and tuned for a better prediction of the airflow (as well as the device motions for a wide application). It is found that, by carefully constructing ANN platform and optimizing the relevant parameters, ANN is capable of predicting the random process a few steps ahead of the real, time with a good accuracy. More importantly, the tuned ANN works for a large range of different types of random, process.
Assessment of continuous fermentative hydrogen and methane co-production using macro- and micro-algae with increasing organic loading rate
(Elsevier, 2018-03-20) Ding, Lingkan; Chan Gutierrez, Enrique; Cheng, Jun; Xia, Ao; O'Shea, Richard; Guneratnam, Amita Jacob; Murphy, Jerry D.; Science Foundation Ireland; Chongqing University; Consejo Nacional de Ciencia y Tecnología; Science and Technology Department of Zhejiang Province; National Natural Science Foundation of China; Consejo De Ciencia, Innovación Y Tecnología Del Estado De Yucatán; Gas Networks Ireland
A two-stage continuous fermentative hydrogen and methane co-production using macro-algae (Laminaria digitata) and micro-algae (Arthrospira platensis) at a C/N ratio of 20 was established. The hydraulic retention time (HRT) of first-stage H2 reactor was 4 days. The highest specific hydrogen yield of 55.3 mL/g volatile solids (VS) was obtained at an organic loading rate (OLR) of 6.0 gVS/L/d. In the second-stage CH4 reactor at a short HRT of 12 days, a specific methane yield of 245.0 mL/gVS was achieved at a corresponding OLR of 2.0 gVS/L/d. At these loading rates, the two-stage continuous system offered process stability and effected an energy yield of 9.4 kJ/gVS, equivalent to 77.7% of that in an idealised batch system. However, further increases in OLR led to reduced hydrogen and methane yields in both reactors. The process was compared to a one-stage anaerobic co-digestion of algal mixtures at an HRT of 16 days. A remarkably high salinity level of 13.3 g/kg was recorded and volatile fatty acid accumulations were encountered in the one-stage CH4 reactor. The two-stage system offered better performances in both energy return and process stability. The gross energy potential of the advanced gaseous biofuels from this algal mixture may reach 213 GJ/ha/yr.
Assessment of pretreatment and digestion temperature on anaerobic digestion of whiskey byproducts and microbial taxonomy
(Elsevier, 2021-09) Kang, Xihui; Lin, Richen; Li, Lianhua; Wu, Benteng; Deng, Chen; O'Shea, Richard; Sun, Yongming; Murphy, Jerry D.; China Scholarship Council; Science Foundation Ireland; Environmental Protection Agency; Strategic Priority Research Program of the Chinese Academy of Sciences; European Regional Development Fund; Pernod Ricard; Gas Networks Ireland
The effects of steam and sulfuric acid pretreatment on anaerobic digestion (AD) of whiskey byproducts (including draff, thin and thick stillage) were investigated in order to improve the digestion performance under both mesophilic and thermophilic temperatures. The results of biomethane potential assays suggested that thermophilic AD facilitated the release of free ammonia (ca. 1200 mg/L) from byproducts, resulting in strong ammonia inhibition and volatile fatty acid accumulation. In contrast, no free ammonia inhibition (ca. 700 mg/L) was observed under mesophilic AD; the methane yield from mesophilic AD was between 375.3 +/- 13.6 mL/g volatile solid (VS; acid-treated sample) and 389.1 +/- 8.5 mL/g VS (untreated sample). Although acid pretreatment (2% acid under 135 degrees C for 15 min) did not improve the methane yield from mesophilic AD, it reduced the digestion time by 14.3% compared to that of the untreated sample. Microbial community analysis showed that irrelevant of pretreatment, hydrogenotrophic methanogens of Methanobrevibacter (28.9%-49.8% in abundance) and Methanoculleus (26.0%-55.9% in abundance) were the dominant archaeal genus under mesophilic AD. In comparison, hydrogenotrophic Methanothermobacter (over 97% in abundance) were dominant in thermophilic AD. This study could be exploited to aid in decarbonizing the whiskey industry by optimizing the biogas process in a circular economy system.
Assessment of primary energy conversions of oscillating water columns. I. Hydrodynamic analysis
(American Institute of Physics, 2014-09-29) Sheng, Wanan; Alcorn, Raymond; Lewis, Anthony; Science Foundation Ireland
This is an investigation on the development of a numerical assessment method for the hydrodynamic performance of an oscillating water column (OWC) wave energy converter. In the research work, a systematic study has been carried out on how the hydrodynamic problem can be solved and represented reliably, focusing on the phenomena of the interactions of the wave-structure and the wave-internal water surface. These phenomena are extensively examined numerically to show how the hydrodynamic parameters can be reliably obtained and used for the OWC performance assessment. In studying the dynamic system, a two-body system is used for the OWC wave energy converter. The first body is the device itself, and the second body is an imaginary “piston,” which replaces part of the water at the internal water surface in the water column. One advantage of the two-body system for an OWC wave energy converter is its physical representations, and therefore, the relevant mathematical expressions and the numerical simulation can be straightforward. That is, the main hydrodynamic parameters can be assessed using the boundary element method of the potential flow in frequency domain, and the relevant parameters are transformed directly from frequency domain to time domain for the two-body system. However, as it is shown in the research, an appropriate representation of the “imaginary” piston is very important, especially when the relevant parameters have to be transformed from frequency-domain to time domain for a further analysis. The examples given in the research have shown that the correct parameters transformed from frequency domain to time domain can be a vital factor for a successful numerical simulation.
Assessment of primary energy conversions of oscillating water columns. II. Power take-off and validations
(American Institute of Physics, 2014-09-29) Sheng, Wanan; Alcorn, Raymond; Lewis, Anthony
This is the second part of the assessment of primary energy conversions of oscillating water columns (OWCs) wave energy converters. In the first part of the research work, the hydrodynamic performance of OWC wave energy converter has been extensively examined, targeting on a reliable numerical assessment method. In this part of the research work, the application of the air turbine power take-off (PTO) to the OWC device leads to a coupled model of the hydrodynamics and thermodynamics of the OWC wave energy converters, in a manner that under the wave excitation, the varying air volume due to the internal water surface motion creates a reciprocating chamber pressure (alternative positive and negative chamber pressure), whilst the chamber pressure, in turn, modifies the motions of the device and the internal water surface. To do this, the thermodynamics of the air chamber is first examined and applied by including the air compressibility in the oscillating water columns for different types of the air turbine PTOs. The developed thermodynamics is then coupled with the hydrodynamics of the OWC wave energy converters. This proposed assessment method is then applied to two generic OWC wave energy converters (one bottom fixed and another floating), and the numerical results are compared to the experimental results. From the comparison to the model test data, it can be seen that this numerical method is capable of assessing the primary energy conversion for the oscillating water column wave energy converters.
Associations between ambient particle radioactivity and lung function
(Elsevier Ltd, 2019-06-11) Nyhan, Marguerite M.; Rice, Mary; Blomberg, Annelise; Coull, Brent A.; Garshick, Eric; Vokonas, Pantel; Schwartz, Joel; Gold, Diane R.; Koutrakis, Petros; U.S. Environmental Protection Agency; NIH (US); National Institute of Environmental Health Sciences; U.S. Department of Veterans Affairs; Epidemiology Research and Information Center (ERIC); Epidemiology Research and Information Center (MAVERIC); VA Boston Healthcare System
Previous studies have suggested increased risk of respiratory diseases and mortality following short-term exposures to ionizing radiation. However, the short-term respiratory effects of low-level environmental radiation associated with air pollution particles have not been considered. Although ambient particulate matter (PM) has been reproducibly linked to decreased lung function and to increased respiratory related morbidity, the properties of PM promoting its toxicity are uncertain. As such, we evaluated whether lung function was associated with exposures to radioactive components of ambient PM, referred to as particle radioactivity (PR). For this, we performed a repeated-measures analysis of 839 men to examine associations between PR exposure and lung function using mixed-effects regression models, adjusting for potential confounders. We examined whether PR-lung function associations changed after adjusting for PM2.5 (particulate matter≤2.5 μm) or black carbon, and vice versa. PR was measured by the USEPA's radiation monitoring network. We found that higher PR exposure was associated with a lower forced vital capacity (FVC) and forced expiratory volume in 1 second (FEV1). An IQR increase in 28-day PR exposure was associated with a 2.4% lower FVC [95% confidence interval (CI): 1.4, 3.4% p < 0.001] and a 2.4% lower FEV1 (95% CI: 1.3, 3.5%, p < 0.001). The PR-lung function associations were partially attenuated with adjustment for PM2.5 and black carbon. This is the first study to demonstrate associations between PR and lung function, which were independent of and similar in magnitude to those of PM2.5 and black carbon. If confirmed, future research should account for PR exposure in estimating respiratory health effects of ambient particles. Because of widespread exposure to low levels of ionizing radiation, our findings may have important implications for research, and environmental health policies worldwide.
Automated crack classification for underground tunnel infrastructure using deep learning
(University College Cork, 2021-11-01) O'Brien, Darragh; Li, Zili; Osborne, John; Irish Centre for Applied Geoscience; CERN
One early sign of tunnel structure deterioration originates in the form of cracking, and therefore crack detection and resultant classification is integral for tunnel structural inspection and maintenance. Conventionally tunnel cracks are manually recorded and classified by trained professionals, which is costly, time-consuming and inevitably subjective. Recent advances in the deep learning space have allowed for automatic cracks detection algorithms to be developed and subsequently utilized in surface structural health assessment of surface buildings, bridges, roads and other civil infrastructure. Nevertheless, these methods of development underperform when implemented for a tunnel structure in an underground environment due to the disparity of illumination combined with the congested image data caused by pipes, steel mesh, wires, and other tunnel amenities. This thesis develops an intuitive crack directional classification approach that increases the accuracy, reduces time and subjectivity in comparison to traditional inspection methods. The detection of cracks by utilising CNN’s is antiquity investigated by in literature however little of the writings develop the algorithm further for classification purposes. The novel of this research is centred on the development of a crack classification algorithm that adheres to the directional classification rationale. The output information of the crack classification is correlated to the structural movement of the lining providing a deeper understanding of the tunnel behaviors. To surmount these challenges, this thesis constructs a Convolution Neural Network (CNN) image-based crack detection method accompanied by an innovative crack classification for underground infrastructure environment. Conventional CNN’s are developed from scratch, the proposed CNN incorporates transfer learning in the form of the VGG16 model with weights transferred from ImageNet. The transfer model was trained under various scenarios to determine the optimal model for the operational task in the tunnel environment. The various models are trained using over 10’000 images validated on 2’500 images all of which are 256 x 256 pixels in size, these models are all subsequently tested using 30 images 3072 x 4096 pixels in size. The transfer learning model used outperforms that of the traditional CNN training method of training from scratch. The optimum transfer model accomplished testing metrics of 96.6%,87.3%,92.4%,89.3% for Accuracy, Precision, Recall and F1 score respectively. The proposed CNN appraises images regarding the existence and subsequent location of cracks. Detected cracks are subjected to the secondary classification CNN where the crack is categorized into one of the four crack classes which include the three directional classes of Horizontal, vertical and diagonal with the last crack classes incorporated to represent complex crack regions. The secondary classification CNN attains an Accuracy of 92.3% a Precision of 83.9% a Recall value of 82.3 % and an F1 score of 81.5%. The performance of the manufactured integrated detection and classification method is analysed by performing a field test to evolve the research from a controlled theoretical setting into a realistic tunnel environment. The field test is performed on three separate tunnel sections with an amassed distance of 150 meters with the section testing the robustness, speed and ultimately prospect of application in the CERN inspection scenario. The outcome from this testing demonstrates that the established CNN crack detector/classifier can effectively overwhelm the unfavourable tunnel environment and accomplish results to a high standard.
Automatic UAV inspection of tunnel infrastructure in GPS-denied underground environment
(Springer Nature Switzerland AG, 2022-06-16) Zhang, Ran; Ouyang, Aohui; Li, Zili
In the Architecture, Engineering and Construction (AEC) industry, unmanned aerial vehicles (UAV) has been widely acknowledged as a promising tool to perform adaptive structural health monitoring automatically. However, there still remains some challenges for drones to collect image data of underground structures, primarily due to low light and no GPS conditions. In order to facilitate data acquisition, this article developed a mobile software development kit (MSDK) for drone using visual positioning and predefined controlling code, which enabled the drone to automatically fly along a designated sinusoidal route, whilst continuously taking videos and images of the tunnel surface. The developed MSDK was able to adjust the drone parameters (e.g., overlapping rate, inspection range, heading, flight direction between frames of the video) for different underground infrastructure conditions. Furthermore, a field test is conducted in an abandoned windless tunnel near Cork (Goggins Hill Tunnel) to test its feasibility. Results show that the 40-m difference between the designated routine and actual routine was 1.9%, and the collected data processed by Pix4Dmapper could reconstruct the complete tunnel scene and surface details. The navigation method proposed in this paper allows UAVs to perform automatic inspection without GPS, and the collected image data is used to build a tunnel panorama view.
Automatically identifying and predicting unplanned wind turbine stoppages using SCADA and alarms system data: case study and results
(IOP Publishing, 2017) Leahy, Kevin; Gallagher, Colm V.; Bruton, Ken; O'Donovan, Peter; O'Sullivan, Dominic T. J.; Science Foundation Ireland
Using 10-minute wind turbine SCADA data for fault prediction offers an attractive way of gaining additional prognostic capabilities without needing to invest in extra hardware. To use these data-driven methods effectively, the historical SCADA data must be labelled with the periods when the turbine was in faulty operation as well the sub-system the fault was attributed to. Manually identifying faults using maintenance logs can be effective, but is also highly time consuming and tedious due to the disparate nature of these logs across manufacturers, operators and even individual maintenance events. Turbine alarm systems can help to identify these periods, but the sheer volume of alarms and false positives generated makes analysing them on an individual basis ineffective. In this work, we present a new method for automatically identifying historical stoppages on the turbine using SCADA and alarms data. Each stoppage is associated with either a fault in one of the turbine's sub-systems, a routine maintenance activity, a grid-related event or a number of other categories. This is then checked against maintenance logs for accuracy and the labelled data fed into a classifier for predicting when these stoppages will occur. Results show that the automated labelling process correctly identifies each type of stoppage, and can be effectively used for SCADA-based prediction of turbine faults
Beyond carbon and energy: the challenge in setting guidelines for life cycle assessment of biofuel systems
(Elsevier Ltd., 2016-11-18) Smyth, Beatrice M.; Murphy, Jerry D.; Czyrnek-Delêtre, Magdalena M.; Science Foundation Ireland; Gas Networks Ireland; Ervia, Ireland; B9 Energy Group, Northern Ireland
Life cycle assessment (LCA) is one of the most suitable tool for a uniform assessment methodology of biofuels’ sustainability. However, there are no binding guidelines for LCA of biofuel systems. Published LCAs use a range of methodologies, different system boundaries, impact categories and functional units, various allocation approaches, and assumptions regarding by- and co-products, as well as different reference systems to which the biofuel system is compared. The European Renewable Energy Directive and the US Renewable Fuel Standard focus on greenhouse gas (GHG) emissions. However, previous LCAs of biofuel systems have shown that a reduction of GHG emissions does not lead automatically to a decrease in other environmental impacts, and might in fact be associated with an increase in impacts such as acidification, eutrophication, and land use change. In order to enable effective comparison of biofuel systems, the authors propose a framework for biofuel LCA. System boundaries should be expanded to include the life cycle of by- and co-products. Results should be reported using more than one functional unit. Burden shifting can be avoided by considering an array of impact categories including global warming potential and energy balance, along with eutrophication and acidification potential, and a land use indicator.
Biological hydrogen methanation systems–an overview of design and efficiency
(Taylor and Francis Inc., 2019-11-03) Rusmanis, Davis; O'Shea, Richard; Wall, David M.; Murphy, Jerry D.; Science Foundation Ireland; Gas Networks Ireland; Irish Distillers Pernod Ricard
The rise in intermittent renewable electricity production presents a global requirement for energy storage. Biological hydrogen methanation (BHM) facilitates wind and solar energy through the storage of otherwise curtailed or constrained electricity in the form of the gaseous energy vector biomethane. Biological methanation in the circular economy involves the reaction of hydrogen – produced during electrolysis – with carbon dioxide in biogas to produce methane (4H2 + CO2 = CH4 + 2H2), typically increasing the methane output of the biogas system by 70%. In this paper, several BHM systems were researched and a compilation of such systems was synthesized, facilitating comparison of key parameters such as methane evolution rate (MER) and retention time. Increased retention times were suggested to be related to less efficient systems with long travel paths for gases through reactors. A significant lack of information on gas-liquid transfer co-efficient was identified.

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