Pressure transients in water distribution networks: understanding their contribution to pipe repairs

Drinking water infrastructure functions to provide a service to meet customer demands and health requirements. Pipe repairs are one of the biggest challenges of ageing water infrastructure in the UK and world wide. Pressure transients resulting from sudden interruptions of the movement of the water can be caused by routine value operations. In a single pipeline one extreme event can burst a pipe. However the occurrences and impact of pressure transients in operational water distribution systems were not currently fully understood. This research developed new insights and understanding of pressure transient occurrences and their contribution to observed pipe repair rates. A large scale field monitoring program, including deploying and managing high-speed (100 Hz) instrumentation for 11 months, was designed and implemented to cover 67 district metered areas (DMA) subdivided into 79 pressure zones. In total 144 locations were monitored. The data was analysed using a novel method, termed transient fingerprint. This allowed the identification of discrete pressure transients and their three fundamental components (magnitude, duration and numbers of occurrences) leading to a quantitative interpretation of pressure transients. Evolutionary polynomial regression modelling was used to assess the impact of directly measured pressure transient data in context with static pressure, age, diameter and soil variables on 64 cast iron pipes. The analysis suggested that high magnitude, short duration repeatedly occurring pressure transients can have an adverse effect on the pipes. The extrapolation of pressure transient analysis into 7978 cast iron pipes showed inconclusive results suggesting that more accurate pressure transient data is required for each pipe in the network. Additional analysis carried out on 25 asbestos cement pipes, with actual measurements of pressure transients for each pipe, confirmed an adverse effect of pressure transient on water network observed in cast iron pipes. This research has provided an understanding of the occurrence of pressure transients that has implications on pipe management strategies. Mitigation techniques to locate pressure transient sources based on the project outcomes could be utilised to better manage distribution systems and ultimately reduce future pipe replacements and associated costs

Pressure transients in water distribution networks: understanding their contribution to pipe repairs

Drinking water infrastructure functions to provide a service to meet customer demands and health requirements. Pipe repairs are one of the biggest challenges of ageing water infrastructure in the UK and world wide. Pressure transients resulting from sudden interruptions of the movement of the water can be caused by routine value operations. In a single pipeline one extreme event can burst a pipe. However the occurrences and impact of pressure transients in operational water distribution systems were not currently fully understood. This research developed new insights and understanding of pressure transient occurrences and their contribution to observed pipe repair rates. A large scale field monitoring program, including deploying and managing high-speed (100 Hz) instrumentation for 11 months, was designed and implemented to cover 67 district metered areas (DMA) subdivided into 79 pressure zones. In total 144 locations were monitored. The data was analysed using a novel method, termed transient fingerprint. This allowed the identification of discrete pressure transients and their three fundamental components (magnitude, duration and numbers of occurrences) leading to a quantitative interpretation of pressure transients. Evolutionary polynomial regression modelling was used to assess the impact of directly measured pressure transient data in context with static pressure, age, diameter and soil variables on 64 cast iron pipes. The analysis suggested that high magnitude, short duration repeatedly occurring pressure transients can have an adverse effect on the pipes. The extrapolation of pressure transient analysis into 7978 cast iron pipes showed inconclusive results suggesting that more accurate pressure transient data is required for each pipe in the network. Additional analysis carried out on 25 asbestos cement pipes, with actual measurements of pressure transients for each pipe, confirmed an adverse effect of pressure transient on water network observed in cast iron pipes. This research has provided an understanding of the occurrence of pressure transients that has implications on pipe management strategies. Mitigation techniques to locate pressure transient sources based on the project outcomes could be utilised to better manage distribution systems and ultimately reduce future pipe replacements and associated costs

A China-EU electricity transmission link: Assessment of potential connecting countries and routes

The report looks at the potential routes for a future power interconnection between EU and China. High voltage direct current technology is considered and its potential is assessed. It analyses the renewable energy sources in the countries along the potential routes as well as the power sector and power grid in the countries crossed. Three potential routes are analysed.JRC.C.3-Energy Security, Distribution and Market

Indoor navigation for the visually impaired : enhancements through utilisation of the Internet of Things and deep learning

Wayfinding and navigation are essential aspects of independent living that heavily rely on the sense of vision. Walking in a complex building requires knowing exact location to find a suitable path to the desired destination, avoiding obstacles and monitoring orientation and movement along the route. People who do not have access to sight-dependent information, such as that provided by signage, maps and environmental cues, can encounter challenges in achieving these tasks independently. They can rely on assistance from others or maintain their independence by using assistive technologies and the resources provided by smart environments. Several solutions have adapted technological innovations to combat navigation in an indoor environment over the last few years. However, there remains a significant lack of a complete solution to aid the navigation requirements of visually impaired (VI) people. The use of a single technology cannot provide a solution to fulfil all the navigation difficulties faced. A hybrid solution using Internet of Things (IoT) devices and deep learning techniques to discern the patterns of an indoor environment may help VI people gain confidence to travel independently. This thesis aims to improve the independence and enhance the journey of VI people in an indoor setting with the proposed framework, using a smartphone. The thesis proposes a novel framework, Indoor-Nav, to provide a VI-friendly path to avoid obstacles and predict the user s position. The components include Ortho-PATH, Blue Dot for VI People (BVIP), and a deep learning-based indoor positioning model. The work establishes a novel collision-free pathfinding algorithm, Orth-PATH, to generate a VI-friendly path via sensing a grid-based indoor space. Further, to ensure correct movement, with the use of beacons and a smartphone, BVIP monitors the movements and relative position of the moving user. In dark areas without external devices, the research tests the feasibility of using sensory information from a smartphone with a pre-trained regression-based deep learning model to predict the user s absolute position. The work accomplishes a diverse range of simulations and experiments to confirm the performance and effectiveness of the proposed framework and its components. The results show that Indoor-Nav is the first type of pathfinding algorithm to provide a novel path to reflect the needs of VI people. The approach designs a path alongside walls, avoiding obstacles, and this research benchmarks the approach with other popular pathfinding algorithms. Further, this research develops a smartphone-based application to test the trajectories of a moving user in an indoor environment

AN ENERGY EFFICIENT CROSS-LAYER NETWORK OPERATION MODEL FOR MOBILE WIRELESS SENSOR NETWORKS

Wireless sensor networks (WSNs) are modern technologies used to sense/control the environment whether indoors or outdoors. Sensor nodes are miniatures that can sense a specific event according to the end user(s) needs. The types of applications where such technology can be utilised and implemented are vast and range from households’ low end simple need applications to high end military based applications. WSNs are resource limited. Sensor nodes are expected to work on a limited source of power (e.g., batteries). The connectivity quality and reliability of the nodes is dependent on the quality of the hardware which the nodes are made of. Sensor nodes are envisioned to be either stationary or mobile. Mobility increases the issues of the quality of the operation of the network because it effects directly on the quality of the connections between the nodes

The Role of the Natural Resource Curse in Preventing Development in Politically Unstable Countries: Case Studies of Angola and Bolivia

For about three decades now, development economics researchers have consistently claimed that third world resource-rich countries were not developing as well and/or as fast as they were expected to, given that their natural resources endowment was considered a great opportunity for development. The phenomenon of underperformances concerning primary commodity exporters relative to non resource-rich countries has been often referred to as to the “Natural Resource Curse”. The authors use an historical and political approach to the manifestations of the curse in the specific cases of Angola and Bolivia, both resource abundant countries, but suffering among the lowest development standards in their respective continents. In chapter one, the authors make a quick review of the literature explaining both causes and manifestations of the Resource Curse. The authors go beyond the classical Dutch Disease explanations and show how natural resources lead to behaviours of looting, rent-seeking and civil confrontations. In chapter two, the authors present the framework where they adjust the “African Anti-growth Policy Syndromes” described by Paul Collier to the specific case of the Natural Resource curse. In addition, they add some considerations of the negative effect of natural resource extraction by analysing externalities on environment, education and inequalities. Chapters three and four analyse the case studies of Angola and Bolivia respectively, emphasizing the role of historical context explaining policy behaviour and the critical impact of unexpected windfalls and sudden price collapses. The authors find that natural resources could sustain long lasting conflicts, but that conditions of fractionalization of society determine the possibility of conflict. A country divided in two rigid political factions is more prone to internal conflict, like in Angola, whether in countries where frontiers between blocks are blurried or the country is multi-polar, like in Bolivia, the risks of long-lasting civil war seem less important. Apart from conflict, the authors show that lack of institutions and inequality make of natural resources a source of political instability that has far more impact on economic performances than other factors.Natural Resource curse, Rent-seeking, Civil War, Angola, Bolivia

Data Management for Structural Integrity Assessment of Offshore Wind Turbine Support Structures: Data Cleansing and Missing Data Imputation

Structural Health Monitoring (SHM) and Condition Monitoring (CM) Systems are currently utilised to collect data from offshore wind turbines (OWTs), to enhance the accurate estimation of their operational performance. However, industry accepted practices for effectively managing the information that these systems provide have not been widely established yet. This paper presents a four-step methodological framework for the effective data management of SHM systems of OWTs and illustrates its applicability in real-time continuous data collected from three operational units, with the aim of utilising more complete and accurate datasets for fatigue life assessment of support structures. Firstly, a time-efficient synchronisation method that enables the continuous monitoring of these systems is presented, followed by a novel approach to noise cleansing and the posterior missing data imputation (MDI). By the implementation of these techniques those data-points containing excessive noise are removed from the dataset (Step 2), advanced numerical tools are employed to regenerate missing data (Step 3) and fatigue is estimated for the results of these two methodologies (Step 4). Results show that after cleansing, missing data can be imputed with an average absolute error of 2.1%, while this error is kept within the [+ 15.2%−11.0%] range in 95% of cases. Furthermore, only 0.15% of the imputed data fell outside the noise thresholds. Fatigue is found to be underestimated both, when data cleansing does not take place and when it takes place but MDI does not. This makes this novel methodology an enhancement to conventional structural integrity assessment techniques that do not employ continuous datasets in their analyses

Harnessing data for wind turbines : machine learning digital-enabled asset management strategies

As interests in offshore wind farms continue to grow, so does the demand to reduce the cost of energy (COE). Maintenance cost and downtime can reduce the COE through greater information on offshore wind assets concerning the operational loads and structural integrity. This has had a significant impact on the interests of digital-enabled asset management (DEAM) using digital twins. Digital twins’ technologies can replicate operational assets computationally, providing more information and increasing one’s confidence in operations and maintenance (O&M). DEAM is a multi-disciplinary field and making advances in this field requires aspects of multiple modelling domains, this thesis aims to develop this and help aid in the future of DEAM. The work carried out in the thesis has been validated against operational data recordings from offshore structures. This provides value and confidence to the results of the state-of-the-art models for real-world engineering systems. This research presents a portfolio of four research areas that have been published in a variety of peer-reviewed journal articles and conference papers. The areas are: 1) A proposal for standardisation of pre-processing data. Current standards have not addressed how to deal with data for machine learning, and this paper aims to begin this discussion with an example. This work implements a trend condition monitoring model that makes predictions on the power of an offshore structure using supervisory control and data acquisition (SCAD) data. There are 5 different machine learning (ML) models used and the data is validated using unused data with the modelling errors quantified. 2) A novel approach to dealing with the limitations of small data sets. This is an innovative way of transferring information from a homogeneous population to increase the accuracy of an artificial neural network (ANN). The ML model is a comparison of a conventional ANN compared to the proposed hard-parameter transfer ANN model. The ML model makes a classification of the error signature from the gearbox using both SCADA data and condition monitoring system (CMS) data. The validation of the comparison uses unseen data during the training process and the errors are measured. 3) Is a case study on Wikinger offshore wind farm population homogeneity where the operational and environmental conditions are compared for all three wind turbines. This case study provides a framework to follow when investigating an offshore wind farm population. This uses operational data from three wind turbines with both SCADA, CMS data, and processed data from RAMBOLL. The outcomes from this paper are used to determine the type of ML model used in the last study. 4) Is the model development of a population-based structural health monitoring (PBSHM) model. This study investigates three domain adaptation techniques suited to strong homogeneous populations. The ML model takes SCADA data as an input and predicts the damage equivalent moments (DEM) on the jacket foundation structure. To validate the PBSHM model data from a structure that is not used during the training of the model is used to quantify the precision of the model. The individual contributions of the developments in each of the constituent areas relate to an overall improvement in modelling approaches that are necessary for DEAM and aid in the realisation of true digital twins. All the areas relate to offshore wind ML and are related to operational data. The link between the measured data and the individual models aid in gaining more information and greater insights into the O&M.As interests in offshore wind farms continue to grow, so does the demand to reduce the cost of energy (COE). Maintenance cost and downtime can reduce the COE through greater information on offshore wind assets concerning the operational loads and structural integrity. This has had a significant impact on the interests of digital-enabled asset management (DEAM) using digital twins. Digital twins’ technologies can replicate operational assets computationally, providing more information and increasing one’s confidence in operations and maintenance (O&M). DEAM is a multi-disciplinary field and making advances in this field requires aspects of multiple modelling domains, this thesis aims to develop this and help aid in the future of DEAM. The work carried out in the thesis has been validated against operational data recordings from offshore structures. This provides value and confidence to the results of the state-of-the-art models for real-world engineering systems. This research presents a portfolio of four research areas that have been published in a variety of peer-reviewed journal articles and conference papers. The areas are: 1) A proposal for standardisation of pre-processing data. Current standards have not addressed how to deal with data for machine learning, and this paper aims to begin this discussion with an example. This work implements a trend condition monitoring model that makes predictions on the power of an offshore structure using supervisory control and data acquisition (SCAD) data. There are 5 different machine learning (ML) models used and the data is validated using unused data with the modelling errors quantified. 2) A novel approach to dealing with the limitations of small data sets. This is an innovative way of transferring information from a homogeneous population to increase the accuracy of an artificial neural network (ANN). The ML model is a comparison of a conventional ANN compared to the proposed hard-parameter transfer ANN model. The ML model makes a classification of the error signature from the gearbox using both SCADA data and condition monitoring system (CMS) data. The validation of the comparison uses unseen data during the training process and the errors are measured. 3) Is a case study on Wikinger offshore wind farm population homogeneity where the operational and environmental conditions are compared for all three wind turbines. This case study provides a framework to follow when investigating an offshore wind farm population. This uses operational data from three wind turbines with both SCADA, CMS data, and processed data from RAMBOLL. The outcomes from this paper are used to determine the type of ML model used in the last study. 4) Is the model development of a population-based structural health monitoring (PBSHM) model. This study investigates three domain adaptation techniques suited to strong homogeneous populations. The ML model takes SCADA data as an input and predicts the damage equivalent moments (DEM) on the jacket foundation structure. To validate the PBSHM model data from a structure that is not used during the training of the model is used to quantify the precision of the model. The individual contributions of the developments in each of the constituent areas relate to an overall improvement in modelling approaches that are necessary for DEAM and aid in the realisation of true digital twins. All the areas relate to offshore wind ML and are related to operational data. The link between the measured data and the individual models aid in gaining more information and greater insights into the O&M

Multiscale Machine Learning and Numerical Investigation of Ageing in Infrastructures

Infrastructure is a critical component of a country’s economic growth. Interaction with extreme service environments can adversely affect the long-term performance of infrastructure and accelerate ageing. This research focuses on using machine learning to improve the efficiency of analysing the multiscale ageing impact on infrastructure. First, a data-driven campaign is developed to analyse the condition of an ageing infrastructure. A machine learning-based framework is proposed to predict the state of various assets across a railway system. The ageing of the bond in fibre-reinforced polymer (FRP)-strengthened concrete elements is investigated using machine learning. Different machine learning models are developed to characterise the long-term performance of the bond. The environmental ageing of composite materials is investigated by a micromechanics-based machine learning model. A mathematical framework is developed to automatically generate microstructures. The microstructures are analysed by the finite element (FE) method. The generated data is used to develop a machine learning model to study the degradation of the transverse performance of composites under humid conditions. Finally, a multiscale FE and machine learning framework is developed to expand the understanding of composite material ageing. A moisture diffusion analysis is performed to simulate the water uptake of composites under water immersion conditions. The results are downscaled to obtain micromodel stress fields. Numerical homogenisation is used to obtain the composite transverse behaviour. A machine learning model is developed based on the multiscale simulation results to model the ageing process of composites under water immersion. The frameworks developed in this thesis demonstrate how machine learning improves the analysis of ageing across multiple scales of infrastructure. The resulting understanding can help develop more efficient strategies for the rehabilitation of ageing infrastructure

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research