Pipeline leakage detection and characterisation with adaptive surrogate modelling using particle swarm optimisation.

Pipelines are often subject to leakage due to ageing, corrosion, and weld defects, and it is difficult to avoid as the sources of leakages are diverse. Several studies have demonstrated the applicability of the machine learning model for the timely prediction of pipeline leakage. However, most of these studies rely on a large training data set for training accurate models. The cost of collecting experimental data for model training is huge, while simulation data is computationally expensive and time-consuming. To tackle this problem, the present study proposes a novel data sampling optimisation method, named adaptive particle swarm optimisation (PSO) assisted surrogate model, which was used to train the machine learning models with a limited dataset and achieved good accuracy. The proposed model incorporates the population density of training data samples and model prediction fitness to determine new data samples for improved model fitting accuracy. The proposed method is applied to 3-D pipeline leakage detection and characterisation. The result shows that the predicted leak sizes and location match the actual leakage. The significance of this study is two-fold: the practical application allows for pipeline leak prediction with limited training samples and provides a general framework for computational efficiency improvement using adaptive surrogate modelling in various real-life applications

Acoustic emission monitoring of pipes; combining finite element simulation and experiment for advanced source location and identification

Impact is a common source of damage in pipes and pipeline systems, detecting the location and nature of damage is vital for reliability and safety of these systems. This work sets out to assess the capacity of Acoustic Emission (AE) to monitor pipes and pipelines for externally applied mechanical damage. AE is a non-destructive testing and monitoring technique that relies on the propagation of elastic (stress) waves generated by impulsive events such as particle impingement, cracking or fluid flow. These waves are recorded at one or more sensors mounted on the surface of the object to be monitored. The key scientific question was to determine the extent to which the structure of a non-impulsive event could be reconstructed using sensors located on the external surface of a pipe. The aim was to combine Finite Element simulations with a series of experiments in order that the relationship between the generating event (source) and the resulting stress-time history at a given point on the surface could be elucidated. Experiments and simulations were carried out with impulsive sources (pencil-lead breaks) and dropped objects, the latter being used to represent a non-impulsive event with a reproducible structure lasting around one second. The AE resulting from these sources was recorded over a period of around 2 seconds for both experiments and simulations. Two test objects, a solid cylindrical steel block of diameter 307mm and length 166mm and various lengths of pipe of diameter 100mm and wall thickness 10mm were used, the former to provide a relatively simple and well-studied platform to examine a number of essential principles. The work on the solid cylinder first validated the simulation of the stress wave from an impulsive source and identified the main modes present, by comparing with analytical solutions. Then it was possible to identify the part of the experimental time series record at a given sensor which is uncontaminated by reflections from the edges and surfaces of the cylinder. The dropped object measurements on the solid cylinder provided clear records of the first and subsequent impacts as the dropped steel balls recoiled and returned back to the surface. There was a clear relationship between the measured AE energy and the estimated incident energy of the dropped objects at a range of timescales irrespective of contamination by reflections. The work on the pipe sections formed the main series of systematic experiments. First it was established that an unloading time in the simulations of around 10-8 seconds gave a reasonable representation of the frequency structure of experimentally observed stress waves. It was also observed from both experiments and simulations that a low amplitude wave travelling at around 5500ms-1 was the first to arrive at any surface sensor. The structure thereafter was complex, probably involving reflections from the inner wall of the cylinder and geometric interference as the wave spreads around the circumference of the pipe. The key finding of this aspect of the work is that the AE line structure of an impulsive source can be reproduced by simulation for short times, for longer times, the damping associated with reflections would require to be measured and introduced into the simulations in order to fully represent the real practical simulation. The degree of damping is important in making a cumulative assessment of multiple impulsive sources. The dropped objects on the pipe confirmed that a mechanical disturbance which is extended in time can be identified from its energy-time imprint carried on the stress wave. The analysis was carried out at three different timescales; short (initial interactions free of reflections), medium (first contact including recoil) and long (involving several bounces). Generally, for medium and short timescales, the AE energy varied with drop height and mass consistently with existing models for balls on plate. For multiple bounces, the behaviour was more erratic probably due to the imprecise control of ball contact point. The simulations of AE worked well at medium and long timescales, providing an idealised framework unto which could be added effects of restitution and damping. At the short timescale, the twin challenges of time and spatial resolution meant that a solution could not be obtained within the limitations of the computing power available. It is generally concluded that AE monitoring can be used to identify the nature of a mechanical disturbance on the surface of a pipe. Suggestions for future work include improvements to the simulations to include attenuation and to better simulate the dynamics of mechanical interactions at the surface, and extensions to the experiments to cover the effect of internal and external pipe environment and the use of mechanical sources which involve actual pipe damage

Development of the Next Generation of Water Distribution Network Modelling Tools Using Inverse Methods

The application of optimisation to Water Distribution Network (WDN) Modelling involves the use of computer-based techniques to many different problems, such as leakage detection and localisation. The success in the application of any model-based methodology for finding leaks highly depends on the availability of a well-calibrated model. Both leak detection and localisation, as well as model calibration are procedures that constitute the field of inverse problems in WDN modelling. The procedures are interlinked and dependent as when a leak is found and the model is updated its quality improves, while when a model is calibrated its ability to detect and localise leaks also improves. This is because both inverse problems are solved with the aim to mimic the behaviour of the real system as closely as possible using field measurements. In this research, both inverse problems are formulated as constrained optimisation problems. Evolutionary Optimisation techniques, of which Genetic Algorithms are the best-known examples, are search methods that are increasingly applied in WDN modelling with the aim to improve the quality of a solution for a given problem. This, ultimately, aids practitioners in these facets of management and operation of WDNs. Evolutionary Optimisation employs processes that mimic the biological process of natural selection and “survival of the fittest” in an artificial framework. Based on this philosophy a population of individual solutions to the problem is manipulated and, over time, “evolves” towards optimal solutions. However, such algorithms are characterised by large numbers of function evaluations. This, coupled with the computational complexity associated with the hydraulic simulation of WDNs incurs significant computational burden, can limit the applicability and scalability of this technology across the Water Industry. In addition, the inverse problem is often “ill-posed”. In practice, the ill-posed condition is typically manifested by the non-uniqueness of the problem solution and it is usually a consequence of inadequate quantity and/or quality of field observations. Accordingly, this thesis presents a methodology for applying Genetic Algorithms to solve leakage related inverse problems in WDN Modelling. A number of new procedures are presented for improving the performance of such algorithms when applied to the complex inverse problems of leak detection and localisation, as well as model calibration. A novel reformulation of the inverse problem is developed as part of a decision support framework that minimizes the impact of the inherent computational complexity and dimensionality of these problems. A search space reduction technique is proposed, i.e., a reduction in the number of possible solution combinations to the inverse problem, to improve its condition considering the accuracy of the available measurements. Eventually, this corresponds to a targeted starting point for initiating the search process and therefore more robust stochastic optimisations. The ultimate purpose is to increase the reliability of the WDN hydraulic model in localising leaks in real District Metered Areas, i.e., to reduce the number false positives. In addition, to speed up the leak search process (both computationally and physically) and, improve the overall model accuracy. A calibrated model of the WDN is not always available for supporting work at distribution mains level. Consequently, two separate problem-specific methods are proposed to meet the abovementioned purpose: (a) a Leak Inspection Method used for the detection and localisation of leaks and; (b) a Calibration Method for producing an accurate average day model that is fit for the purpose of leak detection and localisation. Both methods integrate a three-step Search Space Reduction stage, which is implemented before solving the inverse problem. The aim is to minimize the number of decision variables and the range of possible values, while trying to preserve the optimum solution, i.e., reduce the inverse problem dimensionality. The search space reduction technique is established to generate a reduced set of highly sensitive decision variables. Eventually this is done to provide a viable, scalable technique for accelerating evolutionary optimisation applications in inverse problems being worthwhile on both academic and practical grounds. The novel methodologies presented here for leak detection and localisation, as well as for model calibration are verified successfully on four case studies. The case studies include two real WDN examples with artificially generated data, which investigate the limits of each method separately. The other two case studies implement both methods on real District Metered Areas in the United Kingdom, firstly to calibrate the hydraulic network model and, then, to detect and localise a single leak event that has actually happened. The research results suggest that leaks and unknown closed or open throttle valves that cause a hydraulic impact larger than the sensor data error can be detected and localised with the proposed framework which solves the inverse problem after search space reduction. Moreover, the quality of solutions can dramatically improve for given runtime of the algorithm, as 99.99% of infeasible solution combinations are removed, compared to the case where no search space reduction is performed. The outcomes of the real cases show that the presented search space reduction technique can reduce the search area for finding the leak to within 10% of the WDN (by length). The framework can also contribute to more timely detection and localisation of leakage hotspots, thus reducing economic and environmental impacts. The optimisation model for predicting leakage hotspots can be effective despite the recognized challenges of model calibration and the physical measurement limitations from the pressure and flow field tests