Jonathan Edwards

Numerical transient analysis of random leakage in time and frequency domains

ABSTRACT Models of water pipeline systems should take into account the distribution in space and time of user demands and leakage. In the usual approach such a distribution is simplified lumping the system outflows at a reduced number of nodes. To investigate the effects of such a simplification, in this paper we explore by numerical models, both in the time and in the frequency domain, the uncertainty introduced by the random variation in leak size, location and number. The novelty is also in considering the number of leaks as a parameter. In the time domain, results show that the damping increases with the number of leaks. The spreading of the simulated pressure signals increases with time whereas it decreases with the number of leaks. In the frequency domain, the local minima and maxima values of the impedance are affected by the number of leaks for a given total outflow from the system.

Ensemble decision tree models using RUSBoost for estimating risk of iron failure in drinking water distribution systems

Safe, trusted drinking water is fundamental to society. Discolouration is a key aesthetic indicator visible to customers. Investigations to understand discolouration and iron failures in water supply systems require assessment of large quantities of disparate, inconsistent, multidimensional data from multiple corporate systems. A comprehensive data matrix was assembled for a seven year period across the whole of a UK water company (serving three million people). From this a novel data driven tool for assessment of iron risk was developed based on a yearly update and ranking procedure, for a subset of the best quality data. To avoid a ‘black box’ output, and provide an element of explanatory (human readable) interpretation, classification decision trees were utilised. Due to the very limited number of iron failures, results from many weak learners were melded into one high-quality ensemble predictor using the RUSBoost algorithm which is designed for class imbalance. Results, exploring simplicity vs predictive power, indicate enough discrimination between variable relationships in the matrix to produce ensemble decision tree classification models with good accuracy for iron failure estimation at District Management Area (DMA) scale. Two model variants were explored: ‘Nowcast’ (situation at end of calendar year) and ‘Futurecast’ (predict end of next year situation from this year’s data). The Nowcast 2014 model achieved 100% True Positive Rate (TPR) and 95.3% True Negative Rate (TNR), with 3.3% of DMAs classified High Risk for un-sampled instances. The Futurecast 2014 achieved 60.5% TPR and 75.9% TNR, with 25.7% of DMAs classified High Risk for un-sampled instances. The output can be used to focus preventive measures to improve iron compliance.

Visualising Dynamic Stall Around a Vertical Axis Wind Turbine Blade Through Particle Image Velocimetry

The vertical axis wind turbine aerodynamics are highly complex and unsteady. Inherent in the operation of VAWTs is the presence of the dynamic stall phenomenon that has a major influence in the overall performance of the rotor. The acquisition of a reliable experimental flow field data set presents a means to increase the level of understanding of VAWT performance and flow physics through visualisations. The method developed in this study includes the setup of the PIV system in the wind tunnel, surface treatment of the VAWT blades, verification of test settings, and image processing and data analysis. The measurement of the flow fields around a VAWT blade at tip speed ratios of λ = 2.5 and 4 were carried out and the results show significant differences in the stalling characteristics between different λ with increased occurrence of deep and prolonged separation of flow from the blade surface at lower λ. In both cases, however, dynamic stall is observed. The data acquired is an invaluable reference for VAWT flow physics as well as validation of numerical models such as CFD.

A Numerical Study on the Effects of Unsteady Wind on Vertical Axis Wind Turbine Performance

Numerical simulations using RANS–based CFD have been utilised to carry out investigations on the effects of unsteady wind in the performance of a wind tunnel vertical axis wind turbine. Using a validated CFD model, unsteady wind simulations revealed a fundamental relationship between instantaneous VAWT CP and wind speed. CFD data shows a CP variation in unsteady wind that cuts across the steady CP curve as wind speed fluctuates. A reference case with mean wind speed of 7m/s, wind speed amplitude of ±12%, fluctuating frequency of 0.5Hz and mean tip speed ratio of 4.4 has shown a wind cycle mean power coefficient of 0.33 that equals the steady wind maximum. Increasing wind speed causes the instantaneous tip speed ratio to fall which leads to higher effective angle of attack and deeper stalling on the blades. Stalled flow and rapid changes in angle of attack of the blade induce hysteresis loops in both lift and drag. Decreasing wind speeds limit the perceived angle of attack seen by the blades to near static stall thus reducing the positive effect of dynamic stall on lift generation. Three mean tip speed ratio cases were tested to study the effects of varying conditions of VAWT operation on the overall performance. As the mean tip speed ratio increases, the peak performance also increases.Copyright