James Shucksmith

Bulk Flow Resistance in Vegetated Channels: Analysis of Momentum Balance Approaches Based on Data Obtained in Aging Live Vegetation

Previous work has sought to investigate flow resistance caused by vegetation in open channels. However, many existing flow resistance models are based on data from artificial plant mimics in laboratory channels and are untested with live, aging vegetation and may neglect some of the key effects. This paper presents results from a study using two types of live vegetation grown within a laboratory channel. A series of flow resistance tests were conducted over a time period sufficient to observe changes caused by growth. The effects of the vegetation on bulk flow resistance are presented and discussed. The results are compared with predictions made by existing practical momentum balance based models for flow through emergent vegetation, and new empirical relationships are presented. The work shows that momentum balance methods can provide an accurate prediction of flow depth in live vegetation provided that the relationship between bulk drag coefficient and flow is known. This relationship has been shown to ...

Predicting combined sewer overflows chamber depth using artificial neural networks with rainfall radar data

Combined sewer overflows (CSOs) represent a common feature in combined urban drainage systems and are used to discharge excess water to the environment during heavy storms. To better understand the performance of CSOs, the UK water industry has installed a large number of monitoring systems that provide data for these assets. This paper presents research into the prediction of the hydraulic performance of CSOs using artificial neural networks (ANN) as an alternative to hydraulic models. Previous work has explored using an ANN model for the prediction of chamber depth using time series for depth and rain gauge data. Rainfall intensity data that can be provided by rainfall radar devices can be used to improve on this approach. Results are presented using real data from a CSO for a catchment in the North of England, UK. An ANN model trained with the pseudo-inverse rule was shown to be capable of predicting CSO depth with less than 5% error for predictions more than 1 hour ahead for unseen data. Such predictive approaches are important to the future management of combined sewer systems.

Instantaneous Phase and Frequency for the Detection of Leaks and Features in a Pipeline System

This work focuses on the progress of a new analysis technique to detect pipeline leaks and features based on analysis of a pressure transient. The measured time domain signals of these transients were obtained using a single pressure transducer. The instantaneous phase and frequency of the signals are analyzed using the Hilbert transform (HT) and the Hilbert—Huang transform (HHT). Both simulated and experimental pressure signals were used to evaluate the performance of these transforms. The analysis of simulated signals allowed features in the pipeline network such as leaks and pipe ends to be ascribed to features in the signals. Analysis of the experimental tests corroborates the simulated test results when the HT and the HHT analysis are used.

Comparison between InfoWorks hydraulic results and a physical model of an urban drainage system

Urban drainage systems are frequently analysed using hydraulic modelling software packages such as InfoWorks CS or MIKE-Urban. The use of such modelling tools allows the evaluation of sewer capacity and the likelihood and impact of pluvial flood events. Models can also be used to plan major investments such as increasing storage capacity or the implementation of sustainable urban drainage systems. In spite of their widespread use, when applied to flooding the results of hydraulic models are rarely compared with field or laboratory (i.e. physical modelling) data. This is largely due to the time and expense required to collect reliable empirical data sets. This paper describes a laboratory facility which will enable an urban flood model to be verified and generic approaches to be built. Results are presented from the first phase of testing, which compares the sub-surface hydraulic performance of a physical scale model of a sewer network in Yorkshire, UK, with downscaled results from a calibrated 1D InfoWorks hydraulic model of the site. A variety of real rainfall events measured in the catchment over a period of 15 months (April 2008-June 2009) have been both hydraulically modelled and reproduced in the physical model. In most cases a comparison of flow hydrographs generated in both hydraulic and physical models shows good agreement in terms of velocities which pass through the system.

Validation of 2D shock capturing flood models around a surcharging manhole

Abstract This work offers a detailed validation of finite volume (FV) flood models in the case where horizontal floodplain flow is affected by sewer surcharge flow via a manhole. The FV numerical solution of the 2D shallow water equations is considered based on two approximate Riemann solvers, HLLC and Roe, on both quadrilateral structured and triangular unstructured mesh-types. The models are validated against a high resolution experimental data-set obtained using a physical model of a sewer system linked to a floodplain via a manhole. It was verified that the sensitivity of the models is inversely proportional to the surcharged flow/surface inflow ratio, and therefore requires more calibration from the user especially when concerned with localised modelling of sewer-to-floodplain flow. Our findings provide novel evidence that shock capturing FV-based flood models are applicable to simulate localised sewer-to-floodplain flow interaction.

Quantification of energy losses at a surcharging manhole

Abstract Hydraulic models of sewer systems are commonly used to predict the risk of urban flooding. However, suitable calibration datasets in flood conditions are scarce. The quantification of energy losses within manhole structures is a current source of uncertainty within such models. To address this gap, a scaled physical manhole model is used to quantify hydraulic energy losses during surcharging and non-surcharging conditions. Two different novel configurations were tested; (1) With and without the presence of a manhole lid; (2) With and without the presence of a shallow flow on the surface. Results showed that total head losses were found to increase in surcharging conditions. The presence of the lid also marginally increased total head losses. The datasets are used to assess the performance of a numerical urban flood model (SIPSON) and comparisons highlighted that SIPSON tends to overestimate energy losses in surcharging conditions.