This paper presents the results of the experimental and numerical investigation of interactions between surface flood flow in urban areas and the flow in below ground drainage systems (sewer pipes and manholes). An experimental rig has been set up at the Water Engineering Laboratory at the University of Sheffield. It consists of a full scale gully structure with inlet grating, which connects the 8 m(2) surface area with the pipe underneath that can function as an outfall and is also further connected to a tank so that it can come under surcharging conditions and cause outflow from the gully. A three-dimensional CFD (Computational Fluid Dynamics) model has been set up to investigate the hydraulic performance of this type of gully inlet during the interactions between surface flood flow and surcharged pipe flow. Preliminary results show that the numerical model can replicate various complex 3D flow features observed in laboratory conditions. This agreement is overall better in the case of water entering the gully than for the outflow conditions. The influence of the surface transverse slope on flow characteristics has been demonstrated. It is shown that re-circulation zones can form downstream from the gully. The number and size of these zones is influenced by the transverse terrain slope.

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.

One of the most fundamental tasks for robots inspecting water distribution pipes is localization, which allows for autonomous navigation, for faults to be communicated, and for interventions to be instigated. Pose-graph optimization using spatially varying information is used to enable localization within a feature-sparse length of pipe. We present a novel method for improving estimation of a robot’s trajectory using the measured acoustic field, which is applicable to other measurements such as magnetic field sensing. Experimental results show that the use of acoustic information in pose-graph optimization reduces errors by 39% compared to the use of typical pose-graph optimization using landmark features only. High location accuracy is essential to efficiently and effectively target investment to maximise the use of our aging pipe infrastructure.

This paper presents new experimental and numerical evidence that perforations in a pipe wall result in a low-frequency bandgap within which sound waves rapidly attenuate. These perforations are modelled as an acoustically soft boundary condition on the pipe wall and show that a low frequency bandgap is created from 0 Hz. The upper bound of this bandgap is determined by the dimensions and separation of the perforations. An analytical model based on the transfer matrix method is proposed. This model is validated against numerical predictions for the pipe with varying perforation geometries. A numerical study is undertaken to model the effect of perforations with ideal acoustically soft boundary conditions and surrounded with an air gap. Close agreement is found between the numerical and analytical models. Experimental evidence shows that the width of the bandgap is accurately predicted with the numerical and analytical models.

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