Water companies rely on one-dimensional computer models to predict how pollutants move through rivers and pipes, but these models often get the mixing wrong when flows are slow or channels are irregularly shaped. This matters because inaccurate predictions can lead to poor decisions about where to discharge treated wastewater, how to manage storm overflows, or how to respond to chemical spills. Current models work well for fast, steady flows in straight pipes, but real water networks are full of bends, junctions, and variable flow conditions. The researcher will fill this gap by conducting laboratory experiments and field measurements, then using system identification techniques to build better mathematical representations of mixing into the standard 1D models. If successful, the work will give environmental regulators, engineering consultants, and water utilities a cheap, fast, and reliable tool for predicting water quality across entire catchments. The impact would be invisible to the public but tangible: fewer sewer overflows that go unnoticed, more accurate pollution forecasts, and better-informed investment decisions in drainage and water supply infrastructure. The project spans fundamental fluid dynamics through to practical delivery.
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The management of water quality in rivers, urban drainage and water supply networks is essential for ecological and human well-being. Predicting the effects of management strategies requires knowledge of the hydrodynamic processes covering spatial scales of a few millimetres (turbulence) to several hundred kilometres (catchments), with a similarly large range of timescales from milliseconds to weeks. Predicting underlying water quality processes and their human and ecological impact is complicated as they are dependent on contaminant concentration. Current water quality modelling methods range from complex three dimensional computational fluid dynamics (3D CFD) models, for short time and small spatial scales, to one-dimensional (1D) time dependent models, critical for economic, fast, easy-to-use applications within highly complex situations in river catchments, water supply and urban drainage systems. Mixing effects in channels and pipes of uniform geometry can be represented with some confidence in highly turbulent, steady flows. However, in the majority of water networks, the standard 1D model predictions fall short because of knowledge gaps due to low turbulence, 3D shapes and unsteady flows. This Fellowship will work to address the knowledge gaps, delivering a step change in the predictive capability of 1D water quality network models. It will achieve this via the strategic leadership of a programme of laboratory and full-scale field measurements, the implementation of system identification techniques and active engagement with primary users. The proposal covers aspects from fundamental research, through applications, to end-user delivery, by providing a new modelling methodology to inform design, appraisal and management decisions made by environmental regulators, engineering consultants and water utilities.
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