Deterioration relationships for a variety of water main materials
This studentship has has a stipend of approximately £20,000. All fees will be met separately to that amount.
Funding sourceThis project is jointly funded by the EPSRC CDT in MiNMaT and Thames Water
The project forms part of a larger ongoing programme of research into large diameter (trunk) water mains and trials of pipe condition assessment technology. The wider goal is to improve the sponsor’s ability to understand the condition of its water network for the purposes of long-term asset management, medium-term investment targeting, and short-term tactical planning and risk mitigation.
Thames Water’s network include ferrous (cast iron, ductile iron, steel) and non-ferrous (asbestos cement, polyvinylchloride, polyethylene, concrete and glass reinforced polymer) pipes. Depending on the material, pipes are joined using methods such as socket and spigot, bolted flange and compression couplings, as well as welding. The project aims to establish how both the material and the jointing systems of the pipeline structure are likely to deteriorate and fail in the future.
The existing approach to predicting average future performance in non-ferrous trunk mains is to carry out statistical analysis of past failures of assets, grouped by pipe age, material and diameter. Moving on from using backward-looking analysis as a guide to future failures, Thames Water would like to arrive at a better understanding of the deterioration mechanisms and processes of failure for the individual pipe systems making up its network. This should take into account the material assessment as well as considering the water main as a structure, so that the majority of common failure types (including pipe barrel and joint failures) are accounted for. The work presents challenges in identifying those failure mechanisms and being able to model their future trajectory towards eventual failure. It is anticipated combining this approach with the original failure data will allow for better targeting of asset investment and improved confidence in its long term strategy for the network.
To start with the project will require a comprehensive literature review of scientific publications and industry-specific reports to identify existing research and data that will be relevant to the project. Essentially the Research Engineer would be identifying pertinent knowledge within previously under-utilised research and applying it to provide genuine business need.
Cleansing and analysis of historical pipe failure records will also be necessary, which will require an ability to apply critical judgement, and will provide an understanding of the challenges of data collection and analysis in a large company. Statistical analysis of these datasets, informed by engineering knowledge, needs to be undertaken to produce meaningful mathematical relationships that the sponsor can use in its risk models. The project will also inform the sponsor what changes to routine data capture would be worthwhile.
The intention is that a phased approach will be taken; a basic first pass should be completed to make straightforward improvements to the deterioration relationships used for all pipe materials, before further development work is put into each pipe material type, with effort proportionate to the expected benefit.
The research engineer will be based at the sponsor’s Water Innovation Centre at Kempton Park Water Treatment Works in south-west London.
Related linksEPSRC Centre for Doctoral Training in Micro- and NanoMaterials Technologies Thames Water
UK and EU students are invited to apply.
To be eligible for this studentship, you are required to have a First, 2:1 or merit in a masters degree in a physical sciences subject. As this is a multidisciplinary project, applications are welcomed from candidates from a range of backgrounds including statistics, mathematics, computing, science, physics, materials science, civil/mechanical engineering. Additional experience is welcomed, knowledge of pipeline engineering would be an advantage. The successful candidate would need to have a strong ability in maths/statistics and experience of numerical modelling.
If English is not your first language you are required to have an IELTS of 6.5 or above.