Water is usually distributed through underground pipe networks, which are not easily accessible. Therefore, a condition monitoring system is required not only for leak detection, but also provide information about the structural health and integrity of these underground pipes for preventive measures. Most of the existing system are merely based on the passive leakage detection, which significantly depends on the low amplitude sound wave that must reach to the sensor. However, the proposed method is based on the development of novel meta-material based acoustic reflectometer, which can operate over longer distances. Periodicity-enhanced meta-materials are artificial structures with acoustic properties not found in nature and already been used for controlling sound propagation. The idea is to inversely study the sound field and meta-behaviour in order to map the internal structure of the pipes. The changes in the internal surfaces of the pipes will create impedance discontinuity, which are apparent in acoustic field analysis. Metamaterials can exhibit nearly arbitrary values of effective density and modulus, which may be manipulated to enhance the impedance discontinuity due to changes in the internal structure of pipes. The development of metamaterials-based reflectometry technology will help to identify the low frequency and low amplitude acoustic signatures.
Experimental data will be collected for different types of water leakages. Sound propagation modelling and signal processing methods will be developed to investigate the water leakage acoustic signature. Numerical modelling techniques such as Boundary Element Method can be used to model the internal sound field. Metamaterials based reflectometer system will be capable for monitoring the condition of pipes as well detecting and locating any imminent leakage for predictive maintenance. The proposed work will be expanded to develop a sensor network, capable of mapping the water distribution network. Sensor fusion and big data will be employed to design an intelligent condition monitoring system. Impedance discontinuity in large data sets will be detected through machine learning. Empirical Mode Decomposition is a powerful signal processing technique, identified to be used for data analysis. Overall, the system will be capable of mapping the internal structure of pipes and will be able to identify the degradation for predictive maintenance.
Applicants should hold or expect to obtain a good honours degree (2:1 or above) in a relevant discipline. A masters level qualification in a relevant discipline is desirable, but not essential, as well as a demonstrable understanding of the research area. Further details of the expected background may appear in the specific project details. International students will be subject to the standard entry criteria relating to English language ability, ATAS clearance and, when relevant, UK visa requirements and procedures.
How to Apply
Applicants should apply online for this opportunity at: https://e-vision.tees.ac.uk/si_prod/userdocs/web/apply.html?CourseID=1191
Please use the Online Application (Funded PHD) application form. When asked to specify funding select “other” and enter ‘RDS’ and the title of the PhD project that you are applying for. You should ensure that you clearly indicate that you are applying for a Funded Studentship and the title of the topic or project on the proposal that you will need to upload when applying. If you would like to apply for more than one project, you will need to complete a further application form and specify the relevant title for each application to a topic or project.
Applications for studentships that do not clearly indicate that the application is for a Funded Studentship and state the title of the project applied for on the proposal may mean that your application may not be considered for the appropriate funding.
For academic enquiries, please contact Dr Imran Bashir [Email Address Removed]
For administrative enquiries before or when making your application, contact [Email Address Removed].