Prototype Development of an Autonomous Self-Propelled Capsule Pipeline Inspection Gauge Robot. Self-funded PhD in Engineering. Ref: 3086

About the award

Location:Streatham Campus, University of Exeter, EX4 4QJ

Academic Supervisors:
Dr Yang Liu

Project description:
Inspection and maintenance of pipelines is an ongoing challenge that many industries face, particularly the oil and gas industry. The use of in-line intelligent pipeline inspection gauges is usually the preferred method of inspection to determine the condition of oil pipelines. The current method known as ‘pigging’ relies solely on product flow. However, localised environment issues within pipeline such as excessive corrosion, debris or blockage can cause product flow to reduce, thus causing modern pigging technologies failure. Current pigging technologies have limitation due to the conventional passive driving mechanism in pigging. It is widely accepted by operators that, in the long term, the lack of inspection leads to high risk of production ‘down time’ due to undetected pipe corrosion and ruptures.

This project attempts to address two technical issues encountered by current pigging technologies. (1) Current pigging technologies solely rely on production flow as a means of propulsion, and backwards inspection is impossible. The novelty of the proposed system is that it is a self-propelled robot with the autonomy of mobility in any direction. This would allow the robot to conduct long-term inspection within pipelines and permit repeat inspection of suspicious areas from various angles. (2) One of the bottleneck issues of current pigging technologies is that a large portion of installed pipelines are unpiggable due to the size of pipeline diameters. Current pigging techniques can only fit one pig design into one pipe diameter size. The added benefit of the proposed system is its ability to fit into pipelines of a wide range of diameters. This allows the robot to access the areas that are not possible with current pigging technologies and offers a distinct advantage over contemporary pig systems.

This project aims to develop a prototype of the autonomous self-propelled capsule robot for pipeline inspections. The following approaches will be adopted for this project: (1) to optimise the robot design at various operating conditions, such as water, oil, or gas, to maximise the performance of the robot in terms of energy efficiency, progression rate; (2) to develop a physical prototype at laboratory, and (3) to conduct field test.

For more information about the project and informal enquiries, please contact the primary supervisor: Dr Yang Liu - Y.Liu2@exeter.ac.uk

 

General Information

Information about current fees can be found here: https://www.exeter.ac.uk/pg-research/money/fees/

Information about possible funding sources can be found here: http://www.exeter.ac.uk/pg-research/money/alternativefunding/

Summary

Application deadline:10th June 2018
Value:This project is self-funded
Duration of award:Not applicable
Contact: EMPS PGR Team emps-pgr-ad@exeter.ac.uk

How to apply

You will be asked to submit some personal details and upload a full CV, covering letter and two academic references. Your covering letter should outline your academic interests, prior research experience and reasons for wishing to undertake this project. You may also be asked to upload verified transcripts of your most academic qualification.

If you have any general enquiries about the application process please email emps-pgr-ad@exeter.ac.uk

Please quote reference 3086 on your application and in any correspondence about this project.

 

 

Entry Requirements

Applicants for this research project must have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK, in an appropriate area of science or technology. 


If English is not your first language you will need to meet the English language requirements and provide proof of proficiency. Click here for more information and a list of acceptable alternative tests.