About the Project
The EPSRC Centre for Doctoral Training (CDT) in Embedded Intelligence (EI) at Heriot-Watt University is offering two fully-funded PhD studentship to UK/EU applicants. Heriot-Watt University is recognised as the leading UK research institute in general engineering as per the Research Excellence Framework (REF) results of 2014. The CDT in EI represents a £13M centre covering the integration of intelligence into products, processes and services so they work better and can increase productivity, efficiency and connectivity. The students will be supervised by Dr David Flynn and Dr Valentin Robu of the Smart Systems Group based at Heriot-Watt University.
The central theme of these studentships will be prognostics and health management. The PhD students will spend periods of time working as a team within Baker Hughes as well as Heriot-Watt University. The students will review literature in prognostics and health management (PHM), review test and design specifications of sub-systems, develop design of experiment tests for lifecycle analysis and establish the failure mode mechanism and effect analysis (FMMEA).
This second studentship will focus on a modelling based approach, such as Physics of Failure and Surrogate Modelling, to support predicting asset RUL. Such modelling techniques will be investigated in order to “digitise” critical assets supporting the RUL prediction under varying loading conditions, as well as informing future design iterations of the asset. This position is best suited to someone with a mechanical engineering, Micro-Electro-Mechanical Systems (MEMS), electronic engineering or physics background. A background in analysing dynamic mechanical systems as well as experience in Multiphysics and Finite Element Modelling would be advantageous. Areas of investigation are likely to include vibro-acoustics, electrostatic forces, thermomechanical forces within mechanical systems.
In the final 12 months of research the students will spend some of their time collaborating on the potential of Fusion Prognostics. Fusion prognostics seeks to utilise a hybrid version of data and modelling based methods in order to improve the accuracy of RUL predictions.
Baker Hughes is a top-tier oilfield service company with a century-long track record in delivering solutions that help oil and gas operators make the most of their reservoirs. A history of technology innovation is a cornerstone of their success. Local teams are supported by global centres of excellence where scientists push the boundaries of value-adding technology to find solutions for progressively more complex technical challenges. Baker Hughes have a distinguish track record of academic engagement reflected through its strategic Alliance with Heriot-Watt University.
Baker Hughes will provide technical support and continuous professional development resources to this project beyond the studentship funding, including: technical supervision, access to historical data and design of assets, lifecycle test facilities, in-situ monitoring of assets and technical training. The engineering expertise and test facilities of Baker Hughes are critical assets to the program of research that students are expected to fully utilise.
Smart Systems Group
A primary research theme of the Smart Systems Group (SSG) is PHM, the driver for this activity is to provide new visibility and understanding to the remaining useful life of critical assets. High value assets are deployed in harsh environments across a myriad of industrial sectors, ranging from aviation, space, subsea and energy. These environments are exposed to high temperature, pressure, radiation, shock values, as well as chemically corrosive conditions. Assets in such environments present significant challenges in terms of their design, operation and maintenance due to limitations in the visibility of the asset within the harsh environment. The ability to monitor these assets is impeded by the thermal limits of materials, aggressive ambient conditions influencing sensor drift and failure, power management, data analysis and communication issues. The research vision of this group is to design, manufacture, package and test novel smart microsystems that are reliable, robust, adaptable and impervious to harsh environments to support the intelligent management of high value assets
Applicants should
• Have a first class honours or good upper second class degree in Mechanical Engineering, Materials Science, Computer Science, Mathematics or Embedded Systems or equivalent
• With respect to studentship the candidate should have an understanding and experience of Multiphysics software and ideally FMMEA.
• Candidates should demonstrate a keen interest in FMMEA, prognostics, sensors and systems engineering.
• Working across research teams within academia and industry the candidates will require good organisational and communication skills.
• Experience or knowledge of design methodologies is desirable but not essential.
How to Apply
Applications should be made online: https://www.hw.ac.uk/schools/engineering-physical-sciences/research/phd/isss.htm
Informal enquires about the research projects should be made to Dr David Flynn ([Email Address Removed]).
Funding Notes
Each studentship will run for 4 years and include:
• A fee waiver equivalent to the home/EU rate.
• An enhanced EPSRC tax-free stipend of up to £ £17, 057 p.a. for four years.
• A personal training budget of £10,000 to support specific training needs.
• Periods of placement (estimate 2-4 months per year) and training within Baker Hughes.
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