Statistical Design of Experiments for Accelerated Component Testing in Offshore Renewable Applications - Mathematics - EPSRC DTP funded PhD Studentship Ref: 2908

About the award

This project is one of a number funded by the Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Partnership to commence in September 2018. This project is in direct competition with others for funding; the projects which receive the best applicants will be awarded the funding.

The studentships will provide funding for a stipend which is currently £14,553 per annum for 2017-2018. It will provide research costs and UK/EU tuition fees at Research Council UK rates for 42 months (3.5 years) for full-time students, pro rata for part-time students.

Please note that of the total number of projects within the competition, up to 15 studentships will be filled.

Dr Philipp Thies

Prof Peter Challenor

Prof Lars Johanning

Penryn Campus, Cornwall.

Project Description
Offshore renewable energy offers promising potential for low-carbon energy generation. Offshore wind is being deployed at an industrial scale while tidal and wave energy is moving toward first commercial deployments. 

The continuous operation under offshore loading conditions poses formidable engineering challenges regarding long-term component reliability. Failure mechanisms such as wear and fatigue have to be evaluated and assured for typical project lifetimes of 15 to 20 years, making long-term field tests impractical and inefficient. 

The wind energy industry has resorted to very specific performance and durability testing of the individual wind turbine components (rotor blades, drivetrain, nacelle and foundation) that aim to replicate and accelerate the in-situ load conditions. 

Similar efforts are under way for wave and tidal energy [1] aiming to replicate service load conditions to assess and demonstrate critical components. The large majority of tests are driven by pragmatic engineering requirements, such as the investigation of a particular failure mode or assurance of load capacity. 

The statistical foundations, rooted in the Design of Experiments’ are not generally applied to inform and tailor the acceleration and schedule of component tests.  The Design of experiments approach aims to identify, select and schedule the combinations of governing factors (e.g. frequency, load range, sample properties) for the test regime to achieve the test objective with a minimal use of resource (i.e. cost, test time, sample number), whilst maintaining the statistical significance of the results. 

This involves building a statistical model that approximates the relationship between the governing factors on the desired component response (e.g. lifetime). This in turn allows to quantify the uncertainty of the experiments themselves as well as to make a prediction on the component response. 

This PhD project aims to utilise the statistical Design of Experiments approach to develop new methods that are capable to devise the most efficient test regimes for offshore energy applications. Aspects to consider include the stochastic behaviour of the load environment as well as the possibility to reveal new and largely different failure behaviours for these new, often unproven applications. 

[1] Thies PR, Johanning L, Karikari-Boateng KA, Ng C, McKeever P. (2015) Component reliability test approaches for marine renewable energy, Proc. IMechE, Part O: Journal of Risk and Reliability, vol. 229 (5), pp. 403-416, DOI:10.1177/1748006X15580837;  

Entry Requirements

You should have or expect to achieve at least a 2:1 Honours degree, or equivalent, in [Mathematics, Applied Statistics or Mechanical Engineering]. Experience in [experimental testing or statistical modelling] is desirable.

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.

The majority of the studentships are available for applicants who are ordinarily resident in the UK and are classed as UK/EU for tuition fee purposes.  If you have not resided in the UK for at least 3 years prior to the start of the studentship, you are not eligible for a maintenance allowance so you would need an alternative source of funding for living costs. To be eligible for fees-only funding you must be ordinarily resident in a member state of the EU.  For information on EPSRC residency criteria click here.

Applicants who are classed as International for tuition fee purposes are NOT eligible for funding. International students interested in studying at the University of Exeter should search our funding database for alternative options.


Application deadline:10th January 2018
Value:3.5 year studentship: UK/EU tuition fees and an annual maintenance allowance at current Research Council rate. Current rate of £14,553 per year.
Duration of award:per year
Contact: Doctoral

How to apply

You will be required to upload the following documents:
•       CV
•       Letter of application outlining your academic interests, prior research experience and reasons for wishing to
        undertake the project.
•       Transcript(s) giving full details of subjects studied and grades/marks obtained.  This should be an interim
        transcript if you are still studying.
•       If you are not a national of a majority English-speaking country you will need to submit evidence of your current
        proficiency in English.  For further details of the University’s English language requirements please see

The closing date for applications is midnight (GMT) on Wednesday 10 January 2018.  Interviews will be held at the University of Exeter in late February 2018.

If you have any general enquiries about the application process please email:
Project-specific queries should be directed to the supervisor.

During the application process, the University may need to make certain disclosures of your personal data to third parties to be able to administer your application, carry out interviews and select candidates.  These are not limited to, but may include disclosures to:

• the selection panel and/or management board or equivalent of the relevant programme, which is likely to include staff from one or more other HEIs;
• administrative staff at one or more other HEIs participating in the relevant programme.

Such disclosures will always be kept to the minimum amount of personal data required for the specific purpose. Your sensitive personal data (relating to disability and race/ethnicity) will not be disclosed without your explicit consent.