- Type of research degree
- Application deadline
- Friday 28 April 2023
- Project start date
- Sunday 1 October 2023
- Country eligibility
- UK only
- Competition funded
- Source of funding
- Centre for doctoral training
- Dr Xiaodong Jia
- Additional supervisors
- Prof S Xie, Dr M Dogar and Mr R Hunter (industrial)
- School of Chemical and Process Engineering
The project aims to establish a digital twin for the purpose of testing different packing optimisation algorithms or heuristics before such packing models can be deployed for practical nuclear decommissioning applications.
<p style="margin-bottom:11px">When sorting and packing nuclear waste items, the current practice typically involves human operators wearing full PPEs and/or remotely controlled robotic arms. Major constraints of the current practice include short working shift, large support team, low efficiency, long project duration and high cost. The use of autonomous robotic systems is now an accepted future technological trend, as in principle they can overcome all the major constraints of the current practice, by embedding forward planning based on info from modern scanning technologies, by eliminating human operators’ exposure to harmful radiations, and by making continuous 24/7 operation possible. Some supervisors of this PhD project were involved in a recently completed feasibility demo project (OptiSort), funded by IUK (Innovate UK) and NDA (Nuclear Decommissioning Authority). While the demo project successfully integrated some state-of-the-art technologies into an autonomous system and showcased its potentials, it also revealed some gaps to be filled before the system can be deployed for real-world applications.</p> <p>This PhD project helps to fill one of the gaps: a real-time validation system for packing simulations, comprising a robotic packing setup and a digital twin (DT), such that packing simulation models can be plugged in to be tested, verified and corrected. The physical twin is a simple setup (consisting of a robotic arm, flat work surface to pick up well separated objects from, and a packing box) but equipped with necessary machine vision sensors to allow discrepancy detection and correction to be carried out in real-time. The digital twin is built on an existing packing software (DigiDEM) to provide a common virtual packing platform to handles digitised irregular shapes, their packing and mechanical stability, and an API to allow different packing algorithms or heuristics to be tested and validated.</p> <p>Specifically, the project aims to establish a digital twin for the purpose of testing different packing optimisation algorithms or heuristics before such packing models can be deployed for practical nuclear decommissioning applications. Since in a physical packing setup, gravity and laws of physics apply, an object robotically placed in a position may not stay there; a newly added object may also cause existing packing structure to change. Such possibilities need to be taken into account in the packing simulation environment, hence the use of DigiDEM as a starting point. DEM stands for Discrete Element Method, it is a standard numerical technique for simulating dynamic processes involving discrete particles or objects by solving Newton’s equations of motion for each and every single object. DigiDEM is a digital implementation of DEM, designed to handle irregular shapes in a much simpler and easier manner than alternative methods. Since it is physics based, in principle, relocation of placed objects and bed stability are included in the simulation automatically. In practice, however, incomplete/imperfect digital representation of real objects, inaccurate values for mechanical properties used by the simulation model (e.g., for objects of unknown material or of composite materials), and limitations of simulation model itself, are expected to cause a discrepancy between simulation and reality. The level/range of discrepancy is unknown and needs to be quantified, how tolerant the system is to this discrepancy, and how to detect/correct such discrepancies, will be the focus of this PhD project.</p> <p><a href="https://www.nuclear-energy-cdt.manchester.ac.uk/">EPSRC Centre for Doctoral Training in Nuclear Energy – GREEN</a></p>
<p>Formal applications for research degree study should be made online through the <a href="https://www.leeds.ac.uk/research-applying/doc/applying-research-degrees">University's website</a>. Please state clearly in the Planned Course of Study section that you are applying for <em><strong>EPSRC CDT Nuclear Energy – GREEN</strong></em> and in the research information section that the research degree you wish to be considered for is <em><strong>Developing a digital twin for validation of packing optimisers used for nuclear decommissioning</strong></em> as well as <a href="https://eps.leeds.ac.uk/chemical-engineering/staff/201/dr-xiaodong-jia">Dr Xiaodong Jia</a> as your proposed supervisor.</p> <p>If English is not your first language, you must provide evidence that you meet the University's minimum English language requirements (below).</p> <p><em>As an international research-intensive university, we welcome students from all walks of life and from across the world. We foster an inclusive environment where all can flourish and prosper, and we are proud of our strong commitment to student education. Across all Faculties we are dedicated to diversifying our community and we welcome the unique contributions that individuals can bring, and particularly encourage applications from, but not limited to Black, Asian, people who belong to a minority ethnic community, people who identify as LGBT+ and people with disabilities. Applicants will always be selected based on merit and ability.</em></p> <p class="MsoNoSpacing">Applications will be considered on an ongoing basis. Potential applicants are strongly encouraged to contact the supervisors for an informal discussion before making a formal application. We also advise that you apply at the earliest opportunity as the application and selection process may close early, should we receive a sufficient number of applications or that a suitable candidate is appointed.</p> <p>Please note that you must provide the following documents in support of your application by the closing date of 28 April 2023:</p> <ul> <li>Full Transcripts of all degree study or if in final year of study, full transcripts to date</li> <li>Personal Statement outlining your interest in the project</li> <li>CV</li> <li>Funding information: EPSRC CDT Nuclear Energy – GREEN</li> </ul>
Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. The criteria for entry for some research degrees may be higher, for example, several faculties, also require a Masters degree. Applicants are advised to check with the relevant School prior to making an application. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or Graduate School prior to making an application.
The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid. Some schools and faculties have a higher requirement.
<p class="MsoNoSpacing">A highly competitive EPSRC Centre for Doctoral Training in GREEN studentship consisting of the award of fees with a maintenance grant (currently £17,668 in academic session 2022/23) for 4 years.<br /> <br /> This opportunity is open to UK applicants only. All candidates will be placed into the EPSRC Centre for Doctoral Training in GREEN Studentship Competition and selection is based on academic merit.<br /> <br /> Please refer to the <a href="https://www.ukcisa.org.uk/">UKCISA</a> website for information regarding Fee Status for Non-UK Nationals.</p>
<p>For further information about this project, please contact Dr Xiaodong Jia<br /> e: <a href="mailto:EMAIL@leeds.ac.uk">email@example.com</a>, t: +44 (0)113 343 2801.</p> <p>For further information about tyour application, please contact Doctoral College Admissions<br /> e: <a href="mailto:firstname.lastname@example.org">email@example.com</a></p>
<h3 class="heading heading--sm">Linked research areas</h3>