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FIERCE: Fast IntERactive simulation for Crowds and Environments-G

PGR-P-770

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Key facts

Type of research degree
PhD
Application deadline
Ongoing deadline
Country eligibility
International (open to all nationalities, including the UK)
Funding
Competition funded
Source of funding
Other
Supervisors
Dr He Wang
Schools
School of Computing
<h2 class="heading hide-accessible">Summary</h2>

Understanding and predicting airflows in enclosed environments not only lowers physical dangers in extreme situations but also has a wider and long-term impact on safety, thermal comfort and energy efficiency. Current research uses Computational Fluid Dynamics (CFD) methods for airflow simulations. It has been used for optimising building designing or improving existing ones. However, few people model airflows with crowds together. Coupling two complex systems with different dynamics imposes a great challenge.<br /> <br /> The project aims to combine two systems together into a single framework. Since both are computationally expensive, data-driven methods will be leveraged, especially cutting-edge machine/deep learning methods. Deep learning (DL) has been successful in many problems. Recently, there is a surge in using supervised DL for accelerating CFD in computer graphics and crowd prediction. However, the effort has only been made in two separate fields. <br /> <br /> The project will first extend a DL-based fluid simulator for airflow simulation from fixed boundary conditions (as it is in the literature) to dynamic boundary conditions to accommodate crowds. Next, a data-driven crowd predictor will be incorporated. Finally, a single framework that can predict crowd movements and the corresponding airflows will be proposed.<br /> <br /> The major outcome of the project is a framework that can interactively simulate airflows with crowds. To this end, it will identify the performance bottlenecks in both CFD and crowd simulation in relevant scenarios. It will also propose new methods that can leverage big data and accelerate simulation and prediction. <br /> <br /> The specific form of outcomes includes publications of top venues, an open-source project including data and code, and potentially plug-ins for existing pipeline and software platforms.<br /> <br /> The project is currently accepting PhD applications every year.

<h2 class="heading hide-accessible">Full description</h2>

<p>Have you ever felt uncomfortable or suffocated in crowds in a shopping mall? Do you know how fast an airborne contagious disease can spread in the atrium of a hospital? The indoor air quality is crucial and sometimes life-threateningly important. Being able to understand and predict airflows in enclosed environments not only lowers physical dangers in extreme situations but also has a wider and long-term impact on safety, thermal comfort and energy efficiency.</p> <p>Computational Fluid Dynamics (CFD) is the major tool for airflow simulations. It has been used for optimising the design of new buildings or improving existing ones.&nbsp; However, few people model airflows with crowds together. CFD is already computationally expensive and slow, and crowd simulation/prediction itself is difficulty too. Coupling two complex systems with different dynamics would impose a great challenge.</p> <p>This is the challenge the project attempts to address.&nbsp; The potential solution lies within data-driven methods. Recently, machine/deep learning has had massive successes in different domains, on a variety of problems, previously modelled based on deterministic approaches (such as fluids and crowds). This project will look into solutions bypassing traditional methods in CFD and crowds by leveraging cutting-edge deep learning approaches.</p> <p>The project relies heavily on GPUs and powerful computers. We offer computational resources at three levels. On the group level, Dr Wang&rsquo;s group provides several&nbsp;local workstations with 8&nbsp;top Nvidia cards. On the school level, Computing provides two servers with dedicated GPUs for deep learning. On the university level, ARC3/4 GPU clusters will be also accessible to the student.</p>

<h2 class="heading">How to apply</h2>

<p>Formal applications for research degree study should be made online through the&nbsp;<a href="http://www.leeds.ac.uk/rsa/prospective_students/apply/I_want_to_apply.html">University&#39;s website</a>. Please state clearly in the research information section&nbsp;that the research degree you wish to be considered for is &lsquo;FIERCE: Fast IntERactive simulation for Crowds and Environments-G&rsquo; as well as&nbsp;<a href="https://eps.leeds.ac.uk/computing/staff/868/dr-he-wang">Dr He Wang</a> as your proposed supervisor.</p> <p>If English is not your first language, you must provide evidence that you meet the University&#39;s minimum English language requirements (below).</p> <p><em>We welcome applications from all suitably-qualified candidates, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates. All scholarships will be awarded on the basis of merit.</em></p>

<h2 class="heading heading--sm">Entry requirements</h2>

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.

<h2 class="heading heading--sm">English language requirements</h2>

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.5 overall with at least 6.5 in writing and at 6.0 in reading, 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.

<h2 class="heading">Funding on offer</h2>

<p><strong>Self-Funding Students are welcome to apply.</strong></p> <p><strong>UK&nbsp;students</strong>&nbsp;&ndash;&nbsp;The&nbsp;<a href="https://phd.leeds.ac.uk/funding/53-school-of-computing-scholarship">School of Computing Scholarship&nbsp;</a>are available to UK applicants. <a href="https://phd.leeds.ac.uk/funding/60-alumni-bursary">Alumni Bursary</a> is available to graduates of the University of Leeds.</p> <p><strong>Non-UK students</strong>&nbsp;&ndash; The&nbsp;<a href="https://phd.leeds.ac.uk/funding/53-school-of-computing-scholarship">School of Computing Scholarship&nbsp;</a>&nbsp;is available to support the additional academic fees of international applicants. The&nbsp;<a href="https://phd.leeds.ac.uk/funding/73-leeds-marshall-scholarship">Leeds Marshall Scholarship</a>&nbsp;is available to support US citizens. <a href="https://phd.leeds.ac.uk/funding/60-alumni-bursary">Alumni Bursary</a> is available to graduates of the University of Leeds.</p>

<h2 class="heading">Contact details</h2>

<p>For further information regarding your application,&nbsp;please contact Doctoral College Admissions:<br /> e:&nbsp;<a href="mailto:phd@engineering.leeds.ac.uk">phd@engineering.leeds.ac.uk</a>, t: +44 (0)113 343 5057.</p> <p>For further information regarding the project,&nbsp;please contact Dr He Wang:<br /> e:&nbsp;<a href="mailto:H.E.Wang@leeds.ac.uk">H.E.Wang@leeds.ac.uk</a></p>


<h3 class="heading heading--sm">Linked research areas</h3>