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FIERCE: Fast IntERactive simulation for Crowds and Environments

PGR-P-660

Key facts

Type of research degree
PhD
Application deadline
Friday 17 April 2020
Project start date
Thursday 1 October 2020
Country eligibility
UK and EU
Funding
Competition funded
Source of funding
Research council
Supervisors
Dr He Wang
Additional supervisors
Dr Amirul Khan
Schools
School of Civil Engineering, 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. 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. 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. 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. The specific form of outcomes includes publications of top venues, open-source project including data and code, and potentially plug-ins for existing pipeline and software platforms.

<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. Similarly, Dr Khan&rsquo;s group provides a local workstation with two Nvidia Tesla K40. On the school level, Computing provides two servers with dedicated GPUs for deep learning. On the university level, ARC3 GPU clusters will be also accessible to the student.</p> <p>The supervision team is highly inter-disciplinary with both supervisors at the top of their respective fields. Dr He Wang joined School of Computing as a Lecturer, after working as a Research Associate at the University of Edinburgh and a Senior Research Associate at Disney Research Los Angeles. His research area is computer graphics and vision, where he uses machine learning to understand, model and analyse motions. He will provide the expertise on using machine learning to combine/accelerate fluid and crowd simulation. Dr Khan&rsquo;s&nbsp;research interests span from the theoretical and experimental investigations into the fundamental aspects of fluid turbulence and turbulent dispersion to nonlinear dynamics in cavity flows, as well as novel computational methods for fluid flow. He will provide insights on the fundamentals of fluid dynamics.</p>

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

<p>Formal applications for research degree study should be made online through the&nbsp;<a href="https://eps.leeds.ac.uk/computing-research-degrees/doc/apply">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 &ldquo;FIERCE: Fast IntERactive simulation for Crowds and Environments&rdquo; as well as <a href="https://eps.leeds.ac.uk/computing/staff/868/dr-he-wang">Dr He Wang</a> and <a href="https://eps.leeds.ac.uk/civil-engineering/staff/502/dr-amirul-khan">Dr Amirual Khan</a>&nbsp;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 6.0 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.

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

<p><strong>UK/EU</strong> &ndash;&nbsp;Engineering &amp; Physical Sciences Research Council Studentship&nbsp;for 3.5 years. A full standard studentship consists of academic fees (&pound;4,600 in Session 2020/21), together with a maintenance grant (&pound;15,009 in Session 2019/20) paid at standard Research Council rates. UK applicants will be eligible for a full award paying tuition fees and maintenance. European Union applicants will be eligible for an award paying tuition fees only, except in exceptional circumstances, or where residency has been established for more than 3 years prior to the start of the course.&nbsp;&nbsp;Funding is awarded on a competitive basis.</p>

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

<p>For further information regarding the application procedure, please contact Doctoral College Admissions:<br /> e: <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, please contact:<br /> Dr He Wang,&nbsp;e:&nbsp;<a href="mailto:h.e.wang@leeds.ac.uk">h.e.wang@leeds.ac.uk</a><br /> Dr Amirul Khan, e: <a href="mailto:A.Khan@leeds.ac.uk">A.Khan@leeds.ac.uk</a></p>


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