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Details of Grant 

EPSRC Reference: EP/V000756/1
Title: deeP redUced oRder predIctive Fluid dYnamics model (PURIFY)
Principal Investigator: Xiao, Dr D
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: College of Engineering
Organisation: Swansea University
Scheme: New Investigator Award
Starts: 22 March 2021 Ends: 21 September 2023 Value (£): 315,689
EPSRC Research Topic Classifications:
Artificial Intelligence Fluid Dynamics
Numerical Analysis
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:
Panel DatePanel NameOutcome
05 Aug 2020 Engineering Prioritisation Panel Meeting 5 and 6 August 2020 Announced
07 Apr 2020 Engineering Prioritisation Panel Meeting 7 and 8 April 2020 Deferred
Summary on Grant Application Form
Simulating large area urban turbulence flows in real time accurately remains a pressing challenge, yet to be addressed. Reduce Order Modelling (ROM) provides a means of real time simulation; traditional model reduction methods such as balanced truncation, the reduced-basis method, and (balanced) proper orthogonal decomposition (POD), have been developed. However challenges remain: the traditional methods restrict the state to evolve in a linear subspace; in addition, big area simulation cannot be conducted in real time. In addition, it is hard to define how accuracy the derived ROM is since there is no appropriate error estimator derived for the ROM. Also, existing ROM is either physically or data-driven and there is no physically informed data-driven ROM. Advancing in machine learning and in observational and simulation capabilities offer an opportunity to integrate simulation and data science approaches more intensively.

The proposed research offers scientific advances within the field of fluid flow modelling in order to underpin the establishment of novel sophisticated tools that will allow real-time simulations and prediction of air flows (and subsequently personal exposure to air pollutants); principal features include real-time air pollution predictions for next few hours. This can help minimise exposure to the population, especially vulnerable young/elderly patients. Individuals will be able to decide whether to go outside or not, exercise or make their daily plans.

The proposed new model combines physical models and data sciences technologies, in particular, the deep learning for predicting the non-linearity of turbulence flows, thus making the air flows predictions more reliable and accurate. An autoencoder deep neural network will potentially capture the non-linearity of urban turbulence flows and more accurate than the traditional model reduction methods (e.g. POD). Commercialisation of the research outputs will be undertaken in partnership with VortexIoT, Looker Tech and Spire Global. These are international leading companies in the fields of sensor technology, software company and provision of technical professional services.

The approach is based on an advanced fast-running computational model to manage and predict the airflow, air quality in a city, guide effective responses in emergencies and help people reduce the air pollution exposure time. The development of novel deep learning ROM, reducing computational times by several orders of magnitude, will make currently unsurmountable problems tractable:- e.g. detailed air flows through a big area or an entire city. The specific technology that distinguishes this project are the potential use of deep learning reduced order modelling, new computational domain decomposition, new error analysis for DLROM and data assimilation method.

The research objectives are: to develop deep learning based ROM framework with domain decomposition methods; to develop ROM framework with Autoencoder network that will project the system into a non-linear subspace, thus increasing accuracy; to develop ROMs with variable material properties such as variable initial or boundary conditions (different wind direction for example); ROM based data assimilation and optimisation methods; to perform an error analysis and optimal improvement for ROMs using machine learning methods.

The research has substantive health, environmental and economic impacts. Beneficiaries may include the general public through fast response to avoid exposure to air pollution technical professional services consultancy companies, local and national government environment bodies (e.g. NRW in Wales), computational engineering companies who will benefit from more efficient fluid flow model outcomes. In addition, public sector (spend budget savings in health) and air quality sensor device manufacturers incorporating the proposed modelling approach to enhance their offering (increased profits from sales).
Key Findings
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Organisation Website: http://www.swan.ac.uk