Details of Grant 

EPSRC Reference:	EP/V036777/1
Title:	Risk EvaLuatIon fAst iNtelligent Tool (RELIANT) for COVID19
Principal Investigator:	Cammarano, Dr A
Other Investigators:	Busse, Dr A Arcucci, Dr R Collu, Professor M Buchan, Dr AG Pain, Professor CC Green, Dr R Falcone, Professor G Coraddu, Dr A Zare-Behtash, Dr H Linden, Professor PF Karimi, Dr N Matar, Professor OK
Researcher Co-Investigators:
Project Partners:	Addenbrooke's Hospital NHS Trust University of Genoa ViseUp
Department:	School of Engineering
Organisation:	University of Glasgow
Scheme:	Standard Research
Starts:	28 October 2020	Ends:	27 October 2022	Value (£):	1,356,505
EPSRC Research Topic Classifications:
EPSRC Industrial Sector Classifications:	Environment Healthcare
Related Grants:
Panel History:
Summary on Grant Application Form
This project brings together unique expertise in Computational and Experimental Fluid Dynamics, Model Reduction and Artificial Intelligence, to identify solutions for the management of people and spaces in the current pandemic and post lockdown. A new interactive tool is proposed that evaluates the risk of infection in the indoor environment from droplets and aerosols generated when breathing, talking, coughing and sneezing. This capability will become more critical as winter approaches and building ventilation will need to be limited for comfort considerations. The fluid dynamic behaviour of droplets and aerosols, the effect of using face masks as well as other parameters such as room volume, ventilation and number of occupants are considered. A datahub capable of storing, curating and managing heterogeneous data from sources internal and external to the project will be created. A synergetic experimental and numerical approach will be undertaken. These will complement the existing literature and data from other EPSRC-funded projects providing suitable datasets with adequate resolution in time and space for all the relevant features. To support experiments and numerical simulations, reduced order models capable of interpolating and extrapolating the scenarios collected in the database will be used. This will permit the estimation of droplet and aerosol concentrations and distributions in unknown scenarios at low-computational cost, in near real-time. A state-of-the-art AI-based framework, incorporating descriptive, predictive and prescriptive techniques will extract the knowledge from the data and drive the decision-making process and provide in near real-time the assessment of risk levels.
Key Findings
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Potential use in non-academic contexts
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Impacts
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Sectors submitted by the Researcher
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Project URL:
Further Information:
Organisation Website:	http://www.gla.ac.uk