| EPSRC Reference: |
EP/V036777/1 |
| Title: |
Risk EvaLuatIon fAst iNtelligent Tool (RELIANT) for COVID19 |
| Principal Investigator: |
Cammarano, Dr A |
| Other Investigators: |
| Falcone, Professor G |
Arcucci, Dr R |
Karimi, Dr N |
| Coraddu, Dr A |
Collu, Professor M |
Buchan, Dr AG |
| Busse, Dr A |
Linden, Professor PF |
Pain, Professor CC |
| Green, Dr R |
Matar, Professor OK |
Zare-Behtash, Dr H |
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| Department: |
School of Engineering |
| Organisation: |
University of Glasgow |
| Scheme: |
Standard Research |
| Starts: |
28 October 2020 |
Ends: |
27 October 2022 |
Value (£): |
1,356,505
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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.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.gla.ac.uk |