UNIVERSITY OF LEEDS Aerobiological Simulations Using Arc 1 Dr Cath Noakes; Dr Andy Sleigh; Dr Carl Gilkeson; Dr Miller Camargo-Valero; Dr Amir Khan

UNIVERSITY OF LEEDS Outline 1) Airborne pathogens and natural ventilation 2) Experimental study 3) Computational Fluid Dynamics 4) Results 5) Conclusions 2/24

UNIVERSITY OF LEEDS PaCE Institute  Pathogen Control Engineering Institute (PaCE) directed by Dr Cath Noakes, School of Civil Engineering.  Aerobiology and Infection Control  Experimental 3/24  Computational

UNIVERSITY OF LEEDS Airborne Pathogens  Great threat to human health:  Swine flu campaign…  Effective ventilation can reduce Spanish Influenza ( ) million deaths Spanish Influenza ( ) million deaths Asian flu ( ) million deaths Asian flu ( ) million deaths infection risk in indoor environments. 4/24

UNIVERSITY OF LEEDS Natural Ventilation Nightingale wards characterised by: Nightingale wards characterised by:  High ceilings, Large windows for natural ventilation. Many of these wards exist within the UK. Many of these wards exist within the UK. 5/24

UNIVERSITY OF LEEDS St. Lukes Hospital AnemometerPartitioned Open ward 6/24

UNIVERSITY OF LEEDS Ventilation Tests – Pulse Injection CO2 sensors 3 x latex balloons 7/24

UNIVERSITY OF LEEDS Measured Ventilation Rates Pulse Delayed peak Decay – fresh air mixing Typical Results: Low wind speed 0.4 m/s ACH = 2/h (~30 m 3 /h) High wind speed 5.0 m/s ACH = 30/h (~450 m 3 /h) 8/24

UNIVERSITY OF LEEDS Flow Visualisation: Inlet Turbulence Turbulence Pulsating flow of varying duration Pulsating flow of varying duration Ingestion followed by extraction Ingestion followed by extraction 9/24

UNIVERSITY OF LEEDS Flow Visualisation: Outlet Less turbulent Less turbulent Controlled extraction Controlled extraction Efficient even for small wind speeds Efficient even for small wind speeds 10/24

UNIVERSITY OF LEEDS Flow Visualisation: Internal High-velocity air entry High-velocity air entry Rapidly decaying air velocities Rapidly decaying air velocities Range of length and time scales Range of length and time scales 11/24

UNIVERSITY OF LEEDS Modelling Challenges  Natural ventilation unpredictable, flows are time- dependent, turbulent and reliant on ambient weather conditions.  Simulations in large 3D air volumes are computationally expensive.  Necessitates a steady-state approach – transient simulations unfeasible.  Boundary conditions (inlets/outlets/walls) require careful consideration. 12/24

UNIVERSITY OF LEEDS Computational Fluid Dynamics  CFD is a powerful tool for indoor airflow simulations.  Utilizes the speed and power of computers to solve governing fluid flow equations. Step 1 = CAD Step 2 = Mesh Step 3 = Solve... 13/24

UNIVERSITY OF LEEDS 2D Coupled Flow 2 m/s Velocity contours Pressure contours 14/24

UNIVERSITY OF LEEDS 3D Model 15/ M 3.3 M 9.9 M

UNIVERSITY OF LEEDS 3D Model – Open-plan Ward 16/24

UNIVERSITY OF LEEDS 3D Model – Partitioned Ward 17/24

UNIVERSITY OF LEEDS Pathogen Transport Tests 18/24

UNIVERSITY OF LEEDS Windward Release – Experiment P1 P2 P3 HW2 Source HW1  Open ward: Even spread, dilution.  Partitioned ward: Cross infection reduced (P1, P2), higher concentrations in central bays. 19/24

UNIVERSITY OF LEEDS Windward Release – CFD Open-wardPartitioned-ward Pathogen contained Mixing smears the pathogen 20/24

UNIVERSITY OF LEEDS Leeward Release – Experiment P2 P1 P3 HW2 Source HW1  Open ward: Even spread, average 15% reduction.  Partitioned ward: Lower average infection risk. Concentration 76% lower for healthcare worker by source. 21/24

UNIVERSITY OF LEEDS Leeward Release – CFD Open-wardPartitioned-ward Partition channelling effect hinders progress of pathogen Efficient extraction, prevents spread of infection 22/24

UNIVERSITY OF LEEDS Conclusions  CFD simulations complement the experimental results.  Qualitative and quantitative comparison good, further model validation required.  Arc1 facilitates significantly improvement compared with previous machines (Abax, Everest, White Rose Grid).  Simulation times up to 4 x faster.  Larger and more complex problems can now be undertaken:  Time-dependent simulations.  Higher-fidelity models (more cells)  Enables computation on ever larger air indoor air spaces such as hospitals/offices. 23/24

UNIVERSITY OF LEEDS Thank you for Listening Questions? 24/24