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Aerobiological Simulations Using Arc 1

Aerobiological Simulations Using Arc 1. Dr Cath Noakes ; Dr Andy Sleigh; Dr Carl Gilkeson; Dr Miller Camargo -Valero; Dr Amir Khan. Outline. Airborne pathogens and natural ventilation Experimental study Computational Fluid Dynamics Results Conclusions. PaCE Institute.

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Aerobiological Simulations Using Arc 1

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  1. Aerobiological Simulations Using Arc 1 Dr CathNoakes; Dr Andy Sleigh; Dr Carl Gilkeson; Dr Miller Camargo-Valero; Dr Amir Khan

  2. Outline • Airborne pathogens and natural ventilation • Experimental study • Computational Fluid Dynamics • Results • Conclusions

  3. PaCE Institute • Pathogen Control Engineering Institute (PaCE) directed by Dr Cath Noakes, School of Civil Engineering. • Aerobiology and Infection Control • Experimental • Computational www.engineering.leeds.ac.uk/pace

  4. Airborne Pathogens • Great threat to human health: • Swine flu campaign… • Effective ventilation can reduce • Spanish Influenza (1918-1920) 50 - 100 million deaths • Asian flu (1957-1958) 1.5 - 2 million deaths • infection risk in indoor • environments.

  5. Natural Ventilation 1900 1910 • Nightingale wards characterised by: • High ceilings, Large windows for natural ventilation. • Many of these wards exist within the UK.

  6. St. Lukes Hospital Open ward Partitioned Anemometer

  7. Ventilation Tests – Pulse Injection 3 x latex balloons CO2 sensors

  8. Measured Ventilation Rates • Typical Results: Delayed peak Low wind speed 0.4 m/s ACH = 2/h (~30 m3/h) Decay – fresh air mixing High wind speed 5.0 m/s ACH = 30/h (~450 m3/h) Pulse

  9. Flow Visualisation: Inlet • Turbulence • Pulsating flow of varying duration • Ingestion followed by extraction

  10. Flow Visualisation: Outlet • Less turbulent • Controlled extraction • Efficient even for small wind speeds

  11. Flow Visualisation: Internal • High-velocity air entry • Rapidly decaying air velocities • Range of length and time scales

  12. 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.

  13. 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...

  14. 2D Coupled Flow Velocity contours 2 m/s Pressure contours 2 m/s

  15. 3D Model 1.3 M 3.3 M 9.9 M

  16. 3D Model – Open-plan Ward

  17. 3D Model – Partitioned Ward

  18. Pathogen Transport Tests

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

  20. Windward Release – CFD Open-ward Partitioned-ward Mixing smears the pathogen Pathogen contained

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

  22. Leeward Release – CFD Open-ward Partitioned-ward Efficient extraction, prevents spread of infection Partition channelling effect hinders progress of pathogen

  23. 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.

  24. Thank you for Listening Questions?

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