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Exploring High-Fidelity Computational Fluid Dynamics Approaches for Airborne Pandemic Risk Mitigation

Event Type: 
Date and Time: 
Friday, June 3, 2022 - 16:15
Building 300, Room 300
Event Sponsor: 
Parviz Moin, Director of Center for Turbulence Research
Dr. Stefan P. Domino

In response to the global SARS-CoV-2 transmission pandemic, the Sandia National Laboratories Rapid Lab-Directed Research and Development (LDRD) COVID-19 initiative, in partnership with a multi-laboratory CARES Act research project, deployed a multi-physics, droplet-laden, turbulent low-Mach simulation tool to model pathogen-containing water droplets that emanate from synthetic human coughing and breathing events. A high-fidelity, low-Mach computational fluid dynamics (CFD) simulation tool that includes evaporating droplets and variable-density, buoyant turbulent flow coupling is well-suited to ascertain transmission probability and supports science-based risk mitigation methods development for airborne infectious diseases such as COVID-19. In this seminar, a description of common transmission pathways for viral infection will be overviewed and highlights how CFD represents a viable tool to support risk quantification and mitigation. The presented work will concentrate on droplet disposition distances that are driven from the pulsed inflow physics description of coughing events in addition to transport distances of the pathogen-laced aerosol plume. A complete overview of the numerical methodology will be provided. Credibility of the simulation tool will be established based on validation techniques that compare experimentally determined droplet evaporation rates and buoyant jet dispersal to simulations. An outdoor open-space configuration including a kneeling humanoid figure that mimics the recent open-space social distance strategy of San Francisco is presented that exercises a dose-response model, which is based on previous SARS coronavirus (SARS-CoV) data, to establish relative risk at various locations subjected to various crosswinds.

Dr. Stefan P. Domino is a computational scientist within 1541, Computational Thermal & Fluid Mechanics, Sandia National Laboratories. His research at the lab encompasses the intersection of physics modeling, development of verification and validation techniques, stable and accurate numerical methods development for turbulent low-Mach reacting flow, and high-performance computing code development. Stefan Domino received his Ph.D. in Chemical Engineering from the University of Utah ASCI Alliance Center, Center for the Simulation of Accidental Fires and Explosion (C-SAFE) in 2000. Stefan Domino also holds an Adjunct Professor appointment at Stanford’s School of Engineering Institute for Computational and Mathematical Engineering (ICME).