Performance Assessment of Dynamic Downscaling of WRF to Simulate Convective Conditions during Sagebrush Phase 1 Tracer Experiments

Bhimireddy

2020

Environmental Research

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The spatial patterns of the spreading of the COVID19 indicate the possibility of airborne transmission of the coronavirus. As the cough-jet of an infected person is ejected as a plume of infected viral aerosols into the atmosphere, the conditions in the local atmospheric boundary layer together dictate the fate of the infected plume. For the first time - a high-fidelity numerical simulation study - using Weather-Research-Forecast model coupled with the Lagrangian Hybrid Single-Particle Lagrangian Integrated Trajectory model (WRF-HYSPLIT) model has been conducted to track the infected aerosol plume in real-time during March 9-April 6, 2020, in New York City, the epicenter of the coronavirus in the USA for comparing the morning, afternoon and evening release. Atmospheric stability regimes that result in low wind speeds, low level turbulence and cool moist ground conditions favor the transmission of the disease through turbulence energy-containing large-scale horizontal “rolls” and vertical thermal “updrafts” and “downdrafts”. Further, the wind direction is an important factor that dictates the direction of the transport. From the initial time of release, the virus can spread up to 30 min in the air, covering a 200-m radius at a time, moving 1–2 km from the original source.

Section: Methodsmentioning

confidence: 90%

Local atmospheric factors that enhance air-borne dispersion of coronavirus - High-fidelity numerical simulation of COVID19 case study in real-time

Bhimireddy

2020

Environmental Research

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“…1. The model WRF to simulate PBL at scales similar to this study was previously tested and verified [10][11]. Domains D01, D02, and D03 were nested with feedback turned 'on' with horizontal resolutions 36 km (D01), 12 km (D02), and 3 km (D03).…”

Section: Methodsmentioning

confidence: 81%

The Role Of Airborne Moments In The Spread Of The Coronavirus And The Course Of The Pandemic

Bhimireddy

2020

Preprint

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A numerical study using Weather Research Forecast model and Lagrangian HYSPLIT dispersion model was conducted to understand the meteorological factors influencing the transport and mixing of the blob of Corona virus-filled micro-particles (of radius 0.12mm) released into the atmosphere due to coughing, sneezing by infected patient. The study is offered as an important contribution demonstrating the role of local atmospheric dynamics in coronavirus spread during the period of March 9 – April 6, 2020 in New York City, the epicenter of the coronavirus in the USA. The results demonstrate that from the initial time of release, the virus can spread up to 30 minutes in air, covering a 200-m radius at a time, moving 1 – 2 km from the original source. Turbulence energy containing large-scale horizontal “rolls” and vertical thermal “updrafts” and “downdrafts” contribute to transport and advection processes, before small-scale turbulent eddies rapidly mix and dilute virus concentration.

“…Recent modeling efforts have coupled the macroscale turbulence of the ABL with the plume-generated microscale turbulence by downscaling WRF. 11,12 Using higher resolutions, the plume structures including the plume-generated vorticity and well-defined vortex rings have been identified as the key characteristic features of the atmospheric plumes. 13 In the present work, the guiding strategy to use vorticity as a metric to track the plume source is very effective.…”

Section: Methodsmentioning

confidence: 99%

“…9 More complex models can represent the small-scale effects of turbulence by numerically approximating a filtered form of the Navier-Stokes equations. [10][11][12][13] For real-world events, an initial model can be generated from known information around the estimated release location. Local measurements of meteorological data from weather towers and global satellite imaging can be used to obtain this prior information.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Configurable simulation strategies for testing pollutant plume source localization algorithms using autonomous multisensor mobile robots

Lewis

International Journal of Advanced Robotic Systems

2022

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In hazardous situations involving the dispersion of chemical, biological, radiological, and nuclear pollutants, timely containment of the emission is critical. A contaminant disperses as a dynamically evolving plume into the atmosphere, introducing complex difficulties in predicting the dispersion trajectory and potential evacuation sites. Strategies for predictive modeling of rapid contaminant dispersion demand localization of the emission source, a task performed effectively via unmanned mobile-sensing platforms. With vast possibilities in sensor configurations and source-seeking algorithms, platform deployment in real-world applications involves much uncertainty alongside opportunity. This work aims to develop a plume source detection simulator to offer a reliable comparison of source-seeking approaches and performance testing of ground-based mobile-sensing platform configurations prior to experimental field testing. Utilizing ROS, Gazebo, MATLAB, and Simulink, a virtual environment is developed for an unmanned ground vehicle with a configurable array of sensors capable of measuring plume dispersion model data mapped into the domain. For selected configurations, gradient-based and adaptive exploration algorithms were tested for source localization using Gaussian dispersion models in addition to large eddy simulation models incorporating the effects of atmospheric turbulence. A unique global search algorithm was developed to locate the true source with overall success allowing for further evaluation in field experiments. From the observations obtained in simulation, it is evident that source-seeking performance can improve drastically by designing algorithms for global exploration while incorporating measurements of meteorological parameters beyond solely concentration (e.g. wind velocity and vorticity) made possible by the inclusion of high-resolution large eddy simulation plume data.