Som Dutta
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The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
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Som Dutta has a total of 81 co-authors. Here are the top co-authors by number of shared publications:
Ketan Mittal
9 shared publicationsAnton Vernet
Department d'Enginyeria Mecànica, Universitat Rovira i Virgili, Av. Països Catalans 26, Tarragona 43007, Spain
8 shared publicationsFerran Gisbert
Department d'Enginyeria Mecànica, Universitat Rovira i Virgili, Av. Països Catalans 26, Tarragona 43007, Spain
8 shared publications- Alexandre Fabregat
Departament d'Enginyeria Mecànica, Universitat Rovira i Virgili , Av. Països Catalans, 26, 43007 Tarragona
5 shared publications - Jordi Pallares
Universitat Rovira i Virgili
5 shared publications - Marcelo Horacio Garcia
Ven Te Chow Hydrosystems Laboratory Department of Civil and Environmental Engineering Grainger College of Engineering University of Illinois Urbana‐Champaign Urbana IL USA
5 shared publications
Latest Publications
- Open Access
- 4 June 2026
- Published by Elsevier in SSRN Electronic Journal
Efficient Techniques in Spectral Element Methods for ResolvingTurbulent Flows over Roughness
High-order spectral element methods (SEM) offer an accurate representation of the solution across a wide range of turbulent flows. However, they are often limited by computational cost when resolving flow over roughness, due to the challenges associated with meshing complex and varied geometries. This study is the first to compare different SEM simulations that adopt the overlapping grid approach and adaptive mesh refinement (AMR) techniques. Specifically, we evaluated their capability to resolve a broad range of roughness scales, as well as their computational performance. The comparison was carried out using the classical transverse square bar roughness configuration as described by \citet{leonardi2003direct}. Our results showed that AMR achieved better accuracy while using approximately ${1/8}^\text{th}$ degrees of freedom required by a conforming mesh and approximately one-half of the overlapping method. Although the required number of degrees of freedom for adaptive non-conforming grid is lower than that of overlapping-Schwarz method, the computational cost per time unit for both AMR and overlapping method with Schwarz iteration Q=1 is similar, and it is improved by a factor of $3.5$ compared to conforming grid. Our results showed that AMR can resolve the flow without requiring prior knowledge of flow scales and overlapping Schwarz approach facilitates to separate high resolution regions from the low resolution regions to yield fewer elements. However, both offer similar computational performance compared to conformal methods.- 4 June 2026
- Published by Elsevier in SSRN Electronic Journal
Open AccessEfficient Techniques in Spectral Element Methods for ResolvingTurbulent Flows over Roughness
High-order spectral element methods (SEM) offer an accurate representation of the solution across a wide range of turbulent flows. However, they are often limited by computational cost when resolving flow over roughness, due to the challenges associated with meshing complex and varied geometries. This study is the first to compare different SEM simulations that adopt the overlapping grid approach and adaptive mesh refinement (AMR) techniques. Specifically, we evaluated their capability to resolve a broad range of roughness scales, as well as their computational performance. The comparison was carried out using the classical transverse square bar roughness configuration as described by \citet{leonardi2003direct}. Our results showed that AMR achieved better accuracy while using approximately ${1/8}^\text{th}$ degrees of freedom required by a conforming mesh and approximately one-half of the overlapping method. Although the required number of degrees of freedom for adaptive non-conforming grid is lower than that of overlapping-Schwarz method, the computational cost per time unit for both AMR and overlapping method with Schwarz iteration Q=1 is similar, and it is improved by a factor of $3.5$ compared to conforming grid. Our results showed that AMR can resolve the flow without requiring prior knowledge of flow scales and overlapping Schwarz approach facilitates to separate high resolution regions from the low resolution regions to yield fewer elements. However, both offer similar computational performance compared to conformal methods. - Open Access
Effect of initial Rayleigh mode on drop deformation and breakup under impulsive acceleration
One of the fundamental ways of representing a droplet shape is through its Rayleigh-mode decomposition, in which each mode corresponds to a distinct surface-energy content. The influence of these modes on free oscillation dynamics has been studied extensively; however, their role in droplet deformation, breakup, and fragmentation under impulsive acceleration remains largely unexplored. Here we systematically quantify how prescribed initial axisymmetric Rayleigh modes affect the deformation and breakup of an impulsively accelerated drop. Using experimentally validated, VOF-based multiphase direct numerical simulations, we isolate the coupled effects of finite-amplitude surface oscillation modes and the associated initial surface-energy state by initializing drops with well-defined $(n,0)$ modes (and phases) while conserving volume at finite amplitudes. We show that breakup is governed not simply by the initial drag of the imposed shape, but by the dynamic coupling between the free modal oscillations and the forced aerodynamic (or shear-driven) deformation: constructive superposition can strongly amplify deformation, whereas destructive superposition can stabilize the drop even under otherwise disruptive forcing. Across all systems studied, the outcome is controlled by how efficiently the external work is partitioned into recoverable oscillatory energy versus centre-of-mass translation and viscous dissipation, with viscosity and density ratio acting as key mediators that respectively damp modal interactions and restrict the time window for energy uptake.Open AccessEffect of initial Rayleigh mode on drop deformation and breakup under impulsive acceleration
One of the fundamental ways of representing a droplet shape is through its Rayleigh-mode decomposition, in which each mode corresponds to a distinct surface-energy content. The influence of these modes on free oscillation dynamics has been studied extensively; however, their role in droplet deformation, breakup, and fragmentation under impulsive acceleration remains largely unexplored. Here we systematically quantify how prescribed initial axisymmetric Rayleigh modes affect the deformation and breakup of an impulsively accelerated drop. Using experimentally validated, VOF-based multiphase direct numerical simulations, we isolate the coupled effects of finite-amplitude surface oscillation modes and the associated initial surface-energy state by initializing drops with well-defined $(n,0)$ modes (and phases) while conserving volume at finite amplitudes. We show that breakup is governed not simply by the initial drag of the imposed shape, but by the dynamic coupling between the free modal oscillations and the forced aerodynamic (or shear-driven) deformation: constructive superposition can strongly amplify deformation, whereas destructive superposition can stabilize the drop even under otherwise disruptive forcing. Across all systems studied, the outcome is controlled by how efficiently the external work is partitioned into recoverable oscillatory energy versus centre-of-mass translation and viscous dissipation, with viscosity and density ratio acting as key mediators that respectively damp modal interactions and restrict the time window for energy uptake. - 1 December 2025
- Published by AIP Publishing in Physics of Fluids
- Vol. 37 (12)
Bursting at the surface: Fine droplet generation from an exploding water drop in hot oil
The explosion of a water drop near the surface of hot cooking oil may eject fine droplets into the air. Although such a process is often observed in activity in the kitchen, the formation process of these fine droplets has not been investigated extensively [Kiyama et al., “Morphology of bubble dynamics and sound in heated oil,” Phys. Fluids 34, 062107 (2022)]. In this study, we use a high-speed camera to visualize this phenomenon, showing that the size of these droplets are of the order of 10 μm. The droplet formation process is divided into two sequential stages. The first stage, which is associated with the explosion of a submerged water drop and following rupture of the air-oil interface, largely governs the formation of fine droplets. The magnitude of the fine droplets formation seems to be related through the dimensionless relative distance from the bubble formed on the drop to the oil surface. The formation of the small droplets is driven by the Rayleigh–Taylor instability, which is similar to the fragmentation of a droplet by a laser pulse [Klein et al., “Drop fragmentation by laser-pulse impact,” J. Fluid Mech. 893, A7 (2020)]. The second stage is associated with the splash dynamics. Liquid ligaments dispatch relatively larger droplets at later times. We also recorded sounds in the surrounding air, in which the impulsive sounds are related to rupturing of a bag, which is formed due to a sudden extension of exploded drop. The subsequent splash dynamicsalso have unique acoustic signals at a higher acoustic frequency than that of the enclosed cavity after the surface sealing. These characteristics suggests the acoustics may be useful for a better understanding of this complex fluid phenomenon including fine droplets sizes and cavity closure.
- 1 December 2025
- Published by AIP Publishing in Physics of Fluids
- Vol. 37 (12)
Bursting at the surface: Fine droplet generation from an exploding water drop in hot oil
The explosion of a water drop near the surface of hot cooking oil may eject fine droplets into the air. Although such a process is often observed in activity in the kitchen, the formation process of these fine droplets has not been investigated extensively [Kiyama et al., “Morphology of bubble dynamics and sound in heated oil,” Phys. Fluids 34, 062107 (2022)]. In this study, we use a high-speed camera to visualize this phenomenon, showing that the size of these droplets are of the order of 10 μm. The droplet formation process is divided into two sequential stages. The first stage, which is associated with the explosion of a submerged water drop and following rupture of the air-oil interface, largely governs the formation of fine droplets. The magnitude of the fine droplets formation seems to be related through the dimensionless relative distance from the bubble formed on the drop to the oil surface. The formation of the small droplets is driven by the Rayleigh–Taylor instability, which is similar to the fragmentation of a droplet by a laser pulse [Klein et al., “Drop fragmentation by laser-pulse impact,” J. Fluid Mech. 893, A7 (2020)]. The second stage is associated with the splash dynamics. Liquid ligaments dispatch relatively larger droplets at later times. We also recorded sounds in the surrounding air, in which the impulsive sounds are related to rupturing of a bag, which is formed due to a sudden extension of exploded drop. The subsequent splash dynamicsalso have unique acoustic signals at a higher acoustic frequency than that of the enclosed cavity after the surface sealing. These characteristics suggests the acoustics may be useful for a better understanding of this complex fluid phenomenon including fine droplets sizes and cavity closure.
- Open Access
Quantifying Out-of-Training Uncertainty of Neural-Network based Turbulence Closures
Neural-Network (NN) based turbulence closures have been developed for being used as pre-trained surrogates for traditional turbulence closures, with the aim to increase computational efficiency and prediction accuracy of CFD simulations. The bottleneck to the widespread adaptation of these ML-based closures is the relative lack of uncertainty quantification (UQ) for these models. Especially, quantifying uncertainties associated with out-of-training inputs, that is when the ML-based turbulence closures are queried on inputs outside their training data regime. In the current paper, a published algebraic turbulence closure1 has been utilized to compare the quality of epistemic UQ between three NN-based methods and Gaussian Process (GP). The three NN-based methods explored are Deep Ensembles (DE), Monte-Carlo Dropout (MCD), and Stochastic Variational Inference (SVI). In the in-training results, we find the exact GP performs the best in accuracy with a Root Mean Squared Error (RMSE) of $2.14 \cdot 10^{-5}$ followed by the DE with an RMSE of $4.59 \cdot 10^{-4}$. Next, the paper discusses the performance of the four methods for quantifying out-of-training uncertainties. For performance, the Exact GP yet again is the best in performance, but has similar performance to the DE in the out-of-training regions. In UQ accuracy for the out-of-training case, SVI and DE hold the best miscalibration error for one of the cases. However, the DE performs the best in Negative Log-Likelihood for both out-of-training cases. We observe that for the current problem, in terms of accuracy GP > DE > SV I > MCD. The DE results are relatively robust and provide intuitive UQ estimates, despite performing naive ensembling. In terms of computational cost, the GP is significantly higher than the NN-based methods with a $O(n^3)$ computational complexity for each training stepOpen AccessQuantifying Out-of-Training Uncertainty of Neural-Network based Turbulence Closures
Neural-Network (NN) based turbulence closures have been developed for being used as pre-trained surrogates for traditional turbulence closures, with the aim to increase computational efficiency and prediction accuracy of CFD simulations. The bottleneck to the widespread adaptation of these ML-based closures is the relative lack of uncertainty quantification (UQ) for these models. Especially, quantifying uncertainties associated with out-of-training inputs, that is when the ML-based turbulence closures are queried on inputs outside their training data regime. In the current paper, a published algebraic turbulence closure1 has been utilized to compare the quality of epistemic UQ between three NN-based methods and Gaussian Process (GP). The three NN-based methods explored are Deep Ensembles (DE), Monte-Carlo Dropout (MCD), and Stochastic Variational Inference (SVI). In the in-training results, we find the exact GP performs the best in accuracy with a Root Mean Squared Error (RMSE) of $2.14 \cdot 10^{-5}$ followed by the DE with an RMSE of $4.59 \cdot 10^{-4}$. Next, the paper discusses the performance of the four methods for quantifying out-of-training uncertainties. For performance, the Exact GP yet again is the best in performance, but has similar performance to the DE in the out-of-training regions. In UQ accuracy for the out-of-training case, SVI and DE hold the best miscalibration error for one of the cases. However, the DE performs the best in Negative Log-Likelihood for both out-of-training cases. We observe that for the current problem, in terms of accuracy GP > DE > SV I > MCD. The DE results are relatively robust and provide intuitive UQ estimates, despite performing naive ensembling. In terms of computational cost, the GP is significantly higher than the NN-based methods with a $O(n^3)$ computational complexity for each training step - Open Access
- 10 April 2025
- Published by Elsevier in Journal of Hydrology: Regional Studies
- Vol. 59
Seasonal water and salt cycling in the Great Salt Lake after opening the new causeway breach
- 10 April 2025
- Published by Elsevier in Journal of Hydrology: Regional Studies
- Vol. 59
Open AccessSeasonal water and salt cycling in the Great Salt Lake after opening the new causeway breach
- Open Access
Quantifying Model Uncertainty of Neural Network-based Turbulence Closures
With increasing computational demand, Neural-Network (NN) based models are being developed as pre-trained surrogates for different thermohydraulics phenomena. An area where this approach has shown promise is in developing higher-fidelity turbulence closures for computational fluid dynamics (CFD) simulations. The primary bottleneck to the widespread adaptation of these NN-based closures for nuclear-engineering applications is the uncertainties associated with them. The current paper illustrates three commonly used methods that can be used to quantify model uncertainty in NN-based turbulence closures. The NN model used for the current study is trained on data from an algebraic turbulence closure model. The uncertainty quantification (UQ) methods explored are Deep Ensembles, Monte-Carlo Dropout, and Stochastic Variational Inference (SVI). The paper ends with a discussion on the relative performance of the three methods for quantifying epistemic uncertainties of NN-based turbulence closures, and potentially how they could be further extended to quantify out-of-training uncertainties. For accuracy in turbulence modeling, paper finds Deep Ensembles have the best prediction accuracy with an RMSE of $4.31\cdot10^{-4}$ on the testing inputs followed by Monte-Carlo Dropout and Stochastic Variational Inference. For uncertainty quantification, this paper finds each method produces unique Epistemic uncertainty estimates with Deep Ensembles being overconfident in regions, MC-Dropout being under-confident, and SVI producing principled uncertainty at the cost of function diversity.Open AccessQuantifying Model Uncertainty of Neural Network-based Turbulence Closures
With increasing computational demand, Neural-Network (NN) based models are being developed as pre-trained surrogates for different thermohydraulics phenomena. An area where this approach has shown promise is in developing higher-fidelity turbulence closures for computational fluid dynamics (CFD) simulations. The primary bottleneck to the widespread adaptation of these NN-based closures for nuclear-engineering applications is the uncertainties associated with them. The current paper illustrates three commonly used methods that can be used to quantify model uncertainty in NN-based turbulence closures. The NN model used for the current study is trained on data from an algebraic turbulence closure model. The uncertainty quantification (UQ) methods explored are Deep Ensembles, Monte-Carlo Dropout, and Stochastic Variational Inference (SVI). The paper ends with a discussion on the relative performance of the three methods for quantifying epistemic uncertainties of NN-based turbulence closures, and potentially how they could be further extended to quantify out-of-training uncertainties. For accuracy in turbulence modeling, paper finds Deep Ensembles have the best prediction accuracy with an RMSE of $4.31\cdot10^{-4}$ on the testing inputs followed by Monte-Carlo Dropout and Stochastic Variational Inference. For uncertainty quantification, this paper finds each method produces unique Epistemic uncertainty estimates with Deep Ensembles being overconfident in regions, MC-Dropout being under-confident, and SVI producing principled uncertainty at the cost of function diversity. - Open Access
- 16 April 2024
- Published by American Physical Society (APS) in Physical Review E
- Vol. 109 (4) , 045107
Extremely large water droplet impact onto a deep liquid pool
Most studies of droplet impact on liquid pools focus on droplet diameters up to the capillary length (0.27 cm). We break from convention and study extremely large water droplets (1 to 6 cm diameter) falling into a pool of water. We demonstrate that the depth and width of the cavity formed by large droplet impact is greatly influenced by the deformed shape of the droplet at impact (i.e., prolate, spherical, and oblate), and larger droplets amplify this behavior by flattening before impact. In particular, the maximum cavity depth is a function of the Froude number and axis ratio of the droplet just before impact. Further, the cavity depth is more dependent on the droplet height than width, and the maximum cavity diameter is independent of the droplet height. In general, we observe that more oblate droplets result in decreasing cavity depths for a fixed liquid volume. This is because an increase in horizontal droplet diameter results in a reduced impact energy flux and therefore reduced cavity depth. Published by the American Physical Society 2024
- 16 April 2024
- Published by American Physical Society (APS) in Physical Review E
- Vol. 109 (4) , 045107
Open AccessExtremely large water droplet impact onto a deep liquid pool
Most studies of droplet impact on liquid pools focus on droplet diameters up to the capillary length (0.27 cm). We break from convention and study extremely large water droplets (1 to 6 cm diameter) falling into a pool of water. We demonstrate that the depth and width of the cavity formed by large droplet impact is greatly influenced by the deformed shape of the droplet at impact (i.e., prolate, spherical, and oblate), and larger droplets amplify this behavior by flattening before impact. In particular, the maximum cavity depth is a function of the Froude number and axis ratio of the droplet just before impact. Further, the cavity depth is more dependent on the droplet height than width, and the maximum cavity diameter is independent of the droplet height. In general, we observe that more oblate droplets result in decreasing cavity depths for a fixed liquid volume. This is because an increase in horizontal droplet diameter results in a reduced impact energy flux and therefore reduced cavity depth. Published by the American Physical Society 2024
- Open Access
- 1 February 2023
- Published by The Royal Society in Royal Society Open Science
- Vol. 10 (2) , 220684
Multi-fidelity modelling of shark skin denticle flows: insights into drag generation mechanisms
We investigate the flow over smooth (non-ribletted) shark skin denticles in an open-channel flow using direct numerical simulation (DNS) and two Reynolds averaged Navier–Stokes (RANS) closures. Large peaks in pressure and viscous drag are observed at the denticle crown edges, where they are exposed to high-speed fluid which penetrates between individual denticles, increasing shear and turbulence. Strong lift forces lead to a positive spanwise torque acting on individual denticles, potentially encouraging bristling if the denticles were not fixed. However, DNS predicts that denticles ultimately increase drag by 58% compared to a flat plate. Good predictions of drag distributions are obtained by RANS models, although an underestimation of turbulent kinetic energy production leads to an underprediction of drag. Nevertheless, RANS methods correctly predict trends in the drag data and the regions contributing most to viscous and pressure drag. Subsequently, RANS models are used to investigate the dependence of drag on the flow blockage ratio (boundary layer to roughness height ratio), finding that the drag increase due to denticles is halved when the blockage ratio δ / h is increased from 14 to 45. Our results provide an integrated understanding of the drag over non-ribletted denticles, enabling existing diverse drag data to be explained.
- 1 February 2023
- Published by The Royal Society in Royal Society Open Science
- Vol. 10 (2) , 220684
Open AccessMulti-fidelity modelling of shark skin denticle flows: insights into drag generation mechanisms
We investigate the flow over smooth (non-ribletted) shark skin denticles in an open-channel flow using direct numerical simulation (DNS) and two Reynolds averaged Navier–Stokes (RANS) closures. Large peaks in pressure and viscous drag are observed at the denticle crown edges, where they are exposed to high-speed fluid which penetrates between individual denticles, increasing shear and turbulence. Strong lift forces lead to a positive spanwise torque acting on individual denticles, potentially encouraging bristling if the denticles were not fixed. However, DNS predicts that denticles ultimately increase drag by 58% compared to a flat plate. Good predictions of drag distributions are obtained by RANS models, although an underestimation of turbulent kinetic energy production leads to an underprediction of drag. Nevertheless, RANS methods correctly predict trends in the drag data and the regions contributing most to viscous and pressure drag. Subsequently, RANS models are used to investigate the dependence of drag on the flow blockage ratio (boundary layer to roughness height ratio), finding that the drag increase due to denticles is halved when the blockage ratio δ / h is increased from 14 to 45. Our results provide an integrated understanding of the drag over non-ribletted denticles, enabling existing diverse drag data to be explained.
- Open Access
On the Threshold of Drop Fragmentation under Impulsive Acceleration
We examine the complete landscape of parameters which affect secondary breakup of a Newtonian droplet under impulsive acceleration. A Buckingham-Pi analysis reveals that the critical Weber number $\mathit{We}_\mathit{cr}$ for a non-vibrational breakup depends on the density ratio $(\rho)$, the drop $(\mathit{Oh}_d)$ and the ambient $(\mathit{Oh}_o)$ Ohnesorge numbers. Volume of fluid (VOF) multiphase flow simulations are performed using Basilisk to conduct a reasonably complete parametric sweep of the non-dimensional parameters involved. It is found that, contrary to current consensus, even for $\mathit{Oh}_d \leq 0.1$, a decrease in $\mathit{Oh}_d$ has a substantial impact on the breakup morphology, motivating plume formation. In addition to $\rho$, $\mathit{Oh}_o$ also affects the balance between pressure differences between a droplet's pole and its periphery, and the shear stresses on its upstream surface, which ultimately dictates the flow inside the droplet. This behavior manifests in simulations through the observed pancake shapes and ultimately the breakup morphology (forward or backward bag). All these factors affecting droplet deformation process are specified and theories explaining the observed results are provided. A $\mathit{We}_\mathit{cr}-\mathit{Oh}_d$ plot is provided to summarize all variations in $\mathit{We}_\mathit{cr}$ observed due to changes in the involved non-dimensional parameters. All observed critical pancake and breakup morphologies are summarized using a phase diagram illustrating all deformation paths a droplet might take under impulsive acceleration. Finally, based on the understanding of process of bag breakup gained from this work, a non-dimensional parameter to predict droplet breakup threshold is derived and tested on all simulation data obtained from this work and all experimental data gathered from existing literature.Open AccessOn the Threshold of Drop Fragmentation under Impulsive Acceleration
We examine the complete landscape of parameters which affect secondary breakup of a Newtonian droplet under impulsive acceleration. A Buckingham-Pi analysis reveals that the critical Weber number $\mathit{We}_\mathit{cr}$ for a non-vibrational breakup depends on the density ratio $(\rho)$, the drop $(\mathit{Oh}_d)$ and the ambient $(\mathit{Oh}_o)$ Ohnesorge numbers. Volume of fluid (VOF) multiphase flow simulations are performed using Basilisk to conduct a reasonably complete parametric sweep of the non-dimensional parameters involved. It is found that, contrary to current consensus, even for $\mathit{Oh}_d \leq 0.1$, a decrease in $\mathit{Oh}_d$ has a substantial impact on the breakup morphology, motivating plume formation. In addition to $\rho$, $\mathit{Oh}_o$ also affects the balance between pressure differences between a droplet's pole and its periphery, and the shear stresses on its upstream surface, which ultimately dictates the flow inside the droplet. This behavior manifests in simulations through the observed pancake shapes and ultimately the breakup morphology (forward or backward bag). All these factors affecting droplet deformation process are specified and theories explaining the observed results are provided. A $\mathit{We}_\mathit{cr}-\mathit{Oh}_d$ plot is provided to summarize all variations in $\mathit{We}_\mathit{cr}$ observed due to changes in the involved non-dimensional parameters. All observed critical pancake and breakup morphologies are summarized using a phase diagram illustrating all deformation paths a droplet might take under impulsive acceleration. Finally, based on the understanding of process of bag breakup gained from this work, a non-dimensional parameter to predict droplet breakup threshold is derived and tested on all simulation data obtained from this work and all experimental data gathered from existing literature. - Open Access
- 30 September 2022
- Published by American Association for the Advancement of Science (AAAS) in Science Advances
- Vol. 8 (39) , eabq0593
COVID-19 Exposure Assessment Tool (CEAT): Exposure quantification based on ventilation, infection prevalence, group characteristics, and behavior
The coronavirus disease 2019 (COVID-19) Exposure Assessment Tool (CEAT) allows users to compare respiratory relative risk to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for various scenarios, providing understanding of how combinations of protective measures affect risk. CEAT incorporates mechanistic, stochastic, and epidemiological factors including the (i) emission rate of virus, (ii) viral aerosol degradation and removal, (iii) duration of activity/exposure, (iv) inhalation rates, (v) ventilation rates (indoors/outdoors), (vi) volume of indoor space, (vii) filtration, (viii) mask use and effectiveness, (ix) distance between people (taking into account both near-field and far-field effects of proximity), (x) group size, (xi) current infection rates by variant, (xii) prevalence of infection and immunity in the community, (xiii) vaccination rates, and (xiv) implementation of COVID-19 testing procedures. CEAT applied to published studies of COVID-19 transmission events demonstrates the model’s accuracy. We also show how health and safety professionals at NASA Ames Research Center used CEAT to manage potential risks posed by SARS-CoV-2 exposures.
- 30 September 2022
- Published by American Association for the Advancement of Science (AAAS) in Science Advances
- Vol. 8 (39) , eabq0593
Open AccessCOVID-19 Exposure Assessment Tool (CEAT): Exposure quantification based on ventilation, infection prevalence, group characteristics, and behavior
The coronavirus disease 2019 (COVID-19) Exposure Assessment Tool (CEAT) allows users to compare respiratory relative risk to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for various scenarios, providing understanding of how combinations of protective measures affect risk. CEAT incorporates mechanistic, stochastic, and epidemiological factors including the (i) emission rate of virus, (ii) viral aerosol degradation and removal, (iii) duration of activity/exposure, (iv) inhalation rates, (v) ventilation rates (indoors/outdoors), (vi) volume of indoor space, (vii) filtration, (viii) mask use and effectiveness, (ix) distance between people (taking into account both near-field and far-field effects of proximity), (x) group size, (xi) current infection rates by variant, (xii) prevalence of infection and immunity in the community, (xiii) vaccination rates, and (xiv) implementation of COVID-19 testing procedures. CEAT applied to published studies of COVID-19 transmission events demonstrates the model’s accuracy. We also show how health and safety professionals at NASA Ames Research Center used CEAT to manage potential risks posed by SARS-CoV-2 exposures.