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High-resolution large-eddy simulation of indoor turbulence and its effect on airborne transmission of respiratory pathogens; model validation and infection probability analysis
Fluid Dynamics · Submitted 27 Oct 2021
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim The LES-based results are examined in juxtaposition with the classical Wells-Riley model, which is shown to significantly underestimate the infection probability, highlighting the importance of employing accurate indoor turbulence modeling when evaluating different risk-reduction strategies.
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The abstract, sentence by sentence
High-resolution large-eddy simulation (LES) is exploited to study indoor air turbulence and its effect on the dispersion of respiratory virus-laden aerosols and subsequent transmission risks. The methodology is applied to assess two dissimilar approaches to reduce transmission risks: a strategy to augment the indoor ventilation capacity with portable air purifiers and a strategy to utilize partitioning by exploiting portable space dividers. To substantiate the physical relevance of the LES model, a set of experimental aerosol concentration measurements are carried out, and their results are used for validating the LES model results. The obtained LES dispersion results are subjected to pathogen exposure and infection probability analysis. Wells-Riley probability model is extended to rely on realistic time- and space-dependent concentration fields to yield time- and space-dependent infection probability fields. The use of air purifiers leads to greater reduction in absolute risks compared to the analytical Wells-Riley model, which fails to predict the original risk level. However, the two models do agree on the relative risk reduction. The spatial partitioning strategy is demonstrated to have an undesirable effect when employed without other measures. The partitioning approach may yield positive results when employed together with targeted air purifier units. The LES-based results are examined in juxtaposition with the classical Wells-Riley model, which is shown to significantly underestimate the infection probability, highlighting the importance of employing accurate indoor turbulence modeling when evaluating different risk-reduction strategies.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | High-resolution large-eddy simulation (LES) is exploited to study indoor air turbulence and its effect on the dispersion of respiratory virus-laden aerosols and subsequent transmission risks. | contribution 0.68 | 0.85 | 0.04 | asserted 0.99 |
| s1 | The methodology is applied to assess two dissimilar approaches to reduce transmission risks: a strategy to augment the indoor ventilation capacity with portable air purifiers and a strategy to utilize partitioning by exploiting portable space dividers. | contribution 0.51 | 0.86 | 0.03 | asserted 0.97 |
| s2 | To substantiate the physical relevance of the LES model, a set of experimental aerosol concentration measurements are carried out, and their results are used for validating the LES model results. | method 0.99 | 0.93 | 0.02 | asserted 1.00 |
| s3 | The obtained LES dispersion results are subjected to pathogen exposure and infection probability analysis. | method 0.90 | 0.91 | 0.04 | asserted 0.94 |
| s4 | Wells-Riley probability model is extended to rely on realistic time- and space-dependent concentration fields to yield time- and space-dependent infection probability fields. | method 0.80 | 0.86 | 0.06 | asserted 1.00 |
| s5 | The use of air purifiers leads to greater reduction in absolute risks compared to the analytical Wells-Riley model, which fails to predict the original risk level. | result 0.99 | 0.89 | 0.20 | asserted 1.00 |
| s6 | However, the two models do agree on the relative risk reduction. | result 0.92 | 0.87 | 0.07 | asserted 1.00 |
| s7 | The spatial partitioning strategy is demonstrated to have an undesirable effect when employed without other measures. | result 0.99 | 0.90 | 0.11 | asserted 1.00 |
| s8 | The partitioning approach may yield positive results when employed together with targeted air purifier units. | result 0.84 | 0.84 | 0.12 | hedged possibility 1.00 |
| s9 | The LES-based results are examined in juxtaposition with the classical Wells-Riley model, which is shown to significantly underestimate the infection probability, highlighting the importance of employing accurate indoor turbulence modeling when evaluating different risk-reduction strategies. | interpretation 0.87 | 0.86 | 0.69 | asserted 1.00 |
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The paper
Original abstract
High-resolution large-eddy simulation (LES) is exploited to study indoor air turbulence and its effect on the dispersion of respiratory virus-laden aerosols and subsequent transmission risks. The methodology is applied to assess two dissimilar approaches to reduce transmission risks: a strategy to augment the indoor ventilation capacity with portable air purifiers and a strategy to utilize partitioning by exploiting portable space dividers. To substantiate the physical relevance of the LES model, a set of experimental aerosol concentration measurements are carried out, and their results are used for validating the LES model results. The obtained LES dispersion results are subjected to pathogen exposure and infection probability analysis. Wells-Riley probability model is extended to rely on realistic time- and space-dependent concentration fields to yield time- and space-dependent infection probability fields. The use of air purifiers leads to greater reduction in absolute risks compared to the analytical Wells-Riley model, which fails to predict the original risk level. However, the two models do agree on the relative risk reduction. The spatial partitioning strategy is demonstrated to have an undesirable effect when employed without other measures. The partitioning approach may yield positive results when employed together with targeted air purifier units. The LES-based results are examined in juxtaposition with the classical Wells-Riley model, which is shown to significantly underestimate the infection probability, highlighting the importance of employing accurate indoor turbulence modeling when evaluating different risk-reduction strategies.
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