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Visually Based Characterization of the Incipient Particle Motion in Regular Substrates: From Laminar to Turbulent Conditions
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Predicting contaminant dispersion using modified turbulent Schmidt numbers from different vortex structures.

Fei Li1,2, Junjie Liu2, Jianlin Ren2

  • 1Department of HVAC, College of Urban Construction, Nanjing Tech University, Nanjing 210009, China.

Building and Environment
|April 15, 2020
PubMed
Summary
This summary is machine-generated.

This study improves indoor air quality (IAQ) modeling by adjusting the turbulent Schmidt number based on viscosity. This enhanced approach accurately simulates airborne contaminant dispersion in enclosed spaces like aircraft cabins and offices.

Keywords:
CFDContaminant dispersionEnclosed spaceTurbulent Schmidt numberVortex structure

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Area of Science:

  • Environmental Engineering
  • Fluid Dynamics
  • Indoor Air Quality

Background:

  • Air pollutant transmission significantly impacts indoor air quality (IAQ).
  • Understanding airborne contaminant dispersion mechanisms in enclosed spaces is crucial.
  • The relationship between pollutant diffusion coefficients and viscosity in enclosed environments remains unclear.

Purpose of the Study:

  • To enhance the simulation of airborne contaminant dispersion in enclosed spaces.
  • To investigate the modification of the turbulent Schmidt number (Sc) as a function of turbulent kinematic viscosity.
  • To improve the accuracy of computational fluid dynamics (CFD) models for indoor environments.

Main Methods:

  • Modified the turbulent Schmidt number (Sc) to be dependent on turbulent kinematic viscosity.
  • Conducted experiments in an aircraft cabin mockup to study airborne contaminant transmission.
  • Utilized experimental data from an office room with an underfloor air distribution (UFAD) system.
  • Employed the RNG k-ε turbulence model for simulations.
  • Analyzed model applicability based on airflow vibration frequency.

Main Results:

  • The modified turbulent Schmidt number (Sc) improved simulation accuracy for contaminant dispersion.
  • The RNG k-ε model with the modified Sc outperformed the default Sc value in both aircraft cabin and office room simulations.
  • The study demonstrated better prediction of airborne contaminant dispersion using the viscosity-dependent Sc.

Conclusions:

  • Modifying the turbulent Schmidt number (Sc) based on viscosity offers a more accurate method for simulating indoor airborne contaminant dispersion.
  • The enhanced model shows improved performance across different enclosed spaces, including aircraft cabins and offices.
  • This research provides a foundation for more precise IAQ assessments and control strategies in built environments.