Validation of Computational Fluid Dynamics Models for Evaluating Loose-Fitting Powered Air-Purifying Respirators
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Validation of Computational Fluid Dynamics Models for Evaluating Loose-Fitting Powered Air-Purifying Respirators

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  • English

  • Details:

    • Alternative Title:
      Proc 20th Congr Int Ergon Assoc IEA 2018 I Healthc Ergon (2018)
    • Description:
      Loose-fitting powered air-purifying respirators (PAPRs) are used in healthcare settings to reduce exposure to high-risk respiratory pathogens. Innovative computational fluid dynamics (CFD) models were developed for evaluating loose-fitting PAPR performance. However, the computational results of the CFD models have not been validated using actual experimental data. Experimental testing to evaluate particle facepiece leakage was performed in a test laboratory using two models of loose-fitting PAPRs. Each model was mounted on a static (non-moving) advanced headform placed in a sodium chloride (NaCl) aerosol test chamber. The headform performed cyclic breathing via connection to a breathing machine. High-efficiency particulate air (HEPA)-filtered air was supplied directly to the PAPR facepiece using laboratory compressed supplied-air regulated with a mass-flow controller. One model was evaluated with six supplied-air flowrates from 50-215 L/min (Lpm) and the other model with six flowrates from 50-205 Lpm. Three different workrates (minute volumes) were evaluated: low (25 Lpm), moderate 46 (Lpm), and high 88 (Lpm). Manikin penetration factor (mPF) was calculated as the ratio of chamber particle concentration to the in-facepiece concentration. Overall, data analyses indicated that the mPF results from the simulations were well correlated with the experimental laboratory data for all data combined (r = 0.88). For data at the three different workrates (high, moderate, low) for both models combined, the r-values were 0.96, 0.97, and 0.77, respectively. The CFD models of the two PAPR models were validated and may be utilized for further research.
    • Pubmed ID:
      37987021
    • Pubmed Central ID:
      PMC10658266
    • Document Type:
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