Background and ObjectiveAdditive manufacturing of nasopharyngeal (NP) swabs using 3D printing technology presents a viable alternative to address the immediate shortage problem of standard flock-headed swabs for rapid COVID-19 testing. Recently, several geometrical designs have been proposed for 3D printed NP swabs and their clinical trials are already underway. During clinical testing of the NP swabs, one of the key criteria to compare the efficacy of 3D printed swabs with traditional swabs is the collection efficiency. In this study, we report a numerical framework to investigate the collection efficiency of swabs utilizing the computational fluid dynamics (CFD) approach. MethodsThree-dimensional computational domain comprising of NP swab dipped in the liquid has been considered in this study to mimic the dip test procedure. The volume of fluid (VOF) method has been employed to track the liquid-air interface as the NP swab is pulled out of the liquid. The governing equations of the multiphase model have been solved utilizing finite-volume-based ANSYS Fluent software by imposing appropriate boundary conditions. Taguchi's based design of experiment analysis has also been conducted to evaluate the influence of geometric design parameters on the collection efficiency of NP swabs. The developed model has been validated by comparing the numerically predicted collection efficiency of different 3D printed NP swabs with the experimental findings. ResultsNumerical predictions of the CFD model are in good agreement with the experimental results. It has been found that there prevails huge variability in the collection efficiency of the 3D printed designs of NP swabs available in the literature, ranging from 2 µl to 120 µl. Furthermore, even the smallest alteration in the geometric design parameter of the 3D printed NP swab results in significant changes in the amount of fluid captured. ConclusionsThe proposed framework would assist in quantifying the collection efficiency of the 3D printed designs of NP swabs, rapidly and at a low cost. Moreover, we demonstrate that the developed framework can be extended to optimize the designs of 3D printed swabs to drastically improve the performances of the existing designs and achieve comparable efficacy to that of conventionally manufactured swabs.
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