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In this study, we use Global Sensitivity Analysis (GSA) to rank four non-pharmaceutical interventions (NPIs) in a deterministic compartmental model that might control Covid-19 related deaths in the United States. The NPIs are social distancing, isolation of infected individuals, identifying asymptomatically infected individuals through testing, and the use of face masks. The model uses a fear-based behavioral model that leads unmasked susceptible individuals to wear masks. The model parameters are estimated from the reported deaths for the United States of America from March 1, 2020 to November 26, 2020. Two GSA tools, the Sobol’ sesntivity indices and Partial Rank Correlation Coefficient are used to obtain the rankings of the input parameters at different stages of the disease propagation. We found that social distancing and outward mask efficiency alone decreases the output uncertainty by 25-45%. Sobol’ second order indices show that the combined effect of social distancing with increased mask usage and identifying and isolating asymptomatically infected individuals decreases uncertainty an additional 10%.

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Numerical simulations of high-pressure gas atomization are performed by varying the molten metal and the atomizing gas to understand the physics behind high-pressure gas atomization and the effects of the melt and the atomizing gas on droplet size distributions. The Volume of Fluid method is used in the OpenFoam platform. The three melt-gas combinations used in these simulations are aluminum-nitrogen, aluminum-argon, and low carbon steel-nitrogen. Three interfacial instabilities have been identified in the early stages of all three atomization processes. Comparison of aluminum and steel as the molten metal indicates that steel atomizes more effectively and provides a higher yield than aluminum. However, changing the atomizing gas does not result in a significant change in the atomization process.