The experimental demonstration of a fluidic, multi-axis thrust vectoring (MATV) scheme is presented for a structurally fixed, afterburning nozzle referred to as the conformal fluidic nozzle (CFN). This concept for jet flow control features symmetric injection around the nozzle throat to provide throttling for jet area control, and asymmetric injection to subsonically skew the sonic plane for jet vectoring. The conceptual development of the CFN was presented in a companion paper (Miller et al., 1999). In that study, critical design variables were shown to be the flap length and expansion area ratio of the nozzle, and the location, angle, and distribution of injected flow. Measures of merit were vectoring capability, gross thrust coefficient, and discharge coefficient. A demonstration of MATV was conducted on a 20%-scale CFN test article across a range of nozzle pressure ratios (NPR), injector flow rates, and flow distributions. Both yaw and pitch vector angles of greater than 8° were obtained at NPR of 5.5. Yaw vector angles greater than 10° were achieved at lower NPR. Values of thrust-coefficient for the CFN generally exceeded published measurements of shock-based, vectoring methods. In terms of vectoring effectiveness (ratio of vector angle to percent injected flow), fluidic throat skewing was found to be comparable to shock-based vectoring methods.
We propose the Transmission of Virus in Carriages (TVC) model, a computational model which simulates the potential exposure to SARS‐CoV‐2 for passengers traveling in a subway rail system train. This model considers exposure through three different routes: fomites via contact with contaminated surfaces; close‐range exposure, which accounts for aerosol and droplet transmission within 2 m of the infectious source; and airborne exposure via small aerosols which does not rely on being within 2 m distance from the infectious source. Simulations are based on typical subway parameters and the aim of the study is to consider the relative effect of environmental and behavioral factors including prevalence of the virus in the population, number of people traveling, ventilation rate, and mask wearing as well as the effect of model assumptions such as emission rates. Results simulate generally low exposures in most of the scenarios considered, especially under low virus prevalence. Social distancing through reduced loading and high mask‐wearing adherence is predicted to have a noticeable effect on reducing exposure through all routes. The highest predicted doses happen through close‐range exposure, while the fomite route cannot be neglected; exposure through both routes relies on infrequent events involving relatively few individuals. Simulated exposure through the airborne route is more homogeneous across passengers, but is generally lower due to the typically short duration of the trips, mask wearing, and the high ventilation rate within the carriage. The infection risk resulting from exposure is challenging to estimate as it will be influenced by factors such as virus variant and vaccination rates.
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