Aerodynamic optimisation of Formula student vehicle using computational fluid dynamics

This work aims to improve the external aerodynamic characteristics of the 2017 University of Huddersfield Formula SAE vehicle. To improve dynamic performance in the SAE events, a multiple-element rear wing was developed, which incorporated adjustable elements to constitute a drag reduction system (DRS). A numerical modelling approach was adopted to produce a suitable design. A simplified model of the vehicle was created to obtain baseline coefficients of lift (CL) and drag (CD). The rear wing was optimised to find the peak configuration generating maximum downforce. The results show that the incorporated rear wing improved the vehicle’s CL from 0.21 acting in the positive Y axis (lift) to 1.15 acting in the negative Y axis (downforce), whereas the CD increased from 0.71 to 1.21. However, the DRS configuration reduced the CD to 0.79. Using the obtained lift and drag coefficients, vehicle performance was estimated, such as maximum cornering speed, straight-line top speed and straight-line acceleration capabilities. The rear wing improved the theoretical maximum cornering speed by 3.1% for a corner radius of 13 m. The DRS increased the theoretical top speed by 18.2% compared to a fixed wing configuration. Acceleration potential increased by 15.7% at 25 m/s with the DRS open. The final section of the study used an online simulator (FSAESim) to make predictions of the acceleration event time, which were compared to the track results from the 2017 Hungary SAE event. The results showed a 97% similarity

Droplet Misalignment Limit for Inkjet Printing into Cavities on Textured Surfaces

The control of droplets deposited onto textured surfaces is of great importance for both engineering and medical applications. This research investigates the dynamics of a single droplet deposited into a confined space and its final equilibrium morphology, with emphasis given to droplet deposition under print head misalignment, the effect of nonuniform wettability, and deposition of droplets with varying sizes. A multiphase pseudopotential lattice Boltzmann methodology is used to simulate the process of deposition. The print quality is characterized in terms of a parameter referred to as the wetted fraction, which describes the proportion of the cavity that is wetted by the droplet. Our results show how single and multiple axis misalignment affect the final equilibrium morphology, and it was found for comparable configurations that multiaxis misalignment resulted in a higher wetted fraction. Investigations into wettabilities of the substrate and cavity wall revealed how larger ratios of the contact angles between the two enhance the ability for the droplet to self-align within the cavity. Additionally, a range of uniform wettabilities between the substrate and cavity were found, which mitigate against misalignment. Investigations into varying droplet sizes relative to the cavity revealed how misalignment can be compensated for with larger droplets, and limits for filling a cavity with a single drop are defined. Finally, we explore the deposition with misalignment into closely positioned cavities where it is found that the spacing between cavities is a key factor in determining the maximum permissible misalignment

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

Bridging the Divide Between Iterative Optical Polishing and Automation

Several recent business reports have described the global growth in demand for optical and photonic components, paralleled by technical reports on the growing shortage of skilled manufacturing staff to meet this demand. It is remarkable that producing ultraprecision surfaces remains so dependent on people, in contrast to other sectors of the economy, e.g., car manufacturing. Clearly, training can play some role, but ultimately, only process automation can provide the solution. This paper explores why automation is a challenge and summarizes multidisciplinary work aiming to assemble the building blocks required to realize automation