Surface engineering at the nanoscale is a rapidly developing field that promises to impact a range of applications including energy production, water desalination, self-cleaning and anti-icing surfaces, thermal management of electronics, microfluidic platforms, and environmental pollution control. As the area advances, more detailed insights of dynamic wetting interactions on these surfaces are needed. In particular, the coalescence of two or more droplets on ultra-low adhesion surfaces leads to droplet jumping. Here we show, through detailed measurements of jumping droplets during water condensation coupled with numerical simulations of binary droplet coalescence, that this process is fundamentally inefficient with only a small fraction of the available excess surface energy (≲ 6%) convertible into translational kinetic energy. These findings clarify the role of internal fluid dynamics during the jumping droplet coalescence process and underpin the development of systems that can harness jumping droplets for a wide range of applications.

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Statistical analysis of transitional boundary layers in pressure gradients is performed using the flow fields from direct numerical simulations of bypass transition. Laminar–turbulent discrimination separates the streaky laminar flow from turbulent regions. Individual streaks are identified and tracked in the flow field in order to obtain statistics of the amplitude of the streak population. An extreme value model is proposed for the distribution of streak amplitudes. It is also possible to differentiate those streaks which break down into turbulent spots from innocuous events. It is shown that turbulence onset is due to high-amplitude streaks, with streamwise perturbation velocity exceeding 20 % of the free stream speed. The resulting turbulent spots are tracked downstream. The current analysis allows for the measurement of the lateral spreading angles of individual spots and their spatial extent and volumes. It is demonstrated that the volumetric growth rate of turbulent spots is insensitive to pressure gradient.

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Communicating the results of research to nonscientists presents many challenges. Among these challenges is communicating the effectiveness of an intervention in a way that people untrained in statistics can understand. Use of traditional effect size metrics (e.g., r, r²) has been criticized as being confusing to general audiences. In response, researchers have developed nontraditional effect size indicators (e.g., binomial effect size display, common language effect size indicator) with the goal of presenting information in a more understandable manner. The studies described here present the first empirical test of these claims of understandability. Results show that nontraditional effect size indicators are perceived as more understandable and useful than traditional indicators for communicating the effectiveness of an intervention. People also rated training programs as more effective and were willing to pay more for programs whose effectiveness was described using the nontraditional effect size metrics.