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Liquid droplet impact is a subject that has been investigated in both engineering and non-engineering applications to understand and to control this phenomenon. Spray cooling, ink-jet printing, spray coating and painting, soil erosion prevention, pesticide application, and impact erosion are merely a few examples in which droplet impact is involved. Erosion caused by droplet impact on a solid surface is important in numerous elements of industrial equipment, such as pipelines, steam turbines, and wind turbine blades. Though experimental and modeling studies have been performed on this topic, most failed to perform quantitative investigation especially when it came to the erosion of wind turbine blades. Moreover, most approaches assume that the impacting droplets are completely spherical and unaffected by any local turbulence or vortex shedding. As the droplet erosion process could be affected by several parameters, such as the impact velocity, shape and size of the droplets, this study focuses on investigating droplet properties and movement in a controlled lab environment. High speed imaging and Particle Image Velocimetry (PIV) methods are used for this purpose. PIV is used to measure the velocity, circularity, and size of the falling droplets in both disturbed and un-disturbed flow conditions. High-speed camera imaging provides additional insight to the path of the droplets’ movement in the presence of any turbulence. Experiments are performed at a variety of flow rates utilizing a range of blunt needle gauge sizes to create different droplet sizes. It is observed that the blunt needles produce a train of droplets that are different in size following each leading droplet. This is a crucial observation as it will have a direct impact on the magnitude of erosion and should be considered in the future modeling efforts. 

Water droplet erosion (WDE) is a complex phenomenon that has been investigated for nearly a century. This form of erosion affects a wide range of energy industries from steam turbines and natural gas pipelines to wind turbine blades. The moving droplets impacting at a high relative speed create a high surge in surface pressure on the impacted material and damage the surface. The damage removes materials and can compromise strength for steam turbines and pipelines or affect the lift and drag forces on wind turbine blades. Research on WDE has been ongoing for decades with a majority of the reported results focused on metallic material testing and qualitative analysis comparing methodologies or surface conditions. The ongoing research at The University of Tulsa is conducting experiments with a variety of materials while exposed to an environment where water droplet erosion occurs. Impact velocity and droplet sizes are controlled within the facility and ongoing research with particle image velocimetry (PIV) is in use to characterize the falling droplets. Stainless steel 316, Aluminum 6061, and a variety of non-metallic materials are tested for a variety of conditions. The mass of each specimen is tracked and recorded at set intervals to determine the erosion ratio and erosion rate. Various other factors such as flowrate and rotational velocity are determined before testing as well as the percentage of droplets which impact the surface is determined with the use of a high-speed camera. Scanning electron microscopy (SEM) is also utilized to examine the material’s surfaces before and after testing to investigate the severity of erosion by water droplets. One impact velocity and one impact angle are set for all tested materials. These data points will be the starting point for future tests and modeling work to predict water droplet erosion based on simple factors. 

Subjective value has long been measured using binary choice experiments, yet responses like willingness-to-pay prices can be an effective and efficient way to assess individual differences risk preferences and value. Tony Marley’s work illustrated that dynamic, stochastic models permit meaningful inferences about cognition from process-level data on paradigms beyond binary choice, yet many of these models remain difficult to use because their likelihoods must be approximated from simulation. In this paper, we develop and test an approach that uses deep neural networks to estimate the parameters of otherwise-intractable behavioral models. Once trained, these networks allow for accurate and instantaneous parameter estimation. We compare different network architectures and show that they accurately recover true risk preferences related to utility, response caution, anchoring, and non-decision processes. To illustrate the usefulness of the approach, it was then applied to estimate model parameters for a large, demographically representative sample of U.S. participants who completed a 20-question pricing task — an estimation task that is not feasible with previous methods. The results illustrate the utility of machine-learning approaches for fitting cognitive and economic models, providing efficient methods for quantifying meaningful differences in risk preferences from sparse data. 

We developed an open source, extensible Python‐based framework, that we call the Versatile Modeling Of Deformation (VMOD), for forward and inverse modeling of crustal deformation sources. VMOD abstracts from specific source model implementations, data types and inversion methods. We implement the most common geodetic source models which can be combined to model and analyze multi‐source deformation. VMOD supports Global Navigation Satellite System (GNSS), InSAR, electronic distance measurement, Leveling and tilt data. To infer source characteristics from observations, VMOD implements non‐linear least squares and Markov Chain Monte‐Carlo Bayesian inversions, including joint inversions using different sources of data. VMOD's structure allows for easy integration of new geodetic models, data types, and inversion strategies. We benchmark the forward models against other published results and the inversion approaches against other implementations. We apply VMOD to analyze deformation at Unimak Island, Alaska, observed with continuous and campaign GNSS, and ascending and descending InSAR time series generated from Sentinel‐1 satellite radar acquisitions. These data show an inflation pattern at Westdahl volcano and subsidence at Fisher Caldera. We use VMOD to test a range of source models by jointly inverting the GNSS and InSAR data sets. Our final model simultaneously constrains the parameters of two sources. Our results reveal a depressurizing spheroid under Fisher Caldera ∼4–6 km deep, contracting at a rate of ∼2–3 Mm³/yr, and a pressurizing spherical source underneath Westdahl volcano ∼6–8 km deep, inflating at ∼5 Mm³/yr. This and past applications of VMOD to volcanic unrest benefit from an extensible framework which supports jointly inversions of data sets for parameters of easily composable multi‐source models.

 

Freshwater systems are projected to experience increased hydrologic extremes under climate change. To determine how small streams may be impacted by shifts in flow regimes, we experimentally simulated flow loss over the span of three summers in nine 50 m naturally fed stream channels. The aquatic insect community of these streams was sampled before, during, and after experimental drought treatments as well as following one unforeseen flood event. Abundance, richness, and beta diversity were measured as indicators of biotic effects of altered flow regimes. Abundance declined in proportion to flow loss. In contrast, we observed a threshold response in richness where richness did not decrease except in channels where losses of surface flow occurred and disconnected pools remained. The flood reset this pattern, but communities continued their prior trajectories shortly thereafter. Beta diversity partitions suggested no strong compositional shifts, and that the effect of drought was largely experienced uniformly across taxa until flow cessation. Pools served as a refuge, maintaining stable abundance gradients and higher richness longer than riffles. Upon flow resumption, abundance and richness returned to pre-treatment levels within one year. Our results suggest that many taxa present were resistant to drought conditions until loss in surface flow occurred.

 

Psychologists hypothesize that the effectiveness of normative messaging interventions increases when individuals have more personal attachment and similarity with reference groups. Using readily available energy consumption data, it is now possible to create highly personalized reference groups based on households’ daily energy use in a non-invasive matter. However, it still remains unclear to what degree individuals perceive behavioral reference groups as a cohesive entity. Therefore, this research investigates how individuals perceive energy profile-based groups relative to more standard geographic proximity-based groups. An online survey is conducted with 1,928 U.S. adults. Individuals do not perceive the profile-based groups as very entitative groups. Also, similarity between energy profile-based group members indirectly affects individuals’ identification with the groups via group entitativity. Lastly, this indirect effect is larger than the direct effect of similarity between group members on group identification. These results imply that a better understanding of what affects group entitativity would allow interveners to create more effective normative feedback messages. 

Abstract The 2018 summit and flank eruption of Kīlauea Volcano was one of the largest volcanic events in Hawaiʻi in 200 years. Data suggest that a backup in the magma plumbing system at the long-lived Puʻu ʻŌʻō eruption site caused widespread pressurization in the volcano, driving magma into the lower flank. The eruption evolved, and its impact expanded, as a sequence of cascading events, allowing relatively minor changes at Puʻu ʻŌʻō to cause major destruction and historic changes across the volcano. Eruption forecasting is inherently challenging in cascading scenarios where magmatic systems may prime gradually and trigger on small events. 

Normative messaging interventions have proven to be a cost-effective strategy for promoting pro-environmental behaviors. The effectiveness of normative messages is partially determined by how personally relevant the comparison groups are as well as the lag of feedback. Using readily available energy use data has created opportunities to generate highly personalized reference groups based on households’ behavioral patterns. Unfortunately, it is not well understood how data granularity (e.g., minute, hour) affects the performance of behavioral reference group categorization. This is important because different levels of data granularity can produce conflicting results in terms of group similarity and vary in computational time. Therefore, this research aims to evaluate the performance of clustering methods across different levels of temporal granularity of energy use data. A clustering analysis is conducted using one-year of energy use data from 3,000 households in Holland, Michigan. The clustering results show that behavioral reference groups become the most similar when representing households’ energy use behaviors at a six-hour interval. Computationally, less granular data (i.e., six and twelve hours) takes less time than highly granular data which increases exponentially with more households. Considering the enormous scale that normative messaging interventions need to be applied at, using less granular data (six-hour intervals) will permit interveners to maximize the effectiveness of highly personalized normative feedback messages while minimizing computation burdens.