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The dispersion of an immiscible fluid in a turbulent liquid flow is a frequent occurrence in various natural and technical processes, with particular importance in the chemical, pharmaceutical, mining, petroleum, and food industries. Understanding the dynamics and breakup of liquid droplets is crucial in many scientific and engineering applications, as poor control and optimization of droplet systems results in significant financial losses annually. Although a theoretical background for describing droplet breakup exists, many assumptions still require experimental verification. Numerous mathematical models have been proposed to describe the rate coefficient of droplet breakup and child distribution functions. However, the validation and discrimination between models have been hindered by the lack of experimental data gathered under well-controlled and well characterized conditions. Thus, to validate the current models, novel equipment and methodology for optical droplet breakage research are required. In this work, a von K´arm´an swirling flow apparatus was designed and constructed to carry out optical based droplet breakage experiments under low-intensity, homogeneous turbulent flow. The methodology presented here describes the procedure for generating and controlling the size of the droplets being injected into the homogeneous turbulent flow field. The experiments involved introducing single droplets into the test section, using peanut oil to be the droplet phase and the continuous phase is water. Automated image analysis algorithms were utilized to determine breakage time, breakage probability, and child droplet size distribution for different turbulence intensities. 

Chemical process engineering unit operations such as solvent extraction, liquid–liquid chemical reactions, and emulsion processing are all dependent on turbulent liquid–liquid droplet flow dynamics. The design and operation of equipment used in these applications is often guided by theoretical models for droplet breakup. Although several models for droplet breakage in agitated liquid emulsions have been developed, their utility is limited because they incorporate fitting factors that must be determined empirically by performing experiments using a specific fluid pairing and relevant flow configuration. The need to acquire experimental data to determine model constants is a significant drawback that hinders widespread use of breakage models to design and optimize process equipment. In this work, analytical expressions are formulated to predict the value of a fitting parameter associated with droplet breakage time for two commonly used breakage rate models without having to perform empirical studies. These equations were derived by using the underlying assumptions within each of the two breakage models considered, namely, that droplet breakage is a result of the competition between relevant deformation and restorative stresses. Data from experiments conducted in a homogeneous turbulent von Kármán box as well as from previously published investigations of droplet breakage in heterogeneous flow devices were utilized to validate the derived equations for the breakage time parameters. In general, good agreement was observed between predictions obtained using the derived equations for fitting parameters and those obtained from experiments. 

We introduce an approach to generate direction-controlled circulation around cylindrical obstructions in channels using a piezoelectric transducer embedded porous-channel device fabricated by photolithography. To transmit acoustic signals into the channel, a single piezoelectric transducer was attached, operating at voltage levels of 5, 10, 15, and 20 V. Microscopic particle image velocimetry was employed to analyze the flow patterns in the channels. The analysis revealed two opposing circulation tendencies around the pillars located at two opposite sides of the channel in the longitudinal direction. The strength of circulation was found to be minimal in the middle of the channel and increased gradually toward the two ends of the channels. Furthermore, we observed that the circulation strength was maximum near the axial centerline and minimum at the boundaries along the width of the channels. Comparing the voltage levels, the higher voltage signals produced a higher strength of circulation than the lower voltage signals in all cases. Additionally, we found that the strength of circulation increased almost linearly and then decayed exponentially in the radial direction from the surfaces of the pillars. The observed velocity fields around individual cylinders matched well with the Görtler vortex model. The reported circulation phenomenon around pillars can be applied in non-contact fluid stirring and mixing in bio-chemical systems and lab-on-a-chip systems and may also provide additional degrees of freedom in object tweezing, trapping, and levitation. 

This Grant for Rapid Response Research (RAPID) project will collect and analyze perishable data on historical buildings. The Tumwata Village (formerly known as Blue Heron Paper Mill Site) located by the Willamette Falls in Oregon City, Oregon, has a very intriguing history and was recently purchased by the Confederated Tribes of Grand Ronde with the intent to restore the falls to their natural state and preserve some of the oldest structures. The site presents a unique opportunity to perform rapid investigations to collect and analyze perishable data on these historical buildings and develop new knowledge in the area of building assessments in corrosive environments. This industrial site contains a wide range of structure types (steel frames, concrete frames, timber frames, masonry walls and massive concrete walls) that were built over a period of 150 years and that employ many construction details that are common in older structures. The data collected and the results of the research will be applicable to many buildings in coastal communities throughout the country. Lidar data sets collected from these buildings will support the development of new methods to analyze and synthesize large data sets as well as integrate visual observations and material testing to quantify structural deterioration damages. The challenge in developing artificial intelligence (AI) technologies to find and quantify damage in structural systems using lidar data is the need to train the methods on existing data sets that show a wide range of damage states. The data to be collected from this site will provide an extensive training data set relevant to structural components common to older buildings. Development of such AI technologies for fast identification and quantification of damage would be transformative for the natural hazards research community and would expand the ability to learn from archived lidar datasets. The collected dataset will be available to researchers to serve as high quality training data in algorithm development. 

Abstract Distributions of landslide size are hypothesized to reflect hillslope strength, and consequently weathering patterns. However, the association of weathering and critical zone architecture with mechanical strength properties of parent rock and soil are poorly-constrained. Here we use three-dimensional stability to analyze 7330 landslides in western Oregon to infer combinations of strength - friction angles and cohesion - through analysis of both failed and reconstructed landslide terrain. Under a range of conditions, our results demonstrate that the failure envelope that relates shear strength and normal stress in landslide terrain is nonlinear owing to an exchange in strength with landslide thickness. Despite the variability in material strength at large scales, the observed gradient in proportional cohesive strength with landslide thickness may serve as a proxy for subsurface weathering. We posit that the observed relationships between strength and landslide thickness are associated with the coalescence of zones of low shear strength driven by fractures and weathering, which constitutes a first-order control on the mechanical behavior of underlying soil and rock mass. 

This paper presents an integrative analysis framework combining natural hazards with network mobility to provide insights on disaster preparedness and relief. In particular, this framework characterizes the impact of seismically induced landslides on network mobility to reveal the mobility changes immediately after the events and throughout the course of restoration and recovery efforts. Landslides not only undermine the structural integrity of roadways, but also deposit a significant amount of material on the road surface, usually resulting in partial or complete road closure to traffic. The highly populated Portland, Oregon, Metro is selected as a case study to demonstrate this framework given that the Pacific Northwest is highly prone to large earthquakes as part of the Cascadia Subduction Zone as well as highly susceptible to landslides given its high topographic relief and wet climate. In this case study, travel time to the west and east sides of Willamette River, which divides the Portland Metro area, shows an abrupt change in mobility. In particular, the Portland Hills region with its steep topography is identified as the most vulnerable region. Based on a temporal analysis of recovery, the majority of the network mobility is expected to be restored after 30 days. The results of this study serve as a preliminary assessment of the impact of landslides on network mobility and can facilitate decision making in emergency planning. 

Displacement monitoring is a critical step to understand, manage, and mitigate potential landside hazard and risk. Remote sensing technology is increasingly used in landslide monitoring. While significant advances in data collection and processing have occurred, much of the analysis of remotely-sensed data applied to landslides is still relatively simplistic, particularly for landslides that are slow moving and have not yet “failed”. To this end, this work presents a novel approach, SlideSim, which trains an optical flow predictor for the purpose of mapping 3D landslide displacement using sequential DEM rasters. SlideSim is capable of automated, self-supervised learning by building a synthetic dataset of displacement landslide DEM rasters and accompanying label data in the form of u/v pixel offset flow grids. The effectiveness, applicability, and reliability of SlideSim for landslide displacement monitoring is demonstrated with real-world data collected at a landslide on the Southern Oregon Coast, U.S.A. Results are compared with a detailed ground truth dataset with an End Point Error RMSE = 0.026 m. The sensitivity of SlideSim to the input DEM cell size, representation (hillshade, slope map, etc.), and data sources (e.g., TLS vs. UAS SfM) are rigorously evaluated. SlideSim is also compared to diverse methodologies from the literature to highlight the gap that SlideSim fills amongst current state-of-the-art approaches.