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The storm sewer geyser is a process where an air–water mixture violently erupts from a manhole. Despite the low hydrostatic pressure, violent eruptions can achieve a height of tens of meters above the ground. This current study experimentally investigates large-scale violent geysers using a large air pocket inserted from a pressurized air tank. The total length of the pipe system is approximately 88 m with a 0.1572 m diameter pipe. This large-scale experiment facilitates the investigation of spontaneous geyser eruptions. This study identifies the role of air–water volume ratio and coefficient of pressure (ratio of absolute initial static pressure to initial dynamic pressure) on the geyser intensity using eruption images and pressure plots. A total of 116 cases are tested, in which the volume ratio is parametrically increased from 0 to 1.1 under various operating conditions. A geyser score is defined to quantify the geyser eruption nature based on visual observations. The key findings are as follows: first, a sharp transition in geyser intensity is observed at the critical volume ratio of 0.5, and pre-transition and post-transition intensity exhibit a linear relationship with the volume ratio; and second, the critical volume ratio linearly varies with the coefficient of pressure. 

Abstract Flooding is one of the most frequent natural hazards and causes more economic loss than all the other natural hazards. Fast and accurate flood prediction has significance in preserving lives, minimizing economic damage, and reducing public health risks. However, current methods cannot achieve speed and accuracy simultaneously. Numerical methods can provide high-fidelity results, but they are time-consuming, particularly when pursuing high accuracy. Conversely, neural networks can provide results in a matter of seconds, but they have shown low accuracy in flood map generation by all existing methods. This work combines the strengths of numerical methods and neural networks and builds a framework that can quickly and accurately model the high-fidelity flood inundation map with detailed water depth information. In this paper, we employ the U-Net and generative adversarial network (GAN) models to recover the lost physics and information from ultra-fast, low-resolution numerical simulations, ultimately presenting high-resolution, high-fidelity flood maps as the end results. In this study, both the U-Net and GAN models have proven their ability to reduce the computation time for generating high-fidelity results, reducing it from 7–8 h down to 1 min. Furthermore, the accuracy of both models is notably high. 

Polymers are increasingly being used in higher demanding applications due to their ability to tailor the properties of structures while allowing for a weight and cost reduction. Solvents play an important role in the manufacture of polymeric structures since they allow for a reduction in the polymer’s viscosity or assist with the dispersion of fillers into the polymer matrix. However, the incorrect removal of the solvent affects both the physical and chemical properties of polymeric materials. The presence of residual solvent can also negatively affect the curing kinetics and the final quality of polymers. Destructive testing is mainly performed to characterize the properties of these materials. However, this type of testing involves using lab-type equipment that cannot be taken in-field to perform in situ testing and requires a specific sample preparation. Here, a method is presented to non-destructively evaluate the curing process and final viscoelastic properties of polymeric materials using ultrasonics. In this study, changes in longitudinal sound speed were detected during the curing of an aerospace epoxy adhesive as a result of variations in polymer chemistry. To simulate the presence of residual solvent, samples containing different weight percentages of isopropyl alcohol were manufactured and tested using ultrasonics. Thermogravimetric analysis was used to show changes in the decomposition of the adhesive due to the presence of IPA within the polymer structure. Adding 2, 4, and 6 wt.% of IPA decreased the adhesive’s lap shear strength by 40, 58, and 71%, respectively. Ultrasonics were used to show how the solvent influenced the curing process and the final sound speed of the adhesive. Young’s modulus and Poisson’s ratio were determined using both the longitudinal and shear sound speeds of the adhesive. Using ultrasonics has the potential to non-invasively characterize the quality of polymers in both an in-field and manufacturing settings, ensuring their reliability during use in demanding applications. 

Magnetoelectric coefficient values of above 5 and 2 V cm–1 Oe–1 in 20 nm CoFe2O4–BaTiO3 and NiFe2O4–BaTiO3 core–shell magnetoelectric nanoparticles were demonstrated. These colossal values, compared to 0.1 V cm–1 Oe–1 commonly reported for the 0–3 system, are attributed to (i) the heterostructural lattice-matched interface between the magnetostrictive core and the piezoelectric shell, confirmed through transmission electron microscopy, and (ii) in situ scanning tunneling microscopy nanoprobe-based ME characterization. The nanoprobe technique allows measurements of the ME effect at a single-nanoparticle level which avoids the charge leakage problem of traditional powder form measurements. The difference in the frequency dependence of the ME value between the two material systems is owed to the Ni-ferrite cores becoming superparamagnetic in the near-dc frequency range. The availability of novel nanostructures with colossal ME values promises to unlock many new applications ranging from energy-efficient information processing to nanomedicine and brain–machine interfaces.