Search for: All records

Abstract Overlap between peak hurricane (i.e., strong tropical cyclone) activity and the fall transition represents a period when coastal ocean‐hurricane interactions can be highly variable due to rapid changes in seasonal hydrographic conditions. However, understanding of the response of the coastal ocean to tropical cyclone passage is limited due to the difficulty of collecting observations during such extreme events. Here, observations on the shelf were used to investigate coastal ocean‐atmosphere interactions during the passage of three tropical cyclones in the northern Gulf of Mexico during the fall of 2017. Tropical depression conditions associated with Hurricanes Harvey and Irma and Hurricane Nate impacted the Mississippi Bight region within a 6‐week span (late August–early October) when the fall transition typically begins. Despite large impacts from Harvey and Irma, which pushed the system into a marine cold spell, a rewarming event changed the trajectory of the fall transition, extending the period when the shelf temperature remained favorable for hurricane intensification. In contrast, Nate, the most direct storm hitting the study area, had minimal impacts on shelf conditions, even at sites within the inner core of the storm. All storm responses were linked to moisture disequilibrium and direction of the across‐shelf wind where southward offshore (northward onshore) winds generated higher (lower) vertical gradients in humidity. Overall, these events show the impact that even distant hurricanes can have on the fall transition as well as highlight the need for improving the understanding of factors that influence thermodynamic disequilibrium in the coastal zone. 

We present applications of the full-wave solver, Petra-M code for Earth magnetospheric plasma wave physics by leveraging the current effort of the radio frequency wave project. Because the Petra-M code uses the modular finite element method (MFEM) library, the boundary shapes, plasma density profiles, and realistic planetary magnetic fields can be easily adapted. In order to incorporate realistic Earth’s magnetic field into the Petra-M, we utilize the self-consistent magnetospheric flux models for compressed and stretched magnetic fields and realistic magnetospheric magnetic field geometries extracted from global MHD simulations. Using Petra-M code, we then examine ultra-low frequency (ULF) wave propagations in various magnetic field shapes. For example, left-handed polarized electromagnetic ion cyclotron waves in Earth’s dipole and compressed magnetic field are examined to consider waves in the inner and dayside outer magnetospheres, respectively. Mode-converted Alfvén wave propagation is also demonstrated in the compressed (dayside), stretched(nightside), and realistically stretched magnetic field (magnetotail). Therefore, the Petra-M code successfully demonstrates magnetospheric plasma wave propagation despite the spatial scale differences between the fusion devices (~m) and Earth’s magnetosphere (103 − 104km). 

Modular construction aims at overcoming challenges faced by the traditional construction process such as the shortage of skilled workers, fast-track project requirements, and cost associated with on-site productivity losses and recurrent rework. Since manufacturing is done off-site in controlled factory settings, modular construction is associated with increased productivity and better quality control. However, because every construction project is unique and results in distinct work pieces and building elements to be assembled, modular construction factories necessitate better mechanisms to assist workers during the assembly process in order to minimize errors in selecting the pieces to be assembled and idle times while figuring out the next step in an assembly sequence. Machine intelligence provides opportunities for such assistance; however, a challenge is to rapidly generate large datasets with rich contextual data to train such intelligent agents. This work overviews a mechanism to generate such datasets in virtual environments and evaluates the performance of AI models trained using data generated in virtual environments in recognizing the next installation step in modular assembly sequences. Performance of the trained MV-CNN models (with accuracy of 0.97) shows that virtual environments can potentially be used to generate the required datasets for AI without the costly, time-consuming, and labor-intensive investments needed upfront for capturing real-world data. 

In order to explore the consequences of spin–orbit coupling on spin–phonon interactions in a set of chemically similar mixed metal oxides, we measured the infrared vibrational properties of Co4B2O9 (B = Nb, Ta) as a function of temperature and compared our findings with lattice dynamics calculations and several different models of spin–phonon coupling. Frequency vs temperature trends for the Co2+ shearing mode near 150 cm−1 reveal significant shifts across the magnetic ordering temperature that are especially large in relative terms. Bringing these results together and accounting for noncollinearity, we obtain spin–phonon coupling constants of −3.4 and −4.3 cm−1 for Co4Nb2O9 and the Ta analog, respectively. Analysis reveals that these coupling constants are derived from interlayer (rather than intralayer) exchange interactions and that the interlayer interactions contain competing antiferromagnetic and ferromagnetic contributions. At the same time, beyond-Heisenberg terms are minimized due to fortuitous symmetry considerations, different from most other 4d- and 5d-containing oxides. Comparison with other contemporary oxides shows that spin–phonon coupling in this family of materials is among the strongest ever reported, suggesting an origin for magnetoelectric coupling.