Kibble-Zurek scenario and coarsening across nonequilibrium phase transitions in driven vortices and skyrmions
Not Available
SEARCH · Engineering Papers
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Not Available
Not Available
Abstract not provided.
Causal inference methods are important to complement predictive machine learning models to improve theoretical understanding of the underlying system in climate science. Granger causality (GC) can determine whether time series value of one variable (e.g., X) can predict the future values of another variable (e.g., Y), but not the other way around.
Abstract not provided.
Our research was focused on forecasting the position and shape of the winter stratospheric polar vortex at a subseasonal timescale of 15 days in advance. To achieve this, we employed both statistical and neural network machine learning techniques. The analysis was performed on 42 winter seasons of reanalysis data provided by NASA giving us a total of 6,342 days of data. The state of the polar vortex for determined by using geometric moments to calculate the centroid latitude and the aspect ratio of an ellipse fit onto the vortex. Timeseries for thirty additional precursors were calculated to help improve the predictive capabilities of the algorithm. Feature importance of these precursors was performed using random forest to measure the predictive importance and the ideal number of precursors. Then, using the precursors identified as important, various statistical methods were tested for predictive accuracy with random forest and nearest neighbor performing the best. An echo state network, a type of recurrent neural network that features sparsely connected hidden layer and a reduced number of trainable parameters that allows for rapid training and testing, was also implemented for the forecasting problem. Hyperparameter tuning was performed for each methods using a subset of the training data. The algorithms were trained and tuned on the first 41 years of data, then tested for accuracy on the final year. In general, the centroid latitude of the polar vortex proved easier to predict than the aspect ratio across all algorithms. Random forest outperformed other statistical forecasting algorithms overall but struggled to predict extreme values. Forecasting from echo state network suggested a strong predictive capability past 15 days, but further work is required to fully realize the potential of recurrent neural network approaches.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Tropical cyclogenesis (TCG) is a multiscale process that involves interactions between large-scale circulation and small-scale convection. A near-global aquaplanet cloud-resolving model (NGAqua) with 4-km horizontal grid spacing that produces tropical cyclones (TCs) is used to investigate TCG and its predictability. This study analyzes an ensemble of three 20-day NGAqua simulations, with initial white-noise perturbations of low-level humidity. TCs develop spontaneously from the northern edge of the intertropical convergence zone (ITCZ), where large-scale flows and tropical convection provide necessary conditions for barotropic instability. Zonal bands of positive low-level absolute vorticity organize into cyclonic vortices, some of which develop into TCs. A new algorithm is developed to track the cyclonic vortices. A vortex-following framework analysis of the low-level vorticity budget shows that vertical stretching of absolute vorticity due to convective heating contributes positively to the vorticity spinup of the TCs. A case study and composite analyses suggest that sufficient humidity is key for convective development. TCG in these three NGAqua simulations undergoes the same series of interactions. The locations of cyclonic vortices are broadly predetermined by planetary-scale circulation and humidity patterns associated with ITCZ breakdown, which are predictable up to 10 days. Whether and when the cyclonic vortices become TCs depend on the somewhat more random feedback between convection and vorticity.
Abstract A perturbative solution of simplified primitive equations for nonlinear weakly stratified upwelling over a frictional slope is found that resolves the vertical structure of velocity fields and can satisfy Ertel’s potential vorticity conservation in the stratified inviscid interior. The solution uses assumptions consistent with the model proposed by Lentz and Chapman, including a steady-state, constant cross-shore density gradient, no alongshore gradients, laterally inviscid, and consideration of cross-shore advection of alongshore momentum. The solution resolves the vertical structure of velocity fields (including subsurface maxima of compensational flow, not resolved by Lentz and Chapman) and can satisfy Ertel’s potential vorticity conservation in the stratified inviscid interior. The dynamics are similar to Lentz and Chapman; bottom stress balances alongshore wind stress in a homogeneous density ocean and is replaced by nonlinear cross-shore transport of alongshore momentum as the Burger number (S=αN/f, whereα,N, andfare the bottom slope, buoyancy frequency, Coriolis frequency, respectively) increases. When the solution uses the empirical relation between cross-shore and vertical density gradients proposed by Lentz and Chapman, vorticity conservation is not satisfied and the nonlinear momentum transport estimated by the solution linearly increases withS, asymptotically matching Lentz and Chapman forS< 1. When the solution conserves interior potential vorticity, the momentum transport is proportional toS 2 forS< 1 and is in better agreement with numerical simulations.
The angular momentum of rotating superfluid droplets originates from quantized vortices and capillary waves, the interplay between which remains to be uncovered. Here, the rotation of isolated submicrometer superfluid 4 He droplets is studied by ultrafast x-ray diffraction using a free electron laser. The diffraction patterns provide simultaneous access to the morphology of the droplets and the vortex arrays they host. In capsule-shaped droplets, vortices form a distorted triangular lattice, whereas they arrange along elliptical contours in ellipsoidal droplets. The combined action of vortices and capillary waves results in droplet shapes close to those of classical droplets rotating with the same angular velocity. The findings are corroborated by density functional theory calculations describing the velocity fields and shape deformations of a rotating superfluid cylinder.
Type-II superconductors under magnetic fields remain in a quantum-coherent, non-dissipative state as long as vortices are pinned. Dissipation emerges when vortices depin, a process often driven by thermal fluctuations and commonly associated with a melting transition from a vortex solid to a vortex liquid. Macroscopic experiments almost always observe this transition close to the superconducting critical temperature 𝑇 𝑐 . However, how the vortex solid responds to thermal fluctuations at the scale of individual vortices, far below the melting transition, remains largely unexplored. Here, we use scanning tunneling microscopy (STM) to directly visualize vortices in the iron-based superconductor CaKFe 4 As 4 (𝑇 𝑐 ≈35 K ). We observe the formation of vortex liquid droplets—spatially localized regions where vortices exhibit strong thermal fluctuations—at temperatures as low as 0.5 𝑇 𝑐 . These results demonstrate that the onset of dissipation at the local scale occurs at temperatures significantly below 𝑇𝑐 in type-II superconductors, revealing a previously unrecognized regime of vortex dynamics.
The shielding effect of the downstream cylinder in flow induced oscillation (FIO) of two cylinders arranged in tandem is studied experimentally and numerically at Reynolds number 30,000 to 120,000. Both cylinders are in one degree-of-freedom, transverse-oscillations, and have turbulence stimulation in the form of selective surface roughness to expand FIO beyond vortex-induced vibration (VIV) into galloping. Shielding of the downstream cylinder has a negative effect on harnessing hydrokinetic energy. Further, to study its effect and mechanics, selective cases are studied both numerically and experimentally and discussed to demonstrate the shielding effect on the downstream cylinder and understand its cause. The main conclusions are: (1) The shielding effect for the downstream cylinder shows a strong relation to the damping ratio. As the damping ratio increases, the shielding effect is mitigated. Additionally, the oscillation of the rear cylinder becomes stable and shows stable frequency. (2) In the VIV region, as the stiffness and natural frequency increase, the shielding effect decreases substantially. (3) In the VIV region, the vorticity of the vortices shedding from both the upper and the lower sides of the downstream cylinder does not accumulate enough due to the attraction by the vortices shed from the upstream cylinder, thus resulting in partial suppression of the oscillation on the downstream cylinder. (4) In the galloping region, the shielding effect for the downstream cylinder depends on whether the vorticity near the downstream cylinder is strengthened by the vortices generated by the shear layers of the upstream cylinder or weakened.
Ocean weather comprises vortical and straining mesoscale motions, which play fundamentally different roles in the ocean circulation and climate system. Vorticity determines the movement of major ocean currents and gyres. Strain contributes to frontogenesis and the deformation of water masses, driving much of the mixing and vertical transport in the upper ocean. While recent studies have shown that interactions with the atmosphere damp the ocean’s mesoscale vortices O(100) km in size, the effect of winds on straining motions remains unexplored. Here, we derive a theory for wind work on the ocean’s vorticity and strain. Using satellite and model data, we discover that wind damps strain and vorticity at an equal rate globally, and unveil striking asymmetries based on their polarity. Subtropical winds damp oceanic cyclones and energize anticyclones outside strong current regions, while subpolar winds have the opposite effect. A similar pattern emerges for oceanic strain, where subtropical convergent flow is damped along the west-equatorward east-poleward direction and energized along the east-equatorward west-poleward direction. These findings reveal energy pathways through which the atmosphere shapes ocean weather.
Active colloidal fluids, biological and synthetic, often demonstrate complex self-organization and the emergence of collective behavior. Spontaneous formation of multiple vortices has been recently observed in a variety of active matter systems, however, the generation and tunability of the active vortices not controlled by geometrical confinement remain challenging. Here, we exploit the persistence length of individual particles in ensembles of active rollers to tune the formation of vortices and to orchestrate their characteristic sizes. We use two systems and employ two different approaches exploiting shape anisotropy or polarization memory of individual units for control of the persistence length. We characterize the dynamics of emergent multi-vortex states and reveal a direct link between the behavior of the persistence length and properties of the emergent vortices. We further demonstrate common features between the two systems including anti-ferromagnetic ordering of the neighboring vortices and active turbulent behavior with a characteristic energy cascade in the particles velocity field energy spectra. Finally, our findings provide insights into the onset of spatiotemporal coherence in active roller systems and suggest a control knob for manipulation of dynamic self-assembly in active colloidal ensembles.
We investigate lower-hybrid drift waves (LHDW) in symmetric magnetic reconnection with zero guide field using three-dimensional particle-in-cell simulations. The long-wavelength mode with kρiρe∼1 develops in the bifurcated electron current layer around the X-line within the width of the electron meandering motion from the mid-plane, where ρi(e) is the ion (electron) gyroradius. The short-wavelength mode with kρe∼1 develops in the separatrix region downstream of the electron outflow jet, producing electron vortices in the background flow frame. Electrons follow the E × B drift with corrections from the diamagnetic drift and are heated inside the vortices with diverging electric fields. In the vortices, ions have comparable E × B and inertia drifts, which together mostly cancel the diamagnetic drift. Toward the center of diverging field vortices, ions are decelerated, leading to a decrease in the perpendicular temperature, while the loss of low-energy ions results in an increase in the parallel temperature. Parallel electric fields exist as a combination of the LHDW wave field projected to the magnetic field direction and the penetration of whistler waves that are mainly outside of the LHDW layer. The magnetic flux tube is twisted in the vortices. The twist may potentially lead to slippage reconnection, as indicated by the non-uniform parallel potential variation across field lines, while the periodic variations of the twisting directions are a limiting factor.
Abstract In type-II superconductors, electronic states within magnetic vortices hold crucial information about the paring mechanism and can reveal non-trivial topology. While scanning tunneling microscopy/spectroscopy (STM/S) is a powerful tool for imaging superconducting vortices, it is challenging to isolate the intrinsic electronic properties from extrinsic effects like subsurface defects and disorders. Here we combine STM/STS with basic machine learning to develop a method for screening out the vortices pinned by embedded disorder in iron-based superconductors. Through a principal component analysis of large STS data within vortices, we find that the vortex-core states in Ba(Fe 0.96 Ni 0.04 ) 2 As 2 start to split into two categories at certain magnetic field strengths, reflecting vortices with and without pinning by subsurface defects or disorders. Our machine-learning analysis provides an unbiased approach to reveal intrinsic vortex-core states in novel superconductors and shed light on ongoing puzzles in the possible emergence of a Majorana zero mode.