We studied, by micromagnetic simulations, the characteristic propagation behaviors of specific spin-wave modes along narrow domain walls in a specially designed thin-film-nanostrip cross-structure waveguide as well as their novel interaction behaviors with a single magnetic vortex placed at the cross-point. Only certain specific modes of spin waves well propagate along the given domain walls and then interact with the magnetic vortex. Through this robust interaction, vortex-gyration motions are also stimulated, which exhibit circular- and/or elliptical-shape core trajectories at the same frequencies as those of the pumping spin waves. The elliptical core trajectories of the stimulated vortex gyrations can be interpreted by the superposition of different amplitudes and phases of the clockwise and counterclockwise circular eigenmodes. According to the action–reaction effect, the phase and the amplitude of the propagating spin waves that pass through the vortex structure are modified differently into different arms of the nanostrip cross-structure. Thereby, the propagating spin waves are allowed to be transmitted and scattered with contrasting phases and amplitudes in different branch arms. This work provides a fundamental understanding of the interaction of spin waves propagating along domain walls with a magnetic soliton and also suggests potential applications to magnonic information processing devices.
The macroscopic properties of permanent magnets and the resultant performance required for real implementations are determined by the magnets’ microscopic features. However, earlier micromagnetic simulations and experimental studies required relatively a lot of work to gain any complete and comprehensive understanding of the relationships between magnets’ macroscopic properties and their microstructures. Here, by means of supervised learning, we predict reliable values of coercivity (μ0Hc) and maximum magnetic energy product (BHmax) of granular NdFeB magnets according to their microstructural attributes (e.g. inter-grain decoupling, average grain size, and misalignment of easy axes) based on numerical datasets obtained from micromagnetic simulations. We conducted several tests of a variety of supervised machine learning (ML) models including kernel ridge regression (KRR), support vector regression (SVR), and artificial neural network (ANN) regression. The hyper-parameters of these models were optimized by a very fast simulated annealing (VFSA) algorithm with an adaptive cooling schedule. In our datasets of randomly generated 1,000 polycrystalline NdFeB cuboids with different microstructural attributes, all of the models yielded similar results in predicting both μ0Hc and BHmax. Furthermore, some outliers, which deteriorated the normality of residuals in the prediction of BHmax, were detected and further analyzed. Based on all of our results, we can conclude that our ML approach combined with micromagnetic simulations provides a robust framework for optimal design of microstructures for high-performance NdFeB magnets.
These findings suggest that an elastic chest band combined with inspiratory exercise produces additional positive effect on improving chest function in people with limited rib mobility.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.