Search Results (63)

We briefly review the recent developments in magnetohydrodynamics, which in particular deal with the evolution of magnetic fields in turbulent plasmas. We especially emphasize (i) the necessity and utility of renormalizing equations of motion in turbulence where velocity and magnetic fields become Hölder singular; (ii) the breakdown of Laplacian determinism of classical physics (spontaneous stochasticity or super chaos) in turbulence; and (iii) the possibility of eliminating the notion of magnetic field lines in magnetized plasmas, using instead magnetic path lines as trajectories of Alfvénic wave packets. These methodologies are then exemplified with their application to the problem of magnetic reconnection—rapid change in magnetic field pattern that accelerates plasma—a ubiquitous phenomenon in astrophysics and laboratory plasmas. Renormalizing rough velocity and magnetic fields on any finite scale l in turbulence inertial range, to remove singularities, implies that magnetohydrodynamic equations should be regarded as effective field theories with running parameters depending upon the scale l. A high wave-number cut-off should also be introduced in fluctuating equations of motion, e.g., Navier–Stokes, which makes them effective, low-wave-number field theories rather than stochastic differential equations. Full article

The contact process is a nonequilibrium Hamiltonian model that, even in one dimension, lacks an exact solution and has been extensively studied via Monte Carlo simulations, both in steady-state and time-dependent scenarios. Although the effects of particle mobility and diffusion on criticality have been preliminarily explored, they remain poorly understood in many aspects. In this work, we examine how the critical rate of the model varies with the probability of particle mobility. By analyzing different stochastic evolutions of the system, we employ two modern approaches: (1) Random Matrix Theory (RMT): By building on the success of RMT, particularly Wishart-like matrices, in studying statistical physics of systems with up-down symmetry via magnetization dynamics [R. da Silva, IJMPC 2022], we demonstrate its applicability to models with an absorbing state; (2) Optimized Temporal Power Laws: By using short-time dynamics, we optimize power laws derived from ensemble-averaged evolutions of the system. Both methods consistently reveal that the critical rate decays with mobility according to a simple Belehradek function. Additionally, a straightforward mean-field analysis supports the decay of the critical parameter with mobility, although it predicts a simpler linear dependence. We also demonstrate that the more sophisticated pair approximation mean-field model developed by ben-Avraham and Köhler aligns closely with the Belehradek function, precisely matching our lattice simulation results. Full article

Here, we study the Navier–Stokes equation for the motion of a passive particle based on the Fokker–Planck equation in an incompressible conducting fluid induced by a magnetic field subject to an exponentially correlated Gaussian force in three-time domains. For the hydro-magnetic case of velocity and the time-dependent magnetic field, the mean squared velocity for the joint probability density of velocity and the magnetic field has a super-diffusive form that scales as $\sim t^3$ in $t>>\tau$, while the mean squared displacement for the joint probability density of velocity and the magnetic field reduces to time $\sim t^4$ in $t<<\tau$. The motion of a passive particle for $\tau =0$ and $t>>\tau$ behaves as a normal diffusion with the mean squared magnetic field being $<h^2(t)>\sim t$. In a short-time domain $t<<\tau$, the moment in the magnetic field of the incompressible conducting fluid undergoes super-diffusion with ${\mu }_{2,0,2}^h\sim t^6$, in agreement with our research outcome. Particularly, the combined entropy $H(v,h,t)$ ($H(h,v,t)$) for an active particle with the perturbative force has a minimum value of $\sim \ln t^2$ ($\sim \ln t^2$) in $t>>\tau$ ($\tau =0$), while the largest displacement entropy value is proportional to $\ln t^4$ in $t<<\tau$ and $\tau =0$. Full article

Probabilistic models in physics often require the evaluation of normalized Boltzmann factors, which in turn implies the computation of the partition function Z. Obtaining the exact value of Z, though, becomes a forbiddingly expensive task as the system size increases. A possible way to tackle this problem is to use the Annealed Importance Sampling (AIS) algorithm, which provides a tool to stochastically estimate the partition function of the system. The nature of AIS allows for an efficient and parallel implementation in Restricted Boltzmann Machines (RBMs). In this work, we evaluate the partition function of magnetic spin and spin-like systems mapped into RBMs using AIS. So far, the standard application of the AIS algorithm starts from the uniform probability distribution and uses a large number of Monte Carlo steps to obtain reliable estimations of Z following an annealing process. We show that both the quality of the estimation and the cost of the computation can be significantly improved by using a properly selected mean-field starting probability distribution. We perform a systematic analysis of AIS in both small- and large-sized problems, and compare the results to exact values in problems where these are known. As a result, we propose two successful strategies that work well in all the problems analyzed. We conclude that these are good starting points to estimate the partition function with AIS with a relatively low computational cost. The procedures presented are not linked to any learning process, and therefore do not require a priori knowledge of a training dataset. Full article

This study introduces an innovative, non-contact method for classifying the hardness of austenitic stainless steels (grade AISI 304) based on their intrinsic magnetic fields. Utilizing a 3 × 3 matrix sensor system, this research captures weak magnetic fields to produce precise 2D magnetic field maps of the samples. A key advancement is the application of a modified GoogleNet convolutional neural network, optimized with the stochastic gradient descent with momentum algorithm, which achieves exceptional classification accuracy, ranging from 95% to 100%, and median accuracies of 97.5% to 99%. This method stands out by revealing a novel correlation between annealing temperature and magnetic field strength, particularly a pronounced decline in magnetic properties at temperatures near 1000 °C. This observation underscores the sensitivity of magnetic profiles to heat treatments, offering a groundbreaking approach to material characterization. By enabling reliable, efficient, and fully automated hardness evaluation based on magnetic signatures, this work has the potential to transform materials engineering and manufacturing, setting a new benchmark for non-destructive material analysis techniques. Full article

As an important driving device, the permanent magnet synchronous motor (PMSM) plays a critical role in modern industrial fields. Given the harsh working environment, research into accurate PMSM fault diagnosis methods is of practical significance. Time–frequency analysis captures the rich features of PMSM operating conditions, and convolutional neural networks (CNNs) offer excellent feature extraction capabilities. This study proposes an intelligent fault diagnosis method based on continuous wavelet transform (CWT) and CNNs. Initially, a mechanism analysis is conducted on the inter-turn short-circuit and demagnetization faults of PMSMs, identifying and displaying the key feature frequency range in a time–frequency format. Subsequently, a CNN model is developed to extract and classify these time–frequency images. The feature extraction and diagnosis results are visualized with t-distributed stochastic neighbor embedding (t-SNE). The results demonstrate that our method achieves an accuracy rate of over 98.6% for inter-turn short-circuit and demagnetization faults in PMSMs of various severities. Full article

The exhaust gas recirculation (EGR) valve plays an important role in improving engine fuel economy and reducing emissions. In order to improve the positioning accuracy and robustness of the EGR valve under uncertain dynamics and external disturbances, this paper proposes a positioning servo system design for an electromotive (EM) EGR valve based on the Kalman filter. Taking a novel valve driven by a central winding limited angle torque motor (LATM) as the object, we have fully considered the influence of the motor rotor position and load current, as well as the magnetic field saturation and cogging effect, improved the existing LTAM model, and derived accurate torque expression. The parameter uncertainty of the above internal model and the external stochastic disturbance were unified as “total disturbance”, and a Kalman filter-based observer was designed for disturbance estimations and real-time feed-forward compensation. Furthermore, using non-contact magnetic angle measurements to obtain accurate valve position information, a position control model with real-time response and high accuracy was established. Numerous simulated and experimental data show that in the presence of ± 25% plant model parameter fluctuations and random shock-type disturbances, the servo system scheme proposed in this paper achieves a maximum position deviation of 0.3 mm, a repeatability of positioning accuracy after disturbances of 0.01 mm, and a disturbance recovery time of not more than 250 ms. In addition, the above performance is insensitive to the duration of the disturbance, which demonstrates the strong robustness, high accuracy, and excellent dynamic response capability of the proposed design. Full article

As a type of magnetic sensor known for its high reliability and long lifespan, the reliability issues of Hall current sensors have attracted attention in fields such as electromagnetic compatibility. However, there is still a lack of sufficient failure data for reliability prediction. Therefore, a small-sample reliability prediction method based on the improved Bayes method is proposed. Firstly, the pseudo-failure lifespan data are acquired through the accelerated degradation testing of Hall current sensors subjected to temperature and humidity stressors, and the life is examined by the Weibull distribution; then, the data expanded using the BP neural network model are used as the a priori information, and the parameter estimation of the Weibull distribution is obtained by the Bootstrap method and Gibbs sampling; finally, the Peck accelerated model is implemented to achieve the normal temperature-humidity reliability prediction of Hall current sensors under stress, and the utility of the enhanced Bayes technique is confirmed through the application of the Wiener stochastic process model. Full article

Some recent published works have provided an exhaustive characterization of the plasma dynamics during magnetic reconnections in the presence of a magnetic guide field in MRX laboratory plasmas, including an assessment of the mechanisms that convert from magnetic energy to plasma kinetic energy. Among other results, the measurements indicate the existence of a correlation between the electron temperature and the generation of a spectrum of electric oscillations during the reconnection. In this work, we adapt to MRX conditions the well-known stochastic particle heating mechanism, frequently adopted in the astrophysical literature to justify ion heating by low-frequency large-amplitude electromagnetic waves. We show that, under MRX conditions. it may potentially provide a relevant contribution to electron energization. Full article

Dissipation processes derived from the kinetic theory of gases (shear viscosity and heat conduction) are employed to examine the solar wind that interacts with planetary ionospheres. The purpose of this study is to estimate the mean free path of wave-particle interactions that produce a continuum response in the plasma behavior. Wave-particle interactions are necessary to support the fluid dynamic interpretation that accounts for the interpretation of various features measured in a solar wind–planet ionosphere region; namely, (i) the transport of solar wind momentum to an upper ionosphere in the presence of a velocity shear, and (ii) plasma heating produced by momentum transport. From measurements conducted in the solar wind interaction with the Venus ionosphere, it is possible to estimate that in general terms, the mean free path of wave-particle interactions reaches λ_H ≥ 1000 km values that are comparable to the gyration radius of the solar wind particles in their Larmor motion within the local solar wind magnetic field. Similar values are also applicable to conditions measured by the Mars ionosphere and in cometary plasma wakes. Considerations are made in regard to the stochastic trajectories of the plasma particles that have been implied from the measurements made in planetary environments. At the same time, it is as possible that the same phenomenon is applicable to the interaction of stellar winds with the ionosphere of exoplanets, and also in regions where streaming ionized gases reach objects that are subject to rotational motion in other astrophysical problems (galactic flow–plasma interactions, black holes, etc.). Full article

We investigate the stochastic version of the paradigmatic model of magnetohydrodynamic turbulence. The model can be interpreted as an active vector admixture subject to advective processes governed by turbulent flow. The back influence on fluid dynamics is explicitly taken into account. The velocity field is generated through a fully developed turbulent flow taking into account the violation of spatial parity, which is introduced through the helicity parameter $\rho$. We consider a generalized setup in which parameter A is introduced in model formulation, which is associated with the interaction part of the model, and its actual value represents different physical systems. The model is analyzed by means of the field-theoretic renormalization group. The calculation is performed using $\epsilon$-expansion, where $\epsilon$ is the deviation from the Kolmogorov scaling. Two-loop numerical calculations of the renormalization constant associated with the renormalization of the magnetic field are presented. Full article

Frequency-domain electromagnetic induction (FDEMI) methods are frequently used in non-invasive, area-wise mapping of the subsurface electromagnetic soil properties. A crucial part of data analysis is the geophysical inversion of the data, resulting in either conductivity and/or magnetic susceptibility subsurface distributions. We present a novel 1D stochastic optimization approach that combines dimension-adapting reversible jump Markov chain Monte Carlo (MCMC) with artificial bee colony (ABC) optimization for geophysical inversion, with specific application to frequency-domain electromagnetic induction (FDEMI) data. Several solution models of simplified model geometry and a variable number of model knots, which are found by the inversion method, are used to create re-sampled resulting average models. We present synthetic test inversions using conductivity models based on 14 direct-push (DP) EC logs from Greece, Italy, and Germany, as well as field data applications using multi-coil FDEMI devices from three sites in Azerbaijan and Germany. These examples show that the method can effectively lead to solutions that resemble the known DP input models or image reasonable stratigraphic and archaeological features in the field data. Neighboring 1D solutions on field data examples show high coherence along profiles even though each 1D inversion is independently handled. The computational effort for one 1D inversion is less than 120,000 forward calculations, which is much less than usually needed in MCMC inversions, whereas the resulting models show more plausible solutions due to the dimension-adapting properties of the inversion method. Full article

We show how averages of exponential functions of path-dependent quantities, such as those of Work Fluctuation Theorems, detect phase transitions in deterministic and stochastic systems. State space truncation—the restriction of the observations to a subset of state space with prescribed probability—is introduced to obtain that result. Two stochastic processes undergoing first-order phase transitions are analyzed both analytically and numerically: a variant of the Ehrenfest urn model and the 2D Ising model subject to a magnetic field. In the presence of phase transitions, we prove that even minimal state space truncation makes averages of exponentials of path-dependent variables sensibly deviate from full state space values. Specifically, in the case of discontinuous phase transitions, this approach is strikingly effective in locating the transition value of the control parameter. As this approach works even with variables different from those of fluctuation theorems, it provides a new recipe to identify order parameters in the study of non-equilibrium phase transitions, profiting from the often incomplete statistics that are available. Full article

The major goal of this study is to create a venue for further work on the effect of pulsed magnetic fields on plant metabolism. It deals with metabolite synthesis in the aforementioned conditions in microplants of Pyrus communis L. So far, there have been glimpses into the governing factors of plant biochemistry in vivo, and low-frequency pulsed magnestatic fields have been shown to induce additional electric currents in plant tissues, thus perturbing the value of cell membrane potential and causing the biosynthesis of new metabolites. In this study, sixty-seven metabolites synthesized in microplants within 3–72 h after treatment were identified and annotated. In total, thirty-one metabolites were produced. Magnetic-pulse treatment caused an 8.75-fold increase in the concentration of chlorogenic acid (RT = 8.33 ± 0.01₉₇ min) in tissues and the perturbation of phenolic composition. Aucubin, which has antiviral and antistress biological activity, was identified as well. This study sheds light on the effect of magnetic fields on the biochemistry of low-molecular-weight metabolites of pear plants in vitro, thus providing in-depth metabolite analysis under optimized synthetic conditions. This study utilized high-resolution gas chromatography-mass spectrometry, metabolomics methods, stochastic dynamics mass spectrometry, quantum chemistry, and chemometrics, respectively. Stochastic dynamics uses the relationships between measurands and molecular structures of silylated carbohydrates, showing virtually identical mass spectra and comparable chemometrics parameters. Full article

As the third-generation neural network, the spiking neural network (SNN) has become one of the most promising neuromorphic computing paradigms to mimic brain neural networks over the past decade. The SNN shows many advantages in performing classification and recognition tasks in the artificial intelligence field. In the SNN, the communication between the pre-synapse neuron (PRE) and the post-synapse neuron (POST) is conducted by the synapse. The corresponding synaptic weights are dependent on both the spiking patterns of the PRE and the POST, which are updated by spike-timing-dependent plasticity (STDP) rules. The emergence and growing maturity of spintronic devices present a new approach for constructing the SNN. In the paper, a novel SNN is proposed, in which both the synapse and the neuron are mimicked with the spin transfer torque magnetic tunnel junction (STT-MTJ) device. The synaptic weight is presented by the conductance of the MTJ device. The mapping of the probabilistic spiking nature of the neuron to the stochastic switching behavior of the MTJ with thermal noise is presented based on the stochastic Landau–Lifshitz–Gilbert (LLG) equation. In this way, a simplified SNN is mimicked with the MTJ device. The function of the mimicked SNN is verified by a handwritten digit recognition task based on the MINIST database. Full article