Phase Oscillators Research Articles

Noncontact Measurements of Resonance of Small Round Liquid Surfaces Using Sound Wave to Determine Surface Tension.

Various methods have been developed for measuring the surface tension of liquids, and the present study proposes a new method for measuring the surface tension of liquids. A small (9 mm diameter) round liquid surface was excited by sound waves with a common speaker. Nine liquids possessing various physical properties and functional groups were employed, and three resonance frequencies (frs) were observed for each liquid in a frequency range of 20-180 Hz. The resonances were analyzed with a forced oscillation model. In addition, the amplitudes and phases of the resonance oscillations at various positions of the liquid surface were measured, and the total deformations of the liquid surface were determined. The deformation was compared with the Bessel functions, and the oscillation modes and boundary conditions were decided. Finally, proportional relationships between frs and σ0.5ρ-0.5 (σ: surface tension, ρ: density) with high correlations were obtained, which were supported by a new theoretical equation using hydrodynamics.

The phase coherence of cortical oscillations predicts dynamic changes in perceived visibility.

The phase synchronization of brain oscillations plays an important role in visual processing, perceptual awareness, and performance. Yet, the cortical mechanisms underlying modulatory effects of post-stimulus phase coherence and frequency-specific oscillations associated with different aspects of vision are still subject to debate. In this study, we aimed to identify the post-stimulus phase coherence of cortical oscillations associated with perceived visibility and contour discrimination. We analyzed electroencephalogram data from two masking experiments where target visibility was manipulated by the contrast ratio or polarity of the mask under various onset timing conditions (stimulus onset asynchronies, SOAs). The behavioral results indicated an SOA-dependent suppression of target visibility due to masking. The time-frequency analyses revealed significant modulations of phase coherence over occipital and parieto-occipital regions. We particularly identified modulations of phase coherence in the (i) 2-5Hz frequency range, which may reflect feedforward-mediated contour detection and sustained visibility; and (ii) 10-25Hz frequency range, which may be associated with suppressed visibility through inhibitory interactions between and within synchronized neural pathways. Taken together, our findings provide evidence that oscillatory phase alignments, not only in the pre-stimulus but also in the post-stimulus window, play a crucial role in shaping perceived visibility and dynamic vision.

Passive Islanding Detection of Inverter-Based Resources in a Noisy Environment

Islanding occurs when a load is energized solely by local generators and can result in frequency and voltage instability, changes in current, and poor power quality. Poor power quality can interrupt industrial operations, damage sensitive electrical equipment, and induce outages upon the resynchronization of the island with the grid. This study proposes an islanding detection method employing a Duffing oscillator to analyze voltage fluctuations at the point of common coupling (PCC) under a high-noise environment. Unlike existing methods, which overlook the noise effect, this paper mitigates noise impact on islanding detection. Power system noise in PCC measurements arises from switching transients, harmonics, grounding issues, voltage sags and swells, electromagnetic interference, and power quality issues that affect islanding detection. Transient events like lightning-induced traveling waves to the PCC can also introduce noise levels exceeding the voltage amplitude by more than seven times, thus disturbing conventional detection techniques. The noise interferes with measurements and increases the nondetection zone (NDZ), causing failed or delayed islanding detection. The Duffing oscillator nonlinear dynamics enable detection capabilities at a high noise level. The proposed method is designed to detect the PCC voltage fluctuations based on the IEEE standard 1547 through the Duffing oscillator. For the voltages beyond the threshold, the Duffing oscillator phase trajectory changes from periodic to chaotic mode and sends an islanded operation command to the inverter. The proposed islanding detection method distinguishes switching transients and faults from an islanded operation. Experimental validation of the method is conducted using a 3.6 kW PV setup.

Analysing crack evolution in wind turbine bearings using an oscillation-unified model

In wind turbine bearings subjected to oscillating motion, cracks undergoing reciprocating rolling contact loading experience more complex stress changes compared to those under constant rotational motion. However, oscillation is less explored. This study introduces an Oscillation Unified Surface-Initiated Crack Finite Element Model (OU-CFEM) to address this gap. The model is validated through mesh sensitivity analysis and Oscillation Contact Fatigue (OCF) experimental data. The OU-CFEM enables a detailed investigation into crack evolution characteristics under varying initial angles and friction coefficients. The findings reveal that crack behaviors significantly differ across different oscillation phases, except in cases involving vertically oriented cracks or fully lubricated conditions. The study highlights that larger initial angles and higher friction coefficients promote crack extension, with the initial angle playing a more critical role in crack evolution than the friction coefficient. This research provides new insights into the behavior of cracks in oscillating conditions, offering valuable contributions to the understanding of wind turbine bearing durability under such loading scenarios.

A Lake at the Mt. Fuji (Lake Motosu) Recording Prolonged Negative Arctic Oscillation as Reduction of Aeolian Dust Due To Westerly Pathways During the Holocene

AbstractEast Asia is a major source of dust accounting for 20% of the global dust emission. Work on reconstructing past changes in dust transport in East Asia is complicated by difficulties in distinguishing local sedimentation from aerial material and lack of suitable material for age determination. Here, we address these issues and present a new dust proxy record from Lake Motosu, located on the Pacific side of Japan. The record is anchored by a high‐quality tephra and radiocarbon chronology. Because Lake Motosu is situated in a quartz‐free basaltic volcanic province, all quartz deposited in the lake is likely to be aerially sourced, and variations in quartz content should reflect past changes in dust transport. Our new record detects a low dust deposition event from 3.0 to 2.0 ka. This event corresponds to elevated sea surface temperature in the Sea of Japan and climate conditions similar to a negative phase of the Arctic Oscillation, indicating an association with the weakened Westerlies and with less frequent dust storms in the source region. The increase in spatial resolution of past dust transport reconstructions will further improve our understanding of the mechanism related to dust emission from East Asia.

Training coupled phase oscillators as a neuromorphic platform using equilibrium propagation

Abstract Given the rapidly growing scale and resource requirements of machine learning applications, the idea of building more efficient learning machines much closer to the laws of physics is an attractive proposition. One central question for identifying promising candidates for such neuromorphic platforms is whether not only inference but also training can exploit the physical dynamics. In this work, we show that it is possible to successfully train a system of coupled phase oscillators—one of the most widely investigated nonlinear dynamical systems with a multitude of physical implementations, comprising laser arrays, coupled mechanical limit cycles, superfluids, and exciton-polaritons. To this end, we apply the approach of equilibrium propagation, which permits to extract training gradients via a physical realization of backpropagation, based only on local interactions. The complex energy landscape of the XY/Kuramoto model leads to multistability, and we show how to address this challenge. Our study identifies coupled phase oscillators as a new general-purpose neuromorphic platform and opens the door towards future experimental implementations.

Closed-loop auditory stimulation targeting alpha and theta oscillations during REM sleep induces phase-dependent power and frequency changes.

Alpha and theta oscillations characterize the waking human electroencephalogram (EEG) and can be modulated by closed-loop auditory stimulation (CLAS). These oscillations also occur during rapid eye movement (REM) sleep, but their function here remains elusive. CLAS represents a promising tool to pinpoint how these brain oscillations contribute to brain function in humans. Here we investigate whether CLAS can modulate alpha and theta oscillations during REM sleep in a phase-dependent manner. We recorded high-density EEG during an extended overnight sleep period in 18 healthy young adults. Auditory stimulation was delivered during both phasic and tonic REM sleep in alternating 6s ON and 6s OFF windows. During the ON windows, stimuli were phase-locked to four orthogonal phases of ongoing alpha or theta oscillations detected in a frontal electrode. The phases of ongoing alpha and theta oscillations were targeted with high accuracy during REM sleep. Alpha and theta CLAS induced phase-dependent changes in power and frequency at the target location. Frequency-specific effects were observed for alpha trough (speeding up) and rising (slowing down) and theta trough (speeding up) conditions. CLAS-induced phase-dependent changes were observed during both REM sleep substages, even though auditory evoked potentials were very much reduced in phasic compared to tonic REM sleep. This study provides evidence that faster REM sleep rhythms can be modulated by CLAS in a phase-dependent manner. This offers a new approach to investigate how modulation of REM sleep oscillations affects the contribution of this vigilance state to brain function.

Resonance-induced synchronization in coupled phase oscillators with bimodal frequency distribution and periodic coupling.

Synchronization behaviors in globally coupled phase oscillators with symmetric bimodal frequency distribution and periodic coupling are studied. It is found that by a proper setting of the frequency of the periodic coupling, the synchronization propensity of the oscillators can be markedly improved. Specifically, we show that when the frequency of the periodic coupling matches the distance of the central frequencies in the distribution, the critical coupling characterizing the onset of synchronization can be substantially decreased. The mechanism behind the phenomenon of periodic-coupling-enhanced synchronization is analyzed by the methods of Ott-Antonsen ansatz and synchronization transition tree, and it is revealed that the synchronization enhancement is attributed to the resonance between the synchronization clusters and the periodic coupling.

Optimization research on control strategies for photovoltaic energy storage systems considering multi-mode operation

In this paper, a selective input/output strategy is proposed for improving the life of photovoltaic energy storage (PV-storage) virtual synchronous generator (VSG) caused by random load interference, which can sharply reduce costs of storage device. The strategy consists of two operating modes and a power coordination control method for the VSGs. Firstly, a selective VSG input strategy is proposed based on the magnitude of disturbances, a method of offline solving model equation is used for determine the VSG input time. An online method is used for matching the disturbance frequency variations, which enables a selective VSG startup method, allowing the grid to prioritize the utilization of the generator's physical inertia. Secondly, a dynamic VSG exit strategy is developed based on dynamic frequency characteristics to prevent secondary oscillations in the frequency recovery phase of the PV-storage VSG following grid disturbances. This strategy is crucial as grid variations may affect energy storage lifespan and reduce frequency recovery speed. Finally, the proposed approach is validated for correctness and effectiveness through computer simulations and semi-physical experiments using the NI-PXI + LabVIEW platform. Through the above optimization and research, the selective start of VSG is realized, the energy storage life is improved, the capacity of charge and discharge cycles is reduced by 37.82 % compared with the strategy without investment and withdrawal, and the life loss in the secondary charge and discharge process of PV-storage VSG is avoided, which is conducive to frequency recovery.

Modeling the chaotic dynamics of bubble growth under hydrodynamic interactions in pool boiling

This study seeks to understand the origins of intermittency in quantities of interest in pool boiling, such as bubble departure diameter and growth time. The intermittency of nucleation site activity due to hydrodynamic and thermal interactions with nearby nucleation sites is well-established; however, few mechanistic models have been developed that predict such intermittency. Here we assume a fluctuating pressure field at a nucleation site due to an adjacent bubble which alters bubble growth depending on the phase of the pressure oscillation with respect to the instant of bubble nucleation. The results suggest that even when a single departure frequency is assumed for nearby bubbles, its effect on the nucleation site being considered causes aperiodicity and intermittency in bubble departure quantities. The effects of pressure field phase angle, degree of superheat, and choice of force balance model on the bubble departure quantities are examined. The phase angle is shown to play a major role in determining bubble departure diameter and growth time. The departure diameter is observed to have a broad distribution, belying the assumption of a unique value. Period doubling of the ebullition cycle is observed for some conditions, a phenomenon documented by other investigators. A multifractal analysis of the time series of departure events indicates the presence of long term temporal correlations, which become weaker with increasing superheat. The second order Hurst exponent of the structure functions of departure events appears to have a universal behavior with Jakob number, independent of the choice of liquid. The Hurst exponent rises from zero to a value close to 0.5 at large superheats, suggesting a continuous transition from an ordered state to a disordered state along the boiling curve.

Optimizing real-time phase detection in diverse rhythmic biological signals for phase-specific neuromodulation.

Closed-loop, phase-specific neurostimulation is a powerful method to modulate ongoing brain activity for clinical and research applications. Phase-specific stimulation relies on estimating the phase of an ongoing oscillation in real time and issuing a control command at a target phase. Phase detection algorithms based on Fast Fourier transform (FFT) are widely used due to their computational efficiency and robustness. However, it is unclear how algorithm performance depends on the spectral properties of the input signal and how algorithm parameters can be optimized. We used offline simulation to evaluate the performance of three algorithms (endpoint-corrected Hilbert Transform, Hilbert Transform and phase mapping) on three rhythmic biological signals with distinct spectral properties (rodent hippocampal theta potential, human EEG alpha and human essential tremor). First, we found that algorithm performance was more strongly influenced by signal amplitude and frequency variation compared with signal to noise ratio. Second, our simulations showed that the size of the data window for phase estimation was critical for the performance of FFT-based algorithms, where the optimal data window corresponds to the period of the oscillation. We validated this prediction with real time phase detection of hippocampal theta oscillations in freely behaving rats performing spatial navigation. Our findings define the relationship between signal properties and algorithm performance and provide a convenient method for optimizing FFT-based phase detection algorithms.

Comparative Analysis of Machine Learning Models for Tropical Cyclone Intensity Estimation

Estimating tropical cyclone (TC) intensity is crucial for disaster reduction and risk management. This study aims to estimate TC intensity using machine learning (ML) models. We utilized eight ML models to predict TC intensity, incorporating factors such as TC location, central pressure, distance to land, landfall in the next six hours, storm speed, storm direction, date, and number from the International Best Track Archive for Climate Stewardship Version 4 (IBTrACS V4). The dataset was divided into four sub-datasets based on the El Niño–Southern Oscillation (ENSO) phases (Neutral, El Niño, and La Niña). Our results highlight that central pressure has the greatest effect on TC intensity estimation, with a maximum root mean square error (RMSE) of 1.289 knots (equivalent to 0.663 m/s). Cubist and Random Forest (RF) models consistently outperformed others, with Cubist showing superior performance in both training and testing datasets. The highest bias was observed in SVM models. Temporal analysis revealed the highest mean error in January and November, and the lowest in February. Errors during the Warm phase of ENSO were notably higher, especially in the South China Sea. Central pressure was identified as the most influential factor for TC intensity estimation, with further exploration of environmental features recommended for model robustness.

East Asian summer monsoon evolution during Heinrich Stadial 5

The bi-millennial Heinrich Stadial 5 (HS 5) event, occurring approximately 48 thousand years ago (kyr BP, before 1950 AD), represents a notable cooling episode during the last glacial period in the Northern Hemisphere. However, our comprehension of its impact on Asian summer monsoon variability remains incomplete, largely because of the scarcity of precisely dated and high-resolution records. Here, we present stalagmite δ18O-inferred absolutely dated decadal-resolved evolution of the East Asian summer monsoon (EASM) during HS 5 from Xianyun Cave, southeastern China. Stalagmite chronology constrains the occurrence of HS 5 from 48.67 ± 0.13 to 46.42 ± 0.10 kyr BP. The Xianyun stalagmite record distinctly reveals the three-phase evolution of the EASM during HS 5. It includes a gradual onset lasting for 0.79 kyr (phase 1) from 48.67 to 47.88 kyr BP, a weak EASM oscillation phase (phase 2) with three centennial-scale intensified monsoon events during 47.88–46.94 kyr BP, followed by a gradual 520-yr recovery phase (phase 3). In phases 1 and 2, the synchronous climatic shifts and resemblance to three notable centennial-scale events observed in both the Xianyun and Greenland ice core records suggest a rapid atmospheric teleconnection between the North Atlantic and EASM regions. During phase 3, the 5-century monsoon recovery registered in the Xianyun record contrasts sharply with the rapid 60-yr rebound in the Greenland ice core record. This disagreement suggests a plausible decoupling between low- and high-latitude climates, probably related to tropical sea surface temperatures variabilities and the distinct responses of the regional Intertropical Convergence Zone to El Niño Southern Oscillation variabilities. Our study highlights the essential role of tropical ocean-atmosphere coupling systems in the termination stages of millennial-scale HS events.

Self-organized time crystal in driven-dissipative quantum system

Continuous time crystals (CTCs) are characterized by sustained oscillations that break the time-translation symmetry. Since the ruling out of equilibrium CTCs by no-go theorems, the emergence of such dynamical phases has been observed in various driven-dissipative quantum platforms. The current understanding of CTCs is mainly based on mean-field theories, which fail to address the problem of whether the continuous time-translation symmetry can be broken in noisy, spatially extended systems absent in all-to-all couplings. Here, we propose a CTC realized in a quantum contact model through self-organized bistability. The CTCs stem from the interplay between collective dissipation induced by the first-order absorbing phase transitions and slow constant driving provided by an incoherent pump. The stability of such oscillatory phases in finite dimensions under the action of intrinsic quantum fluctuations is scrutinized by the functional renormalization group method and numerical simulations. Occurring at the edge of many-body synchronization, the CTC phase exhibits an inherent period and amplitude with a coherence time linearly diverging with system size, thus also constituting a boundary time crystal. Our results serve as a solid route towards self-protected CTCs in strongly interacting open systems. Published by the American Physical Society 2024

Predictive grid coding in the medial entorhinal cortex.

The entorhinal cortex represents allocentric spatial geometry and egocentric speed and heading information required for spatial navigation. However, it remains unclear whether it contributes to the prediction of an animal's future location. We discovered grid cells in the medial entorhinal cortex (MEC) that have grid fields representing future locations during goal-directed behavior. These predictive grid cells represented prospective spatial information by shifting their grid fields against the direction of travel. Predictive grid cells discharged at the trough phases of the hippocampal CA1 theta oscillation and, together with other types of grid cells, organized sequences of the trajectory from the current to future positions across each theta cycle. Our results suggest that the MEC provides a predictive map that supports forward planning in spatial navigation.

Ultra‐Stable Phonon Laser Through Closed‐Loop Feedback Control for Optomechanical Sensing

AbstractThe resonant amplification of the optical and mechanical components in cavity optomechanical systems enhances the performance of sensing applications, which rely on precise frequency control and narrowed mechanical linewidth. This study proposes a direct closed‐loop feedback technique for a narrow linewidth phonon laser based on optomechanical self‐oscillation. By continuously pumping a mechanical resonator with a laser, a phonon laser is achieved with an oscillation frequency of 17.58 MHz, exhibiting a narrow linewidth of just 0.44 mHz and an effective mechanical Q of 4 × 1010, while preserving the linear frequency response. The oscillator phase is fed back to the pump laser, yielding a mechanical frequency stability of 7.2 × 10−11 and providing a real‐time output of direct current voltage corresponding to the oscillator frequency changes. The system's real‐time sensing capability is tested using fiber probes and polystyrene particles, demonstrating a performance approach to the theoretical resolution limit. This work opens new avenues for real‐time precision measurement technology and can be extended to different optomechanical systems.

Ratchet loading and multi-ensemble operation in an optical lattice clock

We demonstrate programmable control over the spatial distribution of ultra-cold atoms confined in an optical lattice. The control is facilitated through a combination of spatial manipulation of the magneto-optical trap and atomic population shelving to a metastable state. We first employ the technique to load an extended (5 mm) atomic sample with uniform density in an optical lattice clock (OLC), reducing atomic interactions and realizing remarkable frequency homogeneity across the atomic cloud. We also prepare multiple spatially separated atomic ensembles, and realize multi-ensemble clock operation within the standard one-dimensional (1D) OLC architecture. Leveraging this technique, we prepare two oppositely spin-polarized ensembles that are independently addressable, offering a platform for implementing spectroscopic protocols for enhanced tracking of local oscillator phase. Finally, we demonstrate a relative fractional frequency instability at one second of 2.4(1)×10−17 between two ensembles, useful for characterization of intra-lattice differential systematics.

Echoes from Sensory Entrainment in Auditory Working Memory for Pitch.

Ongoing neural oscillations reflect cycles of excitation and inhibition in local neural populations, with individual neurons being more or less likely to fire depending upon the oscillatory phase. As a result, the oscillations could determine whether or not a sound is perceived and/or whether its neural representation enters into later processing stages. While empirical support for this idea has come from sound detection studies, large gaps in knowledge still exist regarding memory for sound events. In the current study, it was investigated how sensory entrainment impacts the fidelity of working memory representations for pitch. In two separate experiments, an 8 Hz amplitude modulated (AM) entraining stimulus was presented prior to a multitone complex having an f0 between 270 and 715 Hz. This "target" sound could be presented at phases from 0 to 2π radians in relation to the previous AM. After a retention interval of 4 s (Experiment 1; n = 26) or 2 s (Experiment 2; n = 28), listeners were tasked to reproduce the target sound's pitch by moving their finger along the horizontal axis of a response pad. It was hypothesized that if entrainment modulates auditory working memory fidelity, reproductions of a target's pitch would be more accurate and precise when targets were presented in phase with the entrainment. Cosine fits of the average data for both experiments showed a significant entrainment "echo" in the accuracy of pitch matches. There was no apparent echo in the matching precision. Fitting of the individual data accuracy showed that the optimal phase was consistent across individuals, aligning near the next AM peak had the AM continued. The results show that sensory entrainment modulates auditory working memory in addition to stimulus detection, consistent with the proposal that ongoing neural oscillatory activity modulates higher-order auditory processes.

Mid-Holocene climate at mid-latitudes: assessing the impact of Saharan greening

Abstract. During the first half of the Holocene (11 000 to 5000 years ago), the Northern Hemisphere experienced a strengthening of the monsoonal regime, with climate reconstructions robustly suggesting a greening of the Sahara region. Palaeoclimate archives also show that this so-called African humid period (AHP) was accompanied by changes in climate conditions at middle to high latitudes. However, inconsistencies still exist in reconstructions of the mid-Holocene (MH) climate at mid-latitudes, and model simulations provide limited support in reducing these discrepancies. In this paper, a set of simulations performed using a climate model are used to investigate the hitherto unexplored impact of Saharan greening on mid-latitude atmospheric circulation during the MH. Numerical simulations show Saharan greening has a year-round impact on the main circulation features in the Northern Hemisphere, especially during boreal summer (when the African monsoon develops). Key findings include a westward shift in the global Walker Circulation, leading to modifications in the North Atlantic jet stream in summer and the North Pacific jet stream in winter. Furthermore, Saharan greening modifies atmospheric synoptic circulation over the North Atlantic, enhancing the effect of orbital forcing on the transition of the North Atlantic Oscillation phase from predominantly positive to negative in winter and summer. Although the prescription of vegetation in the Sahara does not improve the proxy–model agreement, this study provides the first constraint on the influence of Saharan greening on northern mid-latitudes, opening new opportunities for understanding MH climate anomalies in regions such as North America and Eurasia.

Neural coding of space by time.

The intertwining of space and time poses a significant scientific challenge, transcending disciplines from philosophy and physics to neuroscience. Deciphering neural coding, marked by its inherent spatial and temporal dimensions, has proven to be a complex task. In this paper, we present insights into temporal and spatial modes of neural coding and their intricate interplay, drawn from neuroscientific findings. We illustrate the conversion of a purely spatial input into the temporal form of a singular spike train, demonstrating storage, transmission to remote locations, and recall through spike bursts corresponding to Sharp Wave Ripples. Moreover, the converted temporal representation can be transformed back into a spatiotemporal pattern. The principles of the transformation process are illustrated using a simple feed-forward spiking neural network. The frequencies and phases of Subthreshold Membrane potential Oscillations play a pivotal role in this framework. The model offers insights into information multiplexing and phenomena such as stretching or compressing time of spike patterns.