Power Spectral Density Distribution Research Articles

Evolution of tooth surface morphology and tribological properties of helical gears during mixed lubrication sliding wear

In mixed lubrication, the interplay of lubricant flows, solid asperity contact, and material wear between tooth surfaces creates complex and unpredictable contact states on tooth surface. To comprehensively understand the interaction between the lubrication and wear characteristics of the rough tooth surfaces of helical gears, this study established a mixed lubrication sliding wear calculation model for helical gears based on the mixed elastohydrodynamic lubrication model and Archard’s model. Specifically, the study aimed to examine the effects of surface topography features on average film thickness, contact area ratio, and accumulated wear at the meshing point. The findings demonstrated that the texture and power spectral density distributions of a non-Gaussian reconstructed surface closely resembled those of the actual ground surface. Furthermore, for non-Gaussian rough surfaces, a larger wavelength ratio enhanced microwedge motion, which increased film thickness and reduced wear. Additionally, a negatively skewed surface demonstrated better lubrication performance compared to both positively skewed and Gaussian surfaces. This improved performance is evident in the smaller contact area ratio and lower accumulated wear value of the negatively skewed surface.

Numerical analysis of a parallel triple-jet of liquid-sodium in a turbulent forced convection regime

In the present study, we have applied a combined wall-resolving dynamic Large-Eddy Simulation (LES) (for the velocity field) and Direct Numerical Simulation (DNS) (for the temperature field) approach for mixing of parallel triple-jets with different temperatures of liquid sodium in a turbulent forced convection regime. Because of the high thermal conductivity of sodium (a low-Prandtl fluid), we adopted the dynamic Smagorinsky subgrid closure for the unresolved velocity scales, while the thermal scales are fully resolved. Furthermore, the Time-dependent Reynolds-Averaged Navier-Stokes (T-RANS) approach with the high-Reynolds number variant (i.e. with the wall functions as boundary conditions along solid boundaries) of the four-equation eddy viscosity model (k−ε−kθ−εθ) was applied. The fine-mesh LES/DNS provided a close agreement with the experimental data for both velocity and temperature fields (for both first- and second-moments). In contrast, the coarse-mesh LES/DNS overestimated the turbulent kinetic energy profiles at different distances from the inlet plane. The T-RANS results confirmed a good agreement with the mean streamwise velocity and turbulent kinetic energy, as well as the mean temperature profiles. Finally, the analysis of power spectral density distributions of the temperature signal revealed that all simulation techniques captured a dominant flow frequency originating from the induced Kelvin-Helmholtz instabilities between the side and central jets. The presented combined dynamic LES/DNS approach is recommended for future simulations of the turbulent forced convection flows of low Prandtl fluids, especially if thermal fatigue effects need to be predicted correctly.

Sample entropy-based quantitative assessment of the arc magnetic field spectrum for improved arc welding quality

Arc magnetic field analysis is a valuable approach for assessing the stability of the arc welding process, yet existing methods lack the ability to effectively quantify the disorder within the process. Through an investigation into the characteristics of the arc magnetic field signal, it was observed that the occurrence of low-frequency random fluctuations in arc magnetic field power, induced by unstable factors such as bubbles or short circuits, contributed to increased complexity and randomness in the arc magnetic field signals. To visualise the arc magnetic field signals in a time-frequency domain, a spectrogram was employed, revealing a strong correlation between the distribution of maximum power spectral density (PSD) in the spectrogram and the stability of the arc welding process. Furthermore, a novel method based on sample entropy was introduced to provide a quantitative measure of this relationship. A comprehensive quantitative assessment indicator called arc magnetic field sample entropy (AMFSE) was proposed. This indicator effectively mitigates the influence of varying parameters on the quantitative results, enabling a more accurate and consistent representation of the stability of the arc welding process. The proposed method was validated through testing, yielding an accuracy rate exceeding 90%.

Statistical Properties of Exohiss Waves and Associated Scattering Losses of Radiation Belt Electrons

AbstractExohiss waves are a type of structureless whistler‐mode waves that exist in the low density plasmatrough outside the plasmapause and may potentially perturb the motions of electrons in the radiation belt and ring current. Using data from Van Allen Probe A, we analyze the distribution of magnetic power spectral density (PSD) of exohiss waves in different magnetic local time (MLT) and L‐shell regions near the geomagnetic equator. The results reveal that the peak magnetic PSD of exohiss waves is the weakest at MLT = 0–6 and the strongest at MLT = 12–18. The magnetic PSDs of exohiss waves are much lower than those of chorus and hiss waves except for the MLT = 12–18 sector. In addition, we calculated the quasi‐linear bounce‐averaged pitch angle and momentum diffusion coefficients (〈Dαα〉 and 〈Dpp〉) of electrons caused by exohiss waves. The diffusion coefficients are then compared with those caused by chorus and hiss waves. The peak 〈Dαα〉 of electrons driven by exohiss waves becomes stronger as L‐shell increases at all MLTs and is the greatest on the dayside, especially in the sector of MLT = 12–18. Exohiss waves have more significant effect on the loss of radiation belt electrons with specific energy levels related to MLT and L‐shell region compared to chorus and hiss waves. On the other hand, 〈Dpp〉 of electrons caused by exohiss waves is very small, which illustrates that exohiss waves have almost no acceleration effect on electrons.

Numerical vibrational acoustics and sound quality assessment of pipa strings

This study harmonizes subjective auditory and psychological elements related to plucked strings with objective physical attributes through spectrum analysis and numerical simulation, confirming the congruity between objective assessment systems and subjective perceptions in evaluating pipa string sound quality. The research combines experimental investigation of plucked pipa string vibrations with detailed numerical simulations of vibrational acoustic fields using a specified 3 N input force. Results demonstrate that as frequency increases (from A4: 440 Hz to E6: 1320 Hz), more pronounced vibrations emerge along the pipa body, leading to heightened overall sound pressure within the vibrational acoustic fields. The sound pressure distribution analysis reveals that the rosewood pipa exhibits significantly lower standard deviations of 41.8% and 41.9% compared to the tung wood pipa, and 24.6% and 32.2% lower than the white wood pipa at 440 Hz and 1320 Hz, respectively, highlighting the rosewood pipa’s superior sound pressure uniformity. Additionally, an assessment of the A4 signal’s Power Spectral Density (PSD) distribution underscores even spectral energy dispersion in the rosewood pipa’s A4 sound signal, with minor resonance peaks in higher-order overtones beyond 5000 Hz, further substantiating the superior sound quality of the rosewood pipa, in line with expert reviews.

Data-driven prediction of aerodynamic noise of transonic buffeting over an airfoil

Accurately predicting buffet frequency and aerodynamic noise level is crucial in transonic buffet noise reduction studies. In this study, the Random Forest (RF) algorithm is employed to predict the Power Spectral Density (PSD) and Overall Sound Pressure Level (OASPL) distribution over the supercritical airfoil RAE2822. The study indicates that the RF algorithm exhibits greater advantages over the Multi-Layer Perceptron (MLP). This algorithm does not suffer from the problems of high-frequency divergence or inflection distortion, maintaining a reduction in prediction errors for OASPL and PSD by approximately three to four orders of magnitude. Additionally, this paper proposes a priori criterion for evaluating the accuracy of PSD prediction. When there is a strong correlation between PSD at adjacent points, a data-driven modeling approach can achieve higher prediction accuracy. However, when the root mean square error of the cross-correlation functions, auto-correlation functions, and the PSD between adjacent points are both high, the generalization ability of pure data-driven modeling is insufficient, necessitating the additional monitoring points to ensure prediction accuracy.

Numerical Characterization of Prestall Disturbances in a Compressor Stage

Abstract Compared to the subject of rotating stall, less attention has been given to prestall flow disturbances in turbomachinery. This is mostly due to the fact that these phenomena, often referred to as Rotating Instabilities, do not necessarily or distinctly occur in every compressor and usually last for a long period without posing serious threats to stable operation. The present study aims at numerically characterizing prestall disturbances in detail in a compressor stage in which they have been experimentally observed. Full-annulus computational fluid dynamics (CFD) computations employing a hybrid scale-resolving turbulence model are performed and compared against unsteady experimental data. Assessment of specific prestall disturbance metrics, including power spectral density, coherence, and circumferential mode distribution at the rotor clearance, shows that the numerical model is successful in reproducing the unsteady phenomena close to stall inception. Moreover, dynamic mode decomposition is employed to directly link each peak of the unique prestall disturbance spectral signature to spatial structures with a different circumferential order. This procedure is done for both 1D circumferential modes, based on flush-mounted pressure probes, and for 2D surfaces exposing the tip clearance dynamics. The high-resolution numerical model, supplemented with vortex identification criteria and data-driven decompositions, enhances the understanding of prestall disturbances and their associated coherent flow structures.

Transient quasi-periodic oscillations in the gamma-ray light curves of bright blazars

ABSTRACT Transient quasi-periodic oscillations (QPOs) are extremely interesting observational phenomena. However, the precise physical mechanisms leading to their generation are still hotly debated. We performed a systematic search for transient QPO signals using Weighted Wavelet Z-transforms on the gamma-ray light curves of 134 bright blazars with peak flux exceeding 1 × 10−6 ph cm−2 s−1 as monitored by Fermi-LAT. Artificial light curves were generated from the power spectral density and probability distribution functions of the original light curves to assess the significance level of transient QPO. We discuss several physical mechanisms that produce transient QPOs, with the helical jet model providing the best explanation. This study identified four new transient QPO events. Interestingly, repetitive transient QPOs are observed in PKS 0537-441, and nested transient QPOs are detected in PKS 1424−41. Additionally, we find that transient QPOs tend to occur in the flare state of the blazar. Finally, we estimate the incidence of transient QPO events to be only about 3 per cent.

Variable-Angle Random High-Frequency Voltage Injection Strategy with Cross-Saturation Effect Compensation for Sensorless Synchronous Reluctance Motor Drives

There are usually noise problems when the position sensorless control of a synchronous reluctance motor (SynRM) is carried out by high-frequency (HF) signal injection method. Due to the special structure, the cross-saturation effect of the SynRM is particularly serious, resulting in reduced position observation accuracy. In this paper, a variable-angle random HF voltage injection strategy with cross-saturation effect compensation is proposed for position sensorless SynRM drives. Random number generation based on the chaotic mapping method is used to generate random HF voltage signals with different frequencies for injection; the current power spectral density (PSD) distribution is extended and the HF noise can be reduced. A control strategy based on variable-angle square-wave injection is proposed to suppress the cross-saturation effect. By measuring the position error curves of different loads off-line and polynomial fitting the curves, the position error is achieved by combining with the corresponding signal demodulation algorithm. The proposed method does not require additional hardware resources and can maintain high control accuracy and robustness. Finally, the effectiveness of the proposed sensorless control strategy is verified on a 3 kW SynRM experimental platform.

Noninvasive EEG measurement of sleep in the family cat and comparison with the dog

Abstract We have successfully measured the sleep electroencephalogram (EEG) of 12 family cats during an afternoon nap using a completely noninvasive methodology originally developed and validated for family dogs. Extracting both macrostructural and spectral sleep variables from the acquired data, we: (1) provided a descriptive analysis of sleep structure in cats and the power spectral density (PSD) distribution considering 3 sleep stages—drowsiness, non-rapid eye movement (NREM), and rapid eye movement (REM) sleep; and (2) compared the results to those obtained in family dogs measured under the same conditions and using the same methodology. Importantly, our description of sleep structure and PSD distribution in cats proved to be comparable to those of earlier invasive studies, highlighting that appropriate noninvasive methodologies may provide a viable alternative to those that are invasive in some cases. While no macrostructural differences were found between the sleep of cats and dogs, and the characteristic PSDs were mostly similar across sleep stages within the 2 species, the high-frequency resolution comparison of PSD distributions revealed differences between the 2 species in all sleep stages (concerning the delta, theta, alpha, sigma, and beta bands in drowsiness and NREM sleep; and the delta, alpha, and sigma bands in REM sleep). Potential factors underlying these differences are discussed, including differences in circadian rhythms, sleep homeostatic regulation, experienced stress, or even differential attitudes toward owners—highlighting important links between sleep characteristics and often more complex neural and behavioral features.

A wavelet based PSD approach for fault detection and classification in grid connected inverter interfaced microgrid

The recent progress in the area of microgrid protection has led to the application of data-driven and signal processing techniques for fault detection in microgrids. The deployment of inverter interfaced distributed generators makes the traditional protection schemes ineffective. In this paper a wavelet based power spectral density (PSD) has been utilized to detect fault, identify faulty phases and locate fault in the microgrid. Wavelet based PSD decouples the frequency information from the time information and it is the optimal technique to reveal the change of distribution of harmonics in non-stationary faulty signals as for fault analysis it can consider the faulty frequency band instead of one particular harmonic. The proposed technique can detect fault and identify faulty phases by analyzing current signals from the substation only. The normalized value of PSD of three phase current signals has been considered for fault detection and probabilistic distribution of PSD has been considered for faulty phase identification. The normalized value of PSD of the current signals collected from both ends of the main feeders and one end of the non-DG sub-feeder have been used to locate the faulty feeder and sub-feeder. Appropriate threshold values have been considered to detect fault and to locate the faulty feeder. To locate faults, the technique does not require synchronized signals and thus provides a promising and economical solution for inverter interfaced microgrid. It does not require huge amount of data set for training and testing as it is essentially a threshold based technique and also does not require collection of data at high sampling rate. The proposed scheme has been extensively tested for high impedance fault (HIF), unbalanced loading, load switching, variation of DG parameters, measurement uncertainty and presence of noise in the signal with signal to noise ratio up to 10 dB. This technique provides accurate results for fault resistance from 0 Ω to 300 Ω and up to 10% measurement error. The performance of the proposed technique has been verified for feeder length variation up to 20%. Half cycle of current signal only after fault is required and three levels of wavelet decomposition has been considered. The simulation results and comparative assessment demonstrate the efficacy of the proposed scheme.

A data-driven network decomposition of the temporal, spatial, and spectral dynamics underpinning visual-verbal working memory processes

The brain dynamics underlying working memory (WM) unroll via transient frequency-specific large-scale brain networks. This multidimensionality (time, space, and frequency) challenges traditional analyses. Through an unsupervised technique, the time delay embedded-hidden Markov model (TDE-HMM), we pursue a functional network analysis of magnetoencephalographic data from 38 healthy subjects acquired during an n-back task. Here we show that this model inferred task-specific networks with unique temporal (activation), spectral (phase-coupling connections), and spatial (power spectral density distribution) profiles. A theta frontoparietal network exerts attentional control and encodes the stimulus, an alpha temporo-occipital network rehearses the verbal information, and a broad-band frontoparietal network with a P300-like temporal profile leads the retrieval process and motor response. Therefore, this work provides a unified and integrated description of the multidimensional working memory dynamics that can be interpreted within the neuropsychological multi-component model of WM, improving the overall neurophysiological and neuropsychological comprehension of WM functioning.

An experimental investigation on the thermal behavior of synthetic jets involving non-circular orifices with varying wave patterns

ABSTRACT The present work reports the thermal behavior of synthetic jets (SJs) with different non-circular orifices and various waveforms such as triangular, sinusoidal, and square. Tests are conducted for the different nozzle-to-plate spacing (1–25) for a given value of Reynolds number (5380). The triangular waveform exhibits better heat transfer for all the orifice shapes. The value of maximum heat transfer for rectangular SJ is found to be 32% and 16.5% higher than isosceles and equilateral triangular SJs, respectively. The equilateral triangular SJ exhibits 12.9% higher heat transfer than the isosceles triangular SJ. The power spectral density distribution is plotted for all three waveforms.

Dynamic Detection of Vigorous Worm in Infected Host

The Internet has developed to give many benefits to mankind. The access to information being one of the most important. Worms cause major security threats to the Internet. Worms are software components that are capable of infecting a computer and then using that computer to infect another computer. The cycle is repeated, and the population of worm-infected computers grows rapidly. Smart worms cause most important security threats to the Internet. The ability of smart worms spread in an automated fashion and can flood the internet in a very short time. A new class of smart worms, referred to as Camouflaging worm (C-Worm in short). C-worm is different from traditional worm. C-worm intelligently manipulate its scan traffic volume over time. Motivated by observations, we designed a novel Spectrum Based detection Scheme to detect the C-worm. This scheme uses Power Spectral Density (PSD) distribution of the scan traffic volume and its corresponding Spectral Flatness Measure (SFM) to distinguish the C-worm traffic from background traffic. This scheme is effectively detecting not only the C-worm but traditional worms as well. The goal is to prevent, detect and delete the smart worms as well as traditional worms.

Evaluation of aircraft random vibration under roughness excitation during taxiing

The assessment of runway smoothness or roughness is intimately tied to the vibrational response of aircraft during taxiing. In this study, employing the pseudo excitation method (PEM) based on random vibration analysis, we unearthed the relationship between the random vibrations of five distinct aircraft types and runway irregularities. Initially, we established two three-dimensional (3D) models of aircraft taxiing vibration and derived the response output under roughness excitation. Subsequently, we employed MATLAB to analyze the power spectral characteristics of the vibrational response in different parts of the aircraft. Lastly, we examined the effects of taxiing speed, aircraft type, runway roughness, and lift on the aircraft's vibration. Our findings indicate that the distribution of vibration power spectral density (PSD) exhibits multiple peaks, correlating with the degrees of freedom of the aircraft. We further note that the frequency that aligns most closely with the response peak should be the focus of investigation. High-frequency excitation impacts the pilot and nose landing gear more significantly than the passenger and main landing gear. Absent the consideration of lift, increased taxiing speed amplifies the impact of roughness excitation on aircraft taxiing safety. Larger aircrafts are more sensitive to long-wave roughness. With lift in consideration, all aircraft types exhibit a speed sensitivity to vibration, which should be the primary concern in runway roughness evaluations.

Vibration Response Law of Aircraft Taxiing under Random Roughness Excitation

Understanding the vibration response patterns of aircraft taxiing under runway roughness excitation is crucial for aircraft design and runway performance evaluation. In this paper, we establish pavement roughness models for both asphalt and concrete surfaces, taking into account their unique structural characteristics. We construct a six-degrees-of-freedom aircraft model using multi-rigid-body system dynamics theory and employ the pseudo-excitation method to examine the influence of pavement roughness types on the steady vibration response of aircraft taxiing at constant speeds. Furthermore, we analyze the non-stationary vibration response patterns of aircraft during takeoff and landing taxiing using the method of instantaneous frequency response function with space frequency. Lastly, we explore the effect of stochastic structural parameters on aircraft vibration response using the Monte Carlo method. Our findings reveal that the roughness power spectrum differs between asphalt and concrete pavements, and the established roughness models in this paper demonstrate a strong fit (R2 > 0.95). The type of pavement roughness has a relatively minor impact on the power spectral density distribution of the aircraft vibration response, suggesting that the same roughness model can be used for both asphalt and concrete pavements when high accuracy is not required. The power spectral density distribution of aircraft vibration response varies across different motion attitudes, with the vibration response during landing being significantly larger than that during takeoff. Among the aircraft structural parameters, the randomness of the sprung mass has the most substantial effect on the aircraft vibration response, potentially causing the variation coefficient of dynamic load on the front landing gear to exceed 0.11. Tire stiffness comes next, which can lead to the variation coefficient of dynamic load on the main landing gear reaching 0.07. The results have a guiding role in optimizing aircraft structure design and ensuring pavement performance.

Experimental on the fatigue failure areas of wind turbine blades’ rotating fundamental frequency

The dominant type of vibration in wind turbine blades is flapwise vibration, which causes fatigue loading owing to the out-of-plane alternating load, with blade breakage occurring when the load is below the strength limit. Consequently, the mechanism and distribution of alternating-load generation must be understood to reduce the fatigue load. The strain data in the flapwise direction at the characteristic position of the operating wind turbine were collected to learn the characteristics of the spanwise distribution of the strain power spectral density (PSD) amplitude, to explore the fatigue damage-prone area and the generation mechanism, and to reveal the influence of the rotational speed on the spanwise distribution of the strain. It was evident that linked torsional and edgewise vibrations were predominant and that the strain PSD amplitude of the 1P loading component was nonlinear because of the Brazier effect in the transition region. The fatigue failure susceptible region of the rotating blade is more accurately identified. Except for the 0.10 R position, the blade 1P component 0.70–0.75 R spanwise region is more prone to failure. The rotation speed increases the dynamic fluctuation of spanwise strain.

Adapting to noise distribution shifts in flow-based gravitational-wave inference

Deep learning techniques for gravitational-wave parameter estimation have emerged as a fast alternative to standard samplers---producing results of comparable accuracy. These approaches (e.g., DINGO) enable amortized inference by training a normalizing flow to represent the Bayesian posterior conditional on observed data. By conditioning also on the noise power spectral density (PSD) they can even account for changing detector characteristics. However, training such networks requires knowing in advance the distribution of PSDs expected to be observed and therefore can only take place once all data to be analyzed have been gathered. Here, we develop a probabilistic model to forecast future PSDs, greatly increasing the temporal scope of trained deep learning models. Using PSDs from the second LIGO-Virgo observing run (O2)---plus just a single PSD from the beginning of the third (O3)---we show that we can train a DINGO network to perform accurate inference throughout O3 (on 37 real events). We therefore expect this approach to be a key component to enable the use of deep learning techniques for low-latency analyses of gravitational waves.

Quasi-periodic oscillations in the γ-ray light curves of bright active galactic nuclei

Context. The detection of quasi-periodic oscillations (QPOs) in the light curves of active galactic nuclei (AGNs) can provide insights into the physics of the super-massive black holes (SMBHs) powering these systems and could represent a signature of the existence of SMBH binaries, setting fundamental constraints on SMBH evolution in the Universe. Aims. The identification of long-term QPOs, characterized by periods on the order of several months to years, is particularly challenging and can only be achieved via all-sky monitoring instruments that provide unbiased, continuous light curves of astrophysical objects. The Fermi-LAT satellite, thanks to its monitoring observing strategy, is an ideal instrument for such a goal. Here, we aim to identify QPOs in the γ-ray light curves of the brightest AGNs within the Fermi-LAT catalog. Methods. We analyzed the light curves of the 35 brightest Fermi-LAT AGNs, including data from the beginning of the Fermi mission (August 2008) to April 2021, with energies from 100 MeV to 300 GeV. Two time binnings were investigated: 7 and 30 days. The search for quasi-periodic features was then performed using the continuous wavelet transform. The significance of the result was tested via Monte Carlo simulations of artificial light curves with the same power spectral density and probability distribution function as the original light curves. The significances were then corrected for the look-elsewhere effect and provided as post-trials. Results. We identified 24 quasars with candidate QPOs. Several of our candidates coincide with previous claims in the literature, namely: PKS 0537−441, S5 0716+714, Mrk 421, B2 1520+31, and PKS 2247−131. All our candidates are transient. The most significant multi-year QPO, with a period of about 1100 days, was observed in the quasar S5 1044+71. It is reported here for the first time.

Wind Power Scenario Synthesis With Smoothing Effect Through Spectral Decomposition and Its Application to Flexible Resource Adequacy

We synthesize future scenarios of total wind power generation (WPG) of future wind farms by considering the smoothing effect, in which mid-term variability fades out, but periodicity and short-term variability remain as more geographically distributed wind farms are aggregated. First, we decompose the WPG into a periodic component (PC) and non-periodic component (NC). We design the future PC by modifying the standard deviation and mean of the current PC. Then, we assume that the NC can be decomposed into a variable component (VC), which is a source of mid-term variability, and an uncertain component (UC), which is a source of short-term variability. We separate the VC and UC in the frequency domain. We design the VC by modifying the slope of power spectral density (PSD) to represent the smoothing effect and the UC by synthesizing the PSD distribution to represent randomness. Based on our scenarios, we develop a new flexibility index and a novel recursive algorithm to calculate the required amount of future flexible resources and the value of the smoothing effect to maintain the flexible resource adequacy. Finally, we verify the smoothing effect and the amount of flexible resources by simulating the IEEE test network based on actual WPG in Texas and Australia.