Frequency Characteristics Research Articles

Vibration behavior of a microperforated plate within an acoustic nonlinear framework

Microperforated plates (MPP) can add substantial damping in the low-frequency range. MPP are known to dissipate energy through thermo-viscous interactions between shearing adjacent fluid layers near the perforation solid walls. Under linear operating conditions, a previous work carried out by the authors showed that the added damping reaches a maximum at a characteristic frequency which solely depends on the perforation parameters. However, MPP is also suitable in environments subject to high levels of mechanical excitation and, consequently, high fluid velocity within the perforations. Two types of nonlinearities should then be considered: (1) an acoustic nonlinearity induced by high fluid velocity, and (2) a nonlinearity induced by large structural displacements. This work only explores the former. The acoustic nonlinearity is modelled by the Forchheimer resistivity correction, a function of the fluid-solid relative velocity in the perforations, introduced into the equations subsequently solved numerically. Experimental measurements using a laser vibrometer on a perforated cantilever beam validate the proposed model. Results show that, under high excitation levels and at the characteristic frequency, the maximum added damping can reach a maximum, depending on the MPP parameters, at a critical value of the relative fluid-solid velocity.

Road traffic noise prediction model "ASJ RTN-Model 2023" proposed by the Acoustical Society of Japan - Part 2: Structure-borne noise of viaducts

The latest road traffic noise prediction model "ASJ RTN-Model 2023" was published by the Research Committee on Road Traffic Noise of the Acoustical Society of Japan (ASJ) in this April. In this new version, the method for predicting structure-borne noise from viaducts was also updated. Specifically, the calculation method was updated to separate direct sound and ground-reflected sound, and the sound power levels on structure-borne noise from viaducts have revised accordingly based on the latest research results. This paper describes the method for measuring structure-borne noise, the latest values of noise emission and its frequency characteristics, and the method for predicting structure-borne noise.

Epileptic seizure prediction via multidimensional transformer and recurrent neural network fusion

BackgroundEpilepsy is a prevalent neurological disorder in which seizures cause recurrent episodes of unconsciousness or muscle convulsions, seriously affecting the patient’s work, quality of life, and health and safety. Timely prediction of seizures is critical for patients to take appropriate therapeutic measures. Accurate prediction of seizures remains a challenge due to the complex and variable nature of EEG signals. The study proposes an epileptic seizure model based on a multidimensional Transformer with recurrent neural network(LSTM-GRU) fusion for seizure classification of EEG signals.MethodologyFirstly, a short-time Fourier transform was employed in the extraction of time-frequency features from EEG signals. Second, the extracted time-frequency features are learned using the Multidimensional Transformer model. Then, LSTM and GRU are then used for further learning of the time and frequency characteristics of the EEG signals. Next, the output features of LSTM and GRU are spliced and categorized using the gating mechanism. Subsequently, seizure prediction is conducted.ResultsThe model was tested on two datasets: the Bonn EEG dataset and the CHB-MIT dataset. On the CHB-MIT dataset, the average sensitivity and average specificity of the model were 98.24% and 97.27%, respectively. On the Bonn dataset, the model obtained about 99% and about 98% accuracy on the binary classification task and the tertiary upper classification task, respectively.ConclusionThe findings of the experimental investigation demonstrate that our model is capable of exploiting the temporal and frequency characteristics present within EEG signals.

Acoustic radiation of a fluid-saturated microperforated plate

It is known that fluid-saturated microperforated plates (MPP) dissipate energy and are substantially damped through thermoviscous dissipation taking place in the thermoviscous skin on the solid wall of the perforations. The aim of this work is to explore the acoustic radiation of a microperforated periodic cell to investigate the intercellular coupling and the complexity of near-field radiation, and to derive a criterion related to the near-field far-field transition. A numerical modeling using the finite element method is developed to solve the elastic and thermo-visco-acoustic (TVA) multiphysics problem. The TVA calculates explicitly the thermoviscous losses on the acoustic wave in the periodic cell studied. Numerical results show that the distance of the near-far field transition is significant at low frequencies, passing through a minimum for a frequency depending on perforation parameters (diameter and ratio) before increasing again as a frequency function. The radiated acoustic power and radiation efficiency are compared with those derived from the theoretical MPP vibration model. It is found that the microperforations and resulting added damping, around a characteristic frequency, reduce the acoustic radiation efficiency of the MPP compared to the corresponding non-perforated plate.

Recent investigation on VHF sound exposure sources around us

We have reported recent investigation of sound exposure sources in very-high frequency (VHF) region as the activities for research project of VHF sounds in Japan. Recently, the devices which utilize or generate VHF sounds as like rodent repelling devices, ultrasonic cleaning machines, IH cookers and corner sensors of automobile continue to increase in public space or our living environment. The VHF sounds in high level have physical or/and psychological effects for young people who can hear such sounds. So, in environment around us, we identified VHF sound sources using acoustic camera and measured the sound pressure level for such sources. In this paper, we introduce newly-discovered several VHF sounds: for example, ultrasounds from corner sensors of automobile car, VHF sounds from photovoltaic installation and VHF sounds of interior train, and so on. From both the results measured by acoustic camera and microphones show source positions and frequency characteristics of VHF sounds. However these play important role in our life environment, we need to note that these contain potential hazard for young people who can hear such VHF sounds.

Resonant frequencies of multi-layer building constructions: Estimation by measurement of acceleration amplitudes

The Austrian building regulation requirements for impact sound insulation are based on the weighted standardized impact sound pressure level as single-number specification. In recent years, it has become apparent that not only the single-number specification, but the extended frequency range and frequency characteristics of impact sound insulation of constructions can have noticeable influence on the perceived acoustic performance of a construction. Therefore, the Austrian Classification Standard ÖNORM B 8115-5: 2021-04 includes extended requirements on impact sound insulation. It provides two ways for verification of the sound insulation classes A to C. One way is the verification via the resonance frequency of multi-layer constructions. However, there exist as yet no standardized measurement methods for the estimation of the resonance frequency of multi-layer building constructions. In response, in course of this study a new measurement method for the estimation of the resonance frequencies of multi-layer constructions was developed. For this development acceleration measurements were carried out on both surfaces of particularly double-layer building constructions, that were excited with a rubber hammer. From these measurement results, in conjunction with an analytical model, it was possible to derive a measurement method for estimating the resonant frequency via the frequency characteristics of the acceleration amplitudes.

Study on near-field noise control of stationary equipment

The noise of stationary equipment is usually controlled by the sound insulation shield with the mufflers. However, there is not enough space for the installation of the sound insulation shield in certain places or working conditions, so the noise reduction measures are difficult to apply. The noise control measures at the near-field of stationary equipment, especially those infinitely close to the surface of the equipment, can solve the problem of insufficient installation space. At this time, it is necessary to design a structure of noise control measures to ensure that it have sufficient noise reduction performance. In this paper, a noise reduction structure is designed for near-field noise control of stationary equipment. The experimental results show that the structure have a good noise reduction effect in the certain frequency range. The effective noise reduction frequency can be specially designed according to the frequency characteristics of different sound sources. In addition, a modular installation structure was designed.

Experimental Study on Pressure Oscillation Characteristics of Di-N-Butyl Ether in a Constant Volume Vessel

ABSTRACT This paper conducted experimental research on spontaneous pressure oscillation characteristics of di-n-butyl ether (DBE). Based on a constant volume vessel (CVV) with 210 mm in length and 180 mm in diameter, the initial pressure, temperature, and equivalence ratio ranges of 0.05–0.3 MPa, 373–453 K, and 0.7–1.6. The experimental data of combustion pressure, CVV vibration and sound pressure were analyzed. The results indicate that DBE exhibits the strongest pressure oscillation at high pressures, moderate temperatures and equivalence ratios. The highest oscillation intensity is achieved at 0.3 MPa, 433 K, equivalence ratio of 1.4. The vibration and sound pressure signals exhibit the same patterns as combustion pressure signals. Spectrum analysis is conducted by Fast Fourier Transform of pressure signals, it shows that the amplitude of the characteristic frequency increases with the increase of oscillation intensity. In addition, to further reveal the influence of combustion instability on oscillation intensity, the dimensionless parameter Peclet number and critical radius are compared, it shows that the combustion instability has strong effect on pressure oscillation, and diffusive-thermal instability is the key factor that influences pressure oscillation. This study is believed to be beneficial for organizing better combustion for internal combustion engines.

Accretion tori around rotating neutron stars II. Oscillations and precessions

The four characteristic oscillation frequencies of accretion flows (in addition to the Keplerian orbital frequency) are often discussed in the context of the time variability of black hole and neutron star (NS) low-mass X-ray binaries (LMXBs). These four frequencies are the frequencies of the axisymmetric radial and vertical epicyclic oscillations, and the frequencies of non-axisymmetric oscillations corresponding to the periastron (radial) and Lense-Thirring (vertical) precessions. In this context, we investigated the effect of the quadrupole moment of a slowly rotating NS and provide complete formulae for calculating these oscillation and precession frequencies, as well as convenient approximations. Simple formulae corresponding to the geodesic limit of a slender torus (and test-particle motion) and the limit of a marginally overflowing torus (a torus exhibiting a critical cusp) are presented, and more general approximate formulae are included to allow calculations for arbitrarily thick tori. We provide the Wolfram Mathematica code used for our calculations together with the C++ and PYTHON codes for calculating the frequencies. Our formulae can be used for various calculations regarding the astrophysical signatures of the NS super-dense matter equation of state. For instance, we demonstrate that even for a given fixed number of free parameters, a model that accounts for fluid flow precession matches the frequencies of twin-peak quasiperiodic oscillations observed in NS LMXBs better than a model that uses geodesic precession.

High-sensitivity acceleration sensor detecting micro-mechanomyogram and deep learning approach for parkinson’s disease classification

High-sensitivity acceleration sensors have been independently developed by our research group to detect vibrations that are > 10 dB smaller than those detected by conventional commercial sensors. This study is the first to measure high-frequency micro-vibrations in muscle fibers, termed micro-mechanomyogram (MMG) in patients with Parkinson’s disease (PwPD) using a high-sensitivity acceleration sensor. We specifically measured the extensor pollicis brevis muscle at the base of the thumb in PwPD and healthy controls (HC) and detected not only low-frequency MMG (< 15 Hz) but also micro-MMG (≥ 15 Hz), which was preciously undetectable using commercial acceleration sensors. Analysis revealed remarkable differences in the frequency characteristics of micro-MMG between PwPD and HC. Specifically, during muscle power output, the low-frequency MMG energy was greater in PwPD than in HC, while the micro-MMG energy was smaller in PwPD compared to HC. These results suggest that micro-MMG detected by the high-sensitivity acceleration sensor provides crucial information for distinguishing between PwPD and HC. Moreover, a deep learning model trained on both low-frequency MMG and micro-MMG achieved a high accuracy (92.19%) in classifying PwPD and HC, demonstrating the potential for a diagnostic system for PwPD using micro-MMG.

A Multifrequency Data Fusion Deep Learning Model for Carbon Price Prediction

ABSTRACTIn response to the global need for effective management of carbon emissions and alignment with sustainable development goals, predicting carbon trading prices accurately is critical. This study introduces a multifrequency data fusion carbon price prediction model (MFF‐CPPM), addressing the nonlinear characteristics of carbon trading prices and inconsistent feature factor frequencies. The MFF‐CPPM consists of a feature‐extraction frontend, a multifrequency data fusion transformer, and a fusion regression layer, offering a novel methodological approach in forecasting studies. The model's validity was tested in Guangdong, China's largest carbon trading pilot market. The results demonstrated that the MFF‐CPPM outperformed baseline models in terms of carbon price‐prediction accuracy and trend forecasting. Additional trials conducted in Hubei and Beijing confirmed the model's robustness and generalization capabilities, providing valuable evidence of its effectiveness and reliability across varying market contexts. This study presents a novel predictive model for carbon trading prices, with a unique capability to harness data at differing frequencies. The MFF‐CPPM not only enhances forecasting accuracy but also offers an innovative approach to effectively incorporate multifrequency information. This advancement paves the way for flexible forecasting models in any scenario where data arrive at differing frequencies.

Improving microvascular sensitivity of color doppler using phase mask based flow recycling algorithm.

&#xD;Blood flow sensitivity is a crucial metric for appraising the effectiveness of color Doppler flow imaging (CDFI). Color Doppler velocity maps based on classic autocorrelation techniques are widely used in clinical practice. However, these techniques often produce twinkling artifacts in noisy regions due to the inherent randomness of noise phases. To mitigate artifacts and improve image quality, Power Mask (PoM) technology becomes imperative. Nevertheless, PoM technology unintentionally filters out small flow signals that have similar power and frequency characteristics to noise signals, thereby reducing the imaging system's sensitivity to flow. &#xD;Approach:&#xD;To address this issue, a novel Flow Recycling Algorithm (FRA) based on phase anomaly is introduced in this study. This algorithm, excavating small flow signals from noise, aims to enhance the small flow signals with low-velocity by the phase characteristics of the color Doppler flow information. &#xD;Main results: &#xD;Experiments in multi-organ imaging have shown that the FRA-CDFI approach is more effective in suppressing twinkling artifacts in noisy regions, preserving intricate small flow signals, and markedly improving small blood flow sensitivity. This novel approach provides adequate technical support for clinical ultrasound imaging of organs with dense small blood vessels, such as the brain, kidneys, liver, and more. &#xD;Significance: &#xD;As a novel post-processing method, FRA-CDFI holds significant potential for future deployment in clinical high-frame-rate ultrasound imaging devices.

Extraction of Features for Time Series Classification Using Noise Injection

Time series data often display complex, time-varying patterns, which pose significant challenges for effective classification due to data variability, noise, and imbalance. Traditional time series classification techniques frequently fall short in addressing these issues, leading to reduced generalization performance. Therefore, there is a need for innovative methodologies to enhance data diversity and quality. In this paper, we introduce a method for the extraction of features for time series classification using noise injection to address these challenges. By employing noise injection techniques for data augmentation, we enhance the diversity of the training data. Utilizing digital signal processing (DSP), we extract key frequency features from time series data through sampling, quantization, and Fourier transformation. This process enhances the quality of the training data, thereby maximizing the model’s generalization performance. We demonstrate the superiority of our proposed method by comparing it with existing time series classification models. Additionally, we validate the effectiveness of our approach through various experimental results, confirming that data augmentation and DSP techniques are potent tools in time series data classification. Ultimately, this research presents a robust methodology for time series data analysis and classification, with potential applications across a broad spectrum of data analysis problems.

THz-TDS characterization of stress evolution of EB-PVD TBCs under thermal cycling

The failure of thermal barrier coatings (TBCs) during service is a critical issue affecting aeroengine efficiency. In this study, electron beam physical vapor deposition (EB-PVD) TBCs were subjected to thermal cycling at 1100°C. Terahertz time-domain spectroscopy (THz-TDS) was employed to characterize the relationship between the evolution of characteristic frequency and the number of thermal cycles, along with the development of stress in the ceramic layer. In situ tensile test revealed a linear correlation between the characteristic frequency and micro strain derived from THz-TDS data, with a proportional coefficient of -9.94 between the characteristic frequency and the slope of the refractive index curve. The results show that the stress evolution can be effectively monitored by recording the trend of characteristic frequency. During the non-failure stage, the characteristic frequency followed a parabolic trend as thermal cycles increased, influenced by the growth of thermally grown oxide (TGO), ceramic layer sintering, and thermal mismatch. Approaching the failure stage, stress evolution was dominated by the expansion of vertical cracks in the ceramic layer and the widening of transverse cracks at the interface, which corresponded to a sudden change in the characteristic frequency.

Experimental study on the unsteady behavior and frequency characteristics of high-speed submerged cavitating water jets

The frequency characteristics of cavitation fluctuations in high-speed cavitating jets are intricate due to the coupling mechanisms of unsteady behaviors. This study employs high-speed photography to experimentally investigate the relationship between frequency characteristics and the unsteady behavior of cavitating jets with various cavitation numbers. Temporal evolution patterns of the cavitating jets are analyzed through spatiotemporal (s-t) diagrams. The spatial distribution and temporal evolution of cavitation fluctuation frequencies are examined using fast Fourier transform (FFT) and continuous wavelet transform (CWT), respectively. Proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD) are employed to identify coherent structures and their corresponding frequencies. In results, the s-t diagrams reveal the distinct regions influenced by cavitation shedding and collapse. FFT results indicate that upstream of the jet trajectory, spectral energy is concentrated in the shedding band, shifting toward lower frequencies with increasing axial distance. The CWT spectrum exhibits a single peak in the upstream, identifying it as the shedding frequency. POD modes associated with shedding dominate the energy contribution at higher cavitation numbers, while they become less prominent at lower cavitation numbers. DMD extracts and identifies coherent structures associated with shedding through frequency-specific decomposition.

Analyzing the inspiratory airflow unsteadiness in the respiratory tract using dynamic mode decomposition method

To enhance the understanding of airflow characteristics in the human respiratory tract, the inspiratory airflow field was simulated under both tidal and quasi-steady inspiratory flow rates at the mouth inlet using the large eddy simulation method. Special attention was paid on analyzing the inspiratory airflow unsteadiness using the dynamics mode decomposition (DMD) method based on the vorticity field and comparing it with the proper orthogonal decomposition (POD) method. The following novel findings were obtained. (1) Power spectral density indicates that the inspiratory airflow is highly turbulent in the pharynx–larynx region. The vorticity field in the upper airway is more affected by inspiratory patterns compared to turbulence fluctuations. (2) The DMD results indicate that the shear flow in the pharynx–larynx region is mainly caused by flow under low-frequency modes, while the disturbances of the jet flow are caused by flow under multiple frequency modes. Steady-state inspiratory pattern demonstrates the decay characteristics different from the tidal inspiratory pattern. (3) Compared to the POD method, which may contain multiple frequency components, the DMD decomposition yields modes with a single frequency, enabling a more accurate capture of the frequency and decay characteristics of the respiratory flow under each mode. In conclusion, this study demonstrates that the DMD method is more suitable for studying the respiratory airflow unsteadiness and further confirms the necessity of adopting clinically measured inspiratory data to investigate airflow unsteadiness.

Band Relevance Factor (BRF): A novel automatic frequency band selection method based on vibration analysis for rotating machinery

Monitoring rotating machinery has become a fundamental activity in the industry given the high criticality in production processes. Extracting useful information from signals is a key factor for effective monitoring. Several studies in the areas of Informative Frequency Selection bands (IFB) and Feature Extraction/Selection have demonstrated the need to identify the bands of interest in vibration signals. However, typical methods in these areas focus on identifying bands where impulsive excitations are present or analyzing the relevance of features after signal extraction. Therefore, they do not focus on other regions that may be related to changes in the dynamic behavior of machines or faults. Furthermore, the methods generally require parameter adjustments and are not automatic. To overcome these problems, the present study proposes a new approach called Band Relevance Factor (BRF). BRF aims to perform an automatic selection of all relevant frequency bands for a vibration analysis of a rotating machine based on spectral entropy. In other words, automatically identify all frequency bands that are related to changes in the behavior of the machines or faults. The results are presented through a relevance ranking and can be visually analyzed through a heatmap. The effectiveness of the approach is validated on a synthetically dataset and on two real datasets, showing that BRF is capable of automatically identifying bands that present relevant information for the analysis of rotating machinery.

Quantifying the tangential oscillation of thermal stratification by using principal component analysis

Tangential oscillation of thermal stratification is a new finding in previous investigations on thermal-mixing process at pipeline T-junctions. This phenomenon can create regular temperature changes on the pipeline inner surface and lead to material damage caused by the degradation mechanism of thermal fatigue, which has been frequently reported as a reason for incidents in piping systems of nuclear power plants. Previous investigations cannot provide a quantitative description of this phenomenon, making it difficult to understand or evaluate the fatigue potential due to this phenomenon. In this study, a new method namely principal component analysis has been employed for quantitatively estimating the angular shift of the thermal stratification in pipe tangential direction. Time-dependent angular shift is extracted from the temperature data of the transient simulations of the pipe flow and confirms the initiation of the tangential oscillation of thermal stratification. Moreover, a characteristic frequency of approximately 2.3 Hz has been identified from the frequency spectrum of the time-dependent angular shift. It indicates a potential risk in the piping material due to thermal fatigue. Furthermore, results of this study show a high efficiency and accuracy of the principal component analysis method in quantifying tangential oscillation. It can be applied in further investigations of similar phenomena of thermal stratification.

Simulation and experimental research on the propagation mechanisms of acoustic energy inside the pipeline to detect the leakage

Leakage from pipelines has negative impacts on property and environment. The diameters of internal acoustic detectors in pipelines are small and not easily blocked, making it suitable for pipelines under various working conditions. Studying the characteristics of pipeline leakage sound signal and the propagation mechanisms within pipelines is crucial for timely detection of small leaks. In this study, a 3D simulation model of pipeline leakage was established, and a CFD/CAA hybrid calculation method was used to obtain effective leakage flow field information via LES. The equivalent sound sources in the flow field were extracted based on the Lighthill acoustic analogy theory. Determine the characteristic frequency range of the leakage sound signal was 1–2 kHz. This study founded that an increase in the leakage pressure and aperture size both lead to an enhancement in the leakage sound signal, and the propagation of leakage sound signal in pipelines is affected by background flow. An experimental pipeline leakage system was designed, and the accuracy of the simulation results was verified experimentally.

Improving fluid identification in well logging using Continuous Wavelet Transform and Vision Transformers: An innovative approach

Well logging fluid prediction is one of the key steps in assessing oil and gas reserves. By analyzing downhole logging data, different types of fluids contained in underground rocks, such as crude oil, natural gas, and water, can be determined. This information is crucial for assessing the abundance and recoverable reserves of oil and gas resources and helps guide oil and gas exploration and development work. We have introduced a novel model called CWT (Continuous Wavelet Transform)-ViT (Vision Transformer). CWT can simultaneously provide frequency information at different scales, enabling the model to analyze downhole logging data more comprehensively and accurately at different scales. Underground rock structures often exhibit features at multiple scales, and CWT can effectively capture these features, aiding in better differentiation of different types of fluids. The ViT model utilizes the Transformer architecture, allowing for global attention over input sequences without being limited by sequence length. This enables the model to comprehensively understand the overall information of downhole logging data and extract richer features. For complex geological structures and fluid distributions in geological exploration, the global attention mechanism helps the model better grasp the overall situation, thereby improving the accuracy of fluid prediction. When we used the CWT-ViT method for well logging fluid prediction, we achieved a high accuracy rate of 97.50% in the first dataset, which further improved to 97.77% in the second dataset. These results demonstrate the significant robustness and efficiency of the CWT-ViT method in lithology prediction using well logging data. We also conducted blind well experiments, and our CWT-ViT model outperformed other models, achieving a blind well prediction accuracy of 97.36%. Therefore, the experiments indicate that the key to improving accuracy in well logging fluid prediction with CWT lies in its multiscale analysis capability, effectively capturing different fluid characteristic frequencies. Additionally, CWT enhances signal features and removes noise, increasing the precision of fluid identification. Finally, the integration with ViT further optimizes fluid prediction performance, making it outstanding in complex geological environments. The advantages of ViT in fluid prediction include its excellent sequence modeling capability, effective handling of long-distance dependencies, and enhanced ability to capture fluid characteristics in complex well logging data.