Optimal Frequency Range Research Articles

Wind-Induced Vibration Control of High-Rise Buildings with Double-Skin Façades Using Distributed Multiple Tuned Façade-Dampers-Inerters

To address the shortcomings of tuned mass dampers (TMD), such as excessive internal space occupation and overlarge physical mass, this paper proposes a tuned façade damper inerter (TFDI) that utilizes parts of the outer façades of double-skin façades (DSF) as damping mass, capitalizing on the lightweight and efficient characteristics of inerters. The TFDI effectively resolves the challenge of multi-layer connections of inerters in high-rise buildings by utilizing corridor space. By vertically distributing TFDIs, a distributed multiple TFDI (d-MTFDI) system is formed. The configuration and motion of equations of this system are presented, and the control effectiveness is validated using wind tunnel test data. Two tuning modes are further proposed: unified tuning mode and distributed tuning mode. For the unified tuning mode, analytical expressions for optimal tuning frequency and damping ratio are derived; for the distributed tuning mode, numerical optimization methods are employed to determine the optimal tuning frequency range and damping ratio. Comparative results indicate that the distributed tuning mode achieves higher control efficiency than the unified tuning mode, with a significant reduction in the required optimal damping ratio. Furthermore, comparisons with d-MTMD demonstrate that d-MTFDI significantly enhances wind-induced vibration control performance.

Characterising vibration patterns of winter jujube trees to optimise automated fruit harvesting

Understanding jujube tree dynamic characteristics is crucial for the design and invention of a catch-and-shake machine for fruit harvesting. Currently, the study of vibration characteristics based on the finite element method is the mainstream method for different types of fruit trees. However, limited by the lack of an accurate 3D tree model, there are still gaps between existing simulation analysis and actual tests to explore vibration characteristics. Specifically, the vibration mechanism of winter jujube trees is still unclear in jujube orchards. To address the issue, a multi-view 3D reconstruction technique is employed to acquire precise 3D tree models for simulation analysis. The obtained results from experiments indicate that the determination coefficient R2 of the trunks and branches diameter are 0.96 and 0.91 between reconstructed and actual measurement results. Subsequently, material properties of jujube tree are measured to conduct model analysis and harmonic response analysis to find the optimal frequency range (10–20 Hz) in which a considerable vibration response can be obtained at low vibration energies. Moreover, transient analysis and test experiments are conducted to explore the energy transfer properties under different vibration frequency. Results showed that the acceleration response gradually increased from the bottom to the top of the branch on most branches at non-resonant frequencies. The proposed method can provide informative insights on the design of high-efficiency and low-energy jujube catch-and-shake harvesters.

Appropriate Selection of Frequency Range and Damping Ratio to Reduce the Resonance Effect, Lower Power Consumption, and Improve the Accuracy of the Capacitive Accelerometer

Introduction: In this work, we explored the capacitive accelerometer in depth by highlighting its advantages over other accelerometers. However, a major challenge associated with using capacitive accelerometers lies in choosing the appropriate frequency range. Method: Incorrect selection of the frequency range can cause several problems. It can decrease the accuracy of the accelerometer, increase the measurement error, and increase power consumption. To overcome these challenges, we undertook detailed modeling of the physical behaviour of the capacitive accelerometer. This modeling takes into account the mechanical and electrical properties of the sensor, including its mass, its rigidity, and the characteristics of its capacitive circuit. By simulating the developed model, we analyzed how the accelerometer reacts to different frequencies of vibratory movements. Result: This analysis led to the extraction of an important mathematical relationship that links the natural frequency of the accelerometer to the frequency of the vibratory movements to which it is subjected. Using this mathematical relationship, we determined the optimal frequency range for capacitive accelerometer operation. This precise determination of the frequency range makes it possible to significantly reduce the measurement error by avoiding resonance regimes and ensuring that the sensor operates in its maximum precision zone. Conclusion: Additionally, this approach helps improve overall measurement accuracy and minimize sensor power consumption by avoiding inefficient operating conditions.

Multiphysics Field Coupled to a Numerical Simulation Study on Heavy Oil Reservoir Development via Electromagnetic Heating in a SAGD-like Process

Conventional thermal recovery methods for heavy oil suffer from significant issues such as high water consumption, excessive greenhouse gas emissions, and substantial heat losses. In contrast, electromagnetic heating, as a waterless method for heavy oil recovery, offers numerous advantages, including high thermal energy utilization, reduced carbon emissions, and volumetric heating of the reservoir, making it a focus of recent research in heavy oil thermal recovery technologies. This paper presents a numerical simulation study of electromagnetic heating for heavy oil recovery, using a heavy oil block in the Bohai Bay oilfield in China as a case study. Firstly, a multiphysics field coupled to a mathematical model was established, considering the impact of the temperature on the heavy oil viscosity, the threshold pressure gradient of non-Darcy flow, and the dielectric properties of the reservoir, along with heat dissipation from overlying and undercover sandstone and gravitational effects on fluid flow. Secondly, a numerical simulation method for the coupled multiphysics fields was developed, and the convergence and stability of the numerical simulation method were tested. Finally, a sensitivity analysis based on the numerical simulation results identified the factors affecting heavy oil production. It was found that electromagnetic heating significantly enhances heavy oil production, and the threshold pressure gradient greatly influences the prediction of heavy oil production. Moreover, heat dissipation from the overlying and undercover sandstone severely reduces cumulative oil production. When the production well is located below the electromagnetic heating antenna, larger well spacing results in higher cumulative heavy oil production. Higher heavy oil production is achieved when the antenna is positioned at the center of the reservoir for the studied cases. Power has a big effect on increasing heavy oil production, but its influence diminishes as power increases. There exists an optimal range of electromagnetic frequencies for maximum cumulative production, and higher water saturation leads to poorer electromagnetic heating efficiency. This study provides a theoretical foundation and technical support for the numerical simulation technology and development plan optimization of heavy oil reservoirs subjected to electromagnetic heating.

Targeting the high frequency tail of wave spectra for energy harvesting in marine sensor networks

While the conventional philosophy of wave energy conversion is to target the large amounts of power in the peak of the input wave spectrum, this study proposes that for the application of powering marine sensor networks (MSNs), it is advantageous to target the high frequency tail of the wave spectrum. This strategy is predicated on two primary advantages: the spatial and temporal persistence of the wave energy resource in the high-frequency region and its compatibility with the resonance characteristics of smaller MSN devices. To identify the optimal frequency range for energy harvesting, we conducted a detailed analysis of wave spectra across multiple coastal locations. This involved calculating and comparing the power spectra at different frequencies, using data from long-term wave measurements. The high-frequency tail was defined by determining the frequency above which the energy content showed consistent temporal and spatial stability across all study sites. The quantification of available power was achieved by integrating the wave power spectrum over the identified frequency range. Our case study, focusing on the coast of Queensland, Australia, reveals that frequencies above 2.5 rad/s consistently offer a stable and persistent energy resource. The available power in this range is quantified, totalling an average of 60 W/m, with additional analysis provided within narrower sub-bandwidths to address the inherent narrow-bandedness of wave energy harvesters. This research provides critical insights for the design of efficient wave energy harvesters tailored to the needs of diverse marine environments.

A novel amplitude enhancement method of EMAT for High-frequency Rayleigh-like waves in Circumferential propagation

Currently, in terms of resolution and excitation efficiency for pipeline inspection, the high-frequency Rayleigh-like wave excited by an EMAT with a traditional Rayleigh wave EMAT structure is not optimal when using the same magnet volume. This paper introduces an EMAT performance evaluation method focused on 'bandwidth' in the high-frequency-thickness region of circumferential guided waves. A wavenumber spectrum analysis method utilizing combined equivalent surface stresses is proposed to quantify this optimize design. Comparative studies, including theoretical analysis and experimental validation, demonstrate that incorporating bandwidth significantly improves the design of Rayleigh-like waves at high frequencies. The proposed EMAT achieves a performance improvement of 2.4 times for inside pipe excitation and 2.6 times for outside pipe excitation over the conventional structure. The occurrence of multiple wave packets outside the optimal excitation frequency range is acknowledged. Therefore, this method offers a new approach for optimizing EMATs for Rayleigh-like waves.

An efficient acoustic energy harvester by using deep learning-based traffic prediction

With rapid urbanisation, traffic noise pollution has become a growing community concern. However, traffic noise can be valuable due to its ubiquity and cleanliness, which can be exploited for acoustic energy harvesting systems. This paper aims to develop an efficient acoustic energy harvester to improve energy utilisation efficiency and reduce noise levels by using deep-learning methods. We propose a temporal graph attention convolutional network (TGACN) to predict noise frequency, providing guidance for the acoustic energy harvester design. Power efficiency can be maximised by adjusting the harvester's neck size to the frequency predicted by the TGACN model. The relationship between neck size and the optimum frequency range is obtained through simulations and experiments. We analysed the performance differences among the various cavities in multiple-cavity acoustic energy harvesters and discussed the circuit connection methods between each cavity to identify the optimal configurations. Additionally, we evaluated the superiority of TGACN over the state-of-the-art methods in terms of prediction accuracy by conducting extensive experiments with two real-world datasets.

Assessment of UHF Frequency Range for Failure Classification in Power Transformers.

Ultrahigh-frequency (UHF) sensing is one of the most promising techniques for assessing the quality of power transformer insulation systems due to its capability to identify failures like partial discharges (PDs) by detecting the emitted UHF signals. However, there are still uncertainties regarding the frequency range that should be evaluated in measurements. For example, most publications have stated that UHF emissions range up to 3 GHz. However, a Cigré brochure revealed that the optimal spectrum is between 100 MHz and 1 GHz, and more recently, a study indicated that the optimal frequency range is between 400 MHz and 900 MHz. Since different faults require different maintenance actions, both science and industry have been developing systems that allow for failure-type identification. Hence, it is important to note that bandwidth reduction may impair classification systems, especially those that are frequency-based. This article combines three operational conditions of a power transformer (healthy state, electric arc failure, and partial discharges on bushing) with three different self-organized maps to carry out failure classification: the chromatic technique (CT), principal component analysis (PCA), and the shape analysis clustering technique (SACT). For each case, the frequency content of UHF signals was selected at three frequency bands: the full spectrum, Cigré brochure range, and between 400 MHz and 900 MHz. Therefore, the contributions of this work are to assess how spectrum band limitation may alter failure classification and to evaluate the effectiveness of signal processing methodologies based on the frequency content of UHF signals. Additionally, an advantage of this work is that it does not rely on training as is the case for some machine learning-based methods. The results indicate that the reduced frequency range was not a limiting factor for classifying the state of the operation condition of the power transformer. Therefore, there is the possibility of using lower frequency ranges, such as from 400 MHz to 900 MHz, contributing to the development of less costly data acquisition systems. Additionally, PCA was found to be the most promising technique despite the reduction in frequency band information.

Connectivity study on resting-state EEG between motor imagery BCI-literate and BCI-illiterate groups.

Although Motor Imagery-based Brain-Computer Interface (MI-BCI) holds significant potential, its practical application faces challenges such as BCI-illiteracy. To mitigate this issue, researchers have attempted to predict BCI-illiteracy by using the resting state, as this was found to be associated with BCI performance. As connectivity's significance in neuroscience has grown, BCI researchers have applied connectivity to it. However, the issues of connectivity have not been considered fully. First, although various connectivity metrics exist, only some have been used to predict BCI-illiteracy. This is problematic because each metric has a distinct hypothesis and perspective to estimate connectivity, resulting in different outcomes according to the metric. Second, the frequency range affects the connectivity estimation. In addition, it is still unknown whether each metric has its own optimal frequency range. Third, the way that estimating connectivity may vary depending upon the dataset has not been investigated. Meanwhile, we still do not know a great deal about how the resting state EEG network differs between BCI-literacy and -illiteracy. To address the issues above, we analysed three large public EEG datasets using three functional connectivity (FC) and three effective connectivity (EC) metrics by employing diverse graph theory measures. Our analysis revealed that the appropriate frequency range to predict BCI-illiteracy varies depending upon the metric. The alpha range was found to be suitable for the metrics of the frequency domain, while alpha + theta were found to be appropriate for Multivariate Granger Causality (MVGC). The difference in network efficiency between BCI-literate and -illiterate groups was constant regardless of the metrics and datasets used. Although we observed that BCI-literacy had stronger connectivity, no other significant constructional differences were found. Based upon our findings, we predicted MI-BCI performance for the entire dataset. We discovered that combining several graph features could improve the prediction's accuracy.

IOT Based Portable Fire Extinguisher using Acoustic Setup

Despite their effectiveness, conventional fire extinguishers come with notable drawbacks such as chemical residue and environmental damage. Addressing these concerns, recent research proposes a revolutionary solution: utilizing sound waves to combat fires. This innovative approach introduces a portable extinguisher device designed to efficiently diminish flame energy and extinguish fires by concentrating sound waves through a vortex cannon. Extensive testing across various fuel types, including gas, liquid, and solid, has demonstrated the effectiveness of this technology. Findings indicate that the optimal frequency range for extinguishment is around 60 Hz. These results underscore the capability of this novel method to swiftly extinguish fires of diverse origins. Moreover, beyond its efficacy in extinguishing flames, this technology holds the potential to significantly reduce property loss and human casualties by enabling early-stage fire intervention. By leveraging this strategy, fire safety procedures could undergo a transformative shift, offering a more sustainable, safer, and cleaner solution by mitigating the drawbacks associated with traditional fire extinguishers and introducing a versatile approach applicable across different fuel types, this advancement represents a significant step forward in fire safety technology. Its adoption could lead to profound improvements in fire fighting practices, enhancing both environmental sustainability and public safety.

Tension identification of bridge suspension rods based on multiple impedance parameters from self-inductive coils

ABSTRACT This study introduces a method for identifying tension in suspension rods based on impedance from self-inductive coils, grounded in electromagnetic induction and magnetic elasticity principles. The theoretical feasibility of this method was analysed through a high-frequency inductive coil model. Steel strand tensile tests, utilising self-inductive coil sensors at various frequencies, explored the relationship between resistance, inductance, and capacitance with tension. According to the experimental results, the sensitivity coefficient is used to determine the optimal prediction frequency of each impedance parameter. Within the optimal frequency range, a single impedance parameter regression prediction method based on the impedance difference ratio index was proposed. To improve the accuracy and stability of the predictions, a GA-BP neural network prediction model based on the fusion of multiple impedance parameters was proposed. The results indicate that the GA-BP neural network prediction method, which integrates multiple impedance parameters, achieves higher accuracy in identifying cable forces than the single impedance parameter regression prediction method. Specifically, at a frequency of 5 kHz, the relative error of the GA-BP neural network prediction method, which integrates multiple impedance parameters, was only 3.74%. This research offers a novel approach for future in-service tension identification in bridge suspension rods.

Vibration response characteristics of Jujube trees based on finite element method and structure-from-motion

It is an indispensable step to choose optimal vibration parameters in jujube fruits vibration harvesting. Excessive vibration frequency will cause jujube tree damage, otherwise, the separation of fruit and fruit stalk cannot be achieved, resulting in harvesting failure. In our work, finite element method and vibration tests are applied to find optimal vibration parameters. To improve the accuracy of finite element method, a low-cost 3D reconstruction method is proposed to build jujube tree model. Experimental result showed that the phenotypic information of the reconstructed jujube tree and the real jujube tree have a high similarity, and their determination coefficient R2 reach to 0.984. Moreover, the natural frequencies of jujube tree are analyzed by modal analysis, transient analysis is applied to analyze vibration transfer characteristics. The simulation and experimental results show that the optimal vibration frequency range of jujube tree is 25∼30 Hz, the optimal vibration excitation location is on the main trunk and as high as possible close to the secondary branches, which can obtain maximum vibration response for optimum harvesting efficiency. The average determination coefficient is 0.66 between test and simulation results at different frequencies. This study can provide references for accurate 3D modeling of jujube trees, selection of appropriate vibration parameter range and the development of jujube harvesting machinery.

Dynamic response of Camellia oleifera fruit-branch based on mathematical model and high-speed photography

In order to study the motion law and shedding characteristics of fruits during the vibration harvesting process of Camellia oleifera fruit. The dynamic equation of fruit-branch picking was established, the vibration output solution and picking inertial force of Camellia oleifera fruit forced vibration were solved by mathematical methods, and the spectral curve between picking force and frequency when forced vibration of Camellia oleifera fruit was analysed by MATLAB. The three-dimensional model of fruit-branch was established, and the modal analysis and harmonious response analysis were carried out, and the optimal excitation frequency range was 4–8 Hz, and the optimal excitation load of fruit-branch was 50N. The self-made canopy camellia fruit picker was used to continuously vibrate the side branches of Camellia oleifera, and the high-speed camera was used to record the movement process of Camellia oleifera fruit at different vibration frequencies, and the results showed that the movement trajectory of Camellia oleifera fruit was complex and the result of a combination of multiple movements. With the increase of excitation frequency, the speed and acceleration of fruit and flower buds also increased, but the average shedding force of fruits and buds did not change much, and the average shedding force of flower buds was greater than that of fruits. The results of this experiment are basically consistent with the results of the simulation analysis. The results of this study can provide a theoretical basis for the design, selection and research and development of mechanical harvesting equipment.

Osteoporosis detection with microwave signals: An investigation into natural resonance frequencies

This study analyses the dispersion behavior of microwave signals passing through biological tissues by considering various electromagnetic relaxation phenomena. The investigation concentrates on the impact of this dispersion behaviour on human bone tissues causing the health conditions of osteoporosis. Based on the attenuation profile through biological tissues, an optimum frequency range of operation is identified to capture the comprehensive target tissue profiles without minimal loss of signal strength in surrounding tissues. The dispersion through a human wrist model is analysed by extracting the late-time response. A pioneering aspect of this study lies in its utilization of the natural resonant frequency plane as a tool to gauge the dielectric properties of bone tissue, offering insights into its mineral content. To improve the ease of use and diagnostic accuracy, an automated multi-class classification using standard machine learning algorithms is developed during the final response analysis. The proposed approach effectively and accurately classifies the progression of osteoporosis.

Passive Poststall Flow Control on Nonslender Delta Wings Using Flags

The lift force on two nonslender delta wings with sweep angles of Λ=40 and 50°, with flags attached to the leading edge, was investigated. Substantial lift enhancement in the poststall regime of the clean wings and the stall delay were observed. With a flag, the flowfield measurements showed the reattachment of the shear layer onto the wing surface and the re-formation of the leading-edge vortex, whereas the clean wing had completely stalled flow at the same angle of attack. Over a wide range of angles of attack, mass ratios, and flag lengths, the maximum lift enhancement was attained when the dimensionless frequency of flag oscillations was in an optimal range and the flag-tip velocity had sufficient amplitude. The optimal dimensionless frequency range corresponded to the natural shear layer instabilities of the clean wing. The excitation in this range substantially increased the coherency of the separated flow due to the flag oscillations. The main mechanism of the lift increase is the excitation of the shear layer, which exhibits convective instability. This is very different from the lift enhancement mechanism on airfoils where the flags lock-in to the wake instability, which is known to have an absolute instability at the poststall angles of attack of the clean airfoil.

Frequency Characteristics Analysis of Remote Field Eddy Current Testing on Ferromagnetic Pipes

Pipes made of ferromagnetic alloys are widely installed in petrochemical plants and the steel plants. To implement non-destructive corrosion inspections on these pipes, remote field eddy current testing (ECT) which installs a search coil inside the pipe is a practical inspection method. Remote field ECT has the advantage of being able to detect external surface defects while the distance between the detection coil and the excitation coil is twice of the outer diameter of the testing pipe. Nevertheless, the inspection principle such as the frequency characteristics etc. of ECT signals have not been elucidated, inspection devices based on remote field ECT had not been employed widely. In this paper, we measured the frequency characteristics of ferromagnetic pipes with artificial outer surface defects by remote field ECT, and then detailed finite element analysis are carried out by using the nonlinear eddy current analysis to clarify the inspection principle of the remote field ECT. In both measurement and analysis, peak signal occurs directly under the defect and the optimal frequency range are the same. The phase angle of flux density due to the excitation current and flux density due to eddy current are found to greatly affect the frequency characteristics.

Influence of noise masking on leak pinpointing: Experimental analysis on a laboratory test rig for leak noise correlation.

The efficient management of water distribution networks requires the detection and mitigation of leaks that result in significant water losses, economic costs, and potential health hazards. This experimental study focuses on the optimal frequency range for accurately locating a water leak in a distribution network using the noise correlation approach while mitigating the influence of background noise associated with water consumption from users. Through laboratory experimental tests, a digital bandpass filter was defined in order to pre-process signals and to improve cross-correlation analysis within the pertinent frequency range. This was done by characterising the frequency content of leak-induced noise, noise masking resulting from water consumption, and background noise. The analysed signals were recorded using hydrophones installed within the pipes of a laboratory test rig under different conditions of water flow rate. The spectra analysis assessed the influence of varying water flow rates on the acoustic sound field within the pipeline system. Additionally, the results of the analysis made it possible to discriminate clearly between noise generated by leaks and noise attributed to water consumption. This distinction is useful for implementing suitable filters, and also to establish a threshold ratio of leak flow rate to water consumption flow rate for effective pre-filtering. This study aims to establish a methodological framework that can effectively differentiate between acoustic signals stemming from leaks and noise generated by other sources within the system, to improve leak location accuracy through the noise correlation approach.

Investigation of Dielectric Measurement Model for Coconut Fiber Water Content and the Associated Factors

Coconut fiber has excellent permeability and water retention properties, making it a popular choice as an organic substrate for cultivation in the field of facility agriculture. To investigate the dielectric properties of coconut fiber and develop an accurate measurement model for dielectric water content, a vector network analyzer was used to measure the relative dielectric permittivity of coconut fiber and study the dielectric properties of coconut fiber under different volumetric water contents, temperatures, and salinities. The results indicated that the relative dielectric permittivity of the coconut fiber increased with increasing volumetric water content. The optimal frequency range for measuring the water content of the coconut fiber was 200 MHz–3 GHz, with an optimal frequency of 210 MHz. The dielectric measurement model for the water content in the coconut fiber established at this frequency demonstrated good applicability, with an R2 of 0.9704 and RMSE of 0.0453 cm3/cm3. At the same moisture level (θV of approximately 30% and 50%), the error in the real part of the dielectric permittivity was less than 1.35 between 15 and 35 °C, and the error in the moisture content was less than 0.0339 cm3/cm3. The error due to the salinity was 0.0750 cm3/cm3. The experimental results showed that the modeling of the water content measurement of coconut fiber at 210 MHz can be applied to general greenhouse environmental conditions. This study serves as a valuable reference for calibrating the moisture monitoring sensors used in greenhouses for coir cultivation substrates, emphasizing the need for non-destructive, real-time monitoring of moisture content.

Non-invasive Assessment of Cartilage Damage of the Human Knee using Acoustic Emission Monitoring: a Pilot Cadaver Study.

Knee osteoarthritis is currently one of the top causes of disability in older population, a rate that will only increase in the future due to an aging population and the prevalence of obesity. However, objective assessment of treatment outcomes and remote evaluation are still in need of further development. Acoustic emission (AE) monitoring in knee diagnostics has been successfully adopted in the past; however, a wide discrepancy among the adopted AE techniques and analyses exists. This pilot study determined the most suitable metrics to differentiate progressive cartilage damage and the optimal frequency range and placement of AE sensors. Knee AEs were recorded in the 100-450 kHz and 15-200kH frequency ranges from a cadaver specimen in knee flexion/extension. Four stages of artificially inflicted cartilage damage and two sensor positions were investigated. AE events in the lower frequency range and the following parameters provided better distinction between intact and damaged knee: hit amplitude, signal strength, and absolute energy. The medial condyle area of the knee was less prone to artefacts and unsystematic noise. Multiple reopenings of the knee compartment in the process of introducing the damage negatively affected the quality of the measurements. Results may improve AE recording techniques in future cadaveric and clinical studies. This was the first study to evaluate progressive cartilage damage using AEs in a cadaver specimen. The findings of this study encourage further investigation of joint AE monitoring techniques.

The Influence of Anthropometric Variables and Filtering Frequency on Center of Pressure Data.

Good postural control is considered to be a key component of an active lifestyle, and numerous studies have investigated the Center of Pressure (CoP) as a way of identifying motor deficits. However, the optimal frequency range for assessing CoP variables and the effect of filtering on the relationships between anthropometric variables and CoP are unclear. The aim of this work is to show the relationship between anthropometric variables and different ways of filtering the CoP data. CoP was measured in 221 healthy volunteers using a KISTLER force plate in four different test conditions, both mono and bipedal. The results show no significant changes in the existing correlations of the anthropometric variable values over different filter frequencies between 10 Hz and 13 Hz. Therefore, the findings with regard to anthropometric influences on CoP, with a reasonable but less than ideal filtering of the data, can be applied to other study settings.