Fast Fourier Transform Analysis Research Articles

Kohonen Mapping of the Space of Vibration Parameters of an Intact and Damaged Wheel Rim Structure

The research presented in this paper takes another step towards developing methods for automatic condition verification to detect structural damage to vehicle wheel rims. This study presents the utilisation of vibration spectra via Fast Fourier Transform (FFT) and a neural network’s learning capabilities for evaluating structural damage. Amplitude and time cycles of acceleration were analyzed as the structural response. These cycles underwent FFT analysis, leading to the identification of four diagnostic symptoms described by 20 features of the diagnostic signal, which in turn defined a condition vector. In the subsequent stage, the amplitude and frequency cycles served as input data for the neural network, and based on them, self-organizing maps (SOM) were generated. From these maps, a condition vector was defined for each of the four positions of the rim. Therefore, the technical condition of the wheel rim was determined based on the variance in condition parameter features, using reference frequencies of vibration spectra and SOM visualisations. The outcome of this work is a unique synergetic diagnostic system with innovative features, identifying the condition of a wheel rim through vibration and acoustic analysis along with neural network techniques in the form of Kohonen maps.

Numerical study of performance curve incline and instability flow phenomena in low flow rates according to absolute flow angle change at axial-flow pump inlet

This study predicted the performance of an inlet guide vane to be installed later by applying an absolute flow angle (ß) at the axial pump inlet. The pump’s performance was predicted based on changes in inlet ß, and the influence of variations in inlet ß on the low-flow area was analyzed. For turbulence flow analyses, the Reynolds-averaged Navier–Stokes equations were discretized based on the finite volume method. The internal flow was analyzed in detail through transient analysis. Furthermore, the impact of changes in the absolute flow angle at the inlet on the rotating stall occurring inside the pump in the low-flow-rate region was analyzed. The analysis of the internal flow field confirmed that the absolute flow angle at the inlet affects the stability of the internal flow. Results showed that energy reduction and efficient operation scenarios can be established compared with the existing valve control method by changing inlet ß. Through fast Fourier-transform analysis, it was confirmed that when the inlet angle (θ) is changed in the low-flow-rate region, a more stable operation is possible compared with the existing one.

LES investigation of a H2-fueled supersonic combustor: A two-strut injection approach

Efficient combustion schemes are increasingly vital as scramjet expansion into higher Mach numbers emerges as a leading trend. Thus, in the current work, an LES investigation on mixing improvement caused by a two-strut injector has been carried out for a Mach 2.5 model H2 fueled scramjet combustor. The research focuses on understanding the flow field, flame lift-off characteristics and combustion stabilization in the two-strut combustor. The simulation results indicate that the two-strut configuration exhibits a broader pattern of temperature fluctuation, implying a more efficient spread of combustion downstream compared to the single-strut configuration. Analysis of Fast Fourier Transform (FFT) pressure data reveals inherent instability in the combustion processes for two-strut under supersonic air inflow conditions. The findings also demonstrate the significant improvement in mixing performance achieved by the two-strut design compared to conventional struts. The primary mechanism is a two-strut's ability to create significant streamwise vorticity, which can improve the mixing of H2 fuel with supersonic air. According to the mixing and total pressure loss plots, a two-strut produces a rapid complete mixing at 0.182 m at the expense of a minute rise in total pressure loss. Finally, the entropy change plot reveals that the two-strut configuration exhibits a reduced entropy change compared to the single-strut configuration. This is mainly due to enhanced air-fuel mixing, efficient shock wave management, and enhanced flame stabilization in the two-strut setup. These factors collectively contribute to reduced irreversibilities and disorder in the flow, leading to lower entropy within the system.

Numerical Analyses of Ultrasonic Atomization Utilizing Acoustic Effects of a Beam Diaphragm

To study mechanisms of jet atomization, a novel method of experimentation utilizing the resonation of diaphragms made from thin steel plates has been previously developed. In the experiments, a diaphragm covered by a film of water emitted acoustic sounds, and jet atomization from the water film was observed. Experiments using diaphragms composed of different materials and fast Fourier transformation analysis of the acoustic sound revealed that jet atomization occurred under limited surface conditions of the diaphragm and a specific range of frequency. In this article, the dynamics of a resonating body composed of the diaphragm and water film were analyzed using the finite element method with a combination of theoretical analyses of surface waves of water, such as the well-known Lang’s equation. The present FEA results, from harmonic response analyses with consideration of viscous damping effect due to interaction between the diaphragm and water film, precisely confirmed the results of FFT analysis previously obtained by the experiment. Specifically, the peak frequency of the frequency response agreed well with the FFT results, and the shift of the peak frequency and attenuation due to the interaction in the analyses corresponded with the difference in surface conditions between the hydrophilic and hydrophobic materials of the diaphragm in the experiments. Our interpretation of the mechanism of jet atomization is expanded by the present numerical results.

Generation of patterns and higher harmonics in 1D quantum droplets in tilted and driven quasi-periodic confinements

The generation of patterns by breaking the spatial symmetry in external confinement is a captivating area of physics. The emergence of patterns is a fundamental inquiry spanning various disciplines such as nonlinear optics, condensed matter physics, and fluid dynamics. The article investigates the generation of a variety of patterns in a one-dimensional binary mixture of Bose–Einstein condensate forming quantum droplets. By solving the extended Gross–Pitaevskii equation in the presence of tilted and driven engineered bi-chromatic optical lattices (BOL), the out-of-equilibrium dynamics of droplets under strong dc and ac fields are illustrated. Under the influence of a dc field, a stripe-like pattern emerges in the temporal domain, while the scenario with ac fields demonstrates temporal periodic and bi-periodic oscillations of density waves. The width and period of formed patterns are directly correlated with the strength of ac and dc fields. Moreover, temporal modulation of the BOL potential depth yields various harmonics in the oscillations of the condensate density pattern. Through Fast Fourier Transform (FFT) analysis, it is confirmed that these harmonics encompass multiple and combinational frequencies, suggesting potential applications in generating desired frequency combs within quantum droplets. We have also carried out a thorough numerical stability check of the obtained solutions and found them sufficiently stable.

Remote Vibration FFT Analyzer –Using Mobile App IOT and MATLAB Application

A new system to check machine vibrations from far away has been created. It uses sensor and IoT technology and MATLAB to show data. It's better than old ways because it uses new technology to watch things right away. Here's how it works: M5StickC device on the machine. This thing has a 3-axis IMU digital sensor that measures the acceleration. Then, it sends this info to phones using Bluetooth. This makes it a small, flexible IoT platform that collects reliable data. After that, an app sends this stuff to a cloud platform. There, a MATLAB program does something called Fast Fourier Transform (FFT) analysis. This gives you pictures of the signals in remotely showing how they change over time and how often they happen. Methods like remote vibration tests can spot equipment problems such as unbalance, gears and bearing issues on. This real-time data review and evaluation boost productivity and make things more accurate while cutting down on repair costs and time when machines aren't working. It allows factories to use predictive upkeep, which makes equipment more reliable and accurate. This means less downtime and cheaper maintenance overall. Key Words: IoT (Internet of Things), Fast Fourier Transform (FFT), Predictive maintenance

Static and dynamic analysis of polyethylene terephthalate foam core and different natural fiber-reinforced laminated composite-based sandwich plates through experimental and numerical simulation

This study explores the flexural behavior and free vibration analysis of sandwich plates including natural fiber-reinforced polymer composite faces and Polyethylene Terephthalate (PET) foam cores under a range of edge situations. ABAQUS is used to do numerical simulations with three-dimensional solid elements (C3D8R). To ascertain the natural frequencies of the plates, experimental procedures such as impact hammer testing and Fast Fourier Transform (FFT) analysis are carried out. The inference of different geometrical parameters on the natural frequencies is investigated through numerical simulation in ABAQUS. The study gives brief outlines regarding the sandwich plates for different engineering applications.

Employing Siamese Networks as Quantitative Biomarker for Assessing the Effect of Dolphin-Assisted Therapy on Pediatric Cerebral Palsy.

This study explores the potential of using a Siamese Network as a biomarker for assessing the effectiveness of Dolphin-Assisted Therapy (DAT) in children with Spastic Cerebral Palsy (SCP). The problem statement revolves around the need for objective measures to evaluate the impact of DAT on patients with SCP, considering the subjective nature of traditional assessment methods. The methodology involves training a Siamese network, a type of neural network designed to compare similarities between inputs, using data collected from SCP patients undergoing DAT sessions. The study employed Event-Related Potential (ERP) and Fast Fourier Transform (FFT) analyses to examine cerebral activity and brain rhythms, proposing the use of SNN to compare electroencephalographic (EEG) signals of children with cerebral palsy before and after Dolphin-Assisted Therapy. Testing on samples from four children yielded a high average similarity index of 0.9150, indicating consistent similarity metrics before and after therapy. The network is trained to learn patterns and similarities between pre- and post-therapy evaluations, in order to identify biomarkers indicative of therapy effectiveness. Notably, the Siamese Network's architecture ensures that comparisons are made within the same feature space, allowing for more accurate assessments. The results of the study demonstrate promising findings, indicating different patterns in the output of the Siamese Network that correlate with improvements in symptoms of SCP post-DAT. Confirming these observations will require large, longitudinal studies but such findings would suggest that the Siamese Network could have utility as a biomarker in monitoring treatment responses for children with SCP who undergo DAT and offer them more objective as well as quantifiable manners of assessing therapeutic interventions. Great discrepancies in neuronal voltage perturbations, 7.9825 dB on average at the specific samples compared to the whole dataset (6.2838 dB), imply a noted deviation from resting activity. These findings indicate that Dolphin-Assisted Therapy activates particular brain regions specifically during the intervention.

Artificial intelligence and electrocardiography: A modern approach to heart rate monitoring

The integration of Artificial Intelligence (AI) in Electrocardiography (ECG) and Photoplethysmography (PPG) signifies AI's profound influence on heart rate monitoring and analysis. ECG traditionally offers critical insights into cardiac health, necessitating expert interpretation. This study introduces an AI framework with Fast Fourier Transformation Analysis for swift, human-like interpretation of complex ECG signals. A multilayer AI Network accurately detects intricate features, enhancing ECG analysis precision. Leveraging comprehensive datasets, AI models proficiently identify heart dysfunctions like atrial fibrillation and hypertrophic cardiomyopathy, and can estimate age, sex, and race. The proliferation of mobile ECG technologies has spurred AI-based ECG phenotyping, impacting clinical and population health. This research explores AI's role in enhancing cardiac health assessment and clinical decision-making using MATLAB, acknowledging its transformative potential and inherent limitations.

Effects of air gap eccentricity on different rotor structures for PMSM in electric vehicles

In addition to research related to static eccentricity and dynamic eccentricity examining fault and diagnostic methods in various types of motors, there are also studies conducted for motors with non-uniform air gaps similar to the condition. In these studies, improvements and changes in motor performance for such types of motors can be observed. In this study, the effects of the rotor structures determined in the study and the air gap shape, which is effective by changing the eccentricity ratio, on the motor performance of the interior permanent magnet synchronous motor designed for electric vehicle applications are examined. Besides the elliptical rotor structure, which is widely researched in dynamic eccentricity studies, these eccentric motor structures also include novel rectangular and polygonal eccentric rotor structures. Motor models with non-uniform air gap structures are designed in two dimensions and subjected to simulation and analysis studies using the finite element method. At the end of the study, along with the parameters identified for motor performance, the harmonic components of the stator current, radial air gap flux density and the output torque obtained through Fast Fourier Transform analysis have been provided.

Time-series analysis of radon concentrations for Bengaluru's atmosphere.

The Fast Fourier Transform (FFT) analysis of the activity concentration of radon and selected meteorological parameters was carried out at Department of Physics, Bangalore University, Bengaluru (12056'44"N, 77030'25″E, 840m above MSL). All of the measured parameters, with the exception of pressure, showed a clear diurnal trend, which can be explained by the presence of typical atmospheric processes such as temperature inversion in the morning and greater vertical mixing in the afternoon. Radon's time series has a latent memory of sub-diurnal cycles, as shown via FFT analysis. The monthly average radon has higher levels of activity during winter months compared with monsoon and summer months. Days during the monsoon season had the lowest radon activity, which may be ascribed to the fact that less radon was being exhaled from the soil as a result of the rain. Radon was recorded at 8.06±0.56Bq/m3, temperature at 28.9°C, humidity at 55.2% and pressure at 918mbar.

Analysis of spatiotemporal variation in land surface temperature on the Tibetan Plateau from 2001 to 2020

ABSTRACT With global climate warming intensifying, the Land Surface Temperature (LST) variation on the Tibetan Plateau (TP) has become a hot topic in environmental studies. This letter employs spatial analysis to identify LST distribution characteristics, the Pettitt test to detect abrupt changes, and Fast Fourier Transform (FFT) to analyse periodic variations. Analysing mean annual and seasonal LST from 2001 to 2020, the results indicate the following: (1) Over the past 20 years, the TP’s LST increased slowly at 0.08°C per decade, with significant seasonal variations. Warming trends dominate in spring, summer, and autumn, shifting from the northern to the southwestern plateau. Autumn shows the most significant warming at 0.31°C per decade, while winter shows a cooling trend at −0.46°C per decade. (2) The Pettitt test reveals no statistically significant abrupt changes in mean annual and seasonal LST, though potential change points were detected in 2013 for mean annual LST, and in 2005, 2006, 2014, and 2017 for summer, spring, autumn, and winter, respectively. This indicates relative stability in interannual temperature changes, with some asynchronous seasonal variations. FFT analysis indicates dominant short-term variability with a period of 3.33 years and secondary cycles of 10 and 5 years.

A method to design a fast chaotic oscillator using CCTA

This article introduces a novel method for designing a fast chaotic oscillator using a CCTA (Current Conveyor Transconductance Amplifier) based on Chua's circuit. The proposed method uses innovative configurations and advanced simulation techniques to overcome challenges in high-speed operation, nonlinear dynamics, and Analog Building Block (ABB) selection. The design begins with nonlinear negative resistance, essential for Chua's diode characteristics, including two negative resistances, NR1 and NR2. The circuit integrates one CCTA block, two grounded capacitors, two fixed resistors, one inductor, and one potentiometer. It is simulated using PSPICE with IC (Integrated Circuit) macro-models and 180nm CMOS (Complementary Metal Oxide Semiconductor) technology. Various chaotic waveforms and attractors are produced, validating the theoretical and mathematical predictions. By varying the resistance values (1450Ω, 1650Ω, 1800Ω, 1950Ω), the circuit exhibits different chaotic behaviors, such as large limit cycles, double-scroll attractors, Rossler-type attractors, and I-periodic attractors. FFT (Fast Fourier Transform) analysis confirms the highest dominant operating frequency of 37.5MHz. A Monte Carlo simulation with 100 runs shows maximum voltage variations in the chaotic waveforms of 5.21 % and 4.61 % across the capacitors, demonstrating robustness and reliability. This design offers significant advancements in implementing high-frequency chaotic oscillators, with potential applications in various fields requiring chaotic signal generation.•A novel design of Chua's diode and Chua's chaotic oscillator using only one CCTA block is presented in this paper.•The proposed chaotic oscillator achieves the highest operating frequency of 37.5MHz.•The proposed circuit is simulated using commercially available ICs (MAX435 and AD844) and CMOS 180nm technology in PSPICE to confirm its workability.

A neuro-fuzzy based prediction modeling and mechanical characterization of multiwalled-carbon nanotubes filled Luffa cylindrica hybrid core and isotropic skin-based sandwich plate

The study focuses on mechanical characterization and adaptive neuro-fuzzy inference system (ANFIS)-based prediction modeling of MWCNT-filled Luffa cylindrica hybrid core-based sandwich plates. Experiments determine natural frequencies using modal impact hammer tests with a Fast Fourier Transform (FFT) analyzer. Elastic properties from tensile tests utilized for numerical simulations of natural frequencies in ABAQUS, showing high consistency with experimental results. SEM analysis characterizes the fiber-matrix bond in the hybrid core. ANFIS modeling establishes input–output relationships for unseen data sets. Comparisons of numerical simulations with ANFIS predictions demonstrate an accuracy within a 5% margin of error, providing insights into dynamic behavior and potential applications.

Large-Eddy Simulation of Low-Frequency Flow Oscillations for NACA0012 and Dynarig Sail at Large Attack Angles

Unmanned sailboats, harnessing wind for propulsion, offer great potential for extended marine research due to their virtually unlimited endurance. The sails typically operate at high attack angles, which contrasts with aircraft that maintain small angles to prevent stalling. Despite the reduction in lift during stalling, the resultant increase in drag contributes significantly to the sail’s thrust. However, the sail often experiences vortex shedding due to high attack angles, leading to low-frequency oscillations and erratic navigation. This study employs large-eddy simulations (LESs) on a 3D NACA0012 sail at a Reynolds number of 3.6 × 105, which is validated by experimental data. It observes the lift and drag coefficients across attack angles from 5 to 90 degrees and compares these with a Dynarig sail. The findings reveal that higher attack angles amplify fluctuations in lift and drag coefficients. Vortex shedding, resulting from flow separation, creates pressure changes and oscillations in aerodynamic forces. Fast Fourier transformation (FFT) analysis identifies dominant frequencies between 0.5 and 10 Hz, indicating low-frequency oscillations. The study’s insights into the impact of attack angle and sail type on the oscillation frequency are favorable for the design of unmanned sailboats, aiding in the prediction of wind-induced frequencies and optimal attack angle determination.

Beyond life: Exploring hemodynamic patterns in postmortem mice brains.

We utilize Laser Speckle Contrast Imaging (LSCI) for visualizing cerebral blood flow in mice during and post-cardiac arrest. Analyzing LSCI images, we noted temporal blood flow variations across the brain surface for hours postmortem. Fast Fourier Transform (FFT) analysis depicted blood flow and microcirculation decay post-death. Continuous Wavelet Transform (CWT) identified potential cerebral hemodynamic synchronization patterns. Additionally, non-negative matrix factorization (NMF) with four components segmented LSCI images, revealing structural subcomponent alterations over time. This integrated approach of LSCI, FFT, CWT, and NMF offers a comprehensive tool for studying cerebral blood flow dynamics, metaphorically capturing the 'end of the tunnel' experience. Results showed primary postmortem hemodynamic activity in the olfactory bulbs, followed by blood microflow relocations between somatosensory and visual cortical regions via the superior sagittal sinus. This method opens new avenues for exploring these phenomena, potentially linking neuroscientific insights with mysteries surrounding consciousness and perception at life's end.

Analysis of Properties and Macroscopic Defects of Metallic Bars, Pipes, and Strands through the Spectrum of Low-Frequency Excitations.

It is demonstrated that the application of piezoelectric sensors to metallic bars and strands can enable determining the status of the integrity of these elements through the spectrum of their acoustic excitations. The voltage output of the piezo, secured to metal bars or strands, is fed to the input of a Fast Fourier Transform analyzer, which allows displaying the spectrum of the excitations from which information on the length, overall quality of the metal, and the presence of defects can be obtained. We show that the analysis, performed on several materials and strands of different lengths, could be useful for cases in which visible inspection and/or direct access to the entire body of the metallic elements is not possible. Applications of our study for testing metallic structures embedded in concrete foundations are reported for construction sites.

Experimental investigation of acoustic moiré effect controlled by twisted bilayer gratings

Recently, the moiré pattern has attracted lots of attention by superimposing two planar structures of regular geometries, such as two sets of metasurfaces or gratings. Here, we show the experimental investigation of acoustic moiré effect by using twisted bilayer gratings (i.e., one grating twisted with respect to the other). We observed the guided resonance that occurred when the incident ultrasound beam was coupled with the guiding modes in a meta-grating, significantly influencing the reflection and transmission. Tunable guided resonances from the moiré effect with complete ultrasound reflection at different frequencies were further demonstrated in experiments. Combining the measurements of transmission spectra and the Fast Fourier Transform analyses, we reveal the guided resonance frequencies of moiré ultrasonic metasurface can be effectively controlled by adjusting the twisting angle of the bilayer gratings. Our results can be explained in a simplified model based on the band folding theory, providing a reliable prediction on the precise control of ultrasound reflection via the twisting angle adjustment. Our work extends the moiré metasurface from optics into acoustics, which shows more possibilities for the ultrasound beam engineering from the moiré effect and enables the exploration of functional acoustic devices for ultrasound imaging, treatment and diagnosis.

Using the Hilbert Transform to Evaluate the Effects of Functional Foods on Autonomic Nervous System Activity: A Comparison with the Fast Fourier Transform.

Evaluating the autonomic nervous system (ANS) via heart rate variability (HRV) to investigate the effects of food on human health has attracted attention. However, using a conventional HRV analysis via the fast Fourier transform (FFT), it is difficult to remove artifacts such as body movements and/or abnormal physiological responses (unexpected events) from the HRV analysis results. In this study, an analysis combining bandpass filters and the Hilbert transform was applied to HRV data on functional food intake to compare with FFT analysis. HRV data were obtained from six males by recording electrocardiograms on functional food, γ-aminobutyric acid, intake. HRV indices were calculated by both analysis. In the Hilbert analysis, all HRV indices were obtained for the same number of sampling points as the HRV data. The standard errors of all HRV indices tended to be smaller in the Hilbert analysis than in the FFT analysis. In conclusion, the Hilbert analysis was more suitable than FFT analysis for evaluating ANS via HRV on functional foods intake.

Unveiling Acoustic Cavitation Characterization in Opaque Chambers through a Low-Cost Piezoelectric Sensor Approach

This study investigates the characterization of acoustic cavitation in a water-filled, opaque chamber induced by ultrasonic waves at 20 kHz. It examines the effect of different acoustic radiator geometries on cavitation generation across varying electrical power levels. A cost-effective piezoelectric sensor, precisely positioned, quantifies cavitation under assorted power settings. Two acoustic radiator shape configurations, one with holes and another solid, were examined. The piezoelectric sensor demonstrated efficacy, corroborating with existing literature, in measuring acoustic cavitation. This was achieved through the Fast Fourier Transform (FFT) analysis of voltage data, specifically targeting sub-harmonic patterns, thereby providing a robust method for cavitation detection. Results demonstrate that perforated geometries enhance cavitation intensity at lower power levels, while solid shapes predominantly affect cavitation axially, exhibiting decreased activity at minimal power. The findings recommend using two different shape geometries on the acoustic radiator for efficient cavitation detection, highlighting intense cavitation on radial walls and cavitation generation on the bottom. Due to the stochastic nature of cavitation, averaging data is critical. The spatial limitation of the sensor necessitates prioritizing specific areas over complete coverage, with multiple sensors recommended for comprehensive cavitation pattern analysis.