Frequency Spectrum Research Articles

Unsteady Flow Structure of Rotating Instability in a 1.5-Stage Axial Compressor

Abstract Unsteady flow structure of the rotating instability (RI) in a 1.5-stage axial compressor is investigated through experimental and numerical analyses. In the tested compressor, the total pressure rise of the rotor stagnates at a certain flow coefficient before it increases again towards lower flow rate in a case involving a wide tip clearance. The RI appears beyond this stagnant point as the compressor is throttled. The RI indicates a gentle hump in the frequency spectra of wall pressure or the flow velocity near the tip in a range approximately 20 to 40% of the blade-passing frequency. As the flow rate decreases, the mode order of the RI increases, in contrast to more commonly reported tendency, while the propagation velocity remains constant. These features are well captured by DES of the half-annulus model. At its onset, RI is formed by the collision of the tip leakage vortex onto the pressure surface of the adjacent blade and subsequent vortex segmentation. This vortex structure spans two blade passages and propagates in the direction opposite to the rotor rotation. As the compressor is throttled, RI becomes more dominated by the circumferential propagation of vortex breakdown of tip leakage vortices, which occur simultaneously among neighboring passages with slight phase differences. The mechanism is discussed in relation to the temporal change in the blade tip loading. The frequency of vortex shedding increases with the reduction in flow rate and thus the increase in the number of RI disturbances.

A comparative investigation of neutron and gamma radiation interaction properties of zircaloy-2 and zircaloy-4 with consideration of mechanical properties

Abstract This study has established the radiation shielding efficacy of zircaloy-2 and zircaloy-4 over a wide spectrum of energy levels. Using the Monte Carlo N-Particle (MCNP) method, the gamma and neutron transmission factors (TF and nTF) were calculated for various energy levels. Zircaloy-2 demonstrated the highest gamma-ray absorption capacity and the lowest neutron absorption capacity among the investigated alloys. The results indicate that zircaloy-2 and zircaloy-4 have nearly the same neutron transmission characteristics. Although many studies have examined the structure and physical characteristics of these materials, there has been a lack of Monte Carlo simulations to comprehensively investigate the correlation between gamma absorption, neutron absorption parameters, and mechanical qualities. This research aims to examine the ability of zirconium and its zircaloy-2 and zircaloy-4 alloys, which are critical materials used in the nuclear industry, to absorb gamma and neutron radiation over a broad spectrum of frequencies. According to the results, zircaloy-2 has the best ability to absorb secondary gamma rays and the highest level of resistance to them. Despite the minimal disparity in the nTF between the two alloys, simulation results have shown that zircaloy-2 has a higher level of neutron transmittance. These results have the potential to expedite the development of novel materials with enhanced attributes for various applications.

Modeling magnetic field amplification in supernova remnants driven by laser

Abstract The origin of magnetic fields and their amplification have always been hot topics in fields such as astrophysics and high-energy-density physics. Among them, the turbulent dynamo effect is an important candidate mechanism, and the interaction between supernova remnants (SNRs) is an important carrier for studying the amplification effect of turbulent magnetic fields. In this paper, we use the radiation magnetohydrodynamic simulation program to carry out a scaling simulation study on the amplification effect of turbulent magnetic fields in the interaction of SNRs driven by powerful lasers. We investigate and compare the evolution of turbulence under different laser driving methods, different directions, and different intensities of initial external environmental magnetic fields. Here, we carefully identify the contributions of Biermann self-generated magnetic fields and environmental magnetic fields in the process of magnetic field amplification, present magnetic energy spectra, and magnetic field amplification factors, and analyze the influence of radiative cooling effect on turbulence and magnetic field evolution. The results show that the collision direction component of the environmental magnetic field dominates the process of magnetic field amplification, and the frequency spectrum of turbulence is consistent with Kolmogorov’s law. The research results are necessary for sorting out and elucidating the physical mechanism of magnetic field amplification in SNRs, and have reference significance for regulating turbulence in strong magnetic fields in the future.

Application of Machine Learning Methods to Analyze the Dependence of Vibration Acceleration Signals on the Tightening Forces of Bolted Connections

The article is devoted to the application of machine learning methods for the analysis of vibration acceleration signals in bolted joints that occur under impact. The relevance of the work is due to the need to ensure the reliability and operability of bolted joints under loads. One of the promising areas of non-destructive testing is the analysis of the characteristics of vibration acceleration signals that change with changes in the state of structures, for example, with an increase in the tightening torque. This allows for the timely detection of possible defects and the prevention of emergency situations, ensuring the safety and durability of structures. The vibration acceleration signals under impact action on a bolted joint were analyzed. After calculating the signal characteristics, data sets were formed, including the values of tightening torques and the corresponding calculated signal characteristics, a search for a correlation between the tightening torque of bolted joints and the obtained set of vibration signal parameters was carried out. The frequency spectrum of signals, the Higuchi fractal dimension, detrended fluctuations, spectral power density and position of signal peaks were calculated. Machine learning models such as decision trees, the nearest neighbor method, the k-means method and neural networks were used for the analysis. For these methods, an optimal set of parameters correlating with the tightening torque was searched for. The main results show that machine learning methods are effective in classifying signals and finding correlations with stress state parameters. They allow us to detect the relationship between a set of signal characteristics and tightening torque, which opens up opportunities for more accurate and reliable monitoring of the condition of bolted connections. This should help to increase their operational reliability and durability, as well as reduce the likelihood of failures and accidents. The use of such methods can improve the quality of monitoring and diagnostics of bolted connections.

The Chaotic Behavior of an Optical Artificial Neuron

ABSTRACT The operation of an optical neural network via a feed-forward (FF) configuration is experimentally simulated in the laboratory. The FF setup is tested using optical injection (OI), and the behavior of follower laser diodes (FLDs) subjected to chaotic modulation is examined. The last two laser diodes (LDs) are exposed to different weights of chaotic modulated signals through optical filtration and attenuation. Observations of the emissions from these two FLDs during FF operation are verified by frequency spectra calculated from time-series data. Signal broadening is assessed by measuring the full width at half maximum (FWHM), and chaotic signal spikes are analyzed by counting the number of peaks associated with signal amplitudes for the FLDs. Additionally, LD control parameters, including the bias voltage of the influencer laser diodes (ILD1, ILD2) and two additional FLDs, are examined. A maximum FWHM of 1.25 GHz for FLD2 is observed with a bias voltage of 3.6 V and a modulated signal attenuation of −12 dB. To determine the synchronization state, the correlation between the ILDs and FLDs is calculated. Results indicate fluctuations between negative and positive values, with the best correlation value being 0.4. These results confirm anti-synchronized ILD-FLDs, which are crucial for ensuring privacy in transmitting units within a chaotic optical communication system simulating an optical neural network.

Interaction of a UWB impulse with a layer of sandy soil, having a rough upper boundary

ABSTRACT In this paper, the changes in the character of time shapes, amplitudes, frequency spectra, and time delay of ultra-wideband (UWB) impulses (duration of 0.33 ns) reflected from the layer of wet sand with a rough upper boundary were investigated. It was shown that the attenuation of UWB impulse amplitude, which is reflected from the upper rough boundary of the sand layer, can be described by the model of Fresnel reflection coefficient for a smooth surface using the Gaussian correction factor (FRGC). At this root-mean-square (RMS), height of soil surface roughness was in the range of ~0–21 mm, but the determination coefficient and RMS error (RMSE) were found to be equal to 0.94 and 0.02, respectively. An experiment close to linear increase/decrease in the propagation time of impulses reflected from the upper/lower boundary of the layer, respectively, was found with increasing the RMS heights of upper boundary. It is shown that the propagation time of such impulses can be described by the modified FRGC model with an error from RMSE = 0.09 ns to RMSE = 0.15 ns. The results of these findings are of practical importance for the interpretation of remote sensing data for precise soil moisture measurements from unmanned aerial vehicle platforms using UWB impulses.

Deep CNN for Parkinson’s Disease Classification Using Line Spectral Frequency Images of Sustained Speech Phonation

It is proposed to use Deep Convolution Neural Network (DCNN) which is a good classifier of natural images to learn speech spectrum images of sustained phonation to detect Parkinson’s Disease (PD) as an alternative to the existing feature-based machine learning method. It is shown that the proposed method yields very high accuracy without the need for separate feature computation stage. The speech spectrum representations proposed are Short Time Fourier Transform (STFT) spectrum of size Nx256 and Line Spectral Frequency (LSF) spectrum of size Nx16. LSF reflects the speech production mechanism and it is a novel idea to use LSF spectrum in DCNN to detect PD speech. The spectrum images look like random patterns and the performance is improved when using an additional deeper hidden layer of tampering pattern in the last stage of a fully connected layer. Using a standard PD-sustained phonation dataset the training accuracies achieved are 98.50% and 92.50% for STFT and LSF method, respectively. The validation accuracies achieved are 84.38% for STFT and 100% for LSF. The STFT method results in a sensitivity of 97.05%, a specificity of 88.63%, a precision of 86.84%, an F1-score of 91.66, a false positive rate (FPR) of 11.36%, and a false alarm rate of 12.82%. The LSF method results in a sensitivity 97.05%, a specificity of 95.45%, a precision of 94.28%, an F1-score of 95.65, an FPR of 4.50%, and a false alarm rate of 5.71%. The LSF based method performs better and the performance comparison with the state-of-the-art methods brings out the merits of the LSF spectrum image-based DCNN learning in PD detection using sustained phonation.

Acoustic scene analysis of spatial and temporal distribution of sound arrival direction and frequency spectra

In the outdoor monitoring of aircraft noise, not only the target aircraft sounds to be monitored arrive, but also various environmental sounds also arrive at the monitoring site, and the sound situation changes from moment to moment. The authors have studied and reported on a method to classify environmental sound sources and to evaluate the influence of environmental sounds on aircraft sound monitoring by analyzing the spatial-temporal distribution of sound arrival direction and sound level of various environmental sounds at the monitoring site as a sound scene. This paper will make a report on a result of the acoustic scene analysis by using the sound spectra as additional input data in the scene analysis. We will also explain about a procedure for automatic classification of sound sources using the result of the scene analysis briefly.

Novel approaches to assessing brake squeal propensity: comparative evaluation of two index formulations

The persistence of brake squeal as a significant NVH issue within automotive brake systems necessitates the development of robust methods for assessing squeal propensity. This study introduces two novel squeal index formulations for assessment purposes and evaluates their performance against the formulation defined by the SAE J2521 standard. Experimental data essential for formulating the squeal index are collected on a controlled mass-sliding belt experiment. Experiments are performed at three different spring stiffness levels, and squeal index formulations are derived based on time and frequency domain data. The first formulation places particular emphasis on the time domain, explicitly considering the duration of individual squeal events. Conversely, the second formulation is based on the frequency response characteristics of the system, which considers the number of super-harmonic peaks in frequency spectra. To evaluate the performance and discriminatory capabilities of the newly developed squeal index formulations, extensive comparisons are made against the Squeal Index based on the test procedure described in SAE J2521. In conclusion, through meticulous analysis and interpretation of the experimental results, it is discerned that the squeal index formulation defined by time domain exhibits superior efficacy in discerning the nuanced effects of varying design parameters on the occurrence of squeal-like behavior.

Analysis and suppression research of high lift device noise based on large aeroacoustic wind tunnel test

The exterior noise of large aircrafts is an important aspect for the airworthiness certification. The high-lift device is one of the key noise sources for large aircrafts, thus the analysis and reduction of high-lift device noise is important and meaningful. Based on the 5.5m×4.0m Aeroacoustic Wind Tunnel and a half model for the aeroacoustic study of large scale aircrafts, the high-lift device noise properties were studied experimentally. First, the experimental platform, the test model, the test system and noise reduction design were introduced. Then, based on the experimental results, the noise characteristics of the high-lift devices were analyzed. The effects on the noise from the angle of attack and the wind speed were compared. The effects on the high-lift device noise from the noise reduction design on slat and flap were investigated. The results show that, the high-lift device noise has a broad frequency spectrum, with some peak frequency components. As the angle of attack is increased, the noise of the model is generally increased in the medium to high frequency range. With the wind speed increased, the noise is increased dramatically, and the frequency spectra keep a good similarity. The peak noise is suppressed by the noise reduction design.

Evaluating attentional responses to electric vehicle driving noise through event-related potentials: a frequency bands analysis

The advent of electric vehicles (EVs), known for their quieter operation compared to Internal Combustion Engine (ICE) vehicles, has shifted auditory attention towards wind and road noise. This shift is further accentuated by the anticipated future dominance of autonomous vehicles, which is expected to reduce the cognitive demands of driving, potentially heightening sensitivity to external noise and leading to increased annoyance and discomfort. This research investigates the impact of driving noise on selective attention by identifying sounds that significantly disrupt adaptation to driving noise through the analysis of event-related potentials (ERPs). Unlike previous studies that concentrated on the peak high-frequency driving noise fluctuations around 1000 Hz, our study explores a broader spectrum of higher frequencies. By recording brain responses to driving-related auditory stimuli, we detected variations in ERPs across different frequency bands, thereby pinpointing the acoustic characteristics most disruptive to concentration. Our findings aim to contribute towards enhancing driver focus and improving the acoustic comfort inside vehicle cabins.

Tyre/road noise frequency spectra analyses based on CPX measurement with six microphones

Road traffic noise is affected by many factors, the road surface being one of them. The CPX method (ISO 11819-2) is the method used to measure tyre/road noise, i.e. noise generated by tyre-surface contact. Up to six microphones are positioned around reference tyre (ISO 11819-3) in specified positions as the standard requires. Two microphones are mandatory; four are optional. Conventional stone mastic asphalt, low-noise pavement, exposed aggregate cement concrete and cobblestones were measured by the CPX method using six microphones at a velocity of 80 km/h. Tyre/road noise frequency spectra from each microphone will be shown in this paper. Presented results should be used to discussion about positions or necessity of mandatory and optional microphones.

Extending the frequency range of ISO 717-2 - a look at options and issues

In 2019, the Norwegian Standard NS 8175, which defines requirements for sound insulation in Norwegian buildings, was revised to include low frequency spectrum adaption terms for new dwellings. This change affects the applicability of measurements of weighted improvement of impact sound insulation, delta Lw, as the spectrum for calculating delta Lw, given in ISO 717-2, is limited downwards to 100 Hz. For a floating floor construction, this means that floors with a resonance frequency well below 100 Hz, and correspondingly a relatively high delta Lw, will be overrated in the sense that the measured delta Lw will be unaffected by the poor low frequency performance of the floating floor. To properly address this increased focus on low frequencies, an extended range spectrum is needed. In the work leading to this paper, a selection of extended range values for the reference heavyweight floor have been suggested, starting with a linear extrapolation of the values given in ISO 717-2. Data from laboratory measurements of delta Lw for floating floors have then been used to investigate the suitability of the suggested values. The results are summarised and discussed in this paper.

Direct Identification of the Continuous Relaxation Time and Frequency Spectra of Viscoelastic Materials

Relaxation time and frequency spectra are not directly available by measurement. To determine them, an ill-posed inverse problem must be solved based on relaxation stress or oscillatory shear relaxation data. Therefore, the quality of spectra models has only been assessed indirectly by examining the fit of the experiment data to the relaxation modulus or dynamic moduli models. As the measures of data fitting, the mean sum of the moduli square errors were usually used, the minimization of which was an essential step of the identification algorithms. The aim of this paper was to determine a relaxation spectrum model that best approximates the real unknown spectrum in a direct manner. It was assumed that discrete-time noise-corrupted measurements of a relaxation modulus obtained in the stress relaxation experiment are available for identification. A modified relaxation frequency spectrum was defined as a quotient of the real relaxation spectrum and relaxation frequency and expanded into a series of linearly independent exponential functions that are known to constitute a basis of the space of square-integrable functions. The spectrum model, given by a finite series of these basis functions, was assumed. An integral-square error between the real unknown modified spectrum and the spectrum model was taken as a measure of the model quality. This index was proved to be expressed in terms of the measurable relaxation modulus at uniquely defined sampling instants. Next, an empirical identification index was introduced in which the values of the real relaxation modulus are replaced by their noisy measurements. The identification consists of determining the spectrum model that minimizes this empirical index. Tikhonov regularization was applied to guarantee model smoothness and noise robustness. A simple analytical formula was derived to calculate the optimal model parameters and expressed in terms of the singular value decomposition. A complete identification algorithm was developed. The analysis of the model smoothness and model accuracy for noisy measurements was carried out. The equivalence of the direct identification of the relaxation frequency and time spectra has been demonstrated when the time spectrum is modeled by a series of functions given by the product of the relaxation frequency and its exponential function. The direct identification concept can be applied to both viscoelastic fluids and solids; however, some limitations to its applicability have been pointed out. Numerical studies have shown that the proposed identification algorithm can be successfully used to identify Gaussian-like and Kohlrausch–Williams–Watt relaxation spectra. The applicability of this approach to determining other commonly used classes of relaxation spectra was also examined.

A 2–36 GHz CMOS LNA with π-Network-Based wideband interstage matching technique for software-defined radio systems

With the advance in wireless communication, next-generation software-defined radio (SDR) systems require transceivers to operate in both sub-6GHz and millimeter-wave (mmWave) band and support multiple standards. However, bridging sub-6GHz frequencies with millimeter-wave bands exceeding 30 GHz remains a challenge for conventional wideband LNAs. To surmount this, a 2–36 GHz CMOS low-noise amplifier (LNA) designed for SDR systems is introduced in this paper. An innovative wideband input matching network capable of spanning both frequency domains is proposed. The methodology effectively mitigates the impact of vast parasitic capacitances, achieving wideband input matching against Electrostatic Discharge (ESD) and on-chip decoupling capacitor parasitic. In addition, a π-network-based wideband interstage matching technique is adopted to extend bandwidth of gain. A tri-stage prototype of the proposed LNA, designed using a 40-nm CMOS process, is designed to validate our design strategies. The post-simulation outcomes reveal a peak gain of 14 dB with a -3dB bandwidth ranging from 2 to 36 GHz, equating to a fractional bandwidth of 178 %. The Noise Figure (NF) is commendably uniform across the frequency spectrum, stabilizing at 5 dB. Furthermore, the third-order input intercept point (IIP3) is −4.2dBm to -3dBm across the bandwidth. The performance is achieved with a power of 19.4 mW and within a core area of 0.1 mm2.

INCAPE23-A Fully Differential Active Inductor with Cascode Current Mirror Using 0.18-um Technology for RF Frequency

This paper introduces a fully differential CMOS active inductor integrated with a cascoded current mirror, tailored for applications within the RF frequency range. The active inductor design is meticulously realized using CMOS 0.18-µm technology. The circuit implementation comprises a fully differential cross-coupled pair of transistors, strategically employed to impart negative feedback, thereby enhancing the quality factor (Q). The cascoded current mirror functions as a pivotal biasing component, facilitating control over the current source to modulate negative feedback and Q factor tuning. Simultaneously, two resistors integrated into the differential structure are crucial in managing the frequency spectrum. Simulation results substantiate a Q factor of 16k, coupled with an inductance of 14 nH, prominently achieved at 3 GHz frequency. Additionally, the objective of achieving a robust Q factor at 3.5 GHz, amounting to 500, is successfully realized. Noteworthy is the adaptability in achieving a frequency range spanning 2 GHz to 3.6 GHz by manipulating resistor values. Furthermore, the study observes that manipulation of supply voltage and current source enables the tuning of Q factor values from 70 to 16k. A performance assessment, juxtaposed with previously published works, underscores the viability of the proposed active inductor for applications within the gigahertz frequency range of RF.

Inflation hedging: a comparative wavelet quantile correlation analysis of real estate and alternative assets

PurposeConducting an analysis spanning from 2000 to 2023, this research evaluates the effectiveness of real estate assets in hedging against global and energy inflation, benchmarked against other compelling investment options such as oil, gold, silver and stocks.Design/methodology/approachThis study employs the wavelet quantile correlation (WQC) methodology. The latter sheds light on dynamic market interactions by scrutinising dependency structures across multiple time scales and also by capturing tail dependence. The adaptability of the wavelet transform, across a spectrum of frequencies, emerges as an indispensable tool for studying time series while also unravelling relationships among variables across diverse quantiles.FindingsThe findings reveal that the response to inflationary pressures is contingent upon the asset class, investment horizon and type of inflation under consideration. While precious metals demonstrate effectiveness over short-term horizons, French-listed real estate exhibits compelling inflation-hedging characteristics as the investment horizon extends. Oil emerges as an unequivocal hedge against both global and energy inflation.Practical implicationsTo counteract the effects of inflation, investors and households may feel compelled to refine their investment strategies, opting to bolster their portfolios with instruments proven to serve as reliable safeguards against inflation, as indicated by this study.Originality/valueIn conjunction with a surge in inflationary pressures, this study delves into the hedging capabilities of assets, exploring their efficacy not only across short- and long-term investment horizons but also within diverse scenarios characterized by fluctuating levels of global and energy-related inflation. To the best of our knowledge, no previous article employed the WQC technique to evaluate the inflation-hedging nexus.

Design of a High-Power Nanosecond Electromagnetic Pulse Radiation System for Verifying Spaceborne Detectors

The Spaceborne Global Lightning Location Network (SGLLN) serves the purpose of identifying transient lightning events occurring beneath the ionosphere, playing a significant role in detecting and warning of disaster weather events. To ensure the effective functioning of the wideband electromagnetic pulse detector, which is a crucial component of the SGLLN, it must be tested and verified with specific signals. However, the inherent randomness and unpredictability of lightning occurrences pose challenges to this requirement. Consequently, a high-power electromagnetic pulse radiation system with a 20 m aperture reflector is designed. This system is capable of emitting nanosecond electromagnetic pulse signals under pre-set spatial and temporal conditions, providing a controlled environment for assessing the detection capabilities of SGLLN. In the design phase, an exponentially TEM feed antenna has been designed firstly based on the principle of high-gain radiation. The feed antenna adopts a pulser-integrated design to mitigate insulation risks, and it is equipped with an asymmetric protective loading to reduce reflected energy by 85.7%. Moreover, an innovative assessment method for gain loss, based on the principle of Love’s equivalence, is proposed to quantify the impact of feed antenna on the radiation field. During the experimental phase, a specialized E-field sensor is used in the far-field experiment at a distance of 400 m. The measurements indicate that at this distance, the signal has a peak field strength of 2.2 kV/m, a rise time of 1.9 ns, and a pulse half-width of 2.5 ns. Additionally, the beamwidth in the time domain is less than 10°. At an altitude of 500 km, the spaceborne detector records a signal with a peak field strength of approximately 10 mV/m. Particularly, this signal transformed into a nonlinear frequency-modulated signal in the microsecond range across its frequency spectrum, which is consistent with the law of radio wave propagation in the ionosphere. This study offers a stable and robust radiation source for verifying spaceborne detectors and establishes an empirical foundation for investigating the impact of the ionosphere on signal propagation characteristics.

Optimising Sensor Placement for Tool Condition Monitoring: A Comparative Analysis of Acoustic Emission Data

In the monitoring of machining processes and in the context of tool condition monitoring, the position of acoustic emission sensors affects the quality of the resulting information. This drives this comparative study which presents a comparison of acoustic emission data recorded simultaneously by a sensor attached to the side of the milling table and another sensor near the spindle during a tool wear test on a CNC milling machine. The signal information is clearly influenced by the different positions. Whereas the signals near the spindle are dominated by vibrations of the spindle motor during machining, these strong contributions do not occur in the recorded data of the machining process detected by the sensor mounted to the table. However, the position of the sensor near the spindle has the advantage of maintaining a constant distance to the source of the acoustic emissions of the machining process, as the sensor moves with the tool. In contrast, the sensor attached to the milling table is affected from the fact that the distance to the milling tool is constantly changing. This results in different dominant frequencies, that are analysed in this study. To analyse both the entire frequency range and the higher frequencies, two distinct approaches are employed, one without and one with the use of a high-pass filter. Furthermore, the recorded signals were used to calculate the characteristic features, namely the Partial Powers, thereby enabling assessment of the condition of the tool. The results demonstrate that this single feature type is sufficient for training a machine learning model to predict the tool wear, focusing on the flank wear width. This study analyses the efficiency of wear prediction with respect to both sensors and frequency spectra, and compares the results. In this context, the most accurate predictions were achieved with Gaussian process regression models.

Preparation and investigation of structural, optical, and dielectric properties of PVA/PVP blend films boosted by MWCNTs/AuNPs for dielectric capacitor applications

With the rising global energy demands, there is a pressing need for the invention of efficient and reliable energy storage systems. This research centers on the creation and analysis of flexible dielectric capacitors composed of polymer nanocomposites (PNCs), incorporating a blend of polyvinyl alcohol (PVA) and polyvinyl pyrrolidone (PVP) as the base polymer, with multi-walled carbon nanotubes (MWCNTs) and gold nanoparticles (AuNPs) serving as nanofillers. The AuNPs were produced through an environmentally friendly synthesis method. Films made from PVA/PVP blended with MWCNTs and AuNPs were fabricated using the casting approach. Various characterization methods, including TEM, XRD, FTIR, and UV-Vis spectroscopy, were utilized to evaluate the samples. A detailed analysis of their electrical/dielectric characteristics was conducted. XRD analysis revealed a significant decrease in crystallinity from 55% for the pure PVA/PVP blend to 37% for the 1.6 wt.% nanofiller composite, indicating increased amorphous content, which facilitates better ion mobility. FTIR confirmed strong interactions between the polymer matrix and nanofillers, with intensified vibrational peaks pointing to enhanced molecular dynamics. UV-Vis spectroscopy demonstrated a red shift in the absorption edge, and Tauc's plot analysis showed a reduction in the indirect/direct optical band gap from 4.84 eV/5.68 eV for the pure blend to 4.26/5.35 eV for the nanocomposite with 1.6 wt.% nanofillers. The addition of nanofillers resulted in improvements in their dielectric features, which exhibited a significant performance improvement, with the dielectric constant (ε') reaching approximately 1100 at low frequency for the 1.6 wt.% nanofiller sample, compared to 9 for the pure blend. Additionally, the dielectric loss (ε'') and tangent loss (tan δ) were reduced, with tan δ showing a decrease from 15 for the pure blend to 2 for the 1.6 wt.% nanofiller composite at low frequency, indicating enhanced dielectric efficiency and reduced energy dissipation. The capacitors’ functionality was assessed through capacitance-frequency and conductance-frequency analyses. The capacitors exhibited stable high capacitance across a broad frequency spectrum, making them alternatives for energy storage solutions.