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The purpose of this paper is to solve the local problem involving strong contrast heterogeneous conductive material, with application to gas-filled porous media with both perfect and imperfect Kapitza boundary conditions at the bi-material interface. The effective parameters like the dynamic conductivity and the thermal permeability in the acoustics of porous media are also derived from the cell solution.

The Fourier transform method is used to solve frequency-dependent heat transfer problems. The periodic Lippmann–Schwinger integral equation in Fourier space with source term is first formulated using discrete Green operators and modified wavevectors, which can then be solved by iteration schemes.

Numerical examples show that the schemes converge fast and yield accurate results when compared with analytical solution for benchmark problems.

The formulation of the method is constructed using static and dynamic Green operators and can be applied to pixelized microstructure issued from tomography images.

The purpose of this paper is to exploit a new and robust method to forecast the long-term extreme dynamic responses for wave energy converters (WECs).

A new adaptive binned kernel density estimation (KDE) methodology is first proposed in this paper.

By examining the calculation results the authors has found that in the tail region the proposed new adaptive binned KDE distribution curve becomes very smooth and fits quite well with the histogram of the measured ocean wave dataset at the National Data Buoy Center (NDBC) station 46,059. Carefully studying the calculation results also reveals that the 50-year extreme power-take-off heaving force value forecasted based on the environmental contour derived using the new method is 3572600N, which is much larger than the value 2709100N forecasted via the Rosenblatt-inverse second-order reliability method (ISORM) contour method.

The proposed method overcomes the disadvantages of all the existing nonparametric and parametric methods for predicting the tail region probability density values of the sea state parameters.

It is concluded that the proposed new adaptive binned KDE method is robust and can forecast well the 50-year extreme dynamic responses for WECs.

The purpose of this study is to investigate the effects of nanoadditive lubricants on the vibration and noise characteristics of helical gears compared with conventional lubricants. The experiment aims to analyze whether nanoadditive lubricants can effectively reduce gear vibration and noise under different speeds and loads. It also analyzes the sensitivity of the vibration reduction to load and speed changes. In addition, it compares the axial and radial vibration reduction effects. The goal is to explore the application of nanolubricants for vibration damping and noise reduction in gear transmissions. The results provide a basis for further research on nanolubricant effects under high-speed conditions.

Helical gears of 20CrMnTi were lubricated with conventional oil and nanoadditive oils. An open helical gearbox with spray lubrication was tested under different speeds (200–500 rpm) and loads (20–100 N·m). Gear noise was measured by a sound level meter. Axial and radial vibrations were detected using an M+P VibRunner system and fast Fourier transform analysis. Vibration spectrums under conventional and nanolubrication were compared. Gear tooth surfaces were observed after testing. The experiment aimed to analyze the noise and vibration reduction effects of nanoadditive lubricants on helical gears and the sensitivity to load and speed.

The key findings are that nanoadditive lubricants significantly reduce the axial and radial vibrations of helical gears under low-speed conditions compared with conventional lubricants, with a more pronounced effect on axial vibrations. The vibration reduction is more sensitive to rotational speed than load. At the same load and speed, nanolubrication reduces noise by 2%–5% versus conventional lubrication. Nanoparticles change the friction from sliding to rolling and compensate for meshing errors, leading to smoother vibrations. The nanolubricants alter the gear tooth surfaces and optimize the microtopography. The results provide a basis for exploring nanolubricant effects under high speeds.

The originality and value of this work is the experimental analysis of the effects of nanoadditive lubricants on the vibration and noise characteristics of hard tooth surface helical gears, which has rarely been studied before. The comparative results under different speeds and loads provide new insights into the vibration damping capabilities of nanolubricants in gear transmissions. The findings reveal the higher sensitivity to rotational speed versus load and the differences in axial and radial vibration reduction. The exploration of nanolubricant effects on gear tribological performance and surface interactions provides a valuable reference for further research, especially under higher speed conditions closer to real applications.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2023-0220/

The purpose of this study is to develop a 3D wheel-rail adhesion model under wet condition, which considers the generated surface roughness topography and the traditional braking procedure for high-speed trains.

Wheel-rail adhesion has an important effect on the braking ability of railway vehicle. Based on the deterministic mixed lubrication approach, the model was solved to get the adhesion characteristics of the train during braking. The elastic deformation was calculated with the discrete convolution and fast Fourier transform method. The simulation results of adhesion coefficient were compared with the experimental values. The wheel-rail adhesion characteristics of train braking at several different initial speeds were investigated. The effects of the time-step length and roughness orientation on the contact load ratio were also discussed.

The results show that the adhesion coefficient of the numerical model is in good agreement with the experimental results. At the instant of braking, the adhesion coefficient drops to a lower adhesion level, the value of adhesion coefficient is lower than 0.06, especially at a higher speed (200, 300 and 400 km/h).

It can provide a better understanding of the low adhesion phenomenon of train braking under wet condition.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-02-2023-0040/

The contact and lubrication performances, which were previously estimated assuming a Gaussian surface, are insufficient due to the non-Gaussian surface characteristics of the honing liner. The purpose of this study is to analyze the liner honing surface and examine its effects on the contact and flow performance.

The fast Fourier transform (FFT) method was used to generate the liner honing texture. Subsequently, an elastoplastic contact model based on boundary element theory was constructed and simulated for the honing surface. The results were compared with those obtained using a Gaussian surface. In addition, flow factors of the honing surfaces were also compared.

The contact pressure and flow factors demonstrate significant disparities when dealing with non-Gaussian surfaces. In the deterministic model, the pressure exhibits considerably diminished magnitudes and a more evenly distribution. Moreover, when the gap between surfaces is narrow, the discrepancy in flow factor across different directions on the real honing surface becomes more prominent compared with the Gaussian surface.

The model incorporates the influence of the non-Gaussian honing surface, thereby enabling more accurate prediction.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2023-0198/

This study aims to analyse the non-linear losses of a porous media (stack) composed by parallel plates and inserted in a resonator tube in oscillatory flows by proposing numerical correlations between pressure gradient and velocity.

The numerical correlations origin from computational fluid dynamics simulations, conducted at the microscopic scale, in which three fluid channels representing the porous media are taken into account. More specifically, for a specific frequency and stack porosity, the oscillating pressure input is varied, and the velocity and the pressure-drop are post-processed in the frequency domain (Fast Fourier Transform analysis).

It emerges that the viscous component of pressure drop follows a quadratic trend with respect to velocity inside the stack, while the inertial component is linear also at high-velocity regimes. Furthermore, the non-linear coefficient b of the correlation ax + bx² (related to the Forchheimer coefficient) is discovered to be dependent on frequency. The largest value of the b is found at low frequencies as the fluid particle displacement is comparable to the stack length. Furthermore, the lower the porosity the higher the Forchheimer term because the velocity gradients at the stack geometrical discontinuities are more pronounced.

The main novelty of this work is that, for the first time, non-linear losses of a parallel plate stack are investigated from a macroscopic point of view and summarised into a non-linear correlation, similar to the steady-state and well-known Darcy–Forchheimer law. The main difference is that it considers the frequency dependence of both Darcy and Forchheimer terms. The results can be used to enhance the analysis and design of thermoacoustic devices, which use the kind of stacks studied in the present work.

The noise measurement on magnetoresistive (MR) sensors is generally conducted by techniques including single-channel data sampling and fast Fourier transform (FFT) analysis as well as two-channel cross-correlation. The single-channel method is easy to implement and is widely used in the noise measurement on MR sensors, whereas the two-channel method can only eliminate part of the system noise. This study aims to address two key issues affecting measurement accuracy: calibration of the measurement system and the elimination of system noise.

The system is calibrated by using a low-noise metal film resistor in that the system noise is eliminated through power spectrum subtraction. Noise measurement and analysis are conducted for both thermal noise and detectivity of magnetic tunnel junction (MTJ) sensor.

The thermal noise measurement error is less than 2%. The detectivity of the MTJ sensor reaches 27 pT/Hz^1/2 at 2 kHz.

This study provides a more practical solution for noise measurement and system calibration on MR sensors with a bias voltage and magnetic field.

This paper aims to focus on the finite element method in the frequency domain (FD-FEM) for the transient electric field in the non-sinusoidal steady state under the non-sinusoidal periodic voltage excitation.

Firstly, the boundary value problem of the transient electric field in the frequency domain is described, and the finite element equation of the FD-FEM is derived by Galerkin’s method. Secondly, the constrained electric field equation on the boundary in the frequency domain (FD-CEFEB) is also derived, which can solve the electric field intensity on the boundary and the dielectric interface with high accuracy. Thirdly, the calculation procedures of the FD-FEM with FD-CEFEB are introduced in detail. Finally, a numerical example of the press-packed insulated gate bipolar transistor under the working condition of the repetitive turn-on and turn-off is given.

The FD-CEFEB improves numerical accuracy of electric field intensity on the boundary and interfacial charge density, which can be achieved by modifying the existing FD-FEMs’ code in appropriate steps. Moreover, the proposed FD-FEM and the FD-CEFEB will only increase calculation costs by a little compared with the traditional FD-FEMs.

The FD-CEFEB can directly solve the electric field intensity on the boundary and the dielectric interface with high accuracy. This paper provides a new FD-FEM for the transient electric field in the non-sinusoidal steady state with high accuracy, which is suitable for combined insulation structure with a long time constant.

Due to high speeds, heavy loads, large slide-to-roll ratios (SRR) and other variable operating conditions, some rolling bearings that have been working in harsh conditions may experience flash temperatures in the contact area, which may result in early damage like smearing and then affect service life. This study aims to investigate the flash temperature phenomenon of rolling bearings through theoretical and experimental analysis.

A technology for measuring temperature distribution in rolling ball on disk contact under lubrication was developed. The test-rig can simulate the ball bearing contact. The effects of working conditions such as entrainment speed, load, SRR and lubricating oil viscosity on the flash temperature were investigated.

The results of the theoretical calculation and experiments indicate that the parameters promoting the reduction of film thickness in elastohydrodynamic lubrication are always related with the number of flash points, even film thickness reduced to mixed lubrication. The flash temperature is easier to happen in conditions of high SRR, heavy load, slow entrainment speed and low viscosity oil.

This work conducts an experimental study on the flash temperature phenomenon, providing a test technology for bearing lubrication and failure investigation.

This author has opted into Transparent Peer Review available at: https://publons.com/publon/10.1108/ILT-04-2023-0104

Music sentiment analysis helps to promote the diversification of music information retrieval methods. Traditional music emotion classification tasks suffer from high manual workload and low classification accuracy caused by difficulty in feature extraction and inaccurate manual determination of hyperparameter. In this paper, the authors propose an optimized convolution neural network-random forest (CNN-RF) model for music sentiment classification which is capable of optimizing the manually selected hyperparameters to improve the accuracy of music sentiment classification and reduce labor costs and human classification errors.

A CNN-RF music sentiment classification model is designed based on quantum particle swarm optimization (QPSO). First, the audio data are transformed into a Mel spectrogram, and feature extraction is conducted by a CNN. Second, the music features extracted are processed by RF algorithm to complete a preliminary emotion classification. Finally, to select the suitable hyperparameters for a CNN, the QPSO algorithm is adopted to extract the best hyperparameters and obtain the final classification results.

The model has gone through experimental validations and achieved a classification accuracy of 97 per cent for different sentiment categories with shortened training time. The proposed method with QPSO achieved 1.2 and 1.6 per cent higher accuracy than that with particle swarm optimization and genetic algorithm, respectively. The proposed model had great potential for music sentiment classification.

The dual contribution of this work comprises the proposed model which integrated two deep learning models and the introduction of a QPSO into model optimization. With these two innovations, the efficiency and accuracy of music emotion recognition and classification have been significantly improved.