Mode Mixing Research Articles

Direct cohesive law measurement under mixed-mode loading using semi-circular bend (SCB) specimens

The present paper suggests a new method to obtain pure mode and mixed mode traction-separation laws of adhesives using semi-circular bend (SCB) specimen by employing the digital image correlation (DIC) technique. This specimen was recently proposed by the same authors for fracture energy assessment of adhesives. This test method doesn't require any special apparatus or loading jig for mixed mode fracture tests ranging from pure mode I to pure mode II. Lower cost and higher precision than conventional methods are the most advantages of using SCB fracture tests. The primary drawback of this test method lies in its data reduction process. To address this limitation, this paper introduces a direct approach for measuring fracture energy and cohesive law parameters of the adhesive. In this method, substrate strains at the interface are used to calculate the cohesive stress applied to the adhesive layer at each desired section. Shear and normal separation values are obtained directly from DIC displacement results. The proposed method was applied to the SCB specimen subjected to pure modes I and II and for mixed-mode I/II cohesive laws. Four different SCB specimens were manufactured and tested to cover different mode mixities between pure mode I and pure mode II. The experimentally obtained cohesive laws were evaluated using finite element analysis (FEA). Good agreement between the experimental and numerical results were observed.

Effects of Mode Mixity and Loading Rate on Fracture Behavior of Cracked Thin-Walled 304L Stainless Steel Sheets with Large Non-Linear Plastic Deformation.

This study investigates the mixed-mode I/II fracture behavior of O-notched diagonally loaded square plate (DLSP) samples containing an edge crack within the O-notch. This investigation aims to explore the combined effects of loading rate and mode mixity on the fracture properties of steel 304L, utilizing DLSP samples. The DLSP samples, made from strain-hardening steel 304L, were tested at three different loading rates: 1, 50, and 400 mm/min, covering five mode mixities from pure mode I to pure mode II. Additionally, tensile tests were performed on dumbbell-shaped specimens at the same loading rates to examine their influence on the material's mechanical properties. The findings revealed that stress and strain diagrams derived from the dumbbell-shaped samples were largely independent of the tested loading rates (i.e., 1-400 mm/min). Furthermore, experimental results from DLSP samples showed no significant impact of the loading rates on the maximum load values, but did indicate an increase in the ultimate displacement. In contrast to the loading rate, mode mixity exhibited a notable effect on the fracture behavior of DLSP samples. Ultimately, it was observed that the loading rate had an insignificant effect on the fracture path or trajectory of the tested DLSP samples.

A novel random spectral similar component decomposition method and its application to gear fault diagnosis

Sparse random mode decomposition (SRMD) is a decomposition approach established by combining sparse random feature model with clustering algorithm. It is not subject to the sampling process of signal and can mitigate mode mixing. However, the performance of SRMD is limited by its own hyperparameters, and it is prone to derive inaccurate clustering decomposition results when processing strong noise interference signal. To overcome these defects, a novel method called random spectral similar component decomposition (RSSCD) is proposed. In RSSCD, the time–frequency localized features produced by randomization and sparsification are adopted to represent the input signal. Subsequently, the initial signal components formed by sparse random features are taken as a whole, and the spectral similarity criterion is defined to adaptively form independent random components (RCs), thus improving the accuracy of decomposition. Furthermore, RSSCD is applied to gear fault diagnosis, and a fault significance measure (FSM) index is designed to guide the selection of parameter in sparse random feature representation, which ensures the fault information richness of the required RCs. Finally, the feasibility and effectiveness of RSSCD are fully validated by simulation signals and two experimental cases. The results indicate that RSSCD has excellent decomposition performance and fault feature extraction ability.

Resolving mode mixing in wheel–rail surface defect detection using EMD based on binary time scale

Due to the mode mixing, empirical mode decomposition (EMD) cannot effectively decompose the vibration signal when the signal is intermittent and pulse interference caused by discontinuous vibration. The methods to solve mode mixing often use noise assistance, such as ensemble EMD (EEMD), complete EEMD (CEEMD), etc. These methods can effectively solve mode mixing, but they also have shortcomings. In EEMD, the added noises not only have residual effects and time-consuming. The drawback of CEEMD is that it is difficult to align during set averaging. In this paper, an improved EMD based on binary time scale (EMD-BTS) is proposed for the fault feature extraction of wheel–rail defect detection. Firstly, the generalized intrinsic mode function (GIMF) is defined based on the time-domain characteristics of non-stationary vibration signals. Then, to tackle the drawbacks of EMD which cannot effectively solve mode mixing caused by signal intermittence and pulse interference, the inherent mode is extracted in the EMD-BTS to decompose the raw signals into GIMFs. Finally, the false components generated by over decomposition are combined based on time-domain cross covariance. A simulation case and a actual case of vehicle bogie are utilized to verify the feasibility of the proposed EMD-BTS. The results indicate that the proposed approach exceeds other typical techniques in extracting intermittent fault features of wheel–rail defect detection.

On-chip phonon-magnon reservoir for neuromorphic computing

Reservoir computing is a concept involving mapping signals onto a high-dimensional phase space of a dynamical system called “reservoir” for subsequent recognition by an artificial neural network. We implement this concept in a nanodevice consisting of a sandwich of a semiconductor phonon waveguide and a patterned ferromagnetic layer. A pulsed write-laser encodes input signals into propagating phonon wavepackets, interacting with ferromagnetic magnons. The second laser reads the output signal reflecting a phase-sensitive mix of phonon and magnon modes, whose content is highly sensitive to the write- and read-laser positions. The reservoir efficiently separates the visual shapes drawn by the write-laser beam on the nanodevice surface in an area with a size comparable to a single pixel of a modern digital camera. Our finding suggests the phonon-magnon interaction as a promising hardware basis for realizing on-chip reservoir computing in future neuromorphic architectures.

Real-time prediction of ship motion based on improved empirical mode composition and dynamic residual neural network

Ship motion attitude data have strong random and non-stationary characteristics under severe sea states. Real-time and accurate prediction of the ship's motion attitude can significantly improve the safety of navigation. Therefore, this article proposes a prediction model based on the improved empirical mode decomposition (IEMD) and the dynamic residual recurrent neural network with bidirectional structure and time pattern attention mechanism (TPA-Bi-DRRNN), and proposes a new algorithm: dynamic adaptive beetle swarm antennae search (DABSAS) algorithm to optimise the initial weight and threshold of the prediction model. The input data are adaptively composed into multiple intrinsic mode functions (IMFs) containing their frequency characteristics using IEMD, and a prediction is made for each IMF using TPA-Bi-DRRNN. The model structure can be regulated in real time according to the sliding window. The destroyer DTMB5415 is used as an example to conduct a performance test of the hybrid prediction model and DABSAS. The results show that no mode mixing occurred in the IMFs, the noise in each IMF was significantly reduced, thus dramatically reducing the difficulty of using the input data for prediction; and the optimization performance of DABSAS in applying the TPA-Bi-DRRNN is much better than that of the traditional algorithms. Compared with other models, the difference in the predictive accuracy of TPA-Bi-DRRNN under each condition is the smallest, suggestings that it has extremely high robustness and will always be able to maintain much higher accuracy than other models over a long period, thus meeting the needs for real-time accurate prediction of ship motion attitude.

Damage identification for pile foundation in high-piled wharf using composite energy factors driven by dynamic response under wave impact excitation

Exploring dynamic response-based damage identification under wave excitation is important for establishing a health monitoring system. In our previous study, we analyzed the characteristics of the rigid-body dynamic response and successfully employed them to identify the damage location of a pile foundation. Nevertheless, some recent researches show that the local flexural response resulting from wave impact has stronger energy performance. Therefore, this paper constructed a new damage identification method by making full use of energy and phase slope of the local flexural response. Firstly, a novel frequency domain clustering algorithm based on the Sub-peaks Suppression of Power Spectral Density Method (SSPSDM) was proposed to address the challenge of mode mixing in signal decomposition. Subsequently, the new composite energy factors were developed by combining both energy and phase of the local flexural response to enhance the sensitivity and robustness of damage identification. Finally, the method was validated using the acceleration signals from a laboratorial high-piled wharf model. The results show that the method can effectively extract the damage feature sub-signal and the composite energy factor based on phase slope enables better identification of the damage location and degree, enhancing the accuracy of the damage assessment process.

A Bearing Fault Diagnosis Strategy Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Correlation Coefficient Method

In response to the difficulties in extracting fault features from bearing vibration signals and the serious mode mixing and endpoint effects in traditional empirical mode decomposition (EMD) methods, a bearing fault diagnosis strategy combining complete ensemble empirical mode decomposition with adaptive noise(CEEMDANAN) and correlation coefficient method is proposed. This method fully combines the advantages of CEEMDANAN algorithm and correlation coefficient method in signal random detection. Firstly, CEEMDANAN decomposition is performed on the bearing vibration signals to obtain a series of intrinsic mode function (IMF) components. Select IMF components with high correlation coefficients for Hilbert envelope spectrum analysis to achieve bearing fault diagnosis. The experimental results show that the proposed method can effectively achieve fault diagnosis of bearings.

Complementary Ensemble Empirical Mode Decomposition and Maximum Correlated Kurtosis Deconvolution for Wind Turbine Bearing Fault Feature Extraction

Wind turbine bearing fault signals exhibit characteristics such as nonlinearity, non-stationarity, and susceptibility to external noise interference, making it challenging to extract fault features and identify them accurately. In light of these issues, this paper proposes a fault signal feature extraction method that combines complementary ensemble empirical mode decomposition (CEEMD) and maximum correlated kurtosis deconvolution (MCKD). CEEMD is utilized to decompose the signals, reducing mode mixing and eliminating residual auxiliary noise in the decomposition process, thereby obtaining linear and stationary signals that enhance the significance of the features. MCKD is employed to increase the kurtosis value of the fault signals, facilitating the extraction of continuous transient impacts from weak fault signals. Finally, a Convolutional Neural Network (CNN) is employed to validate that the method enhances fault characteristics and improves the accuracy of fault signal recognition.

Fracture energy and cohesive law analysis of adhesives using a recently developed SCB joint: The influence of joint geometry and mode mixity

In a prior study, the current authors proposed the utilization of semi-circular bend (SCB) specimens for analyzing the fracture energy of adhesive materials. However, similar to standard fracture test specimens, the geometry and size of the joint can impact the measured fracture energy when using SCB samples. Therefore, the objective of this research is to investigate the influence of adhesive layer thickness, substrate thickness, and disk radius of SCB specimens on cohesive law parameters and fracture energy of the adhesive. The study examines the fracture energy under different loading conditions, including pure mode I, pure mode II, and two mixed-mode conditions. By employing an inverse data reduction method, the cohesive law parameters of the tested adhesive are determined for various joint geometries. The results demonstrate that the disk radius does not affect the cohesive law of the joint. Furthermore, it is observed that bondline thicknesses of 0.2 mm and 0.4 mm lead to similar cohesive laws, whereas an adhesive thickness of 1 mm results in lower initial stiffness but higher fracture energy and damage initiation traction. Moreover, the study reveals that an increase in substrate thickness reduces the final fracture separation, indicating a more brittle fracture behavior. This can be attributed to a smaller fracture process zone caused by the plane strain load distribution in the adhesive layer.

Using cohesive zone models with digital image correlation to obtain a mixed mode I/II fracture envelope of a tough epoxy

This work describes a method in which the digital image correlation (DIC) method and finite element analysis (FEA) were used to create a quasi-static mixed-mode fracture envelope for bonded joints consisting of 2024-T3 Al adherends and a tough structural epoxy adhesive. Symmetric and asymmetric versions of double cantilever beam, single-leg bend, and end-notched flexure tests are used to populate the mixed-mode fracture envelope with results at several mode mixities. Experiments are conducted in a universal testing machine while recording images for subsequent DIC analysis. Finite element analysis is used to implement cohesive zone models (CZMs) of the adhesive fracture and to account for plastic deformation of adherends. Mode I and mode II traction separation laws (TSLs) are determined from a property identification method with a Benzeggagh–Kenane mixed-mode coupling law used to model mixed-mode behavior. FEA results are shown to provide a good agreement to both the crosshead displacement and DIC data. The methods in this paper serve as a potential framework for future calibration of mixed-mode fracture envelopes for joints bonded with very tough adhesives that complicate assessment with traditional data analysis methods.

Evaluating U-Space for UAM in Dense Controlled Airspace

The operation of unmanned aircraft systems in shared airspace can serve as an accelerator for the global economy and a sensitive addition to the existing mix of transportation modes. For these reasons, concepts of Unmanned Traffic Management have been recently published, defining advanced rules for all potential participants in the operation of unmanned systems. Airspace primarily dedicated to automated unmanned system operations, referred to as U-space in Europe, needs to be designated with consideration for the surrounding airspace. This is especially important in cases where the airspace is controlled, and when declaring U-space airspace, it is necessary to pay particular attention to the density of surrounding air traffic. The goal of this article is to assess the suitability of establishing U-space airspace for Urban Air Mobility in terms of traffic density in a controlled area above the selected metropolis, which is Prague, Czech Republic. To achieve this goal, data on air traffic in the given area were analyzed to obtain precise information about the traffic distribution. Areas in which the establishment of U-space airspace is possible both without implementing dynamic reconfiguration and with the application of the dynamic reconfiguration concept were also selected. The result is the determination of whether it is possible to establish U-space in airspace, as in the analyzed case of the Ruzyně CTR, U-space can be introduced in 83 % of the territory.

Operational modal identification of structures based on improved empirical wavelet transform

When empirical wavelet transform (EWT) is used to identify the modal parameters of civil engineering structures, the frequency band division is generally not accurate due to the noise effect on the Fourier spectrum. This phenomenon will lead to modal mixing and false modes in the analysis results. Therefore, this article establishes a signal frequency band division method by taking advantages of maximal spectrum technique and spectral skewness index. Combining the random decrement technique (RDT) and the least square method of single component modal parameter identification, an adaptive approach based on the improved EWT for operational modal parameter identification of civil engineering structures under stationary environmental excitations is proposed. The traditional frequency band division method based on EWT can not completely and effectively divide the meaningful frequency bands. While the proposed frequency band division technology based on the spectral skewness index can prevent the phenomenon of insufficient division and excessive division and realize adaptive frequency band division. The effectiveness of the proposed approach is validated by a numerical three-story frame and a field three-span concrete box girder bridge under ambient vibrations. The modal identification results from both numerical and experimental validations demonstrate that the proposed approach can effectively and accurately decompose the vibration responses and identify the structural modal parameters under operational conditions.

Presentation of machine learning methods and multi-objective optimization of fracture indices for asphalt rubber mixtures containing wax-based warm mix additives modified by nano calcium carbonate

Warm mix asphalt (WMA) additives have been proposed to overcome the high viscosity problem of crumb rubber modified asphalt (CRMA) binders. Various additives have been used to improve the low-temperature cracking performance of asphalt mixtures, but no study has been conducted to present the optimum additive content in CRMA binders in different loading modes. Therefore, this research aims to present the optimum content of nano calcium carbonate (NCC) to improve the fracture toughness and energy of asphalt rubber mixtures containing WMA additives (slack wax (SW) and polypropylene wax (PPW)). The semi-circular bend (SCB) fracture test was applied under pure mode I, mixed mode I-II, mixed mode II-I and pure mode II loadings at subzero temperature. Machine learning methods, including multivariate regression (MVR) and artificial neural network (ANN) models of group method of data handling (GMDH) and multilayer perceptron (MLP), were used to provide the prediction models of effective stress intensity factor (Keff) and fracture energy (Gf). Finally, the multi-objective optimization of Keff and Gf was performed to obtain optimum NCC content. The results indicated that in MVR model, the outputs had a small correlation with laboratory values, so that R value of MVR was 0.8406 and 0.8011 for Keff and Gf, respectively. Also, it was revealed in MVR that NCC had the highest impact on Keff and Gf significantly. GMDH model results showed that the relationships between predicted and laboratory values of Keff and Gf are appropriately described with R value of 0.9546 and 0.9229 for Keff and Gf models, respectively. In MLP model, different layer structures of the feed-forward neural network were developed to obtain the most accurate structure. It was indicated that MLP with 4–22–1 and 3–19–1 structures had a higher accuracy for Keff and Gf prediction models with R value of 0.9951 and 0.9978, respectively. Finally, by the use of the best model relationships, the results of the multi-objective optimization indicated that 4.91% and 6.37% NCC were the design optimum contents resulting in a maximum of Keff and Gf simultaneously for SW and PPW-modified CRMA mixtures. Moreover, the optimum NCC contents in loading modes of mode mixity Me of 1, 0.8, 0.4 and 0 were 3.62%, 5.12%, 4.08% and 3.42% for SW-modified CRMA mixtures and 4.35%, 6.51%, 7.19% and 2.84% for PPW-modified CRMA mixtures, respectively.

Designing a simple and suitable laboratory test specimen for investigating the general mixed mode I/II/III fracture problem

A novel test specimen was designed for experimental investigations of pure and mixed mode fracture. This novel test configuration is called “modified edge notched disc bend” (M-ENDB) specimen. The M-ENDB specimen has a circular disc shape with an edge crack along its diameter that is loaded using asymmetric three-point bending. Different fracture parameters including: stress intensity factors (SIFs), geometry factors, T-stress, biaxiality ratio (BR), and out-of-plane constraint factor (Tz) were determined for the suggested disc bend specimen using the extensive finite element (FE) analyses. According to the obtained results, the proposed specimen can present all combinations of mode mixities, including pure modes I, II, and III as well as mixed-mode I/II, I/III, and I/II/III deformations. Afterwards, a large number of pure and mixed-mode fracture toughness experiments were carried out on polyurethane (PUR) foam to examine the capabilities of the suggested M-ENDB specimen. Fracture path, fracture surface, fracture toughness, work of fracture, and critical T-stress of broken foams under general mixed mode conditions were investigated. Mixed mode I/II/III fracture toughness value was smaller than the pure mode I fracture toughness (KIc) value showing the weakness of brittle materials against multi-axial fracture compared to pure tensile type fracture.

Metafiber transforming arbitrarily structured light

Structured light has proven useful for numerous photonic applications. However, the current use of structured light in optical fiber science and technology is severely limited by mode mixing or by the lack of optical elements that can be integrated onto fiber end-faces for wavefront engineering, and hence generation of structured light is still handled outside the fiber via bulky optics in free space. We report a metafiber platform capable of creating arbitrarily structured light on the hybrid-order Poincaré sphere. Polymeric metasurfaces, with unleashed height degree of freedom and a greatly expanded 3D meta-atom library, were 3D laser nanoprinted and interfaced with polarization-maintaining single-mode fibers. Multiple metasurfaces were interfaced on the fiber end-faces, transforming the fiber output into different structured-light fields, including cylindrical vector beams, circularly polarized vortex beams, and arbitrary vector field. Our work provides a paradigm for advancing optical fiber science and technology towards fiber-integrated light shaping, which may find important applications in fiber communications, fiber lasers and sensors, endoscopic imaging, fiber lithography, and lab-on-fiber technology.

Denoising of Raman Spectra Using a Neural Network Based on Variational Mode Decomposition, Empirical Wavelet Transform, and Encoder-Bidirectional Long Short-Term Memory

Raman spectroscopy has been widely applied in numerous fields including bioanalysis, disease diagnosis, and molecular recognition, owing to its unique advantages of being non-invasive, rapid, and highly specific. However, the acquisition of Raman spectral data is often susceptible to various noise interferences, such as shot noise from the internal detector and dark current noise from the instrument. As a result, the weak Raman signals typically become arduous to discern, which affects the localization and identification of characteristic spectral peaks. This study investigates variational mode decomposition (VMD), empirical wavelet transform (EWT), and integrated encoder-bidirectional long short-term memory (EBiLSTM) modules to propose a neural network algorithm for adaptive denoising of Raman spectra. By combining VMD and EWT, the Raman spectra are decomposed into several sub-sequences, which solves the problem of mode mixing between high-frequency signals and noise in empirical mode decomposition, and significantly reduces the complexity of the original Raman spectral lines. The correlation coefficient between each modal component and the original signal is calculated along with the zero-crossing rate index to categorize the noise and signal sequences. Leveraging the linear differences between the ideal spectral lines and the noisy spectral curves, an encoder-bidirectional long short-term memory (EBiLSTM) denoising network is constructed for hierarchical denoising to extract valid spectral feature information from the high-frequency components, realizing refined adaptive denoising of the Raman spectra. Industry standard objective evaluation metrics on the signal-to-noise ratio and root mean square error are utilized to conduct simulation experiments comparing state-of-the-art algorithms, including empirical mode decomposition, VMD, sliding window averaging, and wavelet thresholding. The experimental results demonstrate that the Raman spectral denoising algorithm combining variational mode decomposition and neural networks improves the denoising performance by 13.38% to 72%, exhibiting higher accuracy and reliability.

Single pulse polarization study of pulsars B0950 + 08 and B1642 − 03: micropulse properties and mixing of orthogonal modes

ABSTRACT We present the results of a high-time resolution polarization study of single pulses from pulsars B0950 + 08 and B1642 − 03. Single pulses from pulsar B0950 + 08 sometimes show isolated micropulses without any significant associated subpulse emission. Assuming that the properties of such micropulses represent the intrinsic nature of micropulse emission, we characterize the width and polarization properties of these ‘intrinsic’ microstructures. Most of the ‘intrinsic’ micropulses ($\sim 90~{{\ \rm per\ cent}}$) follow common characteristic polarization properties, while the average width of these micropulses is consistent with the general micropulse population from this pulsar. Single pulses from these pulsars show a diverse range of polarization properties, including depolarization and mixing of two orthogonal modes resulting in polarization position angle jumps. We present a superposition model of the two orthogonal modes which can explain depolarization, the observed position angle jumps, and associated changes in other polarization parameters.

An Improved Denoising Method for Fault Vibration Signals of Wind Turbine Gearbox Bearings

Vibration monitoring (VM) is an important tool for fault diagnosis in key components of wind turbine gearboxes (WTGs). However, due to the influence of white noise and random interference, it is difficult to realize high-quality denoising of WTG-VM signals. To overcome this limitation, a novel joint denoising method for fault WTG-VM signals is proposed in this article, which we have named EWTKC-SVD. First, the empirical wavelet transform (EWT) boundary exploration method is used to optimize frequency band allocation and obtain the multiple intrinsic mode functions (IMFs). Second, the sensitive IMFs are selected according to the calculated correlation coefficient and kurtosis index, avoiding IMF redundancy. Finally, the fault WTG-VM signals are obtained using SVD denoising. Using this approach, the proposed method realizes high-quality denoising of WTG-VM signals. Furthermore, it also effectively solves the existing problems of conventional methods, namely, inefficient IMF selection, high noise, false frequencies, mode mixing, and end effect. Finally, the effectiveness, superiority, and reliability of the proposed method are proved using simulation and practical case results.

Fracture toughness of colored slurry seal at low temperatures and different loading Modes: Comparative study with warm and hot mix asphalt materials

Sustainable pavement, including colored slurry seal, was treated by crack growth under different traffic loads and temperatures. The current research examined colored slurry seals’ fracture toughness at various temperatures under mixed modes I/II and I/III. Three-point bend fixtures were used to prepare edge-notched disk bend (ENDB) disk specimens with edge cracks along one side of the specimens. Then, different temperatures and complete range mode mixity, including I/II and I/III, were used to determine fracture toughness data as well as the critical stress-intensity factor. Results indicated that the samples’ fracture resistance initially declined followed by an increment by an increment in the shear load in I/II mixed mode. From pure mode I to III, colored mixture resistances decreased with crack growth by raising mode III fraction at the crack tip. Moreover, lower temperatures led to better fracture performance between mixtures, especially at −20 °C. Mixtures’ fracture performance was positively influenced by temperature reduction due to bitumen strength characteristics. Comparison of fracture envelopes of cold mix asphalts (CMAs) in I/II and I/III mode mixities with other types of asphalt concrete, such as warm and hot mix asphalt (WMA and HMA), showed higher susceptibility of CMA mixture to cracking failure mode, making it noticeably weaker than WMA and HMA mixtures.