Nonlinear Characteristics Research Articles

Local plasticity-induced nonlinear surface wave scattering in thick-walled structures

Thick-walled structures (TWSs) are frequently utilized as primary load-bearing structures. The early detection of damage is crucial to ensure the operational safety and integrity of these structures. Nonlinear elastic-wave-based techniques provide a potential solution to the problem, which heavily relies on the good understanding of nonlinear wave generation and propagation characteristics. The task, however, is technically challenging due to the complex wave modes existing in a TWS. Targeting a meticulously manufactured local plasticized damage region on the surface of a TWS, which can be regarded as the precursor of incipient damage, this study investigates the scattering features of nonlinear elastic waves resulting from the interaction between the fundamental quasi-surface wave and the local plasticity. Finite element simulations and experiments show that, upon interacting with the nonlinear material zone, both nonlinear quasi-surface waves and shear bulk waves can be generated, with the latter scattered towards the opposite surface of the TWS. The scattered nonlinear shear waves exhibit a strong and predictable directivity with high nonlinear energy content, which is conducive to the detection and localization of the incipient surface damage of the TWS.

Nonlinear Characteristics of Metal-Insulated REBCO Magnet Under Various Switching Frequencies of Half-Bridge Inverter Circuit

Nonlinear Characteristics of Metal-Insulated REBCO Magnet Under Various Switching Frequencies of Half-Bridge Inverter Circuit

The asymmetric effect of fiscal deficits on macroeconomic stability in Nigeria: evidence from nonlinear autoregressive distributed lag (NARDL)

PurposeThe paper examines the asymmetric effects of fiscal deficits on selected macroeconomic variables in Nigeria, which include economic growth, exchange rates and inflation. The existing works of literature are premised on symmetry assumptions with dichotomous findings. In such situations, they suggest using a nonlinear approach as an alternative to checkmate the findings premised on linearity. This is critical, considering the perpetual fiscal deficit trends of Nigeria, which are considered a major economic problem in the country.Design/methodology/approachThe study employs nonlinear autoregressive distributed lag (NARDL) estimator using secondary data collected from the statistical bulletin of the Central Bank of Nigeria (CBN).FindingsThe results show that in the short run, both positive and negative shocks to the fiscal deficit have no effect on Nigeria's economic growth. The same is found on the negative shocks in the long run. However, positive shocks to the fiscal deficit have a long-run positive impact on economic growth. It is further revealed that, in the short run, positive shocks as well as negative shocks to fiscal deficits are positively related to the inflation rate. More so, long-run estimates show that positive shocks to the fiscal deficit have negative impacts on inflation, while negative shocks to the fiscal deficit have positive impacts on inflation.Originality/valueThis study introduces novelties to the understanding of the relationship between fiscal deficits and macroeconomic stability in Nigeria. It accounts for asymmetric and nonlinear features that are more aligned with the socioeconomic realities of real-world phenomena. This study also offers more insightful policy perspectives to enhance the fiscal profile of the country.

A scheme combining feature fusion and hybrid deep learning models for epileptic seizure detection and prediction

Epilepsy is one of the most well-known neurological disorders globally, leading to individuals experiencing sudden seizures and significantly impacting their quality of life. Hence, there is an urgent necessity for an efficient method to detect and predict seizures in order to mitigate the risks faced by epilepsy patients. In this paper, a new method for seizure detection and prediction is proposed, which is based on multi-class feature fusion and the convolutional neural network-gated recurrent unit-attention mechanism (CNN-GRU-AM) model. Initially, the Electroencephalography (EEG) signal undergoes wavelet decomposition through the Discrete Wavelet Transform (DWT), resulting in six subbands. Subsequently, time–frequency domain and nonlinear features are extracted from each subband. Finally, the CNN-GRU-AM further extracts features and performs classification. The CHB-MIT dataset is used to validate the proposed approach. The results of tenfold cross validation show that our method achieved a sensitivity of 99.24% and 95.47%, specificity of 99.51% and 94.93%, accuracy of 99.35% and 95.16%, and an AUC of 99.34% and 95.15% in seizure detection and prediction tasks, respectively. The results show that the method proposed in this paper can effectively achieve high-precision detection and prediction of seizures, so as to remind patients and doctors to take timely protective measures.

Theoretical and experiment optimization research of a frequency up-converted piezoelectric energy harvester based on impact and magnetic force

Harvesting environmental vibrations to power electronic components is an essential approach for addressing the power supply challenge in MEMS. However, conventional vibration energy harvesting systems frequently suffer from limited frequency bandwidth and high-frequency deficiencies. This paper proposes a novel up-frequency structure for piezoelectric vibration energy harvesting (VEH) that relies on both nonlinear magnetic force and piecewise linear force. The proposed VEH’s nonlinear dynamic characteristics are analyzed theoretically, and an experimental prototype machining and vibration test platform are constructed. Theoretical and experimental results are compared and analyzed by conducting basic experiments and key parameter optimization experiments. The research results demonstrate that the proposed VEH can efficiently harvest vibration energy in low-frequency and wide-band environments. Regarding the system parameters, higher vibration acceleration results in increased output voltage and wider working frequency bandwidth. Reducing the gap distance enhances piecewise linear vibration, which broadens the working frequency bandwidth. Furthermore, the proposed VEH’s ability to harvest low-frequency vibrations can be enhanced by reducing the magnet distance, thereby reducing the linear resonance frequency of the system. The findings of this study offer valuable insights for advancing the engineering application of MEMS self-power supply technology.

Soft sensor modeling based on masked convolutional transformer block deep residual shrinkage network

BackgroundData-driven soft sensor technology is currently an important means for industrial data prediction, addressing the challenge of predicting key quality variables in industrial production processes. Convolutional neural networks (CNN) are widely applied in soft sensor modeling due to their excellent nonlinear modeling and feature extraction capabilities. However, CNN also faces several issues, such as poor robustness to interference and difficulties in extracting features from complex process data. MethodsThis paper introduces a novel CNN model called the Masked Convolutional Transformer Block Deep Residual Shrinkage Network (MCTB-DRSN). Firstly, the Masked Convolutional Transformer Block (MCTB) is utilized to extract features from different positions, thereby enabling the network model to focus more on important information. Secondly, the Global Response Normalization (GRN) layer is incorporated into the Deep Residual Shrinkage Network (DRSN) module, which enhances feature competition among channels. Significant FindingsThis method can provide effective monitoring for chemical production process. Compared with the traditional soft sensor method on the debutanizer column dataset, the results show that the prediction accuracy of this model is significantly improved.

Dynamic characteristic analysis and key parameter optimization of throttling orifice type air damping air spring

Abstract The unique hysteretic characteristic of rubber bellows and the nonlinear flow of internal airflow in the system results in the significant nonlinear dynamic characteristic of throttling orifice type air damping air springs (OADASs). To solve the problem of mathematical representation of dynamic characteristic and key parameters optimization of throttling OADAS, this paper comprehensively considers the hysteretic characteristic of rubber bellows under variable pressure, the nonlinear dynamic characteristic model and linear model of throttling OADAS are established based on the concepts of gas thermodynamics and fluid mechanics. The static and dynamic characteristic tests of the throttling OADAS are conducted, to verify the accuracy and effectiveness of the proposed model, and to reveal the influence laws of excitation amplitude, excitation frequency, and throttling orifice diameter on the quantitative characterization indexes. Finally, a complete throttling orifice diameter optimization method is proposed based on the eight-degree-of-freedom model of the entire vehicle. Optimization results illustrate that the RMS values of the vertical acceleration of the body and the vertical acceleration of the driver are decreased by 19.02% and 38.44%, respectively. Overall, the outcomes of this paper can provide the design idea and theoretical basis for air damping matching and active suspension control.

Thermal performance analysis of gas turbine power plant using soft computing techniques: a review

Gas turbines are pivotal in electricity generation and industry, prized for their efficiency and flexibility in meeting diverse power needs. Optimizing their thermal efficiency is essential for improving energy output and sustainability. Soft computing methods such as neural networks, genetic algorithms, and fuzzy logic offer potent tools for this optimization due to their ability to handle the turbines' nonlinear and dynamic characteristics. These techniques facilitate a deeper understanding of the intricate interplay among various parameters affecting thermal performance, thereby enabling the development of intelligent and adaptive turbine systems. By leveraging soft computing, researchers can enhance gas turbine designs to align with modern energy and environmental objectives. This review emphasizes the application of soft computing approaches in analysing and improving gas turbine thermal performance. Such advancements are instrumental in achieving higher energy efficiency, reducing greenhouse gas emissions, and promoting a sustainable energy landscape. Ultimately, integrating soft computing into gas turbine operations promises to advance both technical capabilities and environmental stewardship in the detection of a more resilient and efficient energy infrastructure.

Enhanced Whale optimization algorithms for parameter identification of solar photovoltaic cell models: a comparative study

Parameter identification of solar photovoltaic (PV) cells is crucial for the PV system modeling. However, finding optimal parameters of PV models is an intractable problem due to the highly nonlinear characteristics between currents and voltages in different environments. To address this problem, whale optimization algorithm (WOA)-based meta-heuristic algorithm has turned out to be a feasible and effective approach. As a highly promising optimization algorithm, different enhanced WOA variants have been proposed. Nevertheless, there has been no comparative study of WOA and its variants for parameter identification of PV models so far. To further investigate and analyze the performance of WOA in the studied problem, this work applied and compared WOA and ten enhanced WOA variants for identifying five PV model parameters. Different evaluation indices including solution accuracy, search robustness, and convergence curve were employed to reveal their performance variation. Based on the simulation results, a multi-model statistical analysis with the Friedman test at a confidence level 0.05 was conducted to rank all algorithms. EWOA that hybridizes the sorting-based differential mutation operator and the Lévy flight strategy ranked first and its performance was further verified. Besides, according to the simulation results, possible effective improvement directions for WOA in tackling this intractable problem are concluded to guide future work.

Parameter-dependent conditions for H ∞ stabilisation of polynomial parameter-varying system with delay

This paper considers the problems of achieving exponential stability and H ∞ stabilisation for polynomial parameter-varying system with time-varying delay. Unlike some existing linear parameter-varying results that treat system parameters as time invariant factors, this paper makes full use of the nonlinearity and time-varying characteristics of system through introducing a novel delay-and-parameter-dependent Lyapunov function. The main conclusions are discussed in three cases (system without input, system with disturbance input, system with disturbance input and control input). And the H ∞ stabilisation problem is solved by constructing a controller which is inserted with system parameters and their derivatives. The structures of Lyapunov function and controller lead to that the derived criteria for exponential stabilisation are expressed by state-and-parameter-dependent linear matrix inequalities, which are efficiently solved by sum-of-squares program. The effectiveness and superiority are verified through two numerical examples.

Gaussian process regression driven rapid life-cycle based seismic fragility and risk assessment of laminated rubber bearings supported highway bridges subjected to multiple uncertainty sources

Structural attributes, corrosion of steel rebars in reinforced concrete (RC) columns, thermal oxidative aging of inherent rubber in laminated rubber bearings (LRBs), replacement of LRBs, and the time-dependent seismic hazard level at the bridge site are well recognized as the main factors that affect the accurate assessment of the seismic performance of RC bridges implemented with LRBs. However, the probabilistic seismic demand model (PSDM) and probabilistic structural capacity model (PSCM) of deteriorated bridge components may not exhibit homoscedastic log-linearity. And the expansion of seismic fragility surfaces and seismic risk curves in the temporal dimension requires a considerable computational cost on the nonlinear analysis, which regretfully has not been well addressed in current studies. To fill in this gap, this study proposes a life-cycle based seismic fragility and risk assessment framework for LRBs supported RC bridges considering deterioration mechanisms of bridge components during their life cycles demonstrated by a three-span LRBs supported highway bridge considering multiple uncertainties such as the replacement interval for LRBs. In this framework, Gaussian process regression (GPR) is employed to effectively construct the PSDM and PSCM of deteriorated bridge components, and then the time-dependent seismic fragility surfaces of bridge components and bridge system are established. Life-cycle based seismic risk analysis is conducted to quantify the coupled time-dependent effects of deterioration and site seismic hazard on a newly built bridge during its design service life and a deteriorated bridge within its remaining service life, respectively. Results indicate that GPR can successfully capture the nonlinearity and heteroscedasticity characteristics of PSDM and PSCM, leading to a significant reduced number of nonlinear analysis cases to generate the time-dependent seismic fragility surfaces and seismic risk curves. Consequently, the proposed framework using GPR can significantly improve the accuracy and efficiency of life-cycle based seismic performance assessment when uncertainties of structural attributes, deterioration progress and seismic hazard are taken into consideration.

Multi-condition adaptive detail characterization model of magnetorheological dampers and experimental verification

The theoretical model for predicting the damping characteristics of magnetorheological dampers (MRDs) is significant for enhancing the design efficiency of the control algorithm. However, some existing theoretical models face limitations in characterizing MRD damping characteristics simultaneously in terms of nonlinear detail characterization and adaptability to variable working conditions. Therefore, this paper proposed the Composite Double-Boltzmann (CDB) model combining the Double-Boltzmann (DB) function widely used in the field of biology and chemistry for its strong nonlinear characterization capability. Utilizing this model to fit the sinusoidal vibration testing data of the MRD prototype under variable combination working conditions, obtaining quantitative relationships between the undetermined parameters in the CDB model and the excitation current, vibration frequency, and amplitude to enable the model to address both the nonlinear details characterization of MRDs and adaptability to variable working conditions. Subsequently, the validity of the quantitative relationships were verified by comparing the calculated parameter values using the quantitative relationships with the original accurate parameter values. In order to verify the validity of the CDB model, extensive unknown working condition vibration tests were conducted on the MRD prototype under variable excitation currents, vibration frequencies, amplitudes and random excitation working conditions, employing the CDB and Tanh models to predict the damping characteristics, to compare to demonstrate the CDB model’s capability of adapting to variable working conditions while accurately characterizing the nonlinear details of MRD damping characteristics.

Thermal displacement prediction of high-speed electric spindles based on BWO-BiLSTM

To accurately, efficiently and stably predict the thermal displacement of the spindle, a prediction model based on the beluga whale algorithm (BWO) optimized bi-directional long and short-term memory neural network (BiLSTM) is introduced in this paper. Firstly, the thermal characterization and simulation analysis of the spindle are carried out, and the temperature and thermal displacement change characteristics of the spindle are obtained. Then the thermal deformation experiment of the spindle is carried out, and the temperature and displacement sensors are set up reasonably according to the temperature and thermal displacement change characteristics of the spindle, and the experimental data are collected and analyzed. The adaptive and globally convergent BWO is selected to optimize network parameters of BiLSTM, and the BWO-BiLSTM prediction model is constructed by learning the nonlinear correlation characteristics between spindle temperature and axial thermal displacement. The constructed BWO-BiLSTM prediction model is compared with other prediction models, and it is found through analysis that the prediction results output from the BWO-BiLSTM model have better accuracy and stability. The results of the study can provide a certain theoretical basis and technical support in predicting the spindle thermal displacement, which can help to promote the precision machining production of electric spindles.

The use of nonlinear guided-wave features for detection and assessment of intermetallic compounds within dissimilar joints – an experimental investigation

Abstract Welding dissimilar materials is widely employed in industrial construction and manufacturing to enhance cost-effectiveness and performance, often utilizing non-fusion methods like solid-state and high-energy beam welding. However, a significant challenge is the formation of intermetallic compounds (IMCs) at the joint interface, which can weaken the bond and increase brittleness, leading to hidden internal cracks. Nonlinear ultrasound detection methods are employed as advanced, non-destructive testing techniques for early damage inspection in various materials. This research investigates the assessment of the thickness of the intermetallic layer within dissimilar joints using non-linear ultrasound-wave features. Numerical and experimental investigations were performed using four friction stir welding (FSW) lap joints, between AA5052-H32 aluminum and ASTM 516-70 steel, with various intermetallic thicknesses. The methodology involved examining the generation of second-order harmonic frequency by exciting Lamb waves (LW) at specific frequencies. To determine the necessary LWs' excitation frequency, synchronism, and non-zero power flux conditions were employed. The collected signals were measured and analyzed in the time and frequency domains to understand the behavior of the non-linear parameter β′ with the thickness of the intermetallic layer. The results show that β′ changes in a linear manner with the thickness of the intermetallic compound layer (several micrometers in thickness). This provides strong evidence that nonlinear LW features are sensitive to microstructural variations in the FSW joints, which enables them to effectively evaluate their strength.

Research on fastener lateral nonlinear stiffness characteristics and its impact on wheel-rail stick-slip vibration

Research on fastener lateral nonlinear stiffness characteristics and its impact on wheel-rail stick-slip vibration

A hybrid model for the classification of Autism Spectrum Disorder using Mu rhythm in EEG.

Autism Spectrum Disorder (ASD) is a condition with social interaction, communication, and behavioral difficulties. Diagnostic methods mostly rely on subjective evaluations and can lack objectivity. In this research Machine learning (ML) and deep learning (DL) techniques are used to enhance ASD classification. This study focuses on improving ASD and TD classification accuracy with a minimal number of EEG channels. ML and DL models are used with EEG data, including Mu Rhythm from the Sensory Motor Cortex (SMC) for classification. Non-linear features in time and frequency domains are extracted and ML models are applied for classification. The EEG 1D data is transformed into images using Independent Component Analysis-Second Order Blind Identification (ICA-SOBI), Spectrogram, and Continuous Wavelet Transform (CWT). Stacking Classifier employed with non-linear features yields precision, recall, F1-score, and accuracy rates of 78%, 79%, 78%, and 78% respectively. Including entropy and fuzzy entropy features further improves accuracy to 81.4%. In addition, DL models, employing SOBI, CWT, and spectrogram plots, achieve precision, recall, F1-score, and accuracy of 75%, 75%, 74%, and 75% respectively. The hybrid model, which combined deep learning features from spectrogram and CWT with machine learning, exhibits prominent improvement, attained precision, recall, F1-score, and accuracy of 94%, 94%, 94%, and 94% respectively. Incorporating entropy and fuzzy entropy features further improved the accuracy to 96.9%. This study underscores the potential of ML and DL techniques in improving the classification of ASD and TD individuals, particularly when utilizing a minimal set of EEG channels.

A Nonlinear Active Disturbance Rejection Feedback Control Method for Proton Exchange Membrane Fuel Cell Air Supply Subsystems

The control strategy of the gas supply subsystem is very important to ensure the performance and stability of the fuel cell system. However, due to the inherent nonlinear characteristics of the fuel cell gas supply subsystem, the traditional control strategy is mainly based on proportional integral (PI) control, which has the disadvantages of large limitation, large error, limited immunity, and inconsistent control performance, which seriously affects its effectiveness. In order to overcome these challenges, this paper proposes an optimal control method for air supply subsystems based on nonlinear active disturbance rejection control (ADRC). Firstly, a seven-order fuel cell system model is established, and then, the nonlinear ADRC and traditional PI control strategies are compared and analyzed. Finally, the two strategies are simulated and compared. The validation results indicate that the integral absolute error (IAE) measure of PI control is 0.502, the integral square error (ISE) measure is 0.1382, and the total variation (TV) measure is 399.1248. Compared with the PI control, the IAE and ISE indexes of ADRC were reduced by 61.31% and 58.03%, respectively. ADRC is superior to PI control strategy in all aspects and realizes the efficient adjustment of the system under different working conditions. ADRC is more suitable for the nonlinear characteristics of the gas supply system and is more suitable for the oxygen excess ratio (OER).

Preliminary Study on Recontrol Evaluation of a Magnetic Bearing Rotor Falling on Touch-Down Bearings

The dynamic control of an active magnetic bearing (AMB) rotor after the rotor falls to its touch-down bearings has always been a difficult problem for applications such as flywheel energy storage. The rotor drop process has obvious nonlinear dynamic characteristics. This paper first discusses the structure of AMBs and the basic principles of their control. Starting from the electromagnetic forces that electromagnets can provide, the problem is simplified to the influence of an electromagnetic force with constant damping characteristics on the dynamic characteristics of a dropped rotor. A dynamic model of an AMB rotor with touch-down bearings was built and the contact force model between the rotor and the touch-down bearings was determined. A constant damping electromagnetic force was applied in two ways to verify the dynamic control feasibility of a dropped rotor through magnetic bearings. The simulation results show that the dropped rotor recovery control is feasible by applying a reasonable electromagnetic force.

Wind speed multi-step prediction based on the comparison of wind characteristics and error correction: Focusing on periodic thermally-developed winds

To leverage the abundant wind energy resources available in mountainous regions, an increasing number of wind power facilities and associated transportation infrastructure are being constructed in these areas. There are different types of intense winds in mountainous areas and the non-linear characteristics of the different intense winds vary significantly. The periodic thermally-developed winds are the commonly occurring type of intense wind. Based on the comparison of wind characteristics, this research proposes a prediction model for the periodic thermally-developed winds. The research utilizes the Bi-directional Long Short-Term Memory model(Bi-LSTM), which has been optimized by the Bayesian optimization(BO), to predict wind speed. Based on the relationship between wind field characteristics in periodic thermally-developed winds, a regression correction module is proposed using a joint probability model. Additionally, to address the volatility and uncertainty of wind speed, a Gaussian mixture model is employed for probability prediction. The experimental results demonstrate several findings: 1. The BO-Bi-LSTM model accurately and reliably predicts multi-step results for periodic thermally-developed winds in mountainous areas. 2. The proposed correction module, when combined with other models, yields improved wind speed accuracy compared to the uncorrected values. 3. The Gaussian mixture model produces probabilistic prediction results that consider both interval coverage and interval bandwidth simultaneously. 4. The proposed model can successfully predict periodic thermally-developed winds in mountainous areas for practical engineering purposes, resulting in reliable probabilistic prediction outcomes. It is of great significance for the utilization of wind energy resources and the driving safety of vehicles on ancillary structures.

One-Step Fabrication of 0D Cs4PbBr6 Perovskite with Nonlinear Optical Properties for Ultrafast Pulse Generation.

Low-dimensional lead halide perovskites demonstrate remarkable nonlinear optical characteristics attributed to their distinctive physical structures and electronic properties. Nevertheless, the investigation into their nonlinear optical properties remains in its incipient stages. This study addresses this gap by precisely controlling solvent volumes to synthesize both 0D Cs4PbBr6 and Cs4PbBr6/CsPbBr3 perovskites. Remarkably, as saturable absorbers, both pure Cs4PbBr6 and Cs4PbBr6/CsPbBr3 composites exhibit favorable nonlinear optical properties within the C-band, showcasing modulation depths of 9.22% and 16.83%, respectively. Moreover, for the first time, Cs4PbBr6 and Cs4PbBr6/CsPbBr3 composites have been successfully integrated into erbium-doped fiber lasers to realize the mode-locking operations. The utilization of the Cs4PbBr6/CsPbBr3 composites as a saturable absorber that enables the generation of conventional soliton mode-locked laser pulses with a pulse duration of 688 fs, and a repetition frequency of 10.947MHz at a central wavelength of 1557nm. Cs4PbBr6 is instrumental in generating laser pulses at a frequency of 10.899MHz, producing pulse widths of 642 fs at the central wavelength of 1531.2nm and 1.02ps at the central wavelength of 1565.3nm, respectively. The findings of this investigation underscore the potential utility of 0D Cs4PbBr6 and Cs4PbBr6/CsPbBr3 composites as promising materials for optical modulation within fiber laser applications.