Nonlinear Characteristics Research Articles

Analysis of Weakly Nonlinear Evolution Characteristics of Flow in the Constant Curvature Bend

Analysis of Weakly Nonlinear Evolution Characteristics of Flow in the Constant Curvature Bend

Improved deep learning based state of charge estimation of lithium ion battery for electrified transportation

The increasing interests and recent advancements in artificial intelligence and machine learning have significantly accelerated the development of novel techniques for the state estimation of batteries in the battery management systems (BMS) of electrified vehicles. To ensure the safe and reliable operation of an electric vehicle, it is essential to determine the state of charge (SOC) among the battery's numerous states. Deep learning models have been effective for SOC estimation due to the nonlinear and time-varying characteristics of a battery management system. This article seeks to describe an SOC estimator design method using deep neural network (DNN) models, particularly non-recurrent and recurrent neural networks, owing to their excellent computational intelligence and capacity for self-learning. A thorough investigation has been conducted at various temperatures, and the model is trained on data received from the cell during discharging processes like the urban dynamometer driving schedule (UDDS). Finally, the accuracy of the models is compared based on various performance indices. From the results, it has been concluded that the DNN models acquire a Mean Absolute Error (MAE) of less than 1 % and a Root Mean Square Error (RMSE) of close to 1 % when validated over various datasets at temperatures of 10 °C, 25 °C, and 40 °C.

Nonlinear dynamic characteristics of mechatronics integrated planetary gears considering wear and temperature effects

The electromechanical planetary gear system has high work efficiency and long service life, but factors such as heat, wear, and electrical signals can affect the transmission performance of electromechanical planetary gears. This article comprehensively considers factors such as temperature, tooth surface wear, lubrication, current and voltage, damping ratio, etc. Based on the principle of thermal deformation, Archard wear model, and equivalent circuit principle, a dynamic model of electromechanical planetary gears is established. The existing literature has not comprehensively analyzed the effects of temperature, wear, and electrical signal changes on the nonlinear characteristics of electromechanical planetary gear systems. The results show that when the motor current is between 7 A and 18 A and the voltage is lower than the rated voltage, the system is in a stable state; as the temperature rises, the system tends to stabilize; the gear wear exceeds 30 μm, and the bifurcation characteristics of the system are more pronounced.

Broadband and ascendant nonlinear optical properties of the wide bandgap material GaN nanowires

Gallium nitride (GaN) nanowire, as a type of wide bandgap nanomaterial, has attracted considerable interest because of its outstanding physicochemical properties and applications in energy storage and photoelectric devices. In this study, we prepared GaN nanowires via a facile chemical vapor deposition method and investigated their nonlinear absorption responses ranging from ultraviolet to near-infrared in the z-scan technology under irradiation by picosecond laser pulses. The experiment revealed that GaN nanowires exhibit remarkable nonlinear absorption characteristics attributed to their wide bandgap and nanostructure, including saturable absorption and reverse saturable absorption. When compared to bulk GaN crystals, the nanowires provide a richer and more potent set of nonlinear optical effects. Furthermore, we conducted an analysis of the corresponding electronic transition processes associated with photon absorption. Under high peak power density laser excitation, two-photon absorption or three-photon absorption dominate, with maximum modulation depths of 73.6%, 74.9%, 63.1% and 64.3% at 266 nm, 355 nm, 532 nm, and 1064 nm, respectively, corresponding to absorption coefficients of 0.22 cm/GW, 0.28 cm/GW, 0.08 cm/GW, and 2.82 ×10−4 cm3/GW2. At lower peak energy densities, GaN nanowires demonstrate rare and excellent saturation absorption characteristics at wavelength of 355 nm due to interband transitions, while saturable absorption is also observed at 532 nm and 1064 nm due to band tail absorption. The modulation depths are 85.2%, 41.9%, and 13.7% for 355 nm, 532 nm, and 1064 nm, corresponding to saturation intensities of 3.39 GW/cm2, 5.58 GW/cm2 and 14.13 GW/cm2. This indicates that GaN nanowires can be utilized as broadband optical limiters and high-performance pulse laser modulating devices, particularly for scarce ultraviolet optical limiters, and saturable absorbers for ultraviolet and visible lasers. Furthermore, our study demonstrates the application potential of wide bandgap nanomaterials in nonlinear optical devices.

Offset-Free Koopman Model Predictive Control of Thermal Comfort Regulation for a Variable Refrigerant Flow-Dedicated Outdoor Air System-Combined System

Abstract Variable refrigerant flow (VRF) system has been an appealing solution of air conditioning for residential and commercial buildings, due to its flexibility and cost effectiveness, while lack of ventilation capability is a major drawback. Incorporation of dedicated outdoor air system (DOAS) is a typical practice. However, good coordination between DOAS and VRF is critical for achieving desired thermal comfort is challenging due to the possible complexity of mixed sensible and latent heat exchanges. In this paper, to handle the nonlinear dynamic characteristics of VRF-DOAS system, we propose an offset-free Koopman model predictive control (MPC) strategy for thermal comfort regulation, in which the MPC design is computationally more efficient due to the convex problem formulation and the use of reduced-order Koopman models, and the offset-free MPC structure enhances the robustness to model uncertainties and unmeasured disturbances. A control-oriented model is obtained by hybridizing the first-principle and data-driven modeling approach. The proposed controls strategy is evaluated with a Modelica simulation model of a VRF-DOAS system. A Dymola-Python cosimulation platform is developed via the functional mockup interface (FMI), for which the MPC algorithms are implemented in Python. Simulation results show significantly better performance of the offset-free Koopman MPC in thermal comfort regulation.

Nonlinear seismic response analysis of slopes considering the coupled effect of slope geometry and soil stratigraphy

Summary To investigate the effects of slope geometric parameters and soil stratigraphic properties on the topographic amplification of ground motions, a large number of 2D horizontally layered slope models are constructed. Firstly, the linear and nonlinear seismic responses of a slope model are compared, and the result shows that the nonlinear characteristics of soils should be considered when studying the amplifying effect of slope topography on ground motions. Then, the nonlinear seismic responses of these slope models are analyzed from four aspects: the maximum shear strain in the slopes, the effects of geometry and stratigraphy on the seismic response, the distance between the maximum topographic amplification indicators and the slope crest, and the influence range of slope topography behind the slope crest. The results indicate that the amplifying effect of slope topography on ground motions increases with increasing slope height or decreasing average shear-wave velocity of the overlying soil layers. Besides, the variation of the topographic amplification effect with slope gradient is significantly influenced by soil stratigraphic properties. The distance between the maximum topographic amplification indicators and the slope crest is mainly in the range of 0 ∼ 60 m, and the influence range of slope topography behind the slope crest is mainly in the range of 0 ∼ 150 m. Subsequently, approximate relations are derived based on regression analyses of simulation results, which can provide meaningful references for the seismic design and seismic retrofitting of engineering structures behind the slope crest. Finally, the effects of slope geometric parameters and soil stratigraphic properties on ground motion modifications are further evaluated according to the prediction curves provided by the approximate relations.

Third-order relativistic fluid dynamics at finite density in a general hydrodynamic frame

The motion of water is governed by the Navier–Stokes equations, which are complemented by the continuity equation to ensure local mass conservation. In this work, we construct the relativistic generalization of these equations through a gradient expansion for a fluid with a conserved charge in a curved d-dimensional spacetime. We adopt a general hydrodynamic frame and introduce the irreducible-structure (IS) algorithm, which is based on derivatives of the expansion scalar and the shear and vorticity tensors. By this method, we systematically generate all permissible gradients up to a specified order and derive the most comprehensive constitutive relations for a charged fluid, accurate to third-order in the gradient expansion. These constitutive relations are formulated to apply to ordinary (nonconformal) and conformally invariant charged fluids. Furthermore, we examine the frame dependence of the transport coefficients for a nonconformal charged fluid up to the third order in the gradient expansion. The frame dependence of the scalar, vector, and tensor parts of the constitutive relations is obtained in terms of the (field redefinitions of the) fundamental hydrodynamic variables. Managing the frame dependencies of the constitutive relations is challenging due to their non-linear character. However, in the linear regime, the higher-order transformations become tractable, enabling the identification of a set of frame-invariant coefficients. Subsequently, the equations obtained in the linear regime are solved in momentum space, yielding dispersion relations for shear, sound, and diffusive modes for a non-conformal charged fluid, expressed in terms of a set of frame-invariant transport coefficients.

Finding the optimal reliable energy consumption path for electric vehicles under rainfall conditions

ABSTRACT This paper presents a new path-finding problem to ensure reliable energy consumption for electric vehicles (EVs) under rainfall conditions. The objective function of the proposed model aims to find a reliable path that minimises energy consumption while ensuring a certain probability of completing the trip without exhausting a given battery energy budget. By considering the influence of adverse weather conditions at different periods, the existing model is expanded. To address the non-additivity and non-linearity characteristics of the optimisation model, an enhanced heuristic algorithm is proposed, incorporating inequality techniques, the K-shortest algorithm, and path-updating strategies. Lastly, the proposed algorithm is validated using Hong Kong’s grid-based road network as a case study, which demonstrates the correctness and effectiveness of the algorithm. The results indicate that by considering adverse weather conditions, the estimation of energy consumption can be significantly improved in terms of accuracy, achieving more efficient and reliable optimal path recommendations.

Physical reservoir application of random network of gold nanoparticles fabricated by multi-step immersion in gold colloidal solution

Abstract We fabricated a random network of gold nanoparticles (RN-GNPs) over 12 NiCr/Au electrodes by using a multi-step immersion method, where a sample was immersed in a gold colloid solution three times. 
Nonlinear current-voltage characteristics due to the Coulomb blockade were observed at 77 K. 
For demonstration of physical reservoir applications, input-output characteristics of the RN-GNPs were also measured in a one-input, nine-output terminal configuration. 
Distorted output voltage waveforms were obtained for a sinusoidal voltage input of 100 Hz. 
The higher-order harmonic components were confirmed in the frequency spectra of the outputs. 
The waveform reconstruction task and short-term storage capacity estimation were performed by an echo state network model with ridge regression and linear regression, respectively.

Multi-step forecasting of dissolved oxygen in River Ganga based on CEEMDAN-AdaBoost-BiLSTM-LSTM model

Accurate prediction of Dissolved Oxygen (DO) is an integral part of water resource management. This study proposes a novel approach combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) with AdaBoost and deep learning for multi-step forecasting of DO. CEEMDAN generates Intrinsic Mode Functions (IMFs) with different frequencies, capturing non-linear and non-stationary characteristics of the data. The high-frequency and medium-frequency IMFs, characterized by complex patterns and frequent changes over time, are predicted using Adaboost with Bidirectional Long Short-Term Memory (BiLSTM) as the base estimator. The low-frequency IMFs, characterized by relatively simple patterns, are predicted using standalone Long Short-Term Memory (LSTM). The proposed CEEMDAN-AdaBoost-BiLSTM-LSTM model is tested on data from ten stations of river Ganga. We compare the results with six models without decomposition and four models utilizing decomposition. Experimental results show that using a tailored prediction technique based on each IMF’s distinctive features leads to more accurate forecasts. CEEMDAN-AdaBoost-BiLSTM-LSTM outperforms CEEMDAN-BiLSTM with an average improvement of 25.458% for RMSE and 37.390% for MAE. Compared with CEEMDAN-AdaBoost-BiLSTM, an average improvement of 20.779% for RMSE and 28.921% for MAE is observed. Diebold-Mariano test and t-test suggest a statistically significant difference in performance between the proposed and compared models.

Performance Improvement of an Electric Vehicle Charging Station Using Brain Emotional Learning-Based Intelligent Control

Electric vehicle (EV) charging facilities are essential to their development and deployment. These days, autonomous microgrids that use renewable energy resources to energize charging stations for electric vehicles alleviate pressure on the public electricity grid. Nevertheless, controlling and managing such charging stations’ energy is difficult due to the nonlinearity and irregular character of renewable energy sources. The current research recommends using a Brain Emotional Learning Intelligent Control (BELBIC) controller to enhance an autonomous EV charging station’s performance and power management. The charging station uses a battery to store energy and is primarily powered by photovoltaic (PV) solar energy. The principles of BELBIC are dependent on emotional cues and sensory inputs, and they are based on an emotion processing system in the brain. Noise and parameter variations do not affect this kind of controller. In this study, the performance of a conventional proportional–integral (PI) controller and the suggested BELBIC controller is evaluated for variations in solar insolation. The various parts of an EV charging station are simulated and modelled by the MATLAB/Simulink framework. The findings show that, in comparison to the conventional PI controller, the suggested BELBIC controller greatly enhances the transient responsiveness of the EV charging station’s performance. The EV keeps charging while the storage battery perfectly saves and keeps steady variations in PV power, even in the face of any PV insolation disturbances. The suggested system’s simulation results are provided and scrutinized to confirm the concept’s suitability. The findings validate the robustness of the suggested BELBIC control versus parameter variations.

Multi-strategy modified sparrow search algorithm for hyperparameter optimization in arbitrage prediction models.

Deep learning models struggle to effectively capture data features and make accurate predictions because of the strong non-linear characteristics of arbitrage data. Therefore, to fully exploit the model performance, researchers have focused on network structure and hyperparameter selection using various swarm intelligence algorithms for optimization. Sparrow Search Algorithm (SSA), a classic heuristic method that simulates the sparrows' foraging and anti-predatory behavior, has demonstrated excellent performance in various optimization problems. Hence, in this study, the Multi-Strategy Modified Sparrow Search Algorithm (MSMSSA) is applied to the Long Short-Term Memory (LSTM) network to construct an arbitrage spread prediction model (MSMSSA-LSTM). In the modified algorithm, the good point set theory, the proportion-adaptive strategy, and the improved location update method are introduced to further enhance the spatial exploration capability of the sparrow. The proposed model was evaluated using the real spread data of rebar and hot coil futures in the Chinese futures market. The obtained results showed that the mean absolute percentage error, root mean square error, and mean absolute error of the proposed model had decreased by a maximum of 58.5%, 65.2%, and 67.6% compared to several classical models. The model has high accuracy in predicting arbitrage spreads, which can provide some reference for investors.

Nonlinear evolution characteristics and seepage mechanical model of fluids in broken rock mass based on the bifurcation theory

With the deep extension of coal mining in China, fault water inrush has become one of the major disasters threatening the safety production of coal mine. Based on the control equations of steady state and non-Darcy seepage in fractured rock mass, the multi-parameter nonlinear dynamic seepage equations of fractured rock mass are established in this paper. Based on the nonlinear dynamics theory, the function of the state variable in the system is derived, and the influence of the gradual change of non-Darcy flow factors on the structural stability of seepage system is studied. The research achievements show that there are three branches in the equilibrium state of the seepage system. Specifically, the stability of the equilibrium state changes abruptly near the limit parameter. The seepage dynamic system of fractured rock mass has the delayed bifurcation, and the coal mine disaster such as fault water inrush occurs easily at the bifurcation point. The research results are of great significance to enrich the theory of fault water inrush in coal mine, and to reveal the disastrous mechanism of fault water inrush and guide its prevention and control technology in coal mine, which can provide the theoretical reference for predicting the water seepage stability in fractured rock mass.

Novel techniques to analyze dynamical properties of quantum chaos with peculiar evidence of hybrid systems confinement

Recent results demonstrate the dynamical peculiarities of the quantum chaos within the hybrid systems by chaotic parameters and probe the pattern formation under the influence of condensation. The complex dynamic behavior of the considered systems was determined with numerical simulation and presented an efficient technique that studied fractional systems comprising chaos-coherence fractions. The findings divulge the peculiar association between the coherence structure and the correlations at finite relative momenta. Thus the present study helps to explore the partially chaos hybrid systems in order to stimulate the experimental applications of nonlinear phenomena. The coherent-chaotic parameters can be measured by examining the chaos peculiarities that possess explicit relations with the condensations to demonstrate the environs of the physical systems. We investigate the influence of the multiplicities, chaos, momentum and temperature of the nonlinear system on the coherent-chaotic normalized correlations. The chaotic parameters are suppressed considerably with the coherence fraction and it appears numerically zero at maximum condensation and one at ideal chaos emissions. We procure that the meaningful parameters decrease significantly with the multiplicity of the nonlinear systems and increase with the momentum in the specified regimes. The identical multiplicity leads to contemplating the coherence and thus the normalized chaotic parameters within its spectacular influences exhibit significance worth contemplating in earnest. The findings underscore the significance of cogitating correlations in deciphering the nonlinear system characteristics and bestowing extraordinary perceptiveness into the convoluted essence of complex systems. The contemplated methodology can be applied to evaluating and analyzing the nonlinear systems and such an innovative approach computes the problems of celestial mechanics, heartbeats and chemical reactions in engineering and medical fields.

The SF and remanence evaluation of magnetic shields based on SST for low frequency and degaussing situation

Abstract The permalloy sheet, as the dominant shielding functional material, mainly determines static and low frequency shielding performance of magnetically shielded room (MSR). However, lack of measurement of shielding sheet in practical use would lead to the nonnegligible evaluation error for MSR performance. Therefore, an estimation technique of shielding factor (SF) and the remanence of MSR is proposed in this paper with the consideration of the nonlinear magnetic characteristics of the permalloy sheet tested by single sheet tester for low frequency field and degaussing situation(L-D-SST). First, a high-accuracy measurement system, composed of L-D-SST (for exciting magnetic field and sensing the corresponding B and H) and the control system (for applying excitation to L-D-SST and amplifying, filtering and collecting B and H signal), is established. The weak magnetic fields of low frequencies and decaying alternating demagnetizing field excitations are separately applied to L-D-SST for basic magnetization curves (BM curves) and anhysteretic magnetization curve (AM curve) test. Furthermore, the BM and AM curves are respectively integrated with FEM algorithm for the accurate and reliable estimation of the SF and remanence of MSR in operation state. In addition, the tested magnetic properties are applied to the optimization of MSR shielding quality.

A heavy-haul train longitudinal-vertical coupled dynamics model and its dynamic behaviour under emergency braking

The heavy-haul train dynamic performance under emergency braking is complex and directly affects the reliability and running safety. To reveal the heavy-haul train dynamic behaviour in emergency braking process on the long downhill segments, a comprehensive heavy-haul train longitudinal-vertical coupled dynamics model (HTLVDM) that considers the air braking system and coupler draft gear system is established in this paper. HTLVDM considers the components of the locomotive and wagon, such as the wheelset, bogie frame, and car body. The coupler force model considers the nonlinear hysteresis characteristic of the coupler draft gear system. The braking delay time and pressure variation of the brake cylinders are taken into the air braking model. In addition, the validity of HTLVDM is verified by published data. Based on the real-world data of the Shuozhou-Huanghua Line and the established model, the HTLVDM's dynamic responses are analysed through numerical simulations. The results indicate that both the brake forces and coupler forces have a significant effect on the wheel-rail dynamic interactions and the dynamic response of the car body in the heavy-haul train.

Electric-field tunable Type-I to Type-II band alignment transition in MoSe2/WS2 heterobilayers

Semiconductor heterojunctions are ubiquitous components of modern electronics. Their properties depend crucially on the band alignment at the interface, which may exhibit straddling gap (type-I), staggered gap (type-II) or broken gap (type-III). The distinct characteristics and applications associated with each alignment make it highly desirable to switch between them within a single material. Here we demonstrate an electrically tunable transition between type-I and type-II band alignments in MoSe2/WS2 heterobilayers by investigating their luminescence and photocurrent characteristics. In their intrinsic state, these heterobilayers exhibit a type-I band alignment, resulting in the dominant intralayer exciton luminescence from MoSe2. However, the application of a strong interlayer electric field induces a transition to a type-II band alignment, leading to pronounced interlayer exciton luminescence. Furthermore, the formation of the interlayer exciton state traps free carriers at the interface, leading to the suppression of interlayer photocurrent and highly nonlinear photocurrent-voltage characteristics. This breakthrough in electrical band alignment control, interlayer exciton manipulation, and carrier trapping heralds a new era of versatile optical and (opto)electronic devices composed of van der Waals heterostructures.

The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach

Epilepsy surgery is effective for patients with medication-resistant seizures, however 20–40% of them are not seizure free after surgery. Aim of this study is to evaluate the role of linear and non-linear EEG features to predict post-surgical outcome. We included 123 paediatric patients who underwent epilepsy surgery at Bambino Gesù Children Hospital (January 2009–April 2020). All patients had long term video-EEG monitoring. We analysed 1-min scalp interictal EEG (wakefulness and sleep) and extracted 13 linear and non-linear EEG features (power spectral density (PSD), Hjorth, approximate entropy, permutation entropy, Lyapunov and Hurst value). We used a logistic regression (LR) as feature selection process. To quantify the correlation between EEG features and surgical outcome we used an artificial neural network (ANN) model with 18 architectures. LR revealed a significant correlation between PSD of alpha band (sleep), Mobility index (sleep) and the Hurst value (sleep and awake) with outcome. The fifty-four ANN models gave a range of accuracy (46–65%) in predicting outcome. Within the fifty-four ANN models, we found a higher accuracy (64.8% ± 7.6%) in seizure outcome prediction, using features selected by LR. The combination of PSD of alpha band, mobility and the Hurst value positively correlate with good surgical outcome.

Effect of Interstage Torsional Damping on Nonlinear Dynamic Characteristics of Two-Stage Planetary Gear System

Abstract This paper explores the influence of interstage torsional damping on the nonlinear dynamics of a two-stage planetary gear transmission system. A comprehensive nonlinear dynamic model is developed, incorporating interstage torsional damping, stiffness, time-varying meshing parameters, damping, and tooth side clearance. The nonlinear equations are derived and solved using the fourth-order Runge–Kutta method. The investigation reveals that the changes in torsional damping between stages significantly affect the nonlinear dynamic characteristics of the system, especially under load fluctuations. Bifurcation characteristics are analyzed using phase diagrams, Poincaré diagrams, time history diagrams, and frequency–domain spectrograms. The simulation results demonstrate that heightened torsional damping between stages distinctly affects the motion states across excitation frequencies. Notably, it mitigates the unstable motion state caused by high-frequency excitation when tooth side clearance is minimal.

A coupling method for wheel–rail longitudinal interaction

Longitudinal wheel–rail interaction is often neglected in railway engineering research. In order to analyze the system dynamics with consideration of the braking force, a new longitudinal wheel–rail interaction model is proposed in this paper. Then, the reliability of the proposed model is verified by comparing the results obtained from the traditional vehicle-track coupled dynamic model. The calculated dynamic responses of the system with different time steps are compared to illustrate the stability of the proposed method. In the case study, the influence of the track irregularity on the dynamic response of the system is analyzed in detail, and it is found that the influence of the lateral track irregularity on the longitudinal dynamic response of the system is significant. In addition, the effect of the nonlinearity of ballasted bed longitudinal resistance on the longitudinal dynamic response, and it is found that the nonlinear characteristics of the ballast bed cannot be ignored.