Nonlinear Dynamics Research Articles

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.

An enhanced hybrid scheme for ship roll prediction using support vector regression and TVF-EMD

In order to represent the complex characteristics of ship roll motion such as nonlinearity, uncertainty, and time-varying dynamics thus improving the ship roll prediction accuracy, an enhanced hybrid prediction scheme is proposed by using improved swarm intelligent optimization, time series decomposition, and machine learning. Firstly, the novel time-varying filtering-based empirical mode decomposition (TVF-EMD) is employed to decompose the ship roll motion data into multiple mode components with dynamic time-varying characteristics, which immunes of the mode mixing and intermittency problems of traditional empirical mode decomposition (EMD). Then, the support vector regression is utilized to train and predict mode components. Finally, the predicted results of individual components are reconstructed to achieve the final ship roll prediction angles. To avoid the unfavorable influence of manual parameter selection for TVF-EMD, an improved black widow optimization algorithm is employed to optimize the parameter configuration. The feasibility and effectiveness of the enhanced hybrid model are validated by ship roll prediction simulation based on the measured data of M.V. YuKun at sea. The experimental results show that the enhanced hybrid scheme can improve the prediction accuracy of ship roll motion and outrank those by using methods of EMD, ensemble empirical mode decomposition, and variational mode decomposition.

The relationship between weight-adjusted-waist index and suicidal ideation: evidence from NHANES

BackgroundAmidst growing evidence of the intricate link between physical and mental health, this study aims to dissect the relationship between the waist-to-weight index (WWI) and suicidal ideation within a representative sample of the US population, proposing WWI as a novel metric for suicide risk assessment.MethodsThe study engaged a sample of 9500 participants in a cross-sectional design. It employed multivariate logistic and linear regression analyses to probe the association between WWI and suicidal ideation. It further examined potential nonlinear dynamics using a weighted generalized additive model alongside stratified analyses to test the relationship's consistency across diverse demographic and health variables.ResultsOur analysis revealed a significant positive correlation between increased WWI and heightened suicidal ideation, characterized by a nonlinear relationship that persisted in the adjusted model. Subgroup analysis sustained the association's uniformity across varied population segments.ConclusionsThe study elucidates WWI's effectiveness as a predictive tool for suicidal ideation, underscoring its relevance in mental health evaluations. By highlighting the predictive value of WWI, our findings advocate for the integration of body composition considerations into mental health risk assessments, thereby broadening the scope of suicide prevention strategies.

Nonlinear dynamics of a double pendulum energy harvesting device for continuously rotating systems

Nonlinear dynamics of a double pendulum energy harvesting device for continuously rotating systems

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.

Parameter Tuning Approach for Incremental Nonlinear Dynamic Inversion-Based Flight Controllers

Incremental nonlinear dynamic inversion (INDI) is a widely used approach to controlling UAVs with highly nonlinear dynamics. One key element of INDI-based controllers is the control allocation realizing pseudo controls using available actuators. However, the tracking of commanded pseudo controls is not the only objective considered during control allocation. Since the approach only works locally due to linearization and the solution is often ambiguous, additional aspects like control efforts or penalizing the deviation of certain states must be considered. Conducting the control allocation by solving a quadratic program this results in a considerable number of weighting parameters, which must be tuned during control design. Currently, this is conducted manually and is therefore time consuming. An automated approach for tuning these parameters is therefore highly beneficial. Thus, this paper presents and evaluates a model-based approach automatically tuning the control allocation parameters of a tiltrotor VTOL using an optimization algorithm. This optimization algorithm searches for optimal parameters minimizing a cost functional that reflects the design target. This cost functional is calculated based on a test mission for the VTOL which is conducted within a simulation environment. The test mission represents the common operating range of the VTOL. The simulation environment consists of an aircraft model as well as a model of the INDI-based controller which is dependent on the control allocation parameters. On this basis, model-based optimization is conducted and the optimal parameters are identified. Finally, successful real-world tests on a 4-degrees-of-freedom testbench using the identified parameters are presented. Since the control allocation parameters can significantly influence the aircraft’s stability, the 4-DOF testbench for the aircraft is required for rapid validation of the parameters at a minimum amount of risk.

Flocks with Nonlinear Inherent Dynamics under Fixed and Switching Digraphs

Flocks with Nonlinear Inherent Dynamics under Fixed and Switching Digraphs

Exploring the physical characteristics and nonlinear wave dynamics of a (3+1)-dimensional integrable evolution system

This study comprehensively explores the [Formula: see text]-dimensional Mikhailov–Novikov–Wang [Formula: see text] integrable equation, with the primary objective of elucidating its physical manifestations and establishing connections with analogous nonlinear evolution equations. The investigated model holds significant physical meaning across various disciplines within mathematical physics. Primarily, it serves as a fundamental model for understanding nonlinear wave propagation phenomena, offering insights into wave behaviors in complex media. Moreover, its relevance extends to nonlinear optics, where it governs the dynamics of optical pulses and solitons crucial for optical communication and signal processing technologies. Employing analytical methodologies, namely the unified [Formula: see text], Khater II ([Formula: see text]hat.II) method, and He’s variational iteration [Formula: see text] method, both numerical and analytical solutions are meticulously examined. Through this investigation, the intricate behaviors of the equation are systematically unveiled, shedding illuminating insights on various physical phenomena, notably including wave propagation in complex media and nonlinear optics. The outcomes not only underscore the efficacy of the analytical techniques deployed but also accentuate the equation’s pivotal role in modeling a broad spectrum of nonlinear wave dynamics. Consequently, this research significantly advances our comprehension of complex physical systems governed by nonlinear dynamics, thereby contributing notably to interdisciplinary pursuits in mathematical physics.

Cement production and CO2 emission cycles in the USA: evidence from MS-ARDL and MS-VARDL causality methods with century-long data.

The cement industry is among the top three polluters among all industries and the examination of the nonlinear and cointegration dynamics between cement production and CO2 emissions has not been explored. Focusing on this research gap, the study employs a novel Markov-switching autoregressive distributed lag (MS-ARDL) model and its generalization to vector error correction, the MS-VARDL model, for regime-dependent causality testing. The new method allows the determination of nonlinear long-run and short-run relations, regime duration, and cement-induced-CO2 emission cycles in the USA for a historically long dataset covering 1900-2021. Empirical findings point to nonlinearity in all series and nonlinear cointegration between cement production and cement-induced CO2 emissions. The phases of regimes coincide closely with NBER's official economic cycles for the USA. The second regime, characterized by expansions, lasts twice as long relative to the first, the contractionary regime, which contains severe economic recessions, as well as economic crises, the 1929 Great Depression, the 1973 Oil Crisis, the 2009 Great Recession, and the COVID-19 Shutdown and Wars, including WWI and II. In both regimes, the adverse effects of cement production on CO2 emissions cannot be rejected with varying degrees both in the long and the short run. Markov regime-switching vector autoregressive distributed lag (MS-VARDL) causality tests confirm unidirectional causality from cement production to CO2 emissions in both regimes. The traditional Granger causality test produces an over-acceptance of causality in a discussed set of cases. Industry-level policy recommendations include investments to help with the shift to green kiln technologies and energy efficiency. National-level policies on renewable energy and carbon capture are also vital considering the energy consumption of cement production.

Landing cushioning analysis of new generation heavy airborne armored vehicles

Purpose The purpose of this study is to ensure the successful implementation of a landing cushion for the new generation armored vehicles with significantly enhanced quality. Furthermore, to introduce a high-precision landing cushioning analysis model. Design/methodology/approach To accurately analyze the cushioning performance of the new generation armored vehicles, a nonlinear finite element dynamics model considering the complex travel system was established. The model considered the influence of various nonlinear factors to measure the dynamic response difference between the proposed and traditional models. The cushioning performance of airbags under different landing conditions and their various influence factors were analyzed. Findings The travel system has a large influence on the key points of the vehicle, whose rear end of the upper deck has a larger acceleration fluctuation compared with the traditional model. The increase in the body material stiffness is helpful to reduce this fluctuation. The established nonlinear finite element model can effectively analyze the landing cushioning performance of airborne armored vehicles. The area of the external airbag vent has a large influence on the cushioning performance, and the cushioning system has excellent cushioning performance under various operating conditions. Practical implications This study introduces the travel system, which is ignored by traditional analytical models. The interactions between various types of complex structures are included in the analysis process in its entirety, leading to valuable new conclusions. Quantitatively reveals the analytical errors of traditional simulation models in multiple dimensions and the reasons for their formation. Based on a high-precision simulation model, it is verified that the designed airbag cushioning system has an excellent cushioning effect for the new generation of heavy airborne armored vehicles. Originality/value The novelty of this work comes from the need for smooth landing with low overload for a new type of large-load airborne armored vehicle and provides a high-precision model that quantifies the traditional analytical modeling errors and error principle.

Path tracking with personalized handling stability constraints matched to driving styles

In the path-tracking control process, drivers have different expectations of the lateral response of vehicles. To meet drivers’ expectations of handling stability, this paper proposes a model predictive control (MPC)-based path-tracking controller with personalized handling stability constraints matched to driving style. Firstly, the categories of driving styles are obtained by applying the K-Means algorithm to the collected driving data. Then the sideslip angle-longitudinal velocity map is constructed based on the nonlinear dynamics model of the vehicle to generate the front-wheel steering angle constraints that are matched with the maximum sideslip of the vehicle expected by drivers of different styles. After that, an MPC-based path-tracking controller has been built, and the front-wheel steering angle is incorporated into the MPC controller. Furthermore, compared to the strategy of directly using sideslip angle constraints for handling stability control, this approach avoids introducing additional constraint variables, leading to improved real-time performance. Simulation experiments are conducted to compare the dynamic vehicle responses between the handling stability control that takes into account the driving style and the control system that does not consider the driving style. In addition, the subjective evaluation is conducted to verify the proposed path-tracking control method that considers the stability constraints of driving style. The evaluation results show that the proposed method can obtain better subjective evaluation results than a path-tracking controller that does not consider the driving style. Finally, through the hardware-in-the-loop experiments, the real-time computational capability of the proposed controller is verified.

Updating Nonlinear Stochastic Dynamics of an Uncertain Nozzle Model Using Probabilistic Learning With Partial Observability and Incomplete Dataset

Abstract This article introduces a methodology for updating the nonlinear stochastic dynamics of a nozzle with uncertain computational model. The approach focuses on a high-dimensional nonlinear computational model constrained by a small target dataset. Challenges include the large number of degrees-of-freedom, geometric nonlinearities, material uncertainties, stochastic external loads, underobservability, and high computational costs. A detailed dynamic analysis of the nozzle is presented. An updated statistical surrogate model relating the observations of interest to the control parameters is constructed. Despite small training and target datasets and partial observability, the study successfully applies probabilistic learning on manifolds (PLoM) to address these challenges. PLoM captures geometric nonlinear effects and uncertainty propagation, improving conditional mean statistics compared to training data. The conditional confidence region demonstrates the ability of the methodology to accurately represent both observed and unobserved output variables, contributing to advancements in modeling complex systems.

Fintech's influence on green credit provision: Empirical evidence from China’s listed banking sector

We explore the impact of financial technology (fintech) advancements on green credit provision, investigating publicly traded banks in China from 2007 to 2022. We particularly focus on credit modelling innovation, examining the non-linear dynamics between fintech evolution and green credit distribution. Results reveal a positive U-shaped correlation. Initial stages of fintech are associated with increased green credit risk, negatively affecting the volume of green credit. However, more established fintech infrastructures significantly enhance green credit volumes by improving resource allocation and credit risk assessment. Utilizing a multiple linear regression approach, we highlight the transformative nature of fintech in advancing sustainable banking practices, particularly through innovations in credit modeling that enhance green credit risk management and resource allocation efficiency.

Dynamic linkages in agricultural and energy markets: A quantile impulse response approach

AbstractThis article investigates the dynamic linkages between agricultural and energy markets, with a focus on an econometric analysis of multivariate stochastic dynamics based on the joint distribution of state variables. The analysis relies on a quantile approach followed by the evaluation of a copula. Applied to nonlinear price dynamics, the approach is flexible and supports a general evaluation of impulse response functions representing how prices adjust over time and across markets in response to a given shock. The analysis allows for arbitrary distribution functions; it captures own‐price and cross‐price dynamics that can depend on the nature of shocks; and it also allows current changes to affect all moments of the future price distributions. The usefulness of the approach is illustrated in an econometric investigation of dynamic linkages in US corn, ethanol, and crude oil markets. We show how price adjustments can vary across quantiles, reflecting different speeds of adjustments depending on market conditions. We find evidence of nonlinear dynamics specific to the tails of the price distributions. We uncover evidence of positive contemporaneous codependence, especially tail dependence. We show how price shocks affect mean, variance, skewness as well as kurtosis of future price distributions. These results stress the importance of going beyond a standard mean‐variance analysis. They also shed new light on the deep linkages existing in the food‐fuel nexus.

Nonlinear dynamics and game-theoretic modeling in economics and finance

AbstractIn this foreword to the Special Issue “Nonlinear dynamics and game-theoretical modeling in economics and finance” we review the contributions in the issue highlighting the economic results and the connections with aspects of dynamic analysis. Indeed, the common theme of the contributions is the focus on system dynamics and the latest analytical techniques. This issue is devoted to celebrating the 70th birthday of Professor Laura Gardini, who inspired a generation of scholars in the study of discrete-time systems and global methods of analysis.

Dual Optical Injection in Semiconductor Lasers with Zero Henry Factor

The dynamics of semiconductor lasers subject to dual optical injections are numerically investigated with a zero linewidth enhancement factor. With the aid of stability maps, we show that different chaotic and nonlinear dynamics can be produced (even with zero linewidth enhancement factor) by injecting an additional optical signal. We also study the bifurcation of the system and the carrier dynamics under dual optical injection and a zero linewidth enhancement factor.

Influence of the magnetic flux on the dynamics of a self-sustaining System: Analytical, numerical and analogical investigations

Abstract This paper investigates the nonlinear dynamics of a ferroelectric enzyme-substrate
reaction modeled by \textcolor{red}{the} bi-rhythmic \emph{van der Pol} oscillator coupled to \textcolor{red}{the} magnetic
flux. We derive the equilibrium points and study their stability.
We analyze some bifurcation structures and the variation of the Lyapunov exponents.
The phenomena of symmetric attractors and the
 anti-monotonicity are observed. \textcolor{red}{By} increasing the magnetic flux,
 \textcolor{red}{we find that the equilibrium points are stable},
 tends to control chaotic regimes, and affects regular and quasi-regular ones.
 As the magnetic flux increases, the amplitude of the oscillations around the equilibrium
 points decreases and the \textcolor{red}{amplitude of the}
 limit cycles at the Hopf bifurcation \textcolor{red}{tends} to disappear.
 Further \textcolor{red}{increasing the magnetic flux gives} rise to chaotic dynamics.
 The electrical circuit and analogical simulations are \textcolor{red}{derived} using the PSpice software.
\textcolor{red}{The agreement between analogical and numerical results is acceptable}.

Simulation of nonlinear system dynamics of calcium and dopamine signaling in neurons

Simulation of nonlinear system dynamics of calcium and dopamine signaling in neurons

Nonlinear Dynamics of a Roller Bicycle

Nonlinear Dynamics of a Roller Bicycle

Transcending classical diffusion models: nonlinear dynamics and solitary waves in the fractional Chaffee–Infante equation

Transcending classical diffusion models: nonlinear dynamics and solitary waves in the fractional Chaffee–Infante equation