Narrow Results

We present a critical remark on the pitfalls of calculating the correlation dimension and the largest Lyapunov exponent from time series data when trend and periodicity exist. We consider a special case where a time series Z_i can be expressed as the sum of two subsystems so that Z_i = X_i + Y_i and at least one of the subsystems is deterministic. We show that if the trend and periodicity are not properly removed, correlation dimension and Lyapunov exponent estimations yield misleading results, which can severely compromise the results of diagnostic tests and model identification. We also establish an analytic relationship between the largest Lyapunov exponents of the subsystems and that of the whole system. In addition, the impact of a periodic parameter perturbation on the Lyapunov exponent for the logistic map and the Lorenz system is discussed.

We study the phenomena of stochastic D- and P-bifurcations of randomly forced limit cycles for the Lorenz model. As noise intensity increases, regular multiple limit cycles of this model in a period-doubling bifurcations zone are deformed to be stochastic attractors that look chaotic (D-bifurcation) and their multiplicity is reduced (P-bifurcation). In this paper for the comparative investigation of these bifurcations, the analysis of Lyapunov exponents and stochastic sensitivity function technique are used. A probabilistic mechanism of backward stochastic bifurcations for cycles of high multiplicity is analyzed in detail. We show that for a limit cycle with multiplicity two and higher, a threshold value of the noise intensity which marks the onset of chaos agrees with the first backward stochastic bifurcation.

In this study, we first present a generalized Lorenz model (LM) with $M$ modes, where $M$ is an odd number that is greater than three. The generalized LM (GLM) is derived based on a successive extension of the nonlinear feedback loop (NFL) with additional high wavenumber modes. By performing a linear stability analysis with $\sigma =10$ and $b=8/3$, we illustrate that: (1) within the 3D, 5D, and 7D LMs, the appearance of unstable nontrivial critical points requires a larger Rayleigh parameter in a higher-dimensional LM and (2) within the 9DLM, nontrivial critical points are stable. By comparing the GLM with various numbers of modes, we discuss the aggregated negative feedback enabled by the extended NFL and its role in stabilizing solutions in high-dimensional LMs. Our analysis indicates that the 9DLM is the lowest order generalized LM with stable nontrivial critical points for all Rayleigh parameters greater than one. As shown by calculations of the ensemble Lyapunov exponent, the 9DLM still produces chaotic solutions. Within the 9DLM, a larger critical value for the Rayleigh parameter, $r_c=679.8$, is required for the onset of chaos as compared to a $r_c=24.74$ for the 3DLM, a $r_c=42.9$ for the 5DLM, and a $r_c=116.9$ for the 7DLM. In association with stable nontrivial critical points that may lead to steady-state solutions, the appearance of chaotic orbits indicates the important role of a saddle point at the origin in producing the sensitive dependence of solutions on initial conditions. The 9DLM displays the coexistence of chaotic and steady-state solutions at moderate Rayleigh parameters and the coexistence of limit cycle and steady-state solutions at large Rayleigh parameters. The first kind of coexistence appears within a smaller range of Rayleigh parameters in lower-dimensional LMs (i.e. $24.06<r<24.74$ within the 3DLM) but in a wider range of Rayleigh parameters within the 9DLM (i.e. $679.8<r<1058$). The second kind of coexistence has never been reported in high-dimensional Lorenz systems.

The nonlinear dynamics of a recently derived generalized Lorenz model [Macek & Strumik, 2010] of magnetoconvection is studied. A bifurcation diagram is constructed as a function of the Rayleigh number where attractors and nonattracting chaotic sets coexist inside a periodic window. The nonattracting chaotic sets, also called chaotic saddles, are responsible for fractal basin boundaries with a fractal dimension near the dimension of the phase space, which causes the presence of very long chaotic transients. It is shown that the chaotic saddles can be used to infer properties of chaotic attractors outside the periodic window, such as their maximum Lyapunov exponent.

It is not common in applied sciences to realize simulations which depict fractal representation in attractors’ dynamics, the reason being a combination of many factors including the nature of the phenomenon that is described and the type of differential operator used in the system. In this work, we use the fractal-fractional derivative with a fractional order to analyze the modified proto-Lorenz system that is usually characterized by chaotic attractors with many scrolls. The fractal-fractional operator used in this paper is a combination of fractal process and fractional differentiation, which is a relatively new concept with most of the properties and features still to be known. We start by summarizing the basic notions related to the fractal-fractional operator. After that, we enumerate the main points related to the establishment of proto-Lorenz system’s equations, leading to the $n$th cover of the proto-Lorenz system that contains $n$ scrolls ($n\in \mathbb{N}$). The triple and quadric cover of the resulting fractal and fractional proto-Lorenz system are solved using the Haar wavelet methods and numerical simulations are performed. Due to the impact of the fractal-fractional operator, the system is able to maintain its chaotic state of attractor with many scrolls. Additionally, such attractor can self-replicate in a fractal process as the derivative order changes. This result reveals another great feature of the fractal-fractional derivative with fractional order.

Recent studies using the classical Lorenz model and generalized Lorenz models present abundant features of both chaotic and oscillatory solutions that may change our view on the nature of the weather as well as climate. In this study, the mathematical universality of solutions in different physical systems is presented. Specifically, the main goal is to reveal mathematical similarities for solutions of homoclinic orbits and solitary waves within a three-dimensional nondissipative Lorenz model (3D-NLM), the Korteweg–de Vries (KdV) equation, and the Nonlinear Schrodinger (NLS) equation. A homoclinic orbit for the $X$, $Y$, and $Z$ state variables of the 3D-NLM connects the unstable and stable manifolds of a saddle point. The $X$ and $Z$ solutions for the homoclinic orbit can be expressed in terms of a hyperbolic secant function ($\mathrm{\text{sech}}$) and a hyperbolic secant squared function (${\mathrm{\text{sech}}}^2$), respectively. Interestingly, these two solutions have the same mathematical form as solitary solutions for the NLS and KdV equations, respectively. After introducing new independent variables, the same second-order ordinary differential equation (ODE) and solutions for the $Z$ component and the KdV equation were obtained. Additionally, the ODE for the $X$ component has the same form as the NLS for the solitary wave envelope. Finally, how a logistic equation, also known as the Lorenz error growth model, and an improved error growth model can be derived by simplifying the 3D-NLM is also discussed. Future work will compare the solutions of the 3D-NLM and KdV equation in order to understand the different physical role of nonlinearity in their solutions and the solutions of the error growth model and the 3D-NLM, as well as other Lorenz models, to propose an improved error growth model for better representing error growth at linear and nonlinear stages for both oscillatory and nonoscillatory solutions.

This work considers the problem of predicting wing changes, and their duration, in systems able to support three interconnected wings (spirals) in the space of the variables. This is done by exploring the alignment of covariant Lyapunov vectors (CLVs) known to precede the occurrence of peaks and regime changes in some chaotic systems. Here, the alignment of the CLVs is combined with classification procedures, machine learning techniques and nearest-neighbors analysis to predict the duration inside coupled wings. The classification procedure has distinct classes defined as the number of maxima of one variable inside one wing, proportional to the time spent inside each wing. In general, we observe that predictions of significant duration times (around $17$ oscillations) inside a wing can be efficiently predicted until four subsequently visited wings. For five to ten subsequently visited wings, the prediction efficiency decreases significantly for more than ten oscillations inside each wing. The numerical comparison shows the superiority of the nearest-neighbors methods over the multilayer perceptrons procedure for the predictions. Remarkably, accuracies larger than $0.9$ are obtained for predictions of classes for $\le 3$ subsequently visited wings. Accuracies higher than $0.5$ are obtained for predictions of classes in the $\le 6$ subsequently visited wings.

Accurate predictions for the spread and evolution of epidemics have significant societal and economic impacts. The temporal evolution of infected (or dead) persons has been described as an epidemic wave with an isolated peak and tails. Epidemic waves have been simulated and studied using the classical SIR model that describes the evolution of susceptible (S), infected (I), and recovered (R) individuals. To illustrate the fundamental dynamics of an epidemic wave, the dependence of solutions on parameters, and the dependence of predictability horizons on various types of solutions, we propose a Korteweg–de Vries (KdV)–SIR equation and obtain its analytical solutions. Among classical and simplified SIR models, our KdV–SIR equation represents the simplest system that produces a solution with both exponential and oscillatory components. The KdV–SIR model is mathematically identical to the nondissipative Lorenz 1963 model and the KdV equation in a traveling-wave coordinate. As a result, the dynamics of an epidemic wave and its predictability can be understood by applying approaches used in nonlinear dynamics, and by comparing the aforementioned systems. For example, a typical solitary wave solution is a homoclinic orbit that connects a stable and an unstable manifold at the saddle point within the $I$–$I^{\prime }$ space. The KdV–SIR equation additionally produces two other types of solutions, including oscillatory and unbounded solutions. The analysis of two critical points makes it possible to reveal the features of solutions near a turning point. Using analytical solutions and hypothetical observed data, we derive a simple formula for determining predictability horizons, and propose a method for predicting timing for the peak of an epidemic wave.

Linear and nonlinear stability analyses of Rayleigh–Bénard convection in water-copper and water-alumina nanofluids are studied in the paper by considering a minimal as well as an extended truncated Fourier representation. These representations respectively result in a third-order classical Lorenz model and a five-dimensional extended Lorenz model. The marginal stability plots reveal that the influence of added dilute concentration of nanoparticles in water is to destabilize the system. The rate of destabilization depends on the nanoparticles’ thermophysical properties and their volume fraction. Influence of adding an additional mode in the horizontal direction is to modify the cell size. This can be observed through the marginal curves as well as the stream line plots. Further, from the Nusselt number plots it is evident that the presence of dilute concentration of nanoparticles in water is to enhance heat transport in the system significantly. The dynamical behavior of the minimal and the extended Lorenz models is investigated using the bifurcation diagram. From the study an important finding that emerges is that the Fourier truncated solution is predicted to have different effects in lower-order and higher-order models. The extended penta-modal Lorenz system predicts advanced onset of chaos compared to that predicted by the classical third-order Lorenz model. The individual influence of both nanoparticles in water is to advance the onset of convection as well as to advance the onset of chaos.

We consider simple stochastic climate models, described by slowly time-dependent Langevin equations. We show that when the noise intensity is not too large, these systems can spend substantial amounts of time in metastable equilibrium, instead of adiabatically following the stationary distribution of the frozen system. This behavior can be characterized by describing the location of typical paths, and bounding the probability of atypical paths. We illustrate this approach by giving a quantitative description of phenomena associated with bistability, for three famous examples of simple climate models: Stochastic resonance in an energy balance model describing the Ice Ages; hysteresis in a box model for the Atlantic thermohaline circulation; and bifurcation delay in the case of the Lorenz model for Rayleigh–Bénard convection.