Chaotic and Hyperchaotic Self-Oscillations of Lambda Diode Composed by Generalized Bipolar Transistors

This paper is focused on the investigation of self-oscillation regimes associated with very simple structure of lambda diode. This building block is constructed by using coupled generalized bipolar transistors. In the stage of mathematical modeling, each transistor is considered as two-port described by full admittance matrix with scalar polynomial forward trans-conductance and linear backward trans-conductance. Thorough numerical analysis including routines of dynamical flow quantification indicate the existence of self-excited dense strange attractors. Plots showing first two Lyapunov exponents as functions of adjustable parameters, signal entropy calculated from generated time sequence, sensitivity analysis, and other results are provided in this paper. By the construction of a flow-equivalent chaotic oscillator, robustness and long-time geometrical stability of the generated chaotic attractors is documented by the experimental measurement, namely by showing captured oscilloscope screenshots.

When Bingham meets Bratu: mathematical and computational investigations

In this article, we discuss the numerical solution of the Bingham-Bratu-Gelfand (BBG) problem, a non-smooth nonlinear eigenvalue problem associated with the total variation integral and an exponential nonlinearity. Using the fact that one can view the nonlinear eigenvalue as a possible Lagrange multiplier associated with a constrained minimization problem from Calculus of Variations, we associate with the BBG problem an initial value problem (dynamical flow), well suited to time-discretization by operator-splitting. Various mathematical results are proved, including the convergence of a finite element approximation of the BBG problem. The operator-splitting/finite element methodology discussed in this article is robust and easy to implement. We validate the implementation by first solving the classical Bratu-Gelfand problem, obtaining and reporting results consistent with those found in the literature. We then explore the full capability of the implementation by solving the viscoplastic BBG problem, obtaining and reporting results for several values of the plasticity yield. We conclude by exhibiting and discussing the bifurcation diagrams corresponding to these same values of the plasticity yield, and by reporting and examining some finer details of the solver discovered during the course of our investigation.

Data assimilation using adaptive, non-conservative, moving mesh models

Numerical models solved on adaptive moving meshes have become increasingly prevalent in recent years. Motivating problems include the study of fluids in a Lagrangian frame and the presence of highly localized structures such as shock waves or interfaces. In the former case, Lagrangian solvers move the nodes of the mesh with the dynamical flow; in the latter, mesh resolution is increased in the proximity of the localized structure. Mesh adaptation can include remeshing, a procedure that adds or removes mesh nodes according to specific rules reflecting constraints in the numerical solver. In this case, the number of mesh nodes will change during the integration and, as a result, the dimension of the model's state vector will not be conserved. This work presents a novel approach to the formulation of ensemble data assimilation (DA) for models with this underlying computational structure. The challenge lies in the fact that remeshing entails a different state space dimension across members of the ensemble, thus impeding the usual computation of consistent ensemble-based statistics. Our methodology adds one forward and one backward mapping step before and after the ensemble Kalman filter (EnKF) analysis, respectively. This mapping takes all the ensemble members onto a fixed, uniform reference mesh where the EnKF analysis can be performed. We consider a high-resolution (HR) and a low-resolution (LR) fixed uniform reference mesh, whose resolutions are determined by the remeshing tolerances. This way the reference meshes embed the model numerical constraints and are also upper and lower uniform meshes bounding the resolutions of the individual ensemble meshes. Numerical experiments are carried out using 1-D prototypical models: Burgers and Kuramoto–Sivashinsky equations and both Eulerian and Lagrangian synthetic observations. While the HR strategy generally outperforms that of LR, their skill difference can be reduced substantially by an optimal tuning of the data assimilation parameters. The LR case is appealing in high dimensions because of its lower computational burden. Lagrangian observations are shown to be very effective in that fewer of them are able to keep the analysis error at a level comparable to the more numerous observers for the Eulerian case. This study is motivated by the development of suitable EnKF strategies for 2-D models of the sea ice that are numerically solved on a Lagrangian mesh with remeshing.

Data assimilation using adaptive, non-conservative, moving mesh models

Numerical models solved on adaptive moving meshes have become increasingly prevalent in recent years. Motivating problems include the study of fluids in a Lagrangian frame and the presence of highly localized structures such as shock waves or interfaces. In the former case, Lagrangian solvers move the nodes of the mesh with the dynamical flow; in the latter, mesh resolution is increased in the proximity of the localized structure. Mesh adaptation can include remeshing, a procedure that adds or removes mesh nodes according to specific rules reflecting constraints in the numerical solver. In this case, the number of mesh nodes will change during the integration and, as a result, the dimension of the model’s state vector will not be conserved. This work presents a novel approach to the formulation of ensemble data assimilation for models with this underlying computational structure. The challenge lies in the fact that remeshing entails a different state space dimension across members of the ensemble, thus impeding the usual computation of consistent ensemble-based statistics. Our methodology adds one forward and one backward mapping step before and after the EnKF analysis respectively. This mapping takes all the ensemble members onto a fixed, uniform, reference mesh where the EnKF analysis can be performed. We consider a high- (HR) and a low-resolution (LR) fixed uniform reference mesh, whose resolutions are determined by the remeshing tolerances. This way the reference meshes embed the model numerical constraints and also are upper and lower uniform meshes bounding the resolutions of the individual ensemble meshes. Numerical experiments are carried out using 1D prototypical models: Burgers and Kuramoto-Sivashinsky equations, and both Eulerian and Lagrangian synthetic observations. While the HR strategy generally outperforms that of LR, their skill difference can be reduced substantially by an optimal tuning of the data assimilation parameters. The LR case is appealing in high-dimensions because of its lower computational burden. Lagrangian observations are shown to be very effective in that fewer of them are able to keep the analysis error at a level comparable to the more numerous observers for the Eulerian case. This study is motivated by the development of suitable EnKF strategies for 2D models of the sea-ice that are numerically solved on a Lagrangian mesh with remeshing.

Feature Extraction From Time Resolved Reacting Flow Data Sets

This paper presents measurements of the simultaneous fuel distribution, flame position and flow velocity in a high pressure, liquid fueled combustor. Its objective is to develop methods to process, display and compare large quantities of instantaneous data with computations. However, time-averaged flow fields rarely represent the instantaneous, dynamical flow fields in combustion systems. It is therefore important to develop methods that can algorithmically extract dynamical flow features and be directly compared between measurements and computations. While a number of data-driven approaches have been previously presented in the literature, the purpose of this paper is to propose several approaches that are based on understanding of key physical features of the flow — for this reacting swirl flow, these include the annular jet, the swirling flow which may be precessing, the recirculating flow between the annular jets, and the helical flow structures in the shear layers. This paper demonstrates nonlinear averaging of axial and azimuthal velocity profiles, which provide insights into the structure of the recirculation zone and degree of flow precession. It also presents probability fields for the location of vortex cores that enables a convenient method for comparison of their trajectory and phasing with computations. Taken together, these methods illustrate the structure and relative locations of the annular fluid jet, recirculating flow zone, spray location, flame location, and trajectory of the helical vortices.

Non-Linear Radiation and Chemical Reaction Impacts on Hydromagnetic Particle Suspension Flow in Non-Newtonian Fluids

A two dimensional (2D) boundary layer two-phase MHD (magneto hydrodynamics) flow of Maxwell and Oldroyd-B fluid over a stretching sheet has been explored. Heat and mass transfer phenomena is inspected through Non-linear radiation, viscous dissipation, joule heating, Soret (thermo-diffusion) and Dufour (diffusion-thermo) Impact. The boundary layer governing differential equations are modelled and transformed to a system of ODE’S with the aid of similarity transformations. The final controlled equations along boundary restrictions are resolved numerically by Runge-Kutta Felhberg method. The graphical analysis has been emphasized for the fluid and dust phase velocity, temperature and concentration fields to the influence of sundry dynamical flow quantities. Furthermore, for authentication of the present computation, the achieved results are distinguished with earlier research works in specific cases and marvellous agreement has been noted. The outcomes conveyed here manifest that velocity and boundary layer thickness escalate with boost up the values of ${K_1}$ . Velocity and boundary layer thickness declines with boost up the values of $M$ . Opposite trend is seen in temperature and concentration profiles. The specific heat ratio parameter $\gamma$ enhances the temperature profile declines. Boost up the values of Soret number $Sr$ temperature profile declines and concentration profile enhances. Skin friction factor declines with increasing values $\beta _v$ verses $M$ .