On the Approximation of Stable and Unstable Fiber Bundles of (Non)Autonomous ODEs — A Contour Algorithm

2016 ◽  
Vol 26 (07) ◽  
pp. 1650118 ◽  
Author(s):  
Thorsten Hüls

We propose an algorithm for the approximation of stable and unstable fibers that applies to autonomous as well as to nonautonomous ODEs. The algorithm is based on computing the zero-contour of a specific operator; an idea that was introduced in [Hüls, 2006] for discrete time systems. We present precise error estimates for the resulting contour algorithm and demonstrate its efficiency by computing stable and unstable fibers for a (non)autonomous pendulum equation in two space dimensions. Our second example is the famous three-dimensional Lorenz system for which several approximations of the two-dimensional Lorenz manifold are calculated. In both examples, we observe equally well performance for autonomously and nonautonomously chosen parameters.

2021 ◽  
Vol 31 (10) ◽  
pp. 2150160
Author(s):  
G. Tigan ◽  
O. Brandibur ◽  
E. A. Kokovics ◽  
L. F. Vesa

Generic results for degenerate Chenciner (generalized Neimark–Sacker) bifurcation are obtained in the present work. The bifurcation arises from two-dimensional discrete-time systems with two independent parameters. We define in this work a new transformation of parameters, which enables the study of the bifurcation when degeneracy occurs. By the four bifurcation diagrams we obtained, new behaviors hidden by the degeneracy are brought to light.


Fractals ◽  
2021 ◽  
pp. 2140034
Author(s):  
AMINA-AICHA KHENNAOUI ◽  
ADEL OUANNAS ◽  
SHAHER MOMANI ◽  
ZOHIR DIBI ◽  
GIUSEPPE GRASSI ◽  
...  

In recent years, some efforts have been devoted to nonlinear dynamics of fractional discrete-time systems. A number of papers have so far discussed results related to the presence of chaos in fractional maps. However, less results have been published to date regarding the presence of hyperchaos in fractional discrete-time systems. This paper aims to bridge the gap by introducing a new three-dimensional fractional map that shows, for the first time, complex hyperchaotic behaviors. A detailed analysis of the map dynamics is conducted via computation of Lyapunov exponents, bifurcation diagrams, phase portraits, approximated entropy and [Formula: see text] complexity. Simulation results confirm the effectiveness of the approach illustrated herein.


1974 ◽  
Vol 14 (02) ◽  
pp. 127-138 ◽  
Author(s):  
Ivan Mrosovsky ◽  
R.L. Ridings

Abstract A reservoir simulator is described in which two-dimensional cylindrical models are used for individual wells within the framework of a three-dimensional, rectangular-grid, reservoir model. This approach to simulation should give more realistic results than those incorporating simpler well production routines. Application should be production routines. Application should be particularly pertinent to cases in which gas and particularly pertinent to cases in which gas and water coning are important. Computation cost is increased by this sophisticated treatment of wells, but total running costs are nevertheless low enough that judicious use of the model is warranted. Introduction Changes in pressure and fluid saturations occurring in a reservoir on production may be described by certain well known differential equations. These equations can be solved by finite-difference techniques. To this end a reservoir may be divided, more or less regularly, into a number of blocks. We consider here a three-dimensional model. A rectangular grid is superimposed on the reservoir, which is further divided vertically into several layers. This kind of subdivision is generally adequate for treating most of a reservoir. However, difficulty arises in the neighborhood of a well. Fluid saturations and pressures typically exhibit steep gradients in the neighborhood of a producing well. Moreover, these properties have values near the well that are markedly different from those prevailing over most of the well's drainage area. To prevailing over most of the well's drainage area. To model this situation using finite-difference methods, a fine grid would be necessary in the vicinity of the well. To extend such a fine grid throughout a reservoir, with many wells, would result in an impracticably large number of blocks. Instead, it is customary to simplify and approximate the calculation of well performance. A common recourse is to use a radial flow formula such as(1) Here the external radius, re, is set so that the area of a circle of this radius should equal the area of the block in which the well is situated. Obviously, this formula takes no account of the radial variation of fluid saturations. In this paper a different approach to the calculation of well performance is described. Each well in the three-dimensional (3D) reservoir model is also assigned a two-dimensional (2D) radial model covering its immediate vicinity. These radial models are solved simultaneously with the 3D reservoir model. Thus, precision is concentrated where it is most needed - near the wells; yet the 3D reservoir model is not blown up unduly. The equations and method of solution for the 3D model are briefly sketched in the Appendix. The 2D radial models are basically the same as that described by Letkeman and Ridings. We now discuss some details of the radial well models. In particular we shall be concerned with the interface particular we shall be concerned with the interface between the 2D radial well models and the 3D reservoir model. RADIAL WELL MODEL To make the discussion simpler and more concrete we take, as an example, a reservoir model we have run. This 3D reservoir model had 15 layers. Consider a well situated in one vertical column of 15 blocks. The 2D radial model for this well also has 15 layers. Each layer in the 2D model is further split radially into six concentric blocks. The relationship between the two models is depicted in Fig. 1. The total pore volume in any layer of the 2D model must equal the pore volume of the corresponding block in the same pore volume of the corresponding block in the same layer of the 3D model; that is, the block through which the well passes. Consider now the calculation of one time step, say 30 days, for the 3D reservoir model. To make this calculation, we require a well production rate. Actually we need three rates: one each for oil, water, and gas. These rates are obtained by solving the 2D model over the same 30-day period. SPEJ P. 127


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