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Register a new userPattern formation in the damped Nikolaevskiy equation
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The Nikolaevskiy equation has been proposed as a model for seismic waves, electroconvection and weak turbulence; we show that it can also be used to model transverse instabilities of fronts. This equation possesses a large-scale Goldstone mode that significantly influences the stability of spatially periodic steady solutions; indeed, all such solutions are unstable at onset, and the equation exhibits so-called soft-mode turbulence. In many applications, a weak damping of this neutral mode will be present, and we study the influence of this damping on solutions to the Nikolaevskiy equation. We examine the transition to the usual Eckhaus instability as the damping of the large-scale mode is increased, through numerical calculation and weakly nonlinear analysis. The latter is accomplished using asymptotically consistent systems of coupled amplitude equations. We find that there is a critical value of the damping below which (for a given value of the supercriticality parameter) all periodic steady states are unstable. The last solutions to lose stability lie in a cusp close to the left-hand side of the marginal stability curve.
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Morphogenetic patterns are highly sophisticated dissipative structures. Are they governed by the same general mechanisms as chemical and hydrodynamic patterns? Turings symmetry breaking and Wolperts signalling provide alternative mechanisms. The current evidence points out that the latter is more relevant but reality is still far more complicated.
Self-organization, the ability of a system of microscopically interacting entities to shape macroscopically ordered structures, is ubiquitous in Nature. Spatio-temporal patterns are abundantly observed in a large plethora of applications, encompassing different fields and scales. Examples of emerging patterns are the spots and stripes on the coat or skin of animals, the spatial distribution of vegetation in arid areas, the organization of the colonies of insects in host-parasitoid systems and the architecture of large complex ecosystems. Spatial self-organization can be described following the visionary intuition of Alan Turing, who showed how non-linear interactions between slow diffusing activators and fast diffusing inhibitors could induce patterns. The Turing instability, as the mechanism described is universally referred to, was raised to paradigm status in those realms of investigations where microscopic entities are subject to diffusion, from small biological systems to large ecosystems. Requiring a significant ratio of the assigned diffusion constants however is a stringent constraint, which limited the applicability of the theory. Building on the observation that spatial interactions are usually direction biased, and often strongly asymmetric, we here propose a novel framework for the generation of short wavelength patterns which overcomes the limitation inherent in the Turing formulation. In particular, we will prove that patterns can always set in when the system is composed by sufficiently many cells - the units of spatial patchiness - and for virtually any ratio of the diffusivities involved. Macroscopic patterns that follow the onset of the instability are robust and show oscillatory or steady-state behavior.
We explain some pde2path setups for pattern formation in 1D, 2D and 3D. A focus is on new pde2path functions for branch switching at steady bifurcation points of higher multiplicity, typically due to discrete symmetries, but we also review general concepts of pattern formation and their handling in pde2path, including localized patterns and homoclinic snaking, again in 1D, 2D and 3D, based on the demo sh (Swift-Hohenberg equation). Next, the demos schnakpat (a Schnakenberg reaction-diffusion system) and chemtax (a quasilinear RD system with cross-diffusion from chemotaxis) simplify and unify previous results in a simple and concise way, CH (Cahn-Hilliard) deals with mass constraints, hexex deals with (multiple) branch points of higher degeneracy in a scalar problem on a hexagonal domain, and shgc illustrates some global coupling. The demos acS, actor, schnakS and schnaktor (the Allen-Cahn and Schnakenberg models on spheres and tori) consider pattern formation on curved surfaces, cpol considers a problem of cell polarization described by bulk-surface coupling, and bruosc (Brusselator) explains how to augment autonomous systems by a time periodic forcing. Along the way we also comment on the choice of meshes and mesh adaptation, on time integration, and we give some examples of branch point continuation and Hopf point continuation to approximate stability boundaries.
Pattern formation in systems with a conserved quantity is considered by studying the appropriate amplitude equations. The conservation law leads to a large-scale neutral mode that must be included in the asymptotic analysis for pattern formation near onset. Near a stationary bifurcation, the usual Ginzburg--Landau equation for the amplitude of the pattern is then coupled to an equation for the large-scale mode. These amplitude equations show that for certain parameters all roll-type solutions are unstable. This new instability differs from the Eckhaus instability in that it is amplitude-driven and is supercritical. Beyond the stability boundary, there exist stable stationary solutions in the form of strongly modulated patterns. The envelope of these modulations is calculated in terms of Jacobi elliptic functions and, away from the onset of modulation, is closely approximated by a sech profile. Numerical simulations indicate that as the modulation becomes more pronounced, the envelope broadens. A number of applications are considered, including convection with fixed-flux boundaries and convection in a magnetic field, resulting in new instabilities for these systems.
The surface pattern formation on a gelation surface is analyzed using an effective surface roughness. The spontaneous surface deformation on DiMethylAcrylAmide (DMAA) gelation surface is controlled by temperature, initiator concentration, and ambient oxygen. The effective surface roughness is defined using 2-dimensional photo data to characterize the surface deformation. Parameter dependence of the effective surface roughness is systematically investigated. We find that decrease of ambient oxygen, increase of initiator concentration, and high temperature tend to suppress the surface deformation in almost similar manner. That trend allows us to collapse all the data to a unified master curve. As a result, we finally obtain an empirical scaling form of the effective surface roughness. This scaling is useful to control the degree of surface patterning. However, the actual dynamics of this pattern formation is not still uncovered.
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