Method of matched asymptotic expansions

Last updated

In mathematics, the method of matched asymptotic expansions [1] is a common approach to finding an accurate approximation to the solution to an equation, or system of equations. It is particularly used when solving singularly perturbed differential equations. It involves finding several different approximate solutions, each of which is valid (i.e. accurate) for part of the range of the independent variable, and then combining these different solutions together to give a single approximate solution that is valid for the whole range of values of the independent variable. In the Russian literature, these methods were known under the name of "intermediate asymptotics" and were introduced in the work of Yakov Zeldovich and Grigory Barenblatt.

Contents

Method overview

In a large class of singularly perturbed problems, the domain may be divided into two or more subdomains. In one of these, often the largest, the solution is accurately approximated by an asymptotic series [2] found by treating the problem as a regular perturbation (i.e. by setting a relatively small parameter to zero). The other subdomains consist of one or more small regions in which that approximation is inaccurate, generally because the perturbation terms in the problem are not negligible there. These areas are referred to as transition layers in general, and specifically as boundary layers or interior layers depending on whether they occur at the domain boundary (as is the usual case in applications) or inside the domain, respectively.

An approximation in the form of an asymptotic series is obtained in the transition layer(s) by treating that part of the domain as a separate perturbation problem. This approximation is called the inner solution, and the other is the outer solution, named for their relationship to the transition layer(s). The outer and inner solutions are then combined through a process called "matching" in such a way that an approximate solution for the whole domain is obtained. [3] [4] [5] [6]

A simple example

Consider the boundary value problem

where is a function of independent time variable , which ranges from 0 to 1, the boundary conditions are and , and is a small parameter, such that .

Outer solution, valid for t = O(1)

Since is very small, our first approach is to treat the equation as a regular perturbation problem, i.e. make the approximation , and hence find the solution to the problem

Alternatively, consider that when and are both of size O(1), the four terms on the left hand side of the original equation are respectively of sizes , O(1), and O(1). The leading-order balance on this timescale, valid in the distinguished limit , is therefore given by the second and fourth terms, i.e.,

This has solution

for some constant . Applying the boundary condition , we would have ; applying the boundary condition , we would have . It is therefore impossible to satisfy both boundary conditions, so is not a valid approximation to make across the whole of the domain (i.e. this is a singular perturbation problem). From this we infer that there must be a boundary layer at one of the endpoints of the domain where needs to be included. This region will be where is no longer negligible compared to the independent variable , i.e. and are of comparable size, i.e. the boundary layer is adjacent to . Therefore, the other boundary condition applies in this outer region, so , i.e. is an accurate approximate solution to the original boundary value problem in this outer region. It is the leading-order solution.

Inner solution, valid for t = O(ε)

In the inner region, and are both tiny, but of comparable size, so define the new O(1) time variable . Rescale the original boundary value problem by replacing with , and the problem becomes

which, after multiplying by and taking , is

Alternatively, consider that when has reduced to size , then is still of size O(1) (using the expression for ), and so the four terms on the left hand side of the original equation are respectively of sizes , , O(1) and O(1). The leading-order balance on this timescale, valid in the distinguished limit , is therefore given by the first and second terms, i.e.

This has solution

for some constants and . Since applies in this inner region, this gives , so an accurate approximate solution to the original boundary value problem in this inner region (it is the leading-order solution) is

Matching

We use matching to find the value of the constant . The idea of matching is that the inner and outer solutions should agree for values of in an intermediate (or overlap) region, i.e. where . We need the outer limit of the inner solution to match the inner limit of the outer solution, i.e.,

which gives .

The above problem is the simplest of the simple problems dealing with matched asymptotic expansions. One can immediately calculate that is the entire asymptotic series for the outer region whereas the correction to the inner solution is and the constant of integration must be obtained from inner-outer matching.

Notice, the intuitive idea for matching of taking the limits i.e. doesn't apply at this level. This is simply because the underlined term doesn't converge to a limit. The methods to follow in these types of cases are either to go for a) method of an intermediate variable or using b) the Van-Dyke matching rule. The former method is cumbersome and works always whereas the Van-Dyke matching rule is easy to implement but with limited applicability. A concrete boundary value problem having all the essential ingredients is the following.

Consider the boundary value problem

The conventional outer expansion gives , where must be obtained from matching.

The problem has boundary layers both on the left and on the right. The left boundary layer near has a thickness whereas the right boundary layer near has thickness . Let us first calculate the solution on the left boundary layer by rescaling , then the differential equation to satisfy on the left is

and accordingly, we assume an expansion .

The inhomogeneous condition on the left provides us the reason to start the expansion at . The leading order solution is .

This with van-Dyke matching gives .

Let us now calculate the solution on the right rescaling , then the differential equation to satisfy on the right is

and accordingly, we assume an expansion

The inhomogeneous condition on the right provides us the reason to start the expansion at . The leading order solution is . This with van-Dyke matching gives . Proceeding in a similar fashion if we calculate the higher order-corrections we get the solutions as

Composite solution

To obtain our final, matched, composite solution, valid on the whole domain, one popular method is the uniform method. In this method, we add the inner and outer approximations and subtract their overlapping value, , which would otherwise be counted twice. The overlapping value is the outer limit of the inner boundary layer solution, and the inner limit of the outer solution; these limits were above found to equal . Therefore, the final approximate solution to this boundary value problem is,

Note that this expression correctly reduces to the expressions for and when is and O(1), respectively.

Accuracy

Convergence of approximations. Approximations and exact solutions, which are indistinguishable at this scale, are shown for various
e
{\displaystyle \varepsilon }
. The outer solution is also shown. Note that since the boundary layer becomes narrower with decreasing
e
{\displaystyle \varepsilon }
, the approximations converge to the outer solution pointwise, but not uniformly, almost everywhere. Singular perturbation convergence.svg
Convergence of approximations. Approximations and exact solutions, which are indistinguishable at this scale, are shown for various . The outer solution is also shown. Note that since the boundary layer becomes narrower with decreasing , the approximations converge to the outer solution pointwise, but not uniformly, almost everywhere.

This final solution satisfies the problem's original differential equation (shown by substituting it and its derivatives into the original equation). Also, the boundary conditions produced by this final solution match the values given in the problem, up to a constant multiple. This implies, due to the uniqueness of the solution, that the matched asymptotic solution is identical to the exact solution up to a constant multiple. This is not necessarily always the case, any remaining terms should go to zero uniformly as .

Not only does our solution successfully approximately solve the problem at hand, it closely approximates the problem's exact solution. It happens that this particular problem is easily found to have exact solution

which has the same form as the approximate solution, by the multiplying constant. The approximate solution is the first term in a binomial expansion of the exact solution in powers of .

Location of boundary layer

Conveniently, we can see that the boundary layer, where and are large, is near , as we supposed earlier. If we had supposed it to be at the other endpoint and proceeded by making the rescaling , we would have found it impossible to satisfy the resulting matching condition. For many problems, this kind of trial and error is the only way to determine the true location of the boundary layer. [3]

Harder problems

The problem above is a simple example because it is a single equation with only one dependent variable, and there is one boundary layer in the solution. Harder problems may contain several co-dependent variables in a system of several equations, and/or with several boundary and/or interior layers in the solution.

It is often desirable to find more terms in the asymptotic expansions of both the outer and the inner solutions. The appropriate form of these expansions is not always clear: while a power-series expansion in may work, sometimes the appropriate form involves fractional powers of , functions such as , et cetera. As in the above example, we will obtain outer and inner expansions with some coefficients which must be determined by matching. [7]

Second-order differential equations

Schrödinger-like second-order differential equations

A method of matched asymptotic expansions - with matching of solutions in the common domain of validity - has been developed and used extensively by Dingle and Müller-Kirsten for the derivation of asymptotic expansions of the solutions and characteristic numbers (band boundaries) of Schrödinger-like second-order differential equations with periodic potentials - in particular for the Mathieu equation [8] (best example), Lamé and ellipsoidal wave equations, [9] oblate [10] and prolate [11] spheroidal wave equations, and equations with anharmonic potentials. [12]

Convection-diffusion equations

Methods of matched asymptotic expansions have been developed to find approximate solutions to the Smoluchowski convection-diffusion equation, which is a singularly perturbed second-order differential equation. The problem has been studied particularly in the context of colloid particles in linear flow fields, where the variable is given by the pair distribution function around a test particle. In the limit of low Péclet number, the convection-diffusion equation also presents a singularity at infinite distance (where normally the far-field boundary condition should be placed) due to the flow field being linear in the interparticle separation. This problem can be circumvented with a spatial Fourier transform as shown by Jan Dhont. [13] A different approach to solving this problem was developed by Alessio Zaccone and coworkers and consists in placing the boundary condition right at the boundary layer distance, upon assuming (in a first-order approximation) a constant value of the pair distribution function in the outer layer due to convection being dominant there. This leads to an approximate theory for the encounter rate of two interacting colloid particles in a linear flow field in good agreement with the full numerical solution. [14] When the Péclet number is significantly larger than one, the singularity at infinite separation no longer occurs and the method of matched asymptotics can be applied to construct the full solution for the pair distribution function across the entire domain. [15] [16]

See also

Related Research Articles

In mathematics and applied mathematics, perturbation theory comprises methods for finding an approximate solution to a problem, by starting from the exact solution of a related, simpler problem. A critical feature of the technique is a middle step that breaks the problem into "solvable" and "perturbative" parts. In perturbation theory, the solution is expressed as a power series in a small parameter . The first term is the known solution to the solvable problem. Successive terms in the series at higher powers of usually become smaller. An approximate 'perturbation solution' is obtained by truncating the series, usually by keeping only the first two terms, the solution to the known problem and the 'first order' perturbation correction.

<span class="mw-page-title-main">Boundary layer</span> Layer of fluid in the immediate vicinity of a bounding surface

In physics and fluid mechanics, a boundary layer is the thin layer of fluid in the immediate vicinity of a bounding surface formed by the fluid flowing along the surface. The fluid's interaction with the wall induces a no-slip boundary condition. The flow velocity then monotonically increases above the surface until it returns to the bulk flow velocity. The thin layer consisting of fluid whose velocity has not yet returned to the bulk flow velocity is called the velocity boundary layer.

Various types of stability may be discussed for the solutions of differential equations or difference equations describing dynamical systems. The most important type is that concerning the stability of solutions near to a point of equilibrium. This may be discussed by the theory of Aleksandr Lyapunov. In simple terms, if the solutions that start out near an equilibrium point stay near forever, then is Lyapunov stable. More strongly, if is Lyapunov stable and all solutions that start out near converge to , then is said to be asymptotically stable. The notion of exponential stability guarantees a minimal rate of decay, i.e., an estimate of how quickly the solutions converge. The idea of Lyapunov stability can be extended to infinite-dimensional manifolds, where it is known as structural stability, which concerns the behavior of different but "nearby" solutions to differential equations. Input-to-state stability (ISS) applies Lyapunov notions to systems with inputs.

In mathematics, a singular perturbation problem is a problem containing a small parameter that cannot be approximated by setting the parameter value to zero. More precisely, the solution cannot be uniformly approximated by an asymptotic expansion

<span class="mw-page-title-main">Yang–Mills theory</span> Physical theory unifying the electromagnetic, weak and strong interactions

The phrase Yang–Mills theory means both a quantum field theory for nuclear binding devised by Chen Ning Yang and Robert Mills in 1953 and the class of similar theories. In mathematical physics, Yang–Mills theory is a gauge theory based on a special unitary group SU(n), or more generally any compact Lie group. A Yang–Mills theory seeks to describe the behavior of elementary particles using these non-abelian Lie groups and is at the core of the unification of the electromagnetic force and weak forces (i.e. U(1) × SU(2)) as well as quantum chromodynamics, the theory of the strong force (based on SU(3)). Thus it forms the basis of our understanding of the Standard Model of particle physics.

In mathematics, more specifically in dynamical systems, the method of averaging exploits systems containing time-scales separation: a fast oscillationversus a slow drift. It suggests that we perform an averaging over a given amount of time in order to iron out the fast oscillations and observe the qualitative behavior from the resulting dynamics. The approximated solution holds under finite time inversely proportional to the parameter denoting the slow time scale. It turns out to be a customary problem where there exists the trade off between how good is the approximated solution balanced by how much time it holds to be close to the original solution.

In physics and fluid mechanics, a Blasius boundary layer describes the steady two-dimensional laminar boundary layer that forms on a semi-infinite plate which is held parallel to a constant unidirectional flow. Falkner and Skan later generalized Blasius' solution to wedge flow, i.e. flows in which the plate is not parallel to the flow.

<span class="mw-page-title-main">Hamilton's principle</span> Formulation of the principle of stationary action

In physics, Hamilton's principle is William Rowan Hamilton's formulation of the principle of stationary action. It states that the dynamics of a physical system are determined by a variational problem for a functional based on a single function, the Lagrangian, which may contain all physical information concerning the system and the forces acting on it. The variational problem is equivalent to and allows for the derivation of the differential equations of motion of the physical system. Although formulated originally for classical mechanics, Hamilton's principle also applies to classical fields such as the electromagnetic and gravitational fields, and plays an important role in quantum mechanics, quantum field theory and criticality theories.

The Poisson–Boltzmann equation describes the distribution of the electric potential in solution in the direction normal to a charged surface. This distribution is important to determine how the electrostatic interactions will affect the molecules in solution. The Poisson–Boltzmann equation is derived via mean-field assumptions. From the Poisson–Boltzmann equation many other equations have been derived with a number of different assumptions.

In perturbation theory, the Poincaré–Lindstedt method or Lindstedt–Poincaré method is a technique for uniformly approximating periodic solutions to ordinary differential equations, when regular perturbation approaches fail. The method removes secular terms—terms growing without bound—arising in the straightforward application of perturbation theory to weakly nonlinear problems with finite oscillatory solutions.

In mathematics, an eigenvalue perturbation problem is that of finding the eigenvectors and eigenvalues of a system that is perturbed from one with known eigenvectors and eigenvalues . This is useful for studying how sensitive the original system's eigenvectors and eigenvalues are to changes in the system. This type of analysis was popularized by Lord Rayleigh, in his investigation of harmonic vibrations of a string perturbed by small inhomogeneities.

The topological derivative is, conceptually, a derivative of a shape functional with respect to infinitesimal changes in its topology, such as adding an infinitesimal hole or crack. When used in higher dimensions than one, the term topological gradient is also used to name the first-order term of the topological asymptotic expansion, dealing only with infinitesimal singular domain perturbations. It has applications in shape optimization, topology optimization, image processing and mechanical modeling.

The turbulent Prandtl number (Prt) is a non-dimensional term defined as the ratio between the momentum eddy diffusivity and the heat transfer eddy diffusivity. It is useful for solving the heat transfer problem of turbulent boundary layer flows. The simplest model for Prt is the Reynolds analogy, which yields a turbulent Prandtl number of 1. From experimental data, Prt has an average value of 0.85, but ranges from 0.7 to 0.9 depending on the Prandtl number of the fluid in question.

<span class="mw-page-title-main">Stokes wave</span> Nonlinear and periodic surface wave on an inviscid fluid layer of constant mean depth

In fluid dynamics, a Stokes wave is a nonlinear and periodic surface wave on an inviscid fluid layer of constant mean depth. This type of modelling has its origins in the mid 19th century when Sir George Stokes – using a perturbation series approach, now known as the Stokes expansion – obtained approximate solutions for nonlinear wave motion.

In mathematics, the method of dominant balance is used to determine the asymptotic behavior of solutions to an ordinary differential equation without fully solving the equation. The process is iterative, in that the result obtained by performing the method once can be used as input when the method is repeated, to obtain as many terms in the asymptotic expansion as desired.

The Krylov–Bogolyubov averaging method is a mathematical method for approximate analysis of oscillating processes in non-linear mechanics. The method is based on the averaging principle when the exact differential equation of the motion is replaced by its averaged version. The method is named after Nikolay Krylov and Nikolay Bogoliubov.

In mathematics and physics, multiple-scale analysis comprises techniques used to construct uniformly valid approximations to the solutions of perturbation problems, both for small as well as large values of the independent variables. This is done by introducing fast-scale and slow-scale variables for an independent variable, and subsequently treating these variables, fast and slow, as if they are independent. In the solution process of the perturbation problem thereafter, the resulting additional freedom – introduced by the new independent variables – is used to remove (unwanted) secular terms. The latter puts constraints on the approximate solution, which are called solvability conditions.

<span class="mw-page-title-main">Potential flow around a circular cylinder</span> Classical solution for inviscid, incompressible flow around a cyclinder

In mathematics, potential flow around a circular cylinder is a classical solution for the flow of an inviscid, incompressible fluid around a cylinder that is transverse to the flow. Far from the cylinder, the flow is unidirectional and uniform. The flow has no vorticity and thus the velocity field is irrotational and can be modeled as a potential flow. Unlike a real fluid, this solution indicates a net zero drag on the body, a result known as d'Alembert's paradox.

The narrow escape problem is a ubiquitous problem in biology, biophysics and cellular biology.

Triple-deck theory is a theory that describes a three-layered boundary-layer structure when sufficiently large disturbances are present in the boundary layer. This theory is able to successfully explain the phenomenon of boundary layer separation, but it has found applications in many other flow setups as well, including the scaling of the lower-branch instability (T-S) of the Blasius flow, boundary layers in swirling flows, etc. James Lighthill, Lev Landau and others were the first to realize that to explain boundary layer separation, different scales other than the classical boundary-layer scales need to be introduced. These scales were first introduced independently by James Lighthill and E. A. Müller in 1953. The triple-layer structure itself was independently discovered by Keith Stewartson (1969) and V. Y. Neiland (1969) and by A. F. Messiter (1970). Stewartson and Messiter considered the separated flow near the trailing edge of a flat plate, whereas Neiland studied the case of a shock impinging on a boundary layer.

References

  1. O’Malley, Robert E. (2014), O'Malley, Robert E. (ed.), "The Method of Matched Asymptotic Expansions and Its Generalizations", Historical Developments in Singular Perturbations, Cham: Springer International Publishing, pp. 53–121, doi:10.1007/978-3-319-11924-3_3, ISBN   978-3-319-11924-3 , retrieved 2023-05-04
  2. R.B. Dingle (1973), Asymptotic Expansions: Their Derivation and Interpretation, Academic Press.
  3. 1 2 Verhulst, F. (2005). Methods and Applications of Singular Perturbations: Boundary Layers and Multiple Timescale Dynamics. Springer. ISBN   0-387-22966-3.
  4. Nayfeh, A. H. (2000). Perturbation Methods. Wiley Classics Library. Wiley-Interscience. ISBN   978-0-471-39917-9.
  5. Kevorkian, J.; Cole, J. D. (1996). Multiple Scale and Singular Perturbation Methods. Springer. ISBN   0-387-94202-5.
  6. Bender, C. M.; Orszag, S. A. (1999). Advanced Mathematical Methods for Scientists and Engineers. Springer. ISBN   978-0-387-98931-0.
  7. Hinch, John (1991). Perturbation Methods. Cambridge University Press.
  8. R.B. Dingle and H. J. W. Müller, J. reine angew. Math. 211 (1962) 11-32 and 216 (1964) 123-133; H.J.W. Müller, J. reine angew. Math. 211 (1962) 179-190.
  9. H.J.W. Müller, Mathematische Nachrichten 31 (1966) 89-101, 32 (1966) 49-62, 32 (1966) 157-172.
  10. H.J.W. Müller, J. reine angew. Math. 211 (1962) 33-47.
  11. H.J.W. Müller, J. reine angew. Math. 212 (1963) 26-48.
  12. H.J.W. Müller-Kirsten (2012), Introduction to Quantum Mechanics: Schrödinger Equation and Path Integral, 2nd ed., World Scientific, ISBN   978-9814397742. Chapter 18 on Anharmonic potentials.
  13. An Introduction to the Dynamics of Colloids by J. K. G. Dhont, google books link
  14. Zaccone, A.; Gentili, D.; Wu, H.; Morbidelli, M. (2009). "Theory of activated-rate processes under shear with application to shear-induced aggregation of colloids". Physical Review E. 80 (5): 051404. arXiv: 0906.4879 . Bibcode:2009PhRvE..80e1404Z. doi:10.1103/PhysRevE.80.051404. hdl: 2434/653702 . PMID   20364982. S2CID   22763509.
  15. Banetta, L.; Zaccone, A. (2019). "Radial distribution function of Lennard-Jones fluids in shear flows from intermediate asymptotics". Physical Review E. 99 (5): 052606. arXiv: 1901.05175 . Bibcode:2019PhRvE..99e2606B. doi:10.1103/PhysRevE.99.052606. PMID   31212460. S2CID   119011235.
  16. Banetta, L.; Zaccone, A. (2020). "Pair correlation function of charge-stabilized colloidal systems under sheared conditions". Colloid and Polymer Science. 298 (7): 761–771. arXiv: 2006.00246 . doi: 10.1007/s00396-020-04609-4 .