Infinite domain, transformation

The Laplace transform method is a powerful technique for solving a variety of partial-differential equations, particularly time-dependent boundary condition problems and problems on the semi-infinite domain. After a Laplace transform is performed on the original boundary-value problem, the transformed equation is often easily solved. The transformed solution is then back-transformed to obtain the desired solution. 

The coupled diffusion equations (6) together with the boundary conditions (9) and (10) can be solved in close form in the Laplace transform space, and numerically inverted to the time domain. At early time, the solution behaves according to the solution for a semi-infinite domain. At large time, the solution... 

Transformation of the infinite domain. There are several ways of dealing with an infinite domain. Guertin et al. (21) chose perturbation solutions of the model as basis functions. 

The use of standard piecewise polynomials, together with an appropriate transformation of the infinite domain, overcomes these difficulties, provided some care is taken in the transformation. ... 

Transient heat conduction or mass transfer in solids with constant physical properties (diffusion coefficient, thermal diffusivity, thermal conductivity, etc.) is usually represented by a parabolic partial differential equation. For steady state heat or mass transfer in solids, potential distribution in electrochemical cells is usually represented by elliptic partial differential equations. In this chapter, we describe how one can arrive at the analytical solutions for linear parabolic partial differential equations and elliptic partial differential equations in semi-infinite domains using the Laplace transform technique, a similarity solution technique and Maple. In addition, we describe how numerical similarity solutions can be obtained for nonlinear partial differential equations in semi-infinite domains. 

In this chapter, analytical solutions were obtained for parabolic and elliptic partial differential equations in semi-infinite domains. In section 4.2, the given linear parabolic partial differential equations were converted to an ordinary differential equation boundary value problem in the Laplace domain. The dependent variable was then solved in the Laplace domain using Maple s dsolve command. The solution obtained in the Laplace domain was then converted to the time domain using Maple s inverse Laplace transform technique. Maple is not capable of inverting complicated functions. Two such examples were illustrated in section 4.3. As shown in section 4.3, even when Maple fails, one can arrive at the transient solution by simplifying the integrals using standard Laplace transform formulae. 

Both the Laplace transform and the similarity solution techniques are powerful techniques for partial differential equations in semi-infinite domains. The Laplace transform technique can be used for all linear partial differential equations with all possible boundary conditions. The similarity solution can be used only if the independent variables can be combined and if the boundary conditions in x and t can be converted to boundary conditions in the combined variable. In addition, unlike the Laplace transform technique, the similarity solution technique cannot handle partial differential equations in which the dependent variable appears explicitly. The Laplace transform cannot handle elliptic or nonlinear partial differential equations. The similarity solution can be used for elliptic and for a few nonlinear partial differential equations as shown in section 4.6. There are thirteen examples in this chapter. 

The Fourier transform can be used to solve the Dirichlet problem in the inhnite domain, and the Fourier sine transform can be used in the semi-inhnite domain. The Fourier cosine transform is appropriate for the Neumann problem in the semi-infinite domain. 

Integral transforms can be used to solve ordinary differential equations by converting them to algebraic equations. In what follows, the convolution properties of the different transforms have been listed, followed by the methods of integral transform used to solve (a) one-dimensional diffusion equations in the infinite and semi-infinite domains and (b) Laplace equations in the cylindrical geometries. 

Numerical solution. We can solve Equation 8.43 as an initial-value problem as written with an ODE solver. Because of the semi-infinite domain, we would need to check the solution for a sequence of increasingly large values and terminate the ODE solver when the value of Cs 0) stops changing. Alternatively, we can map the semi-infinite domain onto a finite domain and let the ODE solver do the work for us. Many transformations are possible, such as z = exp(-d), but experience suggests a strongly decreasing function like the exponential... 

Sometimes the domain is semi-infinite, as in boundaiy layer flow. The domain can be transformed from the x domain (O-oo) to the T domain (1-0) using the transformation T = exp ( ). Another approach is to use a variable mesh, perhaps with the same transformation. For example, use T = exp (— x) and a constant mesh size in T the value of is found experimentally. Still another approach is to solve on a finite mesh in which the last point is far enough away that its location does not influence the solution (Ref. 59). A location that is far enough away must be found by trial and error. 

Equations (40.3) and (40.4) are called the Fourier transform pair. Equation (40.3) represents the transform from the frequency domain back to the time domain, and eq. (40.4) is the forward transform from the time domain to the frequency domain. A closer look at eqs. (40.3) and (40.4) reveals that the forward and backward Fourier transforms are equivalent, except for the sign in the exponent. The backward transform is a summation because the frequency domain is discrete for finite measurement times. However, for infinite measurement times this summation becomes an integral. 

In single-scale filtering, basis functions are of a fixed resolution and all basis functions have the same localization in the time-frequency domain. For example, frequency domain filtering relies on basis functions localized in frequency but global in time, as shown in Fig. 7b. Other popular filters, such as those based on a windowed Fourier transform, mean filtering, and exponential smoothing, are localized in both time and frequency, but their resolution is fixed, as shown in Fig. 7c. Single-scale filters are linear because the measured data or basis function coefficients are transformed as their linear sum over a time horizon. A finite time horizon results infinite impulse response (FIR) and an infinite time horizon creates infinite impulse response (HR) filters. A linear filter can be represented as... 

What about the converse does any linear transformation determine a matrix This question raises two issues. First, if the domain is infinite-dimensional, the question is more complicated. Mathematicians usually reserve the word matrix for a finite-dimensional matrix (i.e., an array with a finite number of rows and colunms). Physicists often use matrix to denote a linear transformation between infinite-dimensional spaces, where mathematicians would usually prefer to say linear transformation. Second, even in finitedimensional spaces, one must specify bases in domain and target space to determine the entries in a matrix. We discuss this issue in more detail in Section 2.5 for the special case of linear operators. 

The infinite radial domain (0 — H < oo) was compressed into a finite domain (O- 15 1) by use of the following transformation ... 

A typical characteristic of hypersymmetry operations is that they exercise their influence in well-defined discrete domains. These domains do not overlap—they do not even touch each other. The usual hypersymmetry elements lead to point-group properties. This means that no infinite molecular chains could be selected, for example, to which these hypersymmetry operations would apply. They affect, instead, pairs of molecules or very small groups of molecules. Thus, they can really be considered as local point-group operations. These hypersymmetry elements, accordingly, divide the whole crystalline system into numerous small groups of molecules, or transform the crystal space into a layered structure. 

The first derivation of the inverse Laplace transform into the time domain of the general rate model solution in the Laplace domain was obtained by Rosen [33]. He obtained it in the form of an infinite integral, for the case of a breakthrough curve (step input), and he used contour integration for the final calculation, assuming (i) that axial dispersion can be neglected i.e., Dj, = 0 in Eq. 6.58) and (ii) that the kinetics of adsorption-desorption is infinitely fast i.e., using Eq. 6.66 instead of Eq. 6.63). Hence, he considered in his solution only the effects of intraparticle diffusion and of the external film resistance. Rosen s model is equivalent to Carta s [34]. 

The methodology is the same as that used in section 8.1.3. When Maple fails to invert the Laplace domain solution to the time domain, a short time solution can be obtained by converting the Laplace domain solution to an infinite series in which each term can be easily inverted to time domain. The solution obtained for heat transfer in a rectangle in example 7.1 using the separation of variables method cannot be used at short times. At time t = 0, one would need infinite number of terms in the separation of variables solution. Fortunately, the Laplace transform technique helps us obtain a solution, which can be used efficiently at short times also. This is best illustrated with the following examples. 

Typically when linear partial differential equations are solved using the Laplace transform method the solution obtained in the Laplace domain can be represented as in equation (8.7) and q(s) usually has an infinite number of roots. If s = p . n = 1..00 are the distinct roots of q(s), q(s) can be factorized as... 

Often inversion to time domain solution is not trivial and the time domain involves an infinite series. In section 8.1.4 short time solution for parabolic partial differential equations was obtained by converting the solution obtained in the Laplace domain to an infinite series, in which each term can easily inverted to time domain. This short time solution is very useful for predicting the behavior at short time and medium times. For long times, a long term solution was obtained in section 8.1.5 using Heaviside expansion theorem. This solution is analogous to the separation of variables solution obtained in chapter 7. In section 8.1.6, the Heaviside expansion theorem was used for parabolic partial differential equations in which the solution obtained has multiple roots. In section 8.1.7, the Laplace transform technique was extended to parabolic partial differential equations in cylindrical coordinates. In section 8.1.8, the convolution theorem was used to solve the linear parabolic partial differential equations with complicated time dependent boundary conditions. For time dependent boundary conditions the Laplace transform technique was shown to be advantageous compared to the separation of variables technique. A total of fifteen examples were presented in this chapter. 

The next most familiar part of the picture is the upper right-hand corner. This i s the domain of classical applied mathematics and mathematical physics where the linear partial differential equations live. Here we find Maxwell s equations of electricity and magnetism, the heat equation, Schrodinger s wave equation in quantum mechanics, and so on. These partial differential equations involve an infinite continuum of variables because each point in space contributes additional degrees of freedom. Even though these systems are large, they are tractable, thanks to such linear techniques as Fourier analysis and transform methods. 

Equations (27) and (28) or alternatively Eq. (31) provide the most general formal expression for any type of 4WM process. They show that the nonlinear response function R(t3,t2,t 1), or its Fourier transform (cum + a>n + (oq,com + tu ,aim), contains the complete microscopic information relevant to the calculation of any 4WM signal. As indicated earlier, the various 4WM techniques differ by the choice of ks and ojs and by the temporal characteristics of the incoming fields E, (t), E2(t), and 3(t). A detailed analysis of the response function and of the nonlinear signal will be made in the following sections for specific models. At this point we shall consider the two limiting cases of ideal time-domain and frequency-domain 4WM. In an ideal time-domain 4WM, the durations of the incoming fields are infinitely short, that is,... 

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