Linear Parabolic Partial Differential Equations

The preliminary steps for solving partial differential equations by finite difference methods is similar to the last section. We shall now illustrate this for parabolic partial differential equations. 

Let us start with a linear equation written in nondimensional form as 

This equation describes many transient heat and mass transfer processes, such as the diffusion of a solute through a slab membrane with constant physical properties. The exact solution, obtained by either the Laplace transform, separation of variables (Chapter 10) or the finite integral transform (Chapter 11), is given as 

We will use this exact solution later for evaluating the efficiency of the finite difference solutions. 

we divide the membrane spatial domain into N equal intervals with the grid size being Aa = 1/A. Next, evaluating Eq. 12.128a at N — I interior points (i.e., points within the domain), we have 

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Transient is a C-program for solving systems of generally non-linear, parabolic partial differential equations in two variables (that is, space and time), in particular, reaction-diffusion equations within the generalized Crank-Nicolson Finite Difference Method. 

With the convective derivatives eliminated and the properties constant, the thermal-energy equation is completely decoupled from the system. Moreover the energy equation is a simple, linear, parabolic, partial differential equation. 

This momentum equation is a linear parabolic partial differential equation (for constant p) that can be solved by the method of separation of variables. In this approach the solution can be found to be a product of two functions as w(t, r) = f t)g r). The solution is represented as an infinite series that can be readily evaluated at any time or value of r. Such a solution is available for a variety of boundary conditions, including time-oscillating rotation rates. At this point, however, we choose to proceed with a numerical solution. 

This is a linear parabolic partial differential equation that can be readily solved as soon as boundary conditions are specified. There is a symmetry condition at the centerline, and it is presumed that the mass fraction Yk vanishes at the wall, Yk = 0. It is important to note that it has been implicitly assumed that the velocity profile has been fully developed, such that the similarity solution / is valid. This assumption is analogous to that used in the Graetz problem (Section 4.10). 

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. 

In section 4.4, the given linear parabolic partial differential equation in semi-infinite domain was solved by combining the independent variables (similarity solution). This technique is capable of providing special function solutions as shown in example 4.9. In section 4.5, elliptic partial differential equations were solved using the similarity solution technique. In section 4.6, similarity solution was extended for nonlinear parabolic and elliptic partial differential equations. 

Mathematical modeling of mass or heat transfer in solids involves Pick s law of mass transfer or Fourier s law of heat conduction. Engineers are interested in the distribution of heat or concentration across the slab or the material in which the experiment is performed. This process is usually time varying and eventually reaches a steady state. This process is represented by parabolic partial differential equations with known initial conditions and boundary conditions at two ends. Both linear and nonlinear parabolic partial differential equations will be discussed in this chapter. We will present semianalytical solutions for linear parabolic partial differential equations and numerical solutions for nonlinear parabolic partial differential equations based on the numerical method of lines. 

Hence, we obtain a semianalytical solution, i.e., the dependent variables at all the node points are obtained as an analytical solution of time t. The procedure for solving linear parabolic partial differential equations with nonhomogeneous boundary conditions can be summarized as follows ... 

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. 

Find an approximate solution, valid for long times, to the linear parabolic partial differential equation, describing mass or heat transport ffom/to a sphere with constant physical properties, such as thermal conductivity and diffusion coefficient... 

We have demonstrated the method of finite differences for solving linear parabolic partial differential equations. But the utility of the numerical method is best appreciated when we deal with nonlinear equations. In this section, we will consider a nonlinear parabolic partial differential equation, and show how to deal with the nonlinear terms. 

The system of hyperbolic and parabolic partial differential equations representing the ID or 2D model of monolith channel is solved by the finite differences method with adaptive time-step control. An effective numerical solution is based on (i) discretization of continuous coordinates z, r and t, (ii) application of difference approximations of the derivatives, (iii) decomposition of the set of equations for Ts, T, c and cs, (iv) quasi-linearization of... 

A one-dimensional Fokker-Planck equation was used by Smoluchowski [19], and the bivariate Fokker-Planck equation in phase space was investigated by Klein [21] and Kramers [22], Note that, in essence, the Rayleigh equation [23] is a monovariate Fokker-Planck equation in velocity space. Physically, the Fokker-Planck equation describes the temporal change of the pdf of a particle subjected to diffusive motion and an external drift, manifest in the second- and first-order spatial derivatives, respectively. Mathematically, it is a linear second-order parabolic partial differential equation, and it is also referred to as a forward Kolmogorov equation. The most comprehensive reference for Fokker-Planck equations is probably Risken s monograph [14]. 

Consider a general linear homogeneous parabolic partial differential equation in dimensionless form... 

Using the boundary conditions (equations (5.54) and (5.55)) the boundary values uo and un+i can be eliminated. Hence, the method of lines technique reduces the nonlinear parabolic PDE (equation (5.48)) to a nonlinear system of N coupled first order ODEs (equation (5.52)). This nonlinear system of ODEs is integrated numerically in time using Maple s numerical ODE solver (Runge-Kutta, Gear, and Rosenbrock for stiff ODEs see chapter 2.2.5). The procedure for using Maple to solve nonlinear parabolic partial differential equations with linear boundary conditions can be summarized as follows ... 

Linear first order parabolic partial differential equations in finite domains are solved using the Laplace transform technique in this section. Parabolic PDEs are first order in the time variable and second order in the spatial variable. The method involves applying the Laplace transform in the time variable to convert the partial differential equation to an ordinary differential equation in the Laplace domain. This becomes a boundary value problem (BVP) in the spatial direction with s, the Laplace variable as a parameter. The boundary conditions in x are converted to the Laplace domain and the differential equation in the Laplace domain is solved by using the techniques illustrated in chapter 3.1 for solving linear boundary value problems. Once an analytical solution is obtained in the Laplace domain, the solution is inverted to the time domain to obtain the final analytical solution (in time and spatial coordinates). Certain simple problems can be inverted to the time domain using Maple. This is best illustrated with the following examples. 

In this chapter, analytical solutions were obtained for linear hyperbolic and parabolic partial differential equations in finite domains using Laplace transform technique. In section 8.1.2, a linear hyperbolic partial differential equations was solved using the Laplace transform technique. First, the partial differential equation was converted to an ordinary differential equation by converting the PDF from the time domain to the Laplace domain. For hyperbolic partial differential equations this results in an initial value problem (IVP), which is solved analytically in the Laplace domain as illustrated in chapter 2.1. The analytical solution obtained in the Laplace domain was converted easily to the time domain using Maple s inbuilt Laplace transform package. For parabolic partial differential equations, the governing equation in the Laplace domain is a boundary value problem (BVP), which is solved analytically as in chapter 3.1. For certain simple parabolic partial differential equations, the Laplace domain solution can be inverted to time domain easily using Maple as illustrated in section 8.1.3. 

Based on the above assumptions, the model equations are shown in Table 4. The mass balance equations at the pellet and crystal level are based in the double linear driving model equations or bidisperse model[30]. The solution of the set of parabolic partial differential equations showed in Table 4 was performed using the method of lines. The spatial coordinate was discretized using the method of orthogonal collocation in finite elements. For each element 2 internal collocation points were used and the basis polynomial were calculated using the shifted Jacobi polynomials with weighting function W x) = (a = Q,p=G) hat has equidistant roots inside each element [31]. The set of discretized ordinary differential equations are then solved with DASPK solver [32] which is based on backward differentiation formulas. 

Both partial differential equations (2.6) and (2.7) are linear and of the parabolic type. The solution of these equations should be nonnegative and normalized to unity. Besides, this solution should satisfy the initial condition ... 

The theory for classifying linear, second-order, partial-differential equations is well established. Understanding the classification is quite important to understanding solution algorithms and where boundary conditions must be applied. Partial differential equations are generally classified as one of three forms elliptic, parabolic, or hyperbolic. Model equations for each type are usually stated as... 

The circumferential ((j>) momentum equation is a partial differential equation. Identify some of its basic properties. Is it elliptic, parabolic, or hyperbolic Is it linear of nonlinear What is its order ... 

If the thermal power W is linearly dependent or independent of the temperature d, the heat conduction equation, (2.9), is a second order linear, partial differential equation of parabolic type. The mathematical theory of this class of equations was discussed and extensively researched in the 19th and 20th centuries. Therefore tried and tested solution methods are available for use, these will be discussed in 2.3.1. A large number of closed mathematical solutions are known. These can be found in the mathematically orientated standard work by H.S. Carslaw and J.C. Jaeger [2.1],... 

Linear second-order partial differential equations in two independent variables are further classified into three canonical forms elliptic, parabolic, and hyperbolic. The general form of this class of equations is... 

Such a classification can also be applied to higher order equations involving more than two independent variables. Typically elliptic equations are associated with physical systems involving equilibrium states, parabolic equations are associated with diffusion type problems and hyperbolic equations are associated with oscillating or vibrating physical systems. Analytical closed form solutions are known for some linear partial differential equations. However, numerical solutions must be obtained for most partial differential equations and for almost all nonlinear equations. 

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