Mathematical methods partial differential equations

See also Numerical Analysis and Approximate Methods and General References References for General and Specific Topics—Advanced Engineering Mathematics for additional references on topics in ordinary and partial differential equations. 

Usually the finite difference method or the grid method is aimed at numerical solution of various problems in mathematical physics. Under such an approach the solution of partial differential equations amounts to solving systems of algebraic equations. 

References Brown, J. W., and R. V. Churchill, Fourier Series and Boundary Value Problems, 6th ed., McGraw-Hill, New York (2000) Churchill, R. V, Operational Mathematics, 3d ed., McGraw-Hill, New York (1972) Davies, B., Integral Transforms and Their Applications, 3d ed., Springer (2002) Duffy, D. G., Transform Methods for Solving Partial Differential Equations, Chapman Hall/CRC, New York (2004) Varma, A., and M. Morbidelli, Mathematical Methods in Chemical Engineering, Oxford, New York (1997). 

The diffusion equation is the partial-differential equation that governs the evolution of the concentration field produced by a given flux. With appropriate boundary and initial conditions, the solution to this equation gives the time- and spatial-dependence of the concentration. In this chapter we examine various forms assumed by the diffusion equation when Fick s law is obeyed for the flux. Cases where the diffusivity is constant, a function of concentration, a function of time, or a function of direction are included. In Chapter 5 we discuss mathematical methods of obtaining solutions to the diffusion equation for various boundary-value problems. 

When the diffusion profile is time-dependent, the solutions to Eq. 4.18 require considerably more effort and familiarity with applied mathematical methods for solving partial-differential equations. We first discuss some fundamental-source solutions that can be used to build up solutions to more complicated situations by means of superposition. 

Amundson, N. R. and Aris, R. (1972) Mathematical Methods in Chemical Engineering First Order Partial Differential Equations with Applications. Prentice-Hall, Englewood Cliffs, NJ. 

Mathematical analysis for a chromatographic reactor (with T. Petroulas and R.W. Carr). In R. Vichnevetsky and R.S. Stepleman (eds.), Advances in Computer Methods for Partial Differential Equations- V Proceedings of the Fifth IM-ACS International Symposium on Computer Methods for Partial Differential Equations, New Brunswick IMACS, Dept, of Computer Science, Rutgers University, 1984. 

The aim of molecular orbital theory is to provide a complete description of the energies of electrons and nuclei in molecules. The principles of the method are simple a partial differential equation is set up, the solutions to which are the allowed energy levels of the system. However, the practice is rather different, and, just as it is impossible (at present) to obtain exact solutions to the wave equations for polyelectronic atoms, so it is not possible to obtain exact solutions for molecular species. Accordingly, the application of molecular orbital theory to molecules is in a regime of successive approximations. Numerous rigorous mathematical methods have been utilised in the effort to obtain ever more accurate solutions to the wave equations. This book is not concerned with the details of the methods which have been used, but only with their results. 

The Dimensionless Parameter is a mathematical method to solve linear differential equations. It has been used in Electrochemistry in the resolution of Fick s second law differential equation. This method is based on the use of functional series in dimensionless variables—which are related both to the form of the differential equation and to its boundary conditions—to transform a partial differential equation into a series of total differential equations in terms of only one independent dimensionless variable. This method was extensively used by Koutecky and later by other authors [1-9], and has proven to be the most powerful to obtain explicit analytical solutions. In this appendix, this method will be applied to the study of a charge transfer reaction at spherical electrodes when the diffusion coefficients of both species are not equal. In this situation, the use of this procedure will lead us to a series of homogeneous total differential equations depending on the variable, v given in Eq. (A.l). In other more complex cases, this method leads to nonhomogeneous total differential equations (for example, the case of a reversible process in Normal Pulse Polarography at the DME or the solutions of several electrochemical processes in double pulse techniques). In these last situations, explicit analytical solutions have also been obtained, although they will not be treated here for the sake of simplicity. 

The distributed nature of the tubular plug flow reactor means that variables change with both axial position and time. Therefore the mathematical models consist of several simultaneous nonlinear partial differential equations in time t and axial position z. There are several numerical integration methods for solving these equations. The method of lines is used in this chapter.1... 

The integral equation approach is a general purpose numerical method for solving mathematical problems involving linear partial differential equations with piecewise constant coefficients. It is commonly used in various fields of science and engineering, such as acoustics, electromagnetism, solid and fluid mechanics,... 

Equation 3.42 is a partial differential equation that will, upon solution, yield concentration as a function of time and distance for any sample pulse undergoing uniform translation and diffusion. In theory, we need only specify the initial conditions (i.e., the mathematical shape of the starting peak) along with any applicable boundary conditions and apply standard methods for solving partial differential equations to obtain our solutions. These solutions tend to be unwieldy if the initial peak shape is complicated. Fortunately, a majority of practical cases are described by a relatively simple special case, which we now describe. 

Finite element methods — The finite element method is a powerful and flexible numerical technique for the approximate solution of (both ordinary and partial) differential equations involving replacing the continuous problem with unknown solution by a system of algebraic equations. The method was first introduced by Richard Courant in 1943 [i], and over the next three decades, and particularly in the 1960s, a comprehensive mathematical framework was developed to underpin the method. 

Much of the mathematical analysis required in physical chemistry can be handled by analytical methods. Throughout this book and in all physical chemisby textbooks, a variety of calculus techniques ate used freely differentiation and integration of functions of several variables solution of ordinary and partial differential equations, including eigenvalue problems some integral equations, mostly linear. There is occasional use of other tools such as vectors and vector analysis, coordinate transformations, matrices, determinants, and Fourier methods. Discussion of all these topics will be found in calculus textbooks and in other standard mathematical texts. 

One may conclude therefore that the solution of Pick s second law (a partial differential equation) would proceed smoothly if some mathematical device could be utilized to convert it into the form of a total differential equation. The Laplace transformation method is often used as such a device. 

Courant, R. and D. Hilbert, 1962, Methods of mathematical physics. Vol. II Partial differential equations (Vol. II by R. Courant) Interscience Publishers (a division of John Wiley Sons), New York, London, 830 pp. 

First principle mathematical models These models solve the basic conservation equations for mass and momentum in their form as partial differential equations (PDEs) along with some method of turbulence closure and appropriate initial and boundary conditions. Such models have become more common with the steady increase in computing power and sophistication of numerical algorithms. However, there are many potential problems that must be addressed. In the verification process, the PDEs being solved must adequately represent the physics of the dispersion process especially for processes such as ground-to-cloud heat transfer, phase changes for condensed phases, and chemical reactions. Also, turbulence closure methods (and associated boundary and initial conditions) must be appropriate for the dis-... 

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],... 

The transport approach has been used very early, and most extensively, to calculate the chromatographic response to a given input function (injection condition). This approach is based on the use of an equation of motion. In this method, we search for the mathematical solution of the set of partial differential equations describing the chromatographic process, or rather the differential mass balance of the solute in a slice of column and its kinetics of mass transfer in the column. Various mathematical models have been developed to describe the chromatographic process. The most important of these models are the equilibrium-dispersive (ED) model, the lumped kinetic model, and the general rate model (GRM) of chromatography. We discuss these three models successively. 

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