Distributed systems partial differential equations

The assumption implicit in the discussion so far is that the system to be modelled consists of lumped-parameter elements and thus may be described adequately using ordinary differential equations in time. This will be true for a large number of process 

The distributed parameter component can be introduced into the larger system being simulated in one of two ways either it can be introduced as an integral part through finite differencing in the distance dimension (or dimensions), or else it can be kept as a separate computational entity that communicates with the main simulation only at specified communication intervals. 

To illustrate these concepts, let us take the example of a heat exchanger, where the temperature of the fluid within the tube will vary continuously throughout the length of the heat exchanger. The describing equations will have the form  

T is the temperature of the fluid inside the tube (°C), T is the temperature of the tube wall ( C), c is the velocity of the fluid inside the tube (m/s), r, is the temperature of the shell-side fluid (°C), iti, ki, ki are all heat transfer constants (s ). 

In many cases there would be a partial differential equation similar to (2.31) for the shell-side fluid also. An exception occurs when the shell-side fluid consists of condensing steam, when the shell-side fluid temperature can be characterized by a single value and described by an ordinary differential equation. For simplicity we will consider here this last case. 

---

Partieulate produets, sueh as those from eomminution, erystallization, preeipi-tation ete., are distinguished by distributions of the state eharaeteristies of the system, whieh are not only funetion of time and spaee but also some properties of states themselves known as internal variables. Internal variables eould inelude size and shape if partieles are formed or diameter for liquid droplets. The mathematieal deseription eneompassing internal eo-ordinate inevitably results in an integro-partial differential equation ealled the population balanee whieh has to be solved along with mass and energy balanees to deseribe sueh proeesses. 

The overall set of partial differential equations that can be considered as a mathematical characterization of the processing system of gas-liquid dispersions should include such environmental parameters as composition, temperature, and velocity, in addition to the equations of bubble-size and residence-time distributions that describe the dependence of bubble nucleation and growth on the bubble environmental factors. A simultaneous solution of this set of differential equations with the appropriate initial and boundary conditions is needed to evaluate the behavior of the system. Subject to the Curie principle, this set of equations should include the possibilities of coupling effects among the various fluxes involved. In dispersions, the possibilities of couplings between fluxes that differ from each other by an odd tensorial rank exist. (An example is the coupling effect between diffusion of surfactants and the hydrodynamics of bubble velocity as treated in Section III.) As yet no analytical solution of the complete set of equations has been found because of the mathematical difficulties involved. To simplify matters, the pertinent transfer equation is usually solved independently, with some simplifying assumptions. 

Unlike stirred tanks, piston flow reactors are distributed systems with one-dimensional gradients in composition and physical properties. Steady-state performance is governed by ordinary differential equations, and dynamic performance is governed by partial differential equations, albeit simple, first-order PDEs. Figure 14.6 illustrates a component balance for a differential volume element. 

In this chapter we concentrate on dynamic, distributed systems described by partial differential equations. Under certain conditions, some of these systems, particularly those described by linear PDEs, have analytical solutions. If such a solution does exist and the unknown parameters appear in the solution expression, the estimation problem can often be reduced to that for systems described by algebraic equations. However, most of the time, an analytical solution cannot be found and the PDEs have to be solved numerically. This case is of interest here. Our general approach is to convert the partial differential equations (PDEs) to a set of ordinary differential equations (ODEs) and then employ the techniques presented in Chapter 6 taking into consideration the high dimensionality of the problem. 

The general equation of convective diffusion in liquids, equation (15), is a second-order, partial differential equation with variable coefficients. Its solution yields the spatial distribution of c, as a function of time, namely its transient behaviour. On an analytical level, solution of equation (15) into the transient c(t) is possible only for a number of relatively simple systems with well-defined geometry and flow properties. The problem is greatly simplified if the concentration function Cj(x,y,z) is essentially independent of time t, i.e. in the steady-state. Then equation (15) reduces to ... 

Only digital simulation solutions for ordinary differential equations are presented. To present anything more than a very superficial treatment of simulation techniques for partial differential equations would require more space than is available in this book. This subject is covered in severd texts. In many practical problems, distributed systems are often broken up into a number of lumps which can then be handled by ordinary differential equations. 

The computer-reconstructed catalyst is represented by a discrete volume phase function in the form of 3D matrix containing information about the phase in each volume element. Another 3D matrix defines the distribution of active catalytic sites. Macroporosity, sizes of supporting articles and the correlation function describing the macropore size distribution are evaluated from the SEM images of porous catalyst (Koci et al., 2006 Kosek et al., 2005). Spatially 3D reaction-diffusion system with low concentrations of reactants and products can be described by mass balances in the form of the following partial differential equations (Koci et al., 2006, 2007a). For gaseous components ... 

We first choose variables sufficient to describe the situation. This choice is tentative, for we may need to omit some or recruit others at a later stage (e.g., if V is constant, it can be dismissed as a variable). In general, variables fall into two groups independent (in our example, time) and dependent (volume and concentration) variables. The term lumped is applied to variables that are uniform throughout the system, as all are in our simple example because we have assumed perfect mixing. If we had wished to model imperfect mixing, we would have had either to introduce a number of different zones (each of which would then be described by lumped variables) or to introduce spatial coordinates, in which case the variables are said to be distributed.2 Lumped variables lead to ordinary equations distributed variables lead to partial differential equations. 

This is a partial differential equation, as we should expect from a plug-flow tubular reactor with a single reaction. We note in passing that the solution requires the specification of an initial distribution and a boundary, or feed, value. These are both functions (the first of z because t = 0 the second of t because z = 0) in the distributed system. Of the corresponding quantities, c0 and cin, in the lumped system, the latter is embodied in the ordinary differential equation itself and the former is the initial value. 

The reaction considered is the gas-phase, irreversible, exothermic reaction A + B — C occurring in a packed tubular reactor. The reactor and the heat exchanger are both distributed systems, which are rigorously modeled by partial differential equations. Lumped-model approximations are used in this study, which capture the important dynamics with a minimum of programming complexity. There are no sharp temperature or composition gradients in the reactor because of the low per-pass conversion and high recycle flowrate. 

Noncompartmental models were introduced as models that allow for transport of material through regions of the body that are not necessarily well mixed or of uniform concentration [248]. For substances that are transported relatively slowly to their site of degradation, transformation, or excretion, so that the rate of diffusion limits their rate of removal from the system, the noncompartmental model may involve diffusion or other random walk processes, leading to the solution in terms of the partial differential equation of diffusion or in terms of probability distributions. A number of noncompartmental models deal with plasma time-concentration curves that are best described by power functions of time. 

The last component of the model is a method to solve this system of (simultaneous partial differential) equations, often as a function of time as the concentration distributions evolve during the experiment. The difficulty of solving these systems depends on the complexity of the material balance... 

In general, the functions Hi and Fjc are nonlinear. These norUinearities are usually due to the exponential activation of the electrochemical rate constants by the potential (see Section 5.5). In addition, even for time-invariant electrochemical systems, equations (14.2) can comprise either differential equations, when only kinetic equations are considered to be involved at the interface, or partial differential equations, when distributed processes occur in the bulk of the solution (such as may result from transport of the reacting species or a temperature gradient in the solution). 

Takeuchi et al. 7 reported a membrane reactor as a reaction system that provides higher productivity and lower separation cost in chemical reaction processes. In this paper, packed bed catalytic membrane reactor with palladium membrane for SMR reaction has been discussed. The numerical model consists of a full set of partial differential equations derived from conservation of mass, momentum, heat, and chemical species, respectively, with chemical kinetics and appropriate boundary conditions for the problem. The solution of this system was obtained by computational fluid dynamics (CFD). To perform CFD calculations, a commercial solver FLUENT has been used, and the selective permeation through the membrane has been modeled by user-defined functions. The CFD simulation results exhibited the flow distribution in the reactor by inserting a membrane protection tube, in addition to the temperature and concentration distribution in the axial and radial directions in the reactor, as reported in the membrane reactor numerical simulation. On the basis of the simulation results, effects of the flow distribution, concentration polarization, and mass transfer in the packed bed have been evaluated to design a membrane reactor system. 

Steady state mass or heat transfer in solids and current distribution in electrochemical systems involve solving elliptic partial differential equations. The method of lines has not been used for elliptic partial differential equations to our knowledge. Schiesser and Silebi (1997)[1] added a time derivative to the steady state elliptic partial differential equation and applied finite differences in both x and y directions and then arrived at the steady state solution by waiting for the process to reach steady state. [2] When finite differences are applied only in the x direction, we arrive at a system of second order ordinary differential equations in y. Unfortunately, this is a coupled system of boundary value problems in y (boundary conditions defined at y = 0 and y = 1) and, hence, initial value problem solvers cannot be used to solve these boundary value problems directly. In this chapter, we introduce two methods to solve this system of boundary value problems. Both linear and nonlinear elliptic partial differential equations will be discussed in this chapter. We will present semianalytical solutions for linear elliptic partial differential equations and numerical solutions for nonlinear elliptic partial differential equations based on method of lines. 

In many practical situations the random process under observation is continuous in the sense that (1) the space of possible states is continuous (or it can be transformed to a continuous-like representation by a coarse-graining procedure), and (2) the change in the system state during a small time interval is small, that is, if the system is found in state x at time t then the probability to find it in state y x at time t + St vanishes when St 0. When these, and some other conditions detailed below, are satisfied, we can derive a partial differential equation for the probability distribution, the Fokker-Planck equation, which is discussed in this Section. 

The lowest level of abstraction, here called the geometry level, is the closest to physical reality, in which the physics is described by partial differential equations. This level is the domain of finite-element, boundary-element or related methods (e.g., [7-9]). Due to their high accuracy, these methods are well suited for calculating, for example, the distribution of stresses, distortions and natural resonant frequencies of MEMS structures. But they also entail considerable computational effort. Thus, these methods are used to solve detailed problems only when needed, whereas simulations of complete sensor systems and, in particular, transient analyses are carried out using methods at higher levels of abstraction. 

This is the simplest model of an electrocatalyst system where the single energy dissipation is caused by the ohmic drop of the electrolyte, with no influence of the charge transfer in the electrochemical reaction. Thus, fast electrochemical reactions occur at current densities that are far from the limiting current density. The partial differential equation governing the potential distribution in the solution can be derived from the Laplace Equation 13.5. This equation also governs the conduction of heat in solids, steady-state diffusion, and electrostatic fields. The electric potential immediately adjacent to the electrocatalyst is modeled as a constant potential surface, and the current density is proportional to its gradient ... 

These are systems where the state variables are varying in one or more directions of the space coordinates. The simplest chemical reaction engineering example is the plug flow reactor. These systems are described at steady state either by an ordinary differential equation (where the variation of the state variables is only in one direction of the space coordinates, i.e. one dimensional models, and the independent variable is this space direction), or partial differential equations (when the variation of the state variables is in more than one direction of the space coordinates, i.e. two dimensional models, and the independent variables are these space directions). The ordinary differential equations of the steady state of the one-dimensional distributed model can be either initial value differential equations (e.g. plug flow models) or two-point boundary value differential equations (e.g. models with superimposed axial dispersion). The equations describing the unsteady state of distributed models are invariably partial difierential equations. 

---