FINITE ELEMENT ITERATIVE

As mentioned in Chapter 2, the numerical solution of the systems of algebraic equations is based on the general categories of direct or iterative procedures. In the finite element modelling of polymer processing problems the most frequently used methods are the direet methods. 

Iterative solution methods are more effective for problems arising in solid mechanics and are not a common feature of the finite element modelling of polymer processes. However, under certain conditions they may provide better computer economy than direct methods. In particular, these methods have an inherent compatibility with algorithms used for parallel processing and hence are potentially more suitable for three-dimensional flow modelling. In this chapter we focus on the direct methods commonly used in flow simulation models. 

The right-hand side in Equation (6.18) is known and hence its solution yields the error 5x in the original solution. The procedure can be iterated to improve the solution step-by-step. Note that implementation of this algorithm in the context of finite element computations may be very expensive. A significant advantage of the LU decomposition technique now becomes clear, because using this technique [A] can be decomposed only once and stored. Therefore in the solution of Equation (6.18) only the right-hand side needs to be calculated. 

Elliptic Equations Elhptic equations can be solved with both finite difference and finite element methods. One-dimensional elhptic problems are two-point boundary value problems. Two- and three-dimensional elliptic problems are often solved with iterative methods when the finite difference method is used and direct methods when the finite element method is used. So there are two aspects to consider howthe equations are discretized to form sets of algebraic equations and howthe algebraic equations are then solved. 

Steady-state solutions are found by iterative solution of the nonlinear residual equations R(a,P) = 0 using Newton s methods, as described elsewhere (28). Contributions to the Jacobian matrix are formed explicitly in terms of the finite element coefficients for the interface shape and the field variables. Special matrix software (31) is used for Gaussian elimination of the linear equation sets which result at each Newton iteration. This software accounts for the special "arrow structure of the Jacobian matrix and computes an LU-decomposition of the matrix so that qu2usi-Newton iteration schemes can be used for additional savings. 

Without a solution, formulated mathematical systems (models) are of little value. Four solution procedures are mainly followed the analytical, the numerical (e.g., finite different, finite element), the statistical, and the iterative. Numerical techniques have been standard practice in soil quality modeling. Analytical techniques are usually employed for simplified and idealized situations. Statistical techniques have academic respect, and iterative solutions are developed for specialized cases. Both the simulation and the analytic models can employ numerical solution procedures for their equations. Although the above terminology is not standard in the literature, it has been used here as a means of outlining some of the concepts of modeling. 

All numerical techniques require application of sampling theory. Briefly stated, one chooses a representative sample of points within the region of interest and at each point attempts to calculate iteratively the most accurate solution possible, guided by self-consistency of local solutions with each other and with the specified boundary conditions. We describe two seemingly contrasting techniques finite-difference and finite-element methods (1,2). 

Ciano et al. (2006) have used a finite element approach to model a tubular cell 0.3 m long. The equations are available in Ciano et al. (2006). Table 7.2 shows the partial differential equations and the mesh characteristics. This model is computationally demanding and the equations have been solved by adopting an iterative procedure. Initial guess values for temperature and current density are assumed (current density is calculated by means of a lumped model, as the function of the average temperature and the cell voltage). Momentum equation and continuity equation are... 

The solutions of conductivity problems shown in the previous sections were obtained for zero-order kinetics. When the approximation by zero-order kinetics is not justified, which is the case, especially for autocatalytic reactions, a numerical solution is required. Here the use of finite elements is particularly efficient. The geometry of the container is described by a mesh of cells and the heat balance is established for each of these cells (Figure 13.5). The problem is then solved by iterations. As an example, a sphere can be described by a succession of concentric shells (like onion skins). In each cell, a mass and a heat balance are established. This gives access to the temperature profile if one considers the temperature of the different cells, or the temperature and conversion may be obtained as a function of time. 

Surfaces may found by finite element analysis methods where the curvature of each element of surface is brought iteratively to the correct value. More general energy functions can be imposed in this way. Exact minimal surfaces are merely particular idealizations and their value lies in their being two-dimensional manifolds which have metrics different from that of the euclidean manifold of the plane. 

A number of one dimensional computer models have been developed to analyze thermionic converters. These numerical models solve the nonlinear differential equations for the thermionic plasma either by setting up a finite element mesh or by propagating across the plasma and iterating until the boundary conditions are matched on both sides. The second of these approaches is used in an analytical model developed at Rasor Associates. A highly refined "shooting technique" computer program, known as IMD-4 is used to calculate converter characteristics with the model ( ). 

A powerful tool for EM modeling and inversion is the integral equation (IE) method and the corresponding linear and nonlinear approximations, introduced in the previous chapter. One important advantage which the IE method has over the finite difference (FD) and finite element (FE) methods is its greater suitability for inversion. Integral equation formulation readily contains a sensitivity matrix, which can be recomputed at each inversion iteration at little expense. With finite differences, however, this matrix has to be established anew on each iteration at a cost at least equal to the cost of the full forward simulation. 

As described above, the temperature field is computed using the finite element formulation of the heat conduction equation, with the viscous heat generation being computed from the stress and velocity fields obtained during the first iteration of the problem. The temperature contours, normalized on the maximum centerline temperature Tc = v V2/3K expected for capillary Poiseuille... 

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