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** Chemical source term Jacobian matrix **

** Computational methods Jacobian matrix **

** Dynamical systems Jacobian matrix **

** Jacobian matrix dynamic stability **

** Jacobian matrix equilibrium calculations **

Appendix B Calculation of the Jacobian Matrix of an Averaging Function... [Pg.331]

Obviously the described transformation depends on the existence of an inverse for the Jacobian matrix (i.e. det/must always be non-zero). [Pg.38]

Differentiation of locally defined shape functions appearing in Equation (2.34) is a trivial matter, in addition, in isoparametric elements members of the Jacobian matrix are given in terms of locally defined derivatives and known global coordinates of the nodes (Equation 2.27). Consequently, computation of the inverse of the Jacobian matrix shown in Equation (2.34) is usually straightforward. [Pg.38]

DERIV. Calculates the inverse of the Jacobian matrix used in isoparametric transformations. [Pg.211]

Newton-Raphson method (or any or several variants to it) is used to solve the equations, the jacobian matrix and its LU fac tors are... [Pg.485]

Adesina has shown that it is superfluous to carry out the inversion required by Equation 5-255 at every iteration of the tri-diagonal matrix J. The vector y"is readily computed from simple operations between the tri-diagonal elements of the Jacobian matrix and the vector. The methodology can be employed for any reaction kinetics. The only requirement is that the rate expression be twice differentiable with respect to the conversion. The following reviews a second order reaction and determines the intermediate conversions for a series of CFSTRs. [Pg.345]

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. [Pg.309]

If the Newton-Raphson method is used to solve Eq. (1), the Jacobian matrix (df/3x)u is already available. The computation of the sensitivity matrix amounts to solving the same Eq. (59) with m different right-hand side vectors which form the columns — (3f/<5u)x. Notice that only the partial derivatives with respect to those external variables subject to actual changes in values need be included in the m right-hand sides. [Pg.174]

Algorithmic Details for NLP Methods All the above NLP methods incorporate concepts from the Newton-Raphson method for equation solving. Essential features of these methods are that they rovide (1) accurate derivative information to solve for the KKT con-itions, (2) stabilization strategies to promote convergence of the Newton-like method from poor starting points, and (3) regularization of the Jacobian matrix in Newton s method (the so-called KKT matrix) if it becomes singular or ill-conditioned. [Pg.64]

The term J is the determinant of the Jacobian matrix upon changing from Cartesian to generalized coordinates. It measures the change in volume element between dxdp, and d

The elements of the Jacobian matrix Z are a quantitative measure for the influence of the experimental data yobs>(- on the potential constants, and vice versa. A potential constant can be neglected and the corresponding term removed from the trial force field if the influence of all yoW-quantities on these potential constants is small enough. Thus the Jacobian matrix tells us quantitatively how important the individual potential constants of our force field are. [Pg.176]

The authors describe the use of a Taylor expansion to negate the second and the higher order terms under specific mathematical conditions in order to make any function (i.e., our regression model) first-order (or linear). They introduce the use of the Jacobian matrix for solving nonlinear regression problems and describe the matrix mathematics in some detail (pp. 178-181). [Pg.165]

A more efficient way of solving the DFT equations is via a Newton-Raphson (NR) procedure as outlined here for a fluid between two surfaces. In this case one starts with an initial guess for the density profile. The self-consistent fields are then calculated and the next guess for density profile is obtained through a single-chain simulation. The difference from the Picard iteration method is that an NR procedure is used to estimate the new guess from the density profile from the old one and the one monitored in the single-chain simulation. This requires the computation of a Jacobian matrix in the course of the simulation, as described below. [Pg.126]

Like Newton s method, the Newton-Raphson procedure has just a few steps. Given an estimate of the root to a system of equations, we calculate the residual for each equation. We check to see if each residual is negligibly small. If not, we calculate the Jacobian matrix and solve the linear Equation 4.19 for the correction vector. We update the estimated root with the correction vector,... [Pg.60]

In this section we consider how Newton-Raphson iteration can be applied to solve the governing equations listed in Section 4.1. There are three steps to setting up the iteration (1) reducing the complexity of the problem by reserving the equations that can be solved linearly, (2) computing the residuals, and (3) calculating the Jacobian matrix. Because reserving the equations with linear solutions reduces the number of basis entries carried in the iteration, the solution technique described here is known as the reduced basis method. ... [Pg.60]

The Jacobian matrix contains the partial derivatives of the residuals with respect to each of the unknown values (nw, m, )r. To derive the Jacobian, it is helpful to note that... [Pg.61]

Newton-Raphson formulation because the partial derivatives in the Jacobian matrix are incomplete. We could, in principle, use a hard formulation in which the Jacobian accounts for the devia-tives dy/dm,i and daw/dm,i. The hard formulation sometimes converges in fewer iterations, but in tests, the advantage was more than offset by the extra effort in computing the Jacobian. The soft method also allows us to keep the method for calculating activity coefficients (see Chapter 8) separate from the Newton-Raphson formulation, which simplifies programming. [Pg.66]

To do so, we calculate the Jacobian matrix, which is composed of the partial derivatives of the residual functions with respect to the unknown variables. Differentiating the mass action equations for aqueous species Aj (Eqn. 4.2), we note that,... [Pg.147]

To evaluate the Jacobian matrix, we need to compute values for dmq/dnw, dmq/dnii, and dmq/dmp. For the Kt and Freundlich models,... [Pg.148]

At each step in the Newton-Raphson iteration, we evaluate the residual functions and Jacobian matrix. We then calculate a correction vector as the solution to the matrix equation... [Pg.149]

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** Chemical source term Jacobian matrix **

** Computational methods Jacobian matrix **

** Dynamical systems Jacobian matrix **

** Jacobian matrix dynamic stability **

** Jacobian matrix equilibrium calculations **

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