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Application of automatic differentiation in TOUGH2

Description: Automatic differentiation (AD) is a way to accurately and efficiently compute derivatives of a function written in computer codes. We describe the procedures necessary to apply the AD method to the multiphase, multicomponent, nonisothermal flow simulator TOUGH2. In particular, we apply the AD method to the ECO2 module of the TOUGH2 code to explore a scheme for efficiently calculating the Jacobian matrix, which is required by the Newton-Raphson method for handling the nonlinearities arising at each iteration. The ECO2 module allows TOUGH2 to accurately simulate CO2 sequestration in aquifers. The robustness and efficiency of the AD-generated derivative codes are compared to the conventional derivative computation approach based on first-order finite differences (FD). Our result with the test problem set indicates that the AD-generated derivative code could improve the convergence behavior in the linear solution step, taking less computational time to compute one linear matrix system.
Date: May 12, 2003
Creator: Kim, Jong G. & Finsterle, Stefan
Partner: UNT Libraries Government Documents Department

Application of automatic differentiation for the simulation of nonisothermal, multiphase flow in geothermal reservoirs

Description: Simulation of nonisothermal, multiphase flow through fractured geothermal reservoirs involves the solution of a system of strongly nonlinear algebraic equations. The Newton-Raphson method used to solve such a nonlinear system of equations requires the evaluation of a Jacobian matrix. In this paper we discuss automatic differentiation (AD) as a method for analytically computing the Jacobian matrix of derivatives. Robustness and efficiency of the AD-generated derivative codes are compared with a conventional derivative computation approach based on first-order finite differences.
Date: January 8, 2002
Creator: Kim, Jong G. & Finsterle, Stefan
Partner: UNT Libraries Government Documents Department