AUTOMATIC DIFFERENTIATION OF AN EULERIAN HYDROCODE

Automatic differentiation (AD) is applied to a two-dimensional Eulerian hydrodynamics computer code (hydrocode) to provide gradients that will be used for design optimization and uncertainty analysis. We examine AD in both the forward and adjoint (reverse) mode using Automatic Differentiation of Fortran (ADIFOR, version 3.0). Setup time, accuracy, and run times are described for three problems. The test set consists of a one-dimensional shock-propagation problem, a two-dimensional metal-jet-formation problem and a two-dimensional shell-collapse problem. Setup time for ADIFOR was approximately one month starting from a simplified, fixed-dimension version of the original code. ADIFOR produced accurate (as compared to finite difference) gradients in both modes for all of the problems. These test problems had 17 independent variables. We find that the forward mode is up to 39% slower and the adjoint mode is at least 11% faster than finding the gradient by means of finite differences. Problems of real interest will certainly have more independent variables. The adjoint mode is thus favored since the computational time increases only slightly for additional independent variables