Automatic differentiation for more efficient system analysis and optimization

This paper reports on the benefits of using automatic differentiation (AD) to improve the efficiency of multidisciplinary analysis and optimization processes. Automatic differentiation technology provides researchers with new opportunities to use sensitivities for analysis and optimization. This research confirms that analysis and optimization applications in which the use of sensitivity information had previously been avoided or ruled out, as computationally prohibitive, need to be revisited. In this research, two applications are investigated in which the use of AD is observed to improve efficiency. The efficiency of solution strategies for non-hierarchic (i.e. coupled) system analysis is improved using automatic differentiation generated sensitivities. Calculation of sensitivities via AD allows for efficient application of Newton's method and a modified form of Newton' method to converge non-hierarchic system analyses. The line search strategy employed within a generalized reduced gradient (GRG) optimizer is improved through the use of AD generated sensitivities. Using the AD-based line search strategy within the GRG method results in a significant reduction in the number of system analyses and the CPU time required for optimization.