Journal of Computational Physics
A conserving discretization for the free boundary in a two-dimensional Stefan problem
Journal of Computational Physics
Evaluating derivatives: principles and techniques of algorithmic differentiation
Evaluating derivatives: principles and techniques of algorithmic differentiation
Automatic differentiation of algorithms: from simulation to optimization
Automatic differentiation of algorithms: from simulation to optimization
Adifor 2.0: Automatic Differentiation of Fortran 77 Programs
IEEE Computational Science & Engineering
On the Use of a Differentiated Finite Element Package for Sensitivity Analysis
ICCS '01 Proceedings of the International Conference on Computational Sciences-Part I
Solving large-scale optimization problems with EFCOSS
Advances in Engineering Software
Mathematics and Computers in Simulation
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In process engineering, a single drop is investigated to better understand its physical and chemical behavior. Laboratory experiments using the Nuclear Magnetic Resonance (NMR) technology are prepared by numerical simulations aiming at finding a suitable geometry of the measuring cell. In the underlying numerical optimization problem, derivatives of the flow field around a single drop with respect to geometric parameters are needed. Rather than using numerical differentiation based on divided differencing, a technique called automatic differentiation is used to compute truncation-error free derivative values. It is shown that automatic differentiation is comparable to numerical differentiation in terms of CPU time but eliminates potential problems in accuracy encountered when using numerical differentiation.