BibTeX
@ARTICLE{
         Muller2005OtP,
       author = "J.-D. M{\"u}ller and P. Cusdin",
       title = "On the Performance of Discrete Adjoint {CFD} Codes using Automatic Differentiation",
       journal = "International Journal of Numerical Methods in Fluids",
       year = "2005",
       pages = "939--945",
       volume = "47",
       number = "8--9",
       ad_tools = "ADIFOR, TAF, TAPENADE",
       ad_area = "Computational Fluid Dynamics",
       ad_theotech = "Adjoint, Iteration, Tangent",
       doi = "10.1002/fld.885",
       abstract = "Abstract Adjoint methods are a computationally inexpensive way of deriving
         sensitivity information where there are fewer dependent (cost) variables than there are independent
         (input) variables. Automatic differentiation (AD) software makes it possible to create discrete
         adjoint codes with minimal human effort, an issue that had previously restricted acceptance of
         adjoint CFD codes. In terms of computational performance, automatic code is often assumed to be
         inferior to hand code. The structure of the underlying code is critical to the performance of the
         transformed code. This paper reviews the implementation of AD on Fortran CFD codes and gives details
         of how small rearrangements can be used to produce competitive tangent and adjoint code using source
         transformation AD."
}