BibTeX
@INCOLLECTION{
         Ozkaya2012AoA,
       title = "Application of Automatic Differentiation to an Incompressible {URANS} Solver",
       doi = "10.1007/978-3-642-30023-3_4",
       author = "Emre {\"O}zkaya and Anil Nemili and Nicolas R. Gauger",
       abstract = "This paper deals with the task of generating a discrete adjoint solver from a given
         primal Unsteady Reynolds Averaged Navier-Stokes (URANS) solver for incompressible flows. This
         adjoint solver is to be employed in active flow control problems to enhance the performance of
         aerodynamic configurations. We discuss on how the development of such a code can be eased through
         the use of the reverse mode of Automatic/Algorithmic Differentiation (AD). If AD is applied in a
         black-box fashion then the resulting adjoint URANS solver will have prohibitively expensive memory
         requirements. We present several strategies to circumvent the excessive memory demands. We also
         address the parallelization of the adjoint code and the adjoint counterparts of the MPI directives
         that are used in the primal solver. The adjoint code is validated by applying it to the standard
         test case of a rotating cylinder by active flow control. The sensitivities based on the adjoint code
         are compared with the values obtained from finite differences and forward mode AD code.",
       pages = "35--45",
       crossref = "Forth2012RAi",
       booktitle = "Recent Advances in Algorithmic Differentiation",
       series = "Lecture Notes in Computational Science and Engineering",
       publisher = "Springer",
       address = "Berlin",
       volume = "87",
       editor = "Shaun Forth and Paul Hovland and Eric Phipps and Jean Utke and Andrea Walther",
       isbn = "978-3-540-68935-5",
       issn = "1439-7358",
       year = "2012",
       ad_theotech = "Adjoint, Parallelism"
}