Abstract
AbstractAdjoint 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. Copyright © 2005 John Wiley & Sons, Ltd.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal for Numerical Methods in Fluids
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
