FDOT: A Fast, memory-efficient and automated approach for Discrete adjoint sensitivity analysis using the Operator overloading Technique

A new toolbox based on operator overloading is introduced for automatic differentiation of scientific computing codes – and in particular legacy computational fluid dynamics solvers that are developed using Fortran. The method can be readily implemented into existing iterative solvers with minimal c...

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Vydáno v:Aerospace science and technology Ročník 91; s. 159 - 174
Hlavní autoři: Djeddi, Reza, Ekici, Kivanc
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Masson SAS 01.08.2019
Témata:
Automatic differentiation (AD)
Computational fluid dynamics
Discrete adjoint
Object-oriented programming
Operator overloading
Sensitivity analysis
Discrete adjoint
Automatic differentiation (AD)
Sensitivity analysis
Computational fluid dynamics
Object-oriented programming
Operator overloading
ISSN:1270-9638, 1626-3219
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Shrnutí:A new toolbox based on operator overloading is introduced for automatic differentiation of scientific computing codes – and in particular legacy computational fluid dynamics solvers that are developed using Fortran. The method can be readily implemented into existing iterative solvers with minimal changes to the primal code. The integrated toolbox can efficiently calculate the sensitivities of any objective function with respect to all variables (design or intermediate) that can later be used for gradient-based design optimization, uncertainty quantification, error estimation, and mesh adaptation. The underlying definition of the current automatic differentiation is directly related to the discrete adjoint sensitivity analysis. Unlike most traditional operator overloading-based adjoint approaches reported in the literature, the current technique offers huge reductions in the memory footprint. To demonstrate the advantages of the current approach, various solvers/problems are considered. It is shown that the proposed technique can be used as an efficient toolbox for automatic differentiation of scientific solvers requiring only a handful additional lines of coding.
ISSN:1270-9638
1626-3219
DOI:10.1016/j.ast.2019.05.004