Algorithmic differentiation of the Open CASCADE Technology CAD kernel and its coupling with an adjoint CFD solver. (2nd November 2018)

• Record Type: Journal Article
• Authors: Banović, Mladen; Mykhaskiv, Orest; Auriemma, Salvatore; Walther, Andrea; Legrand, Herve; Müller, Jens-Dominik
• Is Part Of: Optimization methods and software. Volume 33:Number 4/6(2018)
• Page Start: 813
• Page End: 828
• Publication Date: 2018-11-02
• Subjects: algorithmic differentiation -- CAD kernel -- adjoint CFD method -- gradient-based optimization; Mathematical optimization -- Periodicals; Algorithms -- Periodicals; 519.7
• DOI: 10.1080/10556788.2018.1431235
• Languages: English
• ISSNs: 1055-6788

Abstract

Abstract : Computer-aided design (CAD) tools are extensively used to design industrial components, however, contrary to e.g. computational fluid dynamics (CFD) solvers, shape sensitivities for gradient-based optimization of CAD-parametrized geometries have only been available with inaccurate and non-robust finite differences. Here, algorithmic differentiation (AD) is applied to the open-source CAD kernel Open CASCADE Technology using the AD software tool ADOL-C ( Automatic Differentiation by OverLoading in C++ ). The differentiated CAD kernel is coupled with a discrete adjoint CFD solver, thus providing the first example of a complete differentiated design chain built from generic, multi-purpose tools. The design chain is demonstrated on the gradient-based optimization of a squared U-bend turbo-machinery cooling duct to minimize the total pressure loss.