2018/10/31 by Ole Burghardt, Burghardt, Ole, Nicolas R. Gauger +1
Engineering · Physics and Astronomy · #Advanced Numerical Analysis Techniques #Advanced Numerical Methods in Computational Mathematics #Computational Physics (physics.comp-ph) #FOS: Mathematics #FOS: Physical sciences #Model Reduction and Neural Networks #Numerical Analysis (math.NA)
paper · pdf · doi:10.48550/arxiv.1811.00068
openalex publication_date 2018/10/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
As more and more multiphysics effects are entering the field of CFD\nsimulations, this raises the question how they can be accurately captured in\ngradient computations for shape optimization. The latter has been successfully\nenriched over the last years by the use of (discrete) adjoints. One can think\nof them as Lagrange multipliers to the flow field problem linked to an\nobjective function that depends on quantities like pressure or momentums, and\nthey will set also the framework for this paper. It is split into two main\nparts: First, we show how one can compute coupled discrete adjoints using\nautomatic differentiation in an effective way that is still easily extendable\nfor all kinds of other couplings. Second, we suppose that a valuable first\napplication are so-called conjugate heat transfer problems which are gaining\nmore and more interest from the automobile and aeronautics industry. Therefore\nwe present an implementation for this capability within the open-source solver\nSU2 as well as for the generic adjoint computation algorithm.\n