Automatic Differentiation using Operator Overloading (ADOO) for implicit resolution of hyperbolic single phase and two-phase flow models - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Journal of Computational Physics Année : 2019

Automatic Differentiation using Operator Overloading (ADOO) for implicit resolution of hyperbolic single phase and two-phase flow models

Résumé

Implicit time integration schemes are widely used in computational fluid dynamics numerical codes to speed-up computations. Indeed, implicit schemes usually allow for less stringent time-step stability constraints than their explicit counterpart. The derivation of an implicit scheme is however a challenging and time-consuming task, increasing substantially with the model equations complexity since this method usually requires a fairly accurate evaluation of the spatial scheme's matrix Jacobian. This article presents a flexible method to overcome the difficulties associated to the computation of the derivatives, based on the forward mode of automatic differentiation using operator overloading. Flexibility and simplicity of the method are illustrated through implicit resolution of various flow models of increasing complexity such as the compressible Euler equations, a two-phase flow model in full equilibrium (Le Martelot, et al., 2014) and a symmetric variant (Saurel , et al., 2003) of the two-phase flow model of (Baer & Nunziato, 1986) dealing with mixtures in total disequilibrium.
Fichier principal
Vignette du fichier
Implicit-Overloading.pdf (3.63 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02082705 , version 1 (28-03-2019)

Identifiants

Citer

François Fraysse, Richard Saurel. Automatic Differentiation using Operator Overloading (ADOO) for implicit resolution of hyperbolic single phase and two-phase flow models. Journal of Computational Physics, 2019, 399, pp.108942. ⟨10.1016/j.jcp.2019.108942⟩. ⟨hal-02082705⟩
94 Consultations
93 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More