Wednesday, December 16th, 2020. Time: 12 noon
ONLINE! - Link for online session: meet.google.com/gzo-dhcu-hyt
Computational fluid dynamics simulations in industrial design and optimisation procedures involve the exploration of large sets of admissible configurations. Parameters of interest may include boundary conditions, physical properties of the fluid and geometric configurations. Hence, efficient tools to solve multiple queries of the same flow problem are required. Surrogate models based on reduced order methods are commonly employed to ease the computational burden of such high-dimensional problems, allowing to efficiently perform parametric studies and to evaluate quantities of interest in real-time.
In this seminar, the proper generalised decomposition (PGD) will be utilised to devise reduced order models (ROM) for parametric incompressible flows, from microfluidics to viscous laminar and turbulent flows. The resulting surrogate models provide approximations explicitly depending on user-defined parameters and allow a straightforward construction of separated response surfaces of quantities of interest (e.g., drag, pressure drop, …). PGD-based ROMs will be presented encompassing surrogate models relying on high-order hybrid discretisation techniques (viz. the hybridisable discontinuous Galerkin method) and non-intrusive computational vademecums based on a finite volume solver validated by the industry (viz. OpenFOAM), with applications spanning from flows in geometrically parametrised domains to parametric flow control problems.
Matteo Giacomini is a Senior Postdoctoral Researcher at the International Centre for Numerical Methods in Engineering. Before joining CIMNE, he was a Distinguished Postdoctoral Researcher at the Laboratori de Càlcul Numèric (LaCàN) at Universitat Politècnica de Catalunya. He holds a BS and a MS degree in Mathematical Engineering from Politecnico di Milano and a PhD in Applied Mathematics from École Polytechnique. His research interests lie in numerical methods for PDEs (finite element, discontinuous Galerkin, finite volume), reduced order models, PDE-constrained shape optimisation and a posteriori error estimators, with applications to computational fluid dynamics, computational mechanics and image segmentation.