High-fidelity compressible-flow simulations for physical insight and improving low-order models in turbomachinery
Prof. Richard Sandberg
Department of Mechanical Engineering, The University of Melbourne
||20 Mar 2017 (Mon)
||Room 2571B, HKUST (2/F., Lift #27/28)
In this presentation, the challenges of conducting high fidelity compressible simulations for flow and noise studies are introduced. An in-house compressible direct numerical simulation code, purposely developed to exploit modern supercomputing hardware, is presented and its scaling performance shown. Several case studies with relevance to turbomachinery will be presented, such as a series of Direct Numerical Simulations (DNS) of jets to assess Mach number scaling, a parametric study of low-pressure turbine flows to investigate kinetic loss generation mechanisms, and DNS of a high pressure turbine vane to study loss and heat transfer mechanisms. The presentation will conclude with suggesting different paths of how to use high-fidelity simulation data for improved low-order model development, focussing on a gene-expression programming approach.
Richard is Chair of Computational Mechanics in the Department of Mechanical Engineering. His main interest is in high-fidelity simulation of turbulent flows and the associated noise generation in order to gain physical understanding of flow and noise mechanisms and to help assess and improve low-order models that can be employed in an industrial context, in particular using novel machine-learning approaches.
He received his PhD in 2004 at in Aerospace Engineering at the University of Arizona and prior to joining the University of Melbourne, he was a Professor of Fluid Dynamics and Aeroacoustics in the Aerodynamics and Flight Mechanics research group at the University of Southampton and headed the UK Turbulence Consortium (www.turbulence.ac.uk), coordinating the work packages for compressible flows and flow visualisations and databases. He was awarded a veski innovation fellowship in July 2015 entitled: "Impacting Industry by enabling a step-change in simulation fidelity for flow and noise problems".