Engineering and CFD

Principal Investigator: Christian Stemmer, Stefan Hickel , Technische Universität München, Fakultät für Maschinenwesen

HPC Platform used: SuperMUC and SuperMUC-NG of LRZ

Local Project ID: pr45tu

Deceleration of a supersonic flow in a channel by shocks and interaction with the turbulent boundary layer leads to the formation of a complex array of shocks, subsonic and supersonic regions, and recirculation zones. In this project, high-fidelity and well-resolved large-eddy simulations (LES) of such a fully turbulent (Reδ≈105) pseudo-shock system were performed and compared with experimental data. Particular attention is paid to the occurrence of flow instabilities (such as shock motion, shock-boundary layer interaction, and symmetry breaking of the shock system), mixing behaviour in the transonic shear layer, and a comparison with sophisticated RANS turbulence models.

Engineering and CFD

Principal Investigator: Romuald Skoda , Lehrstuhl für Hydraulische Strömungsmaschinen, Ruhr-Universität Bochum

HPC Platform used: JUWELS of JSC

Local Project ID: chbo46, chbo48

While for the design point operation of centrifugal pumps an essentially steady flow field is present, the flow field gets increasingly unsteady towards off-design operation. Particular pump types as e.g. single-blade or positive displace pumps show a high unsteadiness even in the design point operation. Simulation results for the highly unsteady and turbulent flow in a centrifugal pump are presented. For statistical turbulence models an a-priori averaged turbulence spectrum is assumed, and limitations of these state-of-the-art models are discussed. Since the computational effort of a scale-resolving Large-Eddy-Simulation is tremendous, the potential of scale-adaptive turbulence models is highlighted.

Engineering and CFD

Principal Investigator: Wolfgang Polifke , Department of Mechanical Engineering, Technische Universität München

HPC Platform used: SuperMUC, Phase I and II

Local Project ID: pr94yu

Combustion noise is an undesirable, but unavoidable by-product of turbulent combustion in, e.g., stationary gas turbines or aeronautical engines. This project combines Large Eddy Simulation (LES) of turbulent, reacting flow with advanced System Identification (SI) – a form of supervised machine learning –  to infer reduced-order models of combustion noise. Models for the source of noise on the one hand, and the flame dynamic response to acoustic perturbations on the other, are estimated to make possible the flexible and computationally efficient prediction of combustion noise across a wide variety of combustor configurations.