Invited Speakers
The following renowned experts will join us at ParCFD 2024 to enrich our conference with keynote lectures on various topics of interest to our CFD community.
Lennart Schneiders - SIEMENS Digital Industries Software
Lennart Schneiders is a Software Engineer and researcher at Siemens Digital Industries Software. He received his PhD from RWTH Aachen University in 2017. Until 2022, he was a postdoctoral researcher at RWTH Aachen, Jülich Aachen Research Alliance, and California Institute of Technology. Lennart is currently a developer of the multiphysics CFD software Simcenter STAR-CCM+. His research interests lie in numerical method development, turbulent multiphase flow, and high-performance computing.
Christian Hasse - Technical University Darmstadt, Simulation of reactive Thermo-Fluid Systems
Christian Hasse is the head of the institute simulation of reactive thermo-fluid systems at TU Darmstadt. Specializing in turbulent reactive flows for both single and multiphase systems, his research employs high-fidelity direct numerical simulations (DNS) with the objective of comprehending the fundamental physics of combustion and translating this understanding into sophisticated mathematical models.
Niclas Jansson - KTH Royal Institute of Technology,
PDC Center for High Performance Computing
Niclas Jansson is a researcher at PDC Center for High Performance Computing at the KTH Royal Institute of Technology, Stockholm. He received his M.S. in computer science in 2008 and a PhD in numerical analysis in 2013 from KTH. Between 2013 and 2016, Niclas was a postdoctoral researcher at RIKEN Advanced Institute for Computational Science, where he was part of the application development team of the Japanese exascale program, Flagship 2020, focusing on developing extreme-scale multiphysics solvers for the K computer, and held a visiting scientist position at RIKEN between 2018 and 2021. He has extensive experience in extreme-scale computing as a developer of RIKEN's multiphysics framework CUBE, the HPC branch of FEniCS and the next-generation spectral element flow solver Neko, and is currently the coordinator of the EuroHPC Center of Excellence for Exascale CFD.
Ricardo Vinuesa Motilva - KTH Royal Institute of Technology,
School of Engineering Sciences, Teknisk Mekanik, Fluid Mechanics
Dr. Ricardo Vinuesa is an Associate Professor at the Department of Engineering Mechanics, KTH Royal Institute of Technology in Stockholm. He is also Vice Director of the KTH Digitalization Platform and Lead Faculty at the KTH Climate Action Centre. He studied Mechanical Engineering at the Polytechnic University of Valencia (Spain), and he received his PhD in Mechanical and Aerospace Engineering from the Illinois Institute of Technology in Chicago. His research combines numerical simulations and data-driven methods to understand, control and predict complex wall-bounded turbulent flows, such as the boundary layers developing around wings and urban environments. Dr. Vinuesa has received, among others, an ERC Consolidator Grant, the TSFP Kasagi Award, the Goran Gustafsson Award for Young Researchers, the IIT Outstanding Young Alumnus Award, the SARES Young Researcher Award and he leads several large Horizon Europe projects. He is also a member of the Young Academy of Science of Spain.
Title of Ricardo's keynote lecture:
Explaining and controlling turbulent flows through deep learning
Abstract:
In this presentation we first use a framework for deep-learning explainability to identify the most important Reynolds-stress (Q) events in a turbulent channel (simulated via DNS) and a turbulent boundary layer (obtained experimentally). This objective way to assess importance reveals that the most important Q events are not the ones with the highest Reynolds shear stress. This framework is also used to identify completely new coherent structures, and we find that the most important coherent regions in the flow only have an overlap of 70% with the classical Q events. In the second part of the presentation we use deep reinforcement learning (DRL) to discover completely new strategies of active flow control. We show that DRL applied to a blowing-and-suction scheme significantly outperforms the classical opposition control in a turbulent channel: while the former yields 30% drag reduction, the latter only 20%. We conclude that DRL has tremendous potential for drag reduction in a wide range of complex turbulent-flow configurations.
