HIFiLeD Symposium 2022
December 14 - December 16
The simulation of turbulent flows using CFD methods has progressed rapidly over the last decades and has given rise to significant changes in the design processes of many areas of fluid mechanics. However, despite over a century of research, the modelling of turbulence and transition in industrial relevant configurations is still far from being achieved successfully.
With the advent and growing availability of large scale computing power and facilities, a new area of turbulence research is opening with the ability to perform reliable high-fidelity large-eddy simulations (LES) and direct numerical simulations (DNS) for industrial relevant flow configurations.
This availability is opening exciting new avenues towards understanding and modeling turbulence and transition by:
- Direct generation of High-Fidelity LES/DNS to fully predict complex flows, by capturing most or all of the relevant turbulent scales and interactions
- Analyzing in depth the underlying physics, supported in particular by new technologies of Machine Learning
- Confronting available turbulence and transition models, identifying their deficiencies, improving the range of validity of current models and generate new ones.
This novel approach of High-Fidelity LES/DNS data has attracted many researchers in recent years, stimulated by other emerging areas, as Big Data, Artificial Intelligence (AI) and Machine Learning (ML), providing new efficient methodologies for interrogating and investigating very large data sets.
The HiFiLed Symposium will be focusing on all aspects related to these objectives, ranging from issues concerning the complexity, reliability, accuracy and uncertainties in generating the High-Fidelity LES/DNS data, to their application towards turbulence and transition modelling. It will include progress on the underlying high-order numerical methods (HOMs), innovative approaches for CPU acceleration for LES and DNS, exploitation of massive parallel architectures, efficient post-processing on massive parallel hardware, innovative machine learning methods, as well as experimental data. Moreover, the Symposium offers the opportunity to communicate and exchange knowledge for academic researchers, graduate students, industrial engineers, as well as industrial R&D managers and consultants working in the fields of turbulent flow modelling, simulations, measurements and multidisciplinary CFD applications.
CALL FOR CONTRIBUTIONS
Contributions by participants are expected on the following topics:
- Understanding Turbulence and Transition from High-Fidelity LES/DNS simulations
- Understanding Turbulence and Transition from new experimental data
- Advances in Turbulence and Transition modelling, based on LES/DNS databases
- Machine Learning applications to LES/DNS analysis and modelling
- New LES/DNS data generation for reference configurations
- Applications of high fidelity LES/DNS to industrial configurations
- Algorithmic and modelling issues for LES simulations, including Wall Modelled LES (WMLES)
- Advances in high-order methods, including curved grid generation
- HPC related issues on multiple platforms (CPU/GPU)
Mini-symposia are organized on the following topics:
• Turbulence Modeling – Suad Jakirlic (TU Darmstadt)
• Scale Resolving Simulations – Oriol Lehmkuhl (Barcelona Supercomputing Center)
• Artificial Intelligence/Machine Learning in CFD – Corentin Lapeyre (Cerfacs)
Accepted authors will be expected to pay the regsitration fee, travel to the conference and present in-person. Please submit your abstract for consideration by 5:00pm (CET) on Thursday, October 31.
• Ricardo Vinuesa (KTH Stockholm) – Modeling and controlling turbulent flows through deep learning
• Sylvain Laizet (Imperial College London) – Toward exascale scale-resolving simulations of turbulent flows on a Cartesian mesh with high-order finite-difference schemes
• Cetin C. Kiris (NASA Ames Research Center) – Advancements in Scale Resolving Simulations for Certification by Analysis
• Kunal Puri (Cadence Belgium) – Towards industrial High-Order Scale Resolving Simulations
• Chris Rumsey (NASA Langley) – Data-Driven Turbulence Modeling: Summary and Outcomes of the 2022 NASA Symposium