The Organizing Committee is happy to announce the list of the accepted mini symposia.
MS1: Extreme-scale GPU computing in CFD
- Pedro Costa (TU Delft), P.SimoesCosta@tudelft.nl
- Davide Modesti (TU Delft), D.Modesti@tudelft.nl
The aim of this mini-symposium is to present the latest advancements in the development of numerical methods and algorithms for computational ﬂuid dynamics (CFD) that can harness modern, GPU-based extreme-scale computing resources. The ever-increasing scale and availability of computing power have blessed the CFD community in the past decades, allowing us to tackle important outstanding problems in different ﬁelds, such as atmospheric science, energy technology, aerospace engineering, and health/medicine, whose progress has relied heavily on computing resources.
Recently, there has been a paradigm shift in high-end supercomputer architectures, with a strong focus on accelerated computations using GPUs, which oﬀer high computing power and memory bandwidth, resulting in excellent throughputs and parallel performance. Even though the CFD community is aware of the potential gain from exploiting these resources, there are major challenges when it comes to, e.g., adapting legacy CFD solvers for these novel architectures.
This mini-symposium aims to bring together developers and users of GPU-based CFD codes, to discuss the current state of the art and identify the challenges ahead. We welcome different CFD applications to provide a balanced overview of the progress in the field, and to foster exchanges of knowledge and expertise from participants with different perspectives.
Topics of interest of this mini-symposium include, but are not limited to:
- Extreme-scale simulations of turbulent flows (e.g. DNS/LES)
- Computational algorithms tailored for GPU computing
- Numerical methods and their implementation to run on GPUs
- Porting existing numerical codes to different GPU architectures
MS2: Convergence of Artificial Intelligence and High-Performance Computing for Computational Fluid Dynamics (AI+HPC4CFD Pt. 2)
- Andreas Lintermann, Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Germany, A.Lintermann@fz-juelich.de
- Guillaume Houzeaux, Barcelona Supercomputing Center, Spain, firstname.lastname@example.org
- Corentin Lapeyre, Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique, France, email@example.com
Artificial Intelligence (AI) technologies are penetrating into all sectors of research and industry. They automate and accelerate processes, and uncover new unseen relations in huge datasets. Last year’s successful AI+HPC4CFD ParCFD 2022 minisymposium already impressively showed that the Computational Fluid Dynamics (CFD) community drastically benefits from these technologies. AI methods and notably deep learning techniques are used to develop new models for CFD, e.g., reduced-order models, surrogates, and closure models aiming at efficiently modeling complex physics that are otherwise expensive to compute. Their quality is often a function of both the quantity and the accuracy of the underlying data used for training as well as the physical constraints imposed on the training. The generation and processing of high fidelity simulation data necessitates the application of High-Performance Computing (HPC) systems. Modular and heterogeneous systems with accelerator and/or specialized AI-components as blueprints for upcoming Exascale systems have the potential to deal with the demands of complex and intertwined simulations and AI-data processing workflows. This minisymposium aims at continuing the successful 2022 AI+HPC4CFD minisymposium. It will gather experts in the fields of development and application of parallel CFD methods incorporating novel AI methods, and pure AI method developers contributing to the fields of CFD and HPC alike. It will again offer a platform for discussion and exchange in the context of the convergence of AI and HPC with respect to parallel CFD methods that could benefit from the power of next-generation Exascale computing systems.
- turbulence and other subgrid processes modeling with AI
- modeling of reactive processes with AI, e.g., for fluidized beds, atmospheric chemistry, or pollutant formation and reduction applications
- surrogate modeling for CFD
- AI-incorporated full-loop implementations of CFD workflows
- CFD-centric AI training on modular and heterogeneous HPC systems
- physics-informed learning methods, e.g., with deep neural networks, graph neural networks, or generative networks
- application of AI methods to various fields of CFD: aerospace, automotive, energy, coating, bio, environment, etc.
- feature extraction from complex flow fields using AI methods
MS3: Space-Time Parallel CFD Algorithms
- Dr. Stephen Guzik, Stephen.Guzik@colostate.edu
- Dr. Xinfeng Gao, Xinfeng.Gao@colostate.edu
Traditional parallel CFD algorithms are predominantly performed in the spatial domain. As computer power increases, further speedup can be made possible through the parallelization of time stepping after spatial parallelization has saturated. Parallel-in-time methods have recently become an active research area and various approaches for parallel-in-time integration are available. For example, a multigrid-reduction-in-time algorithm has recently shown promise in providing a framework for separately evolving different scales of turbulence and for parallelizing the temporal domain, thereby increasing the concurrency. This mini symposium aims to advance the space-time parallel CFD algorithms.
MS4: HPC-based CFD and CFD-coupled multi-scale / multi-physics problems in biomedical applications
- Beatriz Eguzkitza (BSC), firstname.lastname@example.org
- Hadrien Calmet (BSC), email@example.com
- Guillaume Houzeaux (BSC), firstname.lastname@example.org
- Mariano Vázquez (Elem biotech, BSC), email@example.com
In this minisymposium we will show advancements on the usage of CFD in advanced biomedical applications, particularly in complex problems in which the usage of supercomputers is a must. In particular, we are interested in showing computational achievements on coupled multi-scale and multi-physics problems in which CFD is one of the intervening physics, with a strong focus on the HPC-based implementation. Contributions on CFD coupling with particle transport, thermal ﬂow, chemical reactions, ﬂuid-structure interaction are welcome, including computationally complex pure CFD cases. Regarding the application problems, we will be delighted to present contributions on blood circulatory system, cardiac hemodynamics, bioprosthetic heart valves implants and valve reparation surgical procedures, respiratory ﬂuid mechanics from upper airways down to alveoli, inhalers and nebulizers, lung models, spinal intrathecal ﬂuid mechanics, etc.
MS5: Accelerated Simulations of Complex Flows with Model Reduction and Machine Learning
- Rooh Khurram1, Rooh.Khurram@kaust.edu.sa
- Hong G. Im2, firstname.lastname@example.org
- Francisco E. Hernandez Perez2, email@example.com
1 Supercomputing Laboratory, King Abdullah University of Science and Technology (KAUST) Thuwal, Saudi Arabia
2 Clean Combustion Research Center, King Abdullah University of Science and Technology (KAUST) Thuwal, Saudi Arabia
Predictive simulations of complex flows require the solution of partial differential equation systems with full spatial and temporal resolution, demanding a substantial number of grid points and time steps to capture all relevant physical scales and phenomena. For instance, turbulent reacting flows typically involve tens of chemical species and hundreds of elementary kinetic reactions, spanning a wide spectrum of length and time scales. With the convergence of high-performance scientific computing and data driven science, opportunities for more efficient computational fluid dynamics (CFD) workflows are opening up. The main objective of this mini-symposium is to cover various aspects of state-of-the-art machine learning and algorithmic reduced-order applications to accelerating complex flow simulations, providing insights into common challenges as well as promising and successful strategies.
This mini-symposium seeks contributions in the areas including but not limited to:
- Algorithmic reduced-order strategies suitable for CFD applications
- Machine-learning based integration of stiff ODE systems
- Data-based CFD models
- Convergence of data-driven science and high-performance computing towards efficient CFD
MS6: Parallel computing in granular mechanics
- Jacinto Ulloa, firstname.lastname@example.org
Rigoberto Moncada-López, email@example.com
Ziran Zhou, firstname.lastname@example.org
José E. Andrade, email@example.com
Granular media include a wide variety of conglomerate materials in which discrete solid particles interact, giving rise to complex and intriguing collective responses. These materials are ubiquitous in engineering science, with examples ranging from powders, sand, or rocks at relatively small scales to structures, sea ice floes, or celestial bodies at larger scales. Nevertheless, the mechanical intricacies of granular media are still not fully understood. Thus, a variety of research projects over the past decades have been devoted to micromechanical models that resolve the kinematics of discrete particles while explicitly describing the grain-to-grain interactions. These models are typically based on molecular dynamics (MD), the discrete element method (DEM), or variants thereof, and provide valuable insights into the collective behavior of granular media under various external conditions. The main limitation of discrete element models lies in the high computational cost that results from many interacting particles and the requirement for small time steps in typical integration algorithms. This problem calls for parallelization techniques and high-performance computing (HPC) systems that allow for large-scale simulations. This minisymposium aims at gathering experts in discrete element models for granular media that rely on HPC. The minisymposium will set the stage for discussions on parallelization schemes and efficient integration algorithms that are crucial to render discrete element models applicable to problems in engineering applications.
Parallelization techniques applied to
- MD, DEM, or lattice models describing various processes in granular materials, particularly concerning (but not limited to) geomechanics,
- accurate characterization of particle shapes,
- hybrid methods combining discrete models with the finite element method or peridynamics,
- multiphysics extensions of discrete element models,
- efficient time-stepping algorithms.
The organizers are all members of the European Center of Excellence in Exascale Computing “Research on AI- and Simulation-Based Engineering at Exascale” – CoE RAISE (https://www.coe-raise.eu).