Modern high-order discretizations bear considerable potential for the exascale era due to their high fidelity and the high, local computational load that allows for computational efficiency in massively parallel simulations. To this end, the discontinuous Galerkin (DG) framework FLEXI was selected to demonstrate exascale readiness within the Center of Excellence for Exascale CFD (CEEC) by simulating shock buffet on a three-dimensional wing segment at transsonic flight conditions. This paper summarizes the recent progress made to enable the simulation of this challenging exascale problem. For this, it is first demonstrated that FLEXI scales excellently to over 500 000 CPU cores on HAWK at the HLRS. To tackle the considerable resolution requirements near the wall, a novel wall model is proposed that takes compressibility effects into account and yields decent results for the simulation of a NACA 64A-110 airfoil. To address the shocks in the domain, a finite-volume-based shock capturing method was implemented in FLEXI, which is validated here using the simulation of a linear compressor cascade at supersonic flow conditions, where the method is demonstrated to yield efficient, robust and accurate results. Lastly, we present the TensorFlow-Fortran-Binding (TFFB) as an easy-to-use library to deploy trained machine learning models in Fortran solvers such as FLEXI.

翻译：现代高阶离散化方法因其高保真度以及高局部计算负载（适用于大规模并行模拟的计算效率），为百亿亿次计算时代带来了巨大潜力。为此，我们选择基于间断伽辽金（DG）框架的FLEXI求解器，通过模拟跨声速飞行条件下三维机翼段上的激波抖振现象，以证明其在百亿亿次计算就绪能力（隶属于百亿亿次CFD卓越中心CEEC）。本文总结了为解决这一具有挑战性的百亿亿次问题而取得的最新进展。首先，我们证明FLEXI在HLRS的HAWK集群上可出色地扩展至超过500,000个CPU核心。为应对壁面附近巨大的分辨率需求，提出了一种考虑可压缩性效应的新型壁面模型，该模型在NACA 64A-110翼型模拟中取得了良好结果。针对计算域内的激波问题，我们在FLEXI中实现了一种基于有限体积法的激波捕捉方法，并通过超音速流条件下线性压缩机叶栅的模拟进行了验证：结果表明该方法高效、鲁棒且精确。最后，我们介绍了TensorFlow-Fortran绑定库（TFFB），这是一个易用的工具库，可将训练好的机器学习模型部署至FLEXI等Fortran求解器中。