Title:	Large-scale Simulations in Stability, Transition, Turbulence and Control
DNr:	SNIC 2022/3-25
Project Type:	SNIC Large Compute
Principal Investigator:	Philipp Schlatter <pschlatt@mech.kth.se>
Affiliation:	Kungliga Tekniska högskolan
Duration:	2023-01-01 – 2024-01-01
Classification:	20306 10508 10501
Homepage:	https://www.flow.kth.se
Keywords:

Abstract

This is a large compute application complementary to the large storage and compute LUMI applications with the same title. We present a large-level request for computer time on high-performance computing (HPC) resources within the Swedish National Infrastructure for Computing (SNIC). The proposed projects by the research groups of the KTH Engineering Mechanics department are summarized. The group of applicants consists of a total of 7 senior researchers, 4 application experts, and 24 Postdocs and PhD students, i.e. a total of 35 researchers. We actively promote collaboration within our large user group to facilitate HPC support, sharing of simulation methods, codes, data, post-processing, data management methods and user experience. We have thus found it beneficial to apply for a large-level allocation instead of multiple medium-level requests. In this proposal we describe our scientific projects which rely on HPC resources, grouped into four main areas: 1) aeronautics; 2) wind turbines and geophysical flows; 3) dynamical systems, uncertainty quantification and machine learning; 4) fundamentals of transition and turbulence. Due to the large number of collaborators, we do not list all individual projects, but rather give an overview of the general research directions. Our research makes use of the numerical codes described in Section 2 below, and the specific data management plan described in complementary storage application. Note that we get specific application support through the Swedish e-Science Research Centre (SeRC), the EuroHPC competence centre and via two EU Centres of Excellence (CEEC and Excellerat) in the form of four application experts and we actively develop our codes. In particular, the possibility to use different machines depending on job size and job characteristic is beneficial for the efficient usage of the available computer time; also having a good mix between computer centres has been helpful to us. We have thus found that, depending on problem size and code, Dardel (PDC), Tetralith (NSC), Alvis (C3SE) and LUMI (CSC) are excellent choices.