Data analysis and data reduction for large-scale turbulence simulations
Time: Fri 2025-11-14 10.15
Location: F3, Lindstedtvägen 26
Video link: https://kth-se.zoom.us/j/69836085996
Language: English
Subject area: Engineering Mechanics
Doctoral student: Adalberto Perez Martinez , Strömningsmekanik
Opponent: Professor Artur Tyliszczak, Czestochowa University of Technology
Supervisor: Professor Philipp Schlatter, SeRC - Swedish e-Science Research Centre, Teknisk mekanik, Institute of Fluid Mechanics (LSTM), Friedrich–Alexander Universität Erlangen–Nürnberg (FAU), DE-910 58 Erlangen, Germany; Professor Stefano Markidis, SeRC - Swedish e-Science Research Centre, Beräkningsvetenskap och beräkningsteknik (CST); Saleh Rezaeiravesh, Department of Fluids and Environment, University of Manchester, Manchester, United Kingdom
QC 251013
Abstract
Computational fluid dynamics (CFD) and direct numerical simulations (DNS),when applied to the study of turbulence, have traditionally been treated as acompute bound discipline, where the size of problems of interest is limited bythe capacity of supercomputers. While this remains true, the relatively recentadoption of specialized hardware such as graphics processing units (GPUs) hasallowed researchers to start studying problems that were not thought possiblebefore. This trend brings benefits for scientific discovery, however, it alsoaccentuates the importance of robust methodologies to manage and processthe increasing amount of data that is being produced by the simulations. Thepresent thesis explores techniques to process large scale data sets produced,mainly, by the spectral element method (SEM).This study explores the possibility to exploit the computational resourcesused by the simulations to perform data analysis and transformations in whatis termed, in-situ data processing. It is shown that it is viable to apply amultitude of processing tasks, such as data compression and image visualizationefficiently, as long as the hardware being used is taken into consideration, whichis relevant for modern heterogeneous systems. Furthermore it is shown that datacompression is an efficient technique to reduce storage requirements while keepingaccuracy, even for turbulence research. On this note, this thesis introduces amethod that incorporates uncertainty quantification (UQ) techniques for datacompression to facilitate the data quality evaluation.Data compression is a large focus in the present work, however, methodsto facilitate data analysis are also studied. Streaming and parallel modaldecompositions, in particular proper orthogonal decomposition (POD), aredeveloped and made available to the turbulence community with the additionof uncertainty quantification studies to ease its adoption. It is found thatthis sort of technique is excellent at increasing the interpretability of the data,while being able to exploit computational resources with in-situ execution.Additionally, parallel high-order interpolation techniques are introduced, whichbecome essential to reduce the memory footprint of large data sets whenperforming post-processing tasks, while aiding to simplify the data distributionof traditional SEM meshes.