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LANS Seminar
June 29, 2022 @ 10:30 - 11:30 CDT
Seminar Title: Computational Modeling for Large-Scale Industrial Applications: Challenges and Opportunities Towards Exascale
Speaker: Gianmarco Mengaldo, Assistant Professor, National University of Singapore (NUS)
Date/Time: June 29, 2022 / 10:30 am – 11:30 am
Location: See meeting URL on the cels-seminars website (requires Argonne login)
Host: Romit Maulik
Description: We provide a perspective on the challenges and opportunities of computational modeling for industry-relevant applications towards the Exascale Era. As an example, we present the successful deployment of high-fidelity Large-Eddy Simulation (LES) technologies based on spectral/hp element methods to simulate the aerodynamics of a real automotive car, namely the Elemental Rp1 model. The simulation presents the common challenges of an industry-relevant problem, namely high Reynolds number and complex geometry. To the best of the authors’ knowledge, this simulation represents the first fifth-order accurate transient LES of an entire real car geometry using spectral element methods. Moreover, this constitutes a key milestone towards considerably expanding the computational design envelope currently allowed in industry, where steady-state modelling remains the standard. We also draw some parallels with other fields, namely numerical weather prediction, where large scale simulations play a central role and have been used since the 1950s.
Bio: Dr Gianmarco Mengaldo is an Assistant Professor in the Department of Mechanical Engineering at NUS. He received his BSc and MSc in Aerospace and Aeronautical Engineering from Politecnico di Milano (Italy), and his PhD in Aeronautical Engineering from Imperial College London (United Kingdom). After his PhD he undertook various roles both in industry and academia. Dr Mengaldo’s primary research area is computational science within the context of multidisciplinary applications that arise in engineering and applied science. His main research interests involve (i) the development of high-fidelity simulations tools for multi-physics problems, (ii) the development of data-mining technologies for the systematic identification of coherent patterns in highly unstructured datasets, and (iii) the use of machine learning and statistical tools to predict the behavior of complex systems. Dr Mengaldo’s main application areas include aerospace and mechanical engineering, weather and climate, and financial engineering.
Please note that the meeting URL for this event can be seen on the cels-seminars website, which requires an Argonne login.