In this talk, we will show how to leverage a multidisciplinary platform that integrates numerical analysis, physics, and high-performance computing for the analysis and development of novel numerical methods for ordinary and partial differential equations with provable properties such as nonlinear stability (entropy stability) and conservation, and structure-preserving techniques. These properties are critical for designing reliable, efficient, and self-adaptive solvers for complex geometries – an essential cornerstone for next-generation computational frameworks.
The current classes of partial differential equations that we are working on are the compressible Navier–Stokes equations and the Eulerian model for compressible heat-conducting flows. We also use deep learning to complement and speed up the process of solving efficiently large-scale PDE-based problems. In this talk, we will summarize the progress we made in the last few years in the following areas:
• Numerical analysis and algorithm development for robust, smart compressible flow solvers.
• Development from the ground up of a new scalable hp-adaptive computational fluid dynamics (CFD) framework, a potential prototype of the future compressible solver as chartered by the NASA CFD 2030 vision.
• I will show applications and impacts in the automotive and aerospace industry and its extension for improving flow physics knowledge in detonation and aeroacoustics.
Bio of the Speaker: Matteo Parsani is an Associate Professor of Applied Mathematics & Computational Science and Mechanical Engineering at King Abdullah University of Science and Technology (KAUST). He received his B.Sc. and M.Sc. in aeronautics and astronautics from Politecnico di Milano (Italy) and his Ph.D. in mechanical engineering from the Vrije Universiteit Brussel (Belgium). He trained as a postdoc at KAUST and in the Computational AeroScience branch at NASA Langley Research Center (USA). Professor Matteo Parsani's research interests are related to developing novel, robust, and scalable numerical methods on unstructured grids partial differential equations and their application to solving realistic flow problems in various natural science and engineering areas. The application domains currently driving Matteo's research are compressible computational aerodynamics (e.g., highly-separated turbulent flows), computational aeroacoustics for noise reduction, dense gas flow simulations (e.g., supercritical fluids), and molecular communication. Matteo is the main developer of the fully-discrete entropy-stable adaptive numerical algorithms implemented in the SSDC framework, the compressible computational fluid dynamic framework developed in the Advanced Algorithms and Numerical Simulations Laboratory (AANSLab), part of the Extreme Computing Research Center at KAUST.