Seminars Series: Aeroautics and Aerospace
Online Seminar on SU2-NEMO: an open-source framework for nonequilibrium flows (open to public)
Prof. Marco Fossati from the University of Strathclyde
Prof. Fossati is associate professor in computational aerodynamics and the head of the Future Air-Space Transportation Technologies Laboratory. His research interests are in the area of multiphysics computational aerodynamics, and his expertise is in the field of aircraft aerodynamics and non-equilibrium flows, modal-based Reduced Order Modeling for aircraft aerodynamics, mesh optimisation and generation.
The talk will give an overview on the developments of an open-source code to address high-enthalpy non-equilibrium flows called SU2-NEMO. NEMO is the outcome of a collaborative effort between the University of Strathclyde, the VKI, Stanford University, and the University of Arizona. The rationale behind NEMO is to develop open-source simulation capabilities to address nonequilibrium physics such as finite-rate chemistry and nonequilibrium energy transfer that characterise the aerothermodynamic interaction of objects flying at high Mach regimes.
Online Seminar on Machine Learning Moment Closures for Accurate and Efficient Simulation of Polydisperse Evaporating Sprays (open to public)
Dr. James B.Scoggins, Postdoctoral researcher at the von Karman Institute
Dr. James B. Scoggins to the AR seminar, where he will talk about his paper presented at this year's AIAA SciTech conference:Machine Learning Moment Closures for Accurate and Efficient Simulation of Polydisperse Evaporating Sprays
He will present a novel machine learning moment method for the closure of the moment transport equations associated with the solution of the Williams-Boltzmann equation for polydisperse, evaporating sprays. The method utilizes neural networks to learn optimal closures approximating the dynamics of the kinetic equation using a supervised learning approach. The neural network closure is compared to reference solutions obtained using a Lagrangian random particle method as well as two other state-of-the-art closure models, based on the maximum entropy assumption. Results on 0D and 1D test cases demonstrate that the closures obtained using the machine learning approach is significantly more accurate than the maximum entropy closures with comparable CPU performance. This suggests that such models can be used to replace expensive Lagrangian techniques with similar accuracy at far less cost.
Online Seminar on Characterization of Supersaturated Nitrogen In Hypersonic Wind Tunnels & The Design Of A Fast-Acting Valve In ANDLM6QT (restricted to VKI member)
Erik Hoberg, PhD at University of Notre Dame, USA
Erik is PhD candidate at the University of Notre Dame and he received the 2020 BAEF fellowship to study for 6 months at VKI
Erik received his bachelors degree in aerospace engineering in 2017 at New Mexico State University and his masters in aerospace engineering from the University of Notre Dame January of this year. At Notre Dame, he has worked on flow characterization and design in the arc heated hypersonic wind tunnel and the large hypersonic quiet tunnel.
Seminar on Bayesian aerothermal assessments
Pranay Seshadri from Alan Turing Institute, United Kingdom
Pranay Seshadri is the Group Leader in Aeronautics at the Alan Turing Institute — the UK’s national institute for data science artificial intelligence. He is concurrently a Research Fellow in the Department of Mathematics at Imperial College London. He obtained his PhD in 2016 from the University of Cambridge in robust turbomachinery design. After his approx. 30min talk, Pranay will continue with a mini-workshop on 'equadratures': an open-source code for uncertainty quantification, data-driven dimension reduction, surrogate-based design optimisation and numerical integration. The workshop will finish around 15:00.
Abstract of the talk and mini-workshop:
Abstract (for talk):
In this talk, I will give you an overview of my group’s research that is broadly focused on Bayesian aerothermal measurements — the science and statistics of inferring aerothermal quantities. The “Bayesian” perspective arises from both uncertainties in the measurements and uncertainties in regions where we do not have measurements. I will present two case studies of some of the underlying research that cuts across statistics and aerodynamics. The first case study is the measurement of jet-engine sub-system (fans, compressors, turbines, etc.) efficiencies, where the measurements are sparsely placed pressure and temperature rakes. The second case study is the quantification of density from old Schlieren images, where the sensors are densely sampled pixels that measure the refractive index gradient field.
Abstract (for mini-workshop):
This mini-workshop is focused on ‘equadratures’: an open-source code for uncertainty quantification, data-driven dimension reduction, surrogate-based design optimisation and numerical integration (see: https://equadratures.org/). Although we will not have time for tandem coding, I will aim to show code snippets for some of the aforementioned capabilities, so you can try running the code on your own data-sets. There will be quite a few turbomachinery examples presented, including your very own LS-89.