VKI Seminar Series

Seminars Serie: Aeronautics and Aerospace

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Particle Methods for the Simulation of Fluid Flow 

Professor Angelo Tafuni from the New Jersey Institute of Technology

The presentation covers a brief introduction of particle methods and their role in CFD, followed by a focused section on Smoothed Particle Hydrodynamics (SPH) and its use in the solution of challenging engineering flows. SPH represents a powerful numerical method when simulating flow with large gradients and/or with one or more interfaces. Prof. Tafuni will introduce the DualSPHysics project (dual.sphysics.org), a massively parallel, open-source code that is developed by a consortium of five universities, including NJIT. He will also talk about projects that involve the use of SPH in his research lab, including new models for studying turbulence in a Lagrangian setting and multiphase flow.

Prof. Tafuni received a "Laurea Triennale" and "Laurea Magistrale" in Mechanical Engineering from Politecnico di Bari, Italy, and a Master of Sciences and PhD from the New York University in Brooklyn, NY. Since 2018, he has been a member of the faculty of the New Jersey Institute of Technology, where he is currently an Assistant Professor in the School of Applied Engineering and Technology and the Department of Mechanical and Industrial Engineering. He is engaged in conducting and supervising research on fluid dynamics and heat transfer theory and applications, with an emphasis on developing and validating new models in Computational Fluid Dynamics. 

Quantum-accelerated scientific computing: concepts, programming tools and applications (online seminar - open to public)

Prof. Matthias Moeller from TU Delft

Quantum computing is an emerging technology that has the potential to radically change the way we will be solving computational problems in the future. One of the first quantum algorithms with potential impact on real-world applications is Shor’s integer factorization algorithm which might bring an end to public-key cryptography schemes once quantum computers have reached a maturity level that will allow the execution of practical quantum algorithms with a sufficiently large number of qubits. But what about the many compute-intensive tasks we are facing day in and day out in scientific computing, e.g., the solution of linear systems of equations stemming from the discretization of differential equations?

In this seminar talk we will start with a brief review of the main concepts of quantum computing that form the basis of the potential advantage of quantum computers over classical computers. Next, I will introduce the cross-platform programming framework LibKet that is being developed in our group. LibKet is an open-source C++ template library that enables the development of quantum algorithms as platform-agnostic expressions and provides a unified programming interface to execute them on many different quantum-computing simulators and quantum hardware backends in the cloud. Our primary target group are scientists and practitioners who are not quantum experts and want to explore the opportunities of quantum computing as next-generation acceleration technology.
In the second part of the talk, we will discuss a hybrid quantum-classical algorithm for solving linear systems of equations and, in particular, discuss approaches to apply it to the solution of Poisson’s problem in the context of finite element discretizations. If time permits, we will finally discuss a novel approach to generate resource-efficient quantum circuits that can be directly executed on today’s Noisy Intermediate-Scale Quantum computers. The core idea consists in replacing all software-visible high-level quantum gates by parametrized native gates (e.g., single-qubit rotation gates) and treat the problem of generating hardware-optimized executable circuits as design optimization problem.

Short bio: Dr. rer. nat. Matthias Möller is Associated Professor at the Department of Applied Mathematics at Delft University of Technology. He obtained a Diploma in Mathematics from the Faculty of Mathematics at TU Dortmund University, Germany, in 2003 and received a PhD from the same institution in 2008. He joined the Numerical Analysis group at TU Delft in 2013.
His research interests focus on numerical methods for partial differential equations and their efficient implementation on heterogeneous high-performance computing platforms. He has been working on high-resolution adaptive finite element and isogeometric methods and fast iterative solution techniques, in particular, for convection-dominated transport problems and compressible flows.
Since recently, Matthias is working in the field of quantum-accelerated scientific computing with focus on quantum algorithms for numerical linear algebra and optimization problems.

Advanced Reynolds Stress Modelling of Cooling Flows in Turbomachinery : recent results obtained at ONERA (online seminar - open to public)

Emmanuel Laroche from ONERA. Emmanuel is research engineer in Aerothermal / Heat Transfer within the "Département multi-physique pour l’énergétique"/ High Energy, Aerothermal systems and Turbulence (H.E.A.T).

This seminar will focus on recent results obtained at ONERA on the modelling of turbomachinery cooling flows, using advanced Reynolds Averaged Navier-Stokes (RANS) Models. The main models investigated rely on the innovative elliptic blending approach suggested by Manceau to model flows in the wall region more universally. For film cooling flows, the use of Reynolds Stress models in combination with advanced heat flux models can improve the description of the mixing between the film and the mainstream. This will be illustrated on the Penn State 777 hole configuration, and on a recent hot jet configuration studied at ONERA. Concerning impingement flows, up to now , Reynolds Stress models failed to provide a correct estimation of the impingement heat flux. A recent analysis of a LES carried out with CERFACS on a reference configuration brings an new light on such a behavior and potential cures are discussed.

Towards large-scale optimization of turbomachinery with the discrete adjoint method: from single to multi approach (open to public)

Dr. Matteo Pini from TU Delft

Adjoint-based design methods for turbomachinery are usually based on the assumption of steady state flow to mitigate computational costs. However, because of the inherently unsteady nature of turbomachinery flows and the growing demand for multidisciplinary design capabilities, it is often necessary to resort to time-accurate flow calculations. In this context, reduced order methods have been investigated as a possible cost-effective alternative to time-accurate simulations. The harmonic balance (HB) method, based on spectral discretization in time of the unsteady flow equations, is particularly attractive for the analysis of non-linear dynamic problems dominated by a known set of frequencies, a problem typically encountered in turbomachinery. Thanks to the HB formulation, the time-dependent flow governing equations can be casted in pseudo-steady form, enabling the application of steady adjoint methods to unsteady flow design problems and paving the way to multi-disciplinary design optimization.

Stemming from simple considerations, we will firstly discuss the challenges associated to shape optimization of turbomachinery and learn why the adjoint method is highly suited for large-scale optimization problems. We will then dive into unsteady design problems and show how these can be effectively tackled with the discrete adjoint method. Finally, we will present and discuss the lessons learned from the development of adjoint-based optimization methods for unsteady flow design problems within the SU2 open-source software. To this end, we will show several examples to provide quantitative insight on the capability of the methods for aerodynamic and aero-elastic design optimization of turbomachinery. We will close the talk by providing our view on the next research steps and long-term perspective.

The two-phase flows addressed in this presentation includes the flashing injection in the combustion chamber, the film cooling of small rocket engines and the sloshing investigation. Different techniques will be presented aiming quantitative measurements of liquid temperature, velocity and thickness and free surface characterization in the aforementioned two-phase flows.

The conditions for such techniques to be applied in flight demonstrators will be considered for an open discussion!

Optical techniques for multi-phase systems: a support to space propellant behavior (open to public)

Dr. Alessia Simonini, Research Engineer, von Karman Institute

Deep space exploration, robotic and humans, need to be supported by efficient space engines. Several two-phase flows problematics need to be investigated in such systems to increase their reliability and reduce safety margins. Numerical codes and reduced models, used for the design of space systems, need a strong support for validation by means of experimental databases. Optical techniques might serve to this scope: they can provide a new insight on two-phase phenomena happening at low TRL providing a unique database of test cases.

The two-phase flows addressed in this presentation includes the flashing injection in the combustion chamber, the film cooling of small rocket engines and the sloshing investigation. Different techniques will be presented aiming quantitative measurements of liquid temperature, velocity and thickness and free surface characterization in the aforementioned two-phase flows.

The conditions for such techniques to be applied in flight demonstrators will be considered for an open discussion!

Moment Methods for Non-Equilibrium Low-Temperature Plasmas with Application to Electric Propulsion (open to public)

PhD Defense of Stefano Boccelli, Collaborative PhD VKI/Politecnico di Milano,

Electric space propulsion devices are able to outperform the efficiency of classical chemical rockets, by showing a much higher specific impulse. This has profound effects on the spacecraft design, as significantly more mass can be destined to the payload with respect to the previous technologies. The Hall effect thruster is one of the most employed electric propulsion devices and is based on low-temperature plasma technology. While Hall thrusters have been successfully employed for decades, the design of up- or down-scaled Hall thrusters for future manned missions and for micro-satellites requires renewed modeling efforts. Due to the low pressure of the space environment, together with the presence of electric and magnetic fields, these devices show strong deviations from thermodynamic equilibrium. An accurate computer modeling requires such effects to be carefully considered.

This work discusses the application of the Maximum-Entropy moment methods to such non-equilibrium conditions. In particular, the order-4 Maximum-Entropy methods will be considered, resulting in a set of 14 moment equations that extend the validity of the classical Euler/Navier-Stokes-Fourier fluid formulations towards strong non-equilibrium conditions. After briefly discussing the structure and properties of the 14-moment method, it will be shown that such formulation allows to describe accurately both the low-collisional ion dynamics inside the thruster channel and the strongly anisotropic and asymmetric electron distribution function in presence of crossed electric and magnetic fields. The computational cost of the method appears larger than the simpler fluid methods, but still advantageous with respect to more expensive fully-kinetic simulations.

The work was carried at Politecnico di Milano, in collaboration with the von Karman Institute for Fluid Dynamics. The work have also benefited from visits at the Laboratoire de Physique des Plasmas (LPP, Paris) and at the University of Ottawa.

Turning Data into Value - Sustainability through Probabilistic Analytics (open to public)

Dr. Georg Rollmann , Siemens Energy, Germany

Georg Rollmann has been working on Data Science methodologies ad applications to Gas Turbines R&D and Service business and other areas of the power generation industry for more than 14 years. He is responsible for the Technology Field “Data Analytics & AI” across Siemens Energy leading a cross-organizational team of experts.

Abstract: Across all areas in the energy business, sustainability and reliability aspects are becoming more and more important. For example, power plant operators are looking for condition-based maintenance concepts and optimized products and solutions to fit their specific needs in the heterogeneous energy landscape. For this, a good understanding of the potential failure modes and associated risks is inevitable. A key challenge here is given by the large number of uncertainties from different sources that influence system behavior, such as manufacturing variations, material imperfections, and power plant operating conditions. The Probabilistic approach, that combines domain-specific, e.g. physics-based, modelling with data science and statistical methods, aims at modelling these uncertainties in an explicit way. This allows for more accurate risk assessments and can be leveraged to enable a more sustainable use of natural resources while maintaining, or even improving, system reliability.

Recent advances in variational data assimilation at CMRE (open to public)

Dr. Paolo Oddo, CMRE, Centre for Maritime Research and Experimentation, Italy

Data assimilation is here introduced in its variational formulation as used at CMRE. The classical stationary formulation of the variational cost function is then augmented by including the information retrieved from ensemble simulations to improve the adherence of the background error covariance matrix to the time-evolving regime-dependent errors. The hybrid scheme is used to both correct the systematic error and to improve the representation of small-scale errors in the background error covariance matrix. Optimal exploitation of remotely sensed data is also investigated. To this end, the “hybrid” variational scheme is modified relaxing the usual assumption of uncorrelated observation errors, and physics or statistical based bias procedures are proposed and assessed. Furthermore, the “adjoint free” version of selected observation operators is tested using a new data-driven approach, based on canonical correlation analysis or neural network, to assimilate remotely observed quantities. Real or synthetic experiments data are used to illustrate the benefit deriving from the recent developments.

ARGO: a high order multiphysics solver (open to public)

Dr. Pierre Schrooyen, Senior Research engineer at Cenaero (VKI graduates, PhD UCL)

Pierre's talk will present the Argo platform, which is a multi-physics code based on Discontinous Galerkin discretization. Discontinuous Galerkin methods provides high order accuracy on unstructured meshes while ensuring local conservation of physical quantities. This type of method handles a wide variety of elements types and are well suited for local adaptation in mesh size and interpolation order. The method will be briefly presented and the current capabilities and limitations of the Argo software will be discussed.

Since his graduation, Pierre has been working as a research engineer at CENAERO and is also an invited professor at UCL, giving the Aerospace dynamics course. He is collaborating with VKI since his PhD work on several research projects, further developing ARGO.

Online Seminar on MLOps – the data science solution to efficiently manage the full lifecycle of computational software in the Cloud (open to public)

Dr. David Vanden Abeele, partner and senior quantitative modeller at Credo Software and Radovan Parrák, Data Scientist and YQ product owner

Developing, testing, deploying, running, monitoring and maintaining a computational code on your own PC is easy.

Doing it with a team is harder.

Doing it with a team on a cluster is even harder.

Doing it with a team on a cluster for tens or hundreds of codes simultaneously becomes a complete mess unless you are organized for it and supported by adequate tooling.

The good news is that you are not alone! This problem also appears in Data Science, where things are even worse. ‘Machine Learning Operations’ (MLOps, the Data Science equivalent of DevOps) offers a promising solution to these challenges in the Cloud. This talk discusses the benefits and pitfalls of building and deploying computational software in an MLOps fashion, based on practical experiences in the financial sector, on AWS and Azure Cloud platforms.

Online Seminar on shock-layer radiative heating for the Mars 2020 mission, which just delivered the Perseverance Rover on 18 February this year (open to public)

Dr. Christopher Johnston from NASA Langley Research Center 

The impact of shock-layer radiative heating to the backshell of capsules entering Earth or Mars was assumed negligible until less than 10 years ago. Since then, modeling improvements and new experimental data have shown that radiative heating is actually the dominant heating contributor, relative to convective, over much of the backshell surface. This talk presents the story of how this backshell radiative heating component was ignored for so long for Mars entry, how it was discovered, and how its modeling was refined and utilized for Mars 2020.

Chris Johnston has worked at NASA Langley since 2006, with the main focus of modeling shock-layer radiative heating. He is the primary author of the HARA radiation code, which is one NASA’s standard aerothermal tools. Has worked on the Mars 2020 and Orion programs, and is currently working on the Mars Sample Return Earth Entry vehicle.


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.

Design challenge for re-entry vehicles: ablation, shape change and aerodynamic performance during re-entry