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von Karman Institute for Fluid Dynamics

Ground testing facilities have been used since long in aeronautics and aerospace, as they have been developed for environmental and industrial applications. Nowadays they are still major sources of key data for investigations in all fields of fluid dynamics. This hybrid lecture series (online / on-site) is organized for student and engineers to provide a general understanding of the design, operation and application of ground testing facilities from all speed regimes. Experimental methodology is exposed as well as the interpretation of the data and their extrapolation to real situation. The course includes an on-site presentation of the VKI facilities during a lab visit (for on-site participation only). A question/answer session will be organized for the online participants. The schedule of this session will be communicated later. This Lecture series is based on the long tradition and experience developed at VKI on experimental testing.

von Karman Institute for Fluid Dynamics

The objective of these hybrid (online / on-site) lecture series on "Introduction to CFD" is to provide an elementary tutorial presentation on computational fluid dynamics (CFD), emphasizing the fundamentals and surveying a variety of solution techniques whose applications range from low speed incompressible flow to hypersonic flow. At the conclusion of the course, an attendee will be well prepared to understand the literature in this field, to follow more sophisticated state-of-the-art lecture series, and to begin the application of CFD to his or her special areas of concern. The techniques to be discussed will be applicable to all fields of fluid dynamics including aeronautics, industrial flows, wind energy etc.

Thermodynamics, Fluid Dynamics and Statistical Physics,    Computational Physics and Numerical Simulation

von Karman Institute for Fluid Dynamics | Universit� libre de Bruxelles (ULB)

The integration of machine learning methods in fluid mechanics continues to grow and open new frontiers. Combining first principles and machine learning offers new avenues to improve predictions and control design, identify patterns, reduce computational costs, enhance measurement techniques, and more generally, get deeper insights into complex fluid phenomena. This lecture series provides an overview of the current state of the art and a pedagogical introduction to the fundamentals of machine learning tools for fluid dynamics. The course is in its second edition. It follows the successful lecture series held in 2020, which attracted more than 200 participants and resulted in an edited book published by Cambridge University Press.

Thermodynamics, Fluid Dynamics and Statistical Physics,    Neural Networks and Artificial Intelligence, Machine Learning

von Karman Institute for Fluid Dynamics

This short course is the regular course on radial compressors thought at VKI to the turbomachinery students specializing in compressors in the context of the ‘Research Master in Fluid Dynamics’. It provides an overview of the present knowledge and flow models and intends to provide a good background for the people interested in the aerodynamic design of radial compressors. This 5 day course is now made available to a limited number of people from outside VKI.

Thermodynamics, Fluid Dynamics and Statistical Physics,    Applied Physics (general)

von Karman Institute for Fluid Dynamics

The course gives a practical introduction to the structure and usability of OpenFoam and OpenFOAM related applications (snappyHexMesh, paraview). Besides the general introduction the participants will perform a complete numerical simulation starting from choosing the solver till post-processing. The second day will be partially dedicated to the open source mesh generator snappyHexMesh and a multiphase simulation will be performed with the obtained mesh.

Thermodynamics, Fluid Dynamics and Statistical Physics,    Computational Physics and Numerical Simulation

von Karman Institute for Fluid Dynamics

The programing course shows the how-to for the most common programming issues in OpenFOAM® covering the implementation of new solver, adding own boundary conditions, modifying turbulence models and defining user-specific utilities. During these programing experiences the participants will get familiar with the highly templated language of OpenFOAM®, with the difference of static and dynamic linking. Basic knowledge of setting up and running OpenFOAM® cases is requested in order to be able to follow the course efficiently!

Thermodynamics, Fluid Dynamics and Statistical Physics,    Computational Physics and Numerical Simulation

von Karman Institute for Fluid Dynamics

The purpose of this Lecture Series is to offer a full overview of the present development and the potential of the Large Eddy Simulation (LES) of turbulent flows. The first part of the course will introduce and discuss fundamental principles, present state-of-the-art applications. The specific numerical problems related to the implementation of LES will be also presented. The second part will present applications of these approaches to various engineering fields of wide interest. The course will be concluded discussing the possible developments of LES and Detached Eddy Simulation (DES) which represents the most promising technique to extend the usefulness of LES for high Reynolds-number flows. The course will be delivered by internationally recognized experts. Its content is addressed both to researchers interested in the fundamental simulation of turbulence and engineers wanting to apply the LES technique or LES solvers to the accurate simulations of turbulent flows.

Thermodynamics, Fluid Dynamics and Statistical Physics,    Computational Physics and Numerical Simulation

von Karman Institute for Fluid Dynamics

This course gives an overview and practical hands-on experience in integrating machine learning in fluid dynamics. The course originated as a compressed version of the course Machine Learning for Fluid Dynamics, given at the Research Master program at the von Karman Institute. After a brief review of the machine learning landscape, we show how to frame problems in fluid mechanics as machine learning problems, and we explore challenges and opportunities. Attendees will be guided through a series of tutorial sessions in Python and will tackle several relevant applications: aeroacoustics' noise prediction, turbulence modelling, reduced-order modelling and forecasting, meshless integration of (partial) differential equations, super-resolution and flow control.

Thermodynamics, Fluid Dynamics and Statistical Physics,    Neural Networks and Artificial Intelligence, Machine Learning

von Karman Institute for Fluid Dynamics

Innovative optimization and design techniques for modern aircraft (manned or UAV/UCAV) and engine systems aiming at maximum performance in a multidisciplinary context (aerodynamic efficiency, safety, drag, losses, weight, strength, heat fluxes, emission, noise, …), are now rapidly moving from research labs to industrial real and virtual platforms. To reach concurrently this level of excellence, emergent optimization methodologies require more and more robust and efficient associated software for a daily use in industrial collaborative design environments. This course intends to provide the basic concepts and tools behind this technology, both in single discipline (single point or multi point design) and multidisciplinary (fluid-structure interaction, fluid-acoustics, conjugate heat transfer, …) context. Subjects which will be treated in detail include: gradient based and steepest descent methods, adjoint methods, one shot or goal oriented methods, evolutionary/differential evolution algorithms on parallel environments, game strategies like Pareto Fronts and Nash Equilibrium, parameterization, surrogate and reduced-order modeling (Radial Basis functions, Artificial Neural Networks, Kriging …), multifidelity modeling approaches, robust design, … The content of this Course is oriented towards junior and experienced engineers and researchers involved in the field of multi disciplinary design and looking for innovative numerical solutions - or set of solutions- for complex multi criteria optimization problems.

Thermodynamics, Fluid Dynamics and Statistical Physics,    Mechanical engineering