Course overview
Nov 2021
Solution methods in computational mechanics (TU/e-JMBC), 3-4 November 2021
Location: TUE Coordinator: Jan ten Thije Boonkkamp, Martijn Anthonissen (TUE) Partial differential equations (PDEs) are ubiquitous in mechanics, describing a wide range of phenomena like stresses in a solid or waves. In this course we will address some numerical methods for PDEs. In particular, we will discuss discretisation methods for PDEs and time integration methods for the resulting ODE systems. The following topics are included: - Classification of second order PDEs - Finite difference methods for the Poisson equation (central differences,…
Find out more »Experimental techniques in fluid mechanics, 15 – 19 November 2021
Location: UT Coordinators: Alvaro Marin (UT), Michel Versluis (UT) Lecturers: Nico Dam (TU/e), Fulvio Scarano (TUD), Jan van Dijk (TU/e), Michel Versluis (UT), Alvaro Marin (UT), Christian Poelma (TUD), Willem van de Water (TUD), Rudie Kunnen (TU/e) This course for JMBC PhD students gives a general overview of concepts of experimental methods for flow, pressure, concentration and temperature measurements. The course will discuss various classic techniques (thermocouples, Pitot tubes, hot-wire anemometry) and optical techniques such as shadowgraphy and Schlieren. The…
Find out more »Jan 2022
CFD 1, 10-14 January 2022
Location: TUD Coordinators: Mark Gerritsma Lecturers: Mark Gerritsma, Barry Koren, Fred Wubs, Roel Verstappen The course discusses the basic methods for solving the equations that describe the motion of fluids. It is organized as a series of lectures and computer exercises. The basic model problem is the convection-diffusion equation with dominating convection. A number of spatial discretization methods (non-uniform grids) will be discussed with their pros and cons (upwind/central, lower/higher order, finite-difference/finite-volume). Also the stability and accuracy of time-integration methods…
Find out more »Turbulence, 31 January – 4 February 2022
Location: TUD Coordinators: Wim Paul Breugem, Rene Pecnik This course gives an introduction to the fundamentals of turbulence. It is aimed at PhD students and other researchers from the JM Burgerscentrum. Prerequisite knowledge of turbulence is not required, though may be helpful to keep up with the pace of the lectures. Basic knowledge of fluid mechanics is assumed. The first 2 days of the course are dedicated to classical turbulence theory such as can be found in the textbooks of…
Find out more »Apr 2022
Complex flows and complex fluids, 4 – 8 April 2022
Location: CISM, Udine (Italy) Coordinator: Federico Toschi (TUE) Lecturers: Fedrico Toschi (TUE), Roberto Benzi (Roma), Elisabeth Guazzelli (Marseille), Detlef Lohse (UT), Peter Schall (Univ. of Amsterdam) This lecture course is organised jointly with CISM, Udine (I). The course will present a broad overview on fluids and flows ranging from the dynamics of complex fluids to the statistical description of complex flows. In particular the course will address the physics of yield stress materials, the rheology of dense fluid suspensions, the…
Find out more »May 2022
Capillarity-driven flows in microfluidics, 9 – 13 May 2022
Location: UT Coordinators: Frieder Mugele (UT), Jacco Snoeijer (UT) Lecturers: Frieder Mugele, Jacco Snoeijer, Anton Darhuber (TUE) Wetting and interfacial tensions play a crucial role for the behaviour of fluids on length scales below the capillary length, which is typically of order 1 mm. Typical application areas include well-established traditional fields such as coating technology, emulsification and oil recovery as well as more recent fields such as microfluidic systems, inkjet printing technology, and immersion lithography. The course will cover the…
Find out more »Jun 2022
Machine learning in fluid mechanics, 13 – 17 June 2022
Location: TUE Coordinators: Federico Toschi (TUE), Alessandro Corbetta (TUE) Lecturers: Federico Toschi (TUE), Alessandro Corbetta (TUE), and others Machine learning and deep learning are allowing the progressive automation of tasks that only few years ago were impossible or required labour-intensive human activity. Image processing, language processing/translation, automated control, digital pathology are examples of recently revolutionized sectors. From a scientific perspective, there are strong evidences that deep learning methods are much better capable of quantifying properties of complex and/or chaotic dynamical…
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