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Job Record #15509
TitleFlow reconstruction by short-horizon data assimilation
LocationFrance, Meudon
InternationalYes, international applications are welcome
Closure DateMonday, December 31, 2018
Title: Flow reconstruction by short-horizon data assimilation

The global objective of the internship is to estimate complex unsteady flow
fields around bodies by combining measurements from an optical technique and a
physical model. Currently, one of the paths of research to overcome the
technological limits of measurement sensors (acquisition frequency, resolution,
noise...) is to exploit apriori knowledge from underlying physical models, in
the framework of so-called data assimilation techniques. Originally developed in
geophysical sciences, such methods receive an increasing attention in the fluid
mechanics and aeronautical research fields, as they enable to combine in a
possibly optimal way experiments and simulations.

The context of this internship will be that of high-speed 3D PIV/PTV (Particle
Image Velocimetry/Particle Tracking Velocimetry) measurements, which is one of
the richest and most recent techniques in the field of velocity measurements.
These techniques allow to obtain three-component velocity vectors in a whole
flow volume at high frequencies (typically of the order of a few kHz), however
still with some limitations. PIV processing yields velocity fields on a regular
3D grid, but smoothes the smallest spatial structures, may exhibit significant
noise and lack accuracy close to solid walls. In PTV, velocity vectors are
obtained with a higher accuracy, but are only available at the particles
positions. As the seeding density for 3D measurements has often to be limited
due to signal constraints, this leads to a result which is mostly sparse in
space. The objective of the internship is to propose new data assimilation
strategies, to alleviate these limitations by enforcing physical conservation
laws (incompressible unsteady Navier-Stokes equations). A few strategies have
been proposed already in the literature in the case of PTV
[Gesemann16,Schneiders16]. In this internship, several new formulations based on
a variational formalism already available at ONERA [Yegavian17] will be
compared, and used by taking both PTV and PIV data as input. The objective will
be to comparatively assess the ability of the schemes to denoise, super-resolve
missing spatial and/or temporal scales, interpolate and extrapolate missing
data, and compare the respective computational costs. To do so, the intern will
first work on numerical data, and then will be able to apply his/her algorithms
on results of 3D PIV and PTV of the flow around a wall-mounted cube, at
Reynolds numbers of the order of 2,000 to 8,000. As a second step, if time
allows, new ideas to extend the algorithms to different situations will be
explored: for instance, by considering different input quantities. In
that respect, an interesting path to explore would be to use, instead of the
processed velocities, either the particles reconstructed in 3D, or even their 2D
images on the cameras. Also, another interesting direction could be to extend
the techniques in order to enable them to use dye visualization as input data,
which would allow to turn qualitative visualizations into quantitative information.

[Gesemann16] Gesemann, S., Huhn, F., Schanz, D., Schröder, A., From Noisy
Particle Tracks to Velocity, Acceleration and Pressure Fields using B-splines
and Penalties, 18 th Lisgon Int. Symp. 2016.

[Schneiders16] Schneiders, J. F., Scarano, F., Dense velocity reconstruction
from tomographic PTV with material derivatives, Exp. Fluids 57(9), 2016.

[Yegavian17] Yegavian, R., Model-based approaches for flow estimation using
Particle Image Velocimetry,Thèse de l'Ecole Polytechnique, 2017.

Period: February-August 2019

Profile of the candidate: Master 2/Engineer school student in Fluid Mechanics

Contact Information:
Please mention the CFD Jobs Database, record #15509 when responding to this ad.
NameVincent Mons
Email ApplicationYes
8 rue des Vertugadins
92190, Meudon, France
Record Data:
Last Modified14:15:07, Thursday, December 06, 2018

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