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Job Record #16599
TitlePostdoc in data assimilation of turbulent thermal flows
CategoryPostDoc Position
EmployerONERA
LocationFrance, Toulouse
InternationalYes, international applications are welcome
Closure DateThursday, December 31, 2020
Description:
Title : Data assimilation of aero-optical measurements for thermal flow
reconstruction

Duration: 12 month, may be renewed – Net salary : approx 25 k€ / year
Location: ONERA, Toulouse, FRANCE
Starting date: approx January 2021

Requirements: PhD in Fluid Mechanics or Applied Mathematics, skills in data
assimilation techniques,
knowledge in turbulence models for thermal flows would be appreciated,
publishing experience.

Keywords: Data assimilation, fluid mechanics, aerodynamic and aerothermal
turbulence, Background
Oriented Schlieren (BOS)

Subject:
Thermal and compressible flows are characterized by inhomogeneous density fields
that are usually difficult to measure. One experimental technique aiming at
measuring instantaneous and averaged 3D density fields is the 3D Background
Oriented Schlieren (3DBOS), developed at ONERA over the past ten years [1]. This
technique relies on the visualization of a textured background using a
camera. The flow of interest is placed in-between, inducing aero-optical effects
and apparent displacements of the textured background. Correlation algorithms
can then be used to evaluate apparent displacement maps that are related to
light ray deviations induced by gradients of refractive index associated with
the flow. Using multiple cameras placed with different point of views, a 3D
reconstruction of the density field can be achieved using a tomographic
algorithm. This approach was shown to be adequate to study free shear flows such
as jets [2] where boundary conditions and cameras positions are straightforward
to set. In more complex configurations such as jets in cross-flow or jets
impinging on a plate for instance, the technique is more difficult to implement,
yielding potentially erroneous reconstructions.

A promising way to gain access to 3D density fields and to overcome the current
limitations of 3DBOS may be found in data assimilation techniques aiming at
providing numerical simulations closely resembling the experimental data
[3]. Such numerical results calibrated on available measurements and sometimes
referred to as hybrid flows are governed by equations of motions and are
constrained by well controlled boundary conditions. They thus provide a priori
physical solutions, enforcing correct symmetries and giving access to quantities
that were not initially probed during the experiments. Recent efforts have been
mainly focusing on using surface pressure measurements and velocity maps
estimated using PIV (Particle Image Velocimetry) to produce hybrid flow fields
for purely aerodynamic configurations [4]. The present project aims at building
on such works by investigating the capability of data assimilation to produce
hybrid aerothermal flow fields by assimilating displacement maps obtained by BOS
using several cameras.

The configuration of interest will be a turbulent hot jet in a cross-flow at ambient
temperature for which a database composed of Reynolds-Averaged Navier-Stokes
(RANS) simulation results, Large Eddy Simulation (LES) results, PIV measurements
and BOS displacement maps is already available. The post-doctoral project will
focus on the restitution of mean (stationary) flow fields of such a jet using
RANS models. Various aerothermal turbulence models will be investigated and a
particular emphasis will be given on the choice of forcing or calibration
parameters that should be considered to provide satisfactory hybrid flows. These
parameters should encompass turbulence models parameters, boundary conditions
and uncertainties on cameras parameters. Data assimilation will be performed by
comparing the displacement maps obtained by BOS with the synthetic ones
evaluated on each camera by raytracing through the computation volume.  Since
adjoint models are not available in the present set of tools, stochastic
assimilation techniques will be preferred over variational ones. Given the large
dimension of the problem, a particular attention will be given to approaches
based on (but not restricted to) Ensemble Kalman Filters (EnKF) [5] and
iterative versions developed for inverse problems [6] in order to assess their
relevance and limitations for the present objectives.

[1] Nicolas et al. "A direct approach for instantaneous 3D density field
reconstruction from background-
oriented schlieren (BOS) measurements." Experiments in Fluids 57, no. 1 (2016): 13.
[2] Nicolas et al. "3D reconstruction of a compressible flow by synchronized
multi-camera BOS."
Experiments in Fluids 58.5 (2017): 46.
[3] Foures et al. "A data-assimilation method for Reynolds-averaged
Navier–Stokes-driven mean flow
reconstruction." Journal of Fluid Mechanics 759 (2014): 404-431.
[4] Symon et al. "Data assimilation of mean velocity from 2D PIV measurements of
flow over an idealized
airfoil." Experiments in Fluids 58.5 (2017): 61.
[5] Roth et al, "The Ensemble Kalman filter: a signal processing perspective"
EURASIP Journal on
Advances in Signal Processing (2017) 2017:56
[6] Iglesias et al, “Ensemble Kalman methods for inverse problems” Inverse
Problems 29 (2013) 045001


Contact Information:
Please mention the CFD Jobs Database, record #16599 when responding to this ad.
NameO. Léon
Emailolivier.leon@onera.fr
Email ApplicationYes
URLhttps://w3.onera.fr/formationparlarecherche/sites/w3.onera.fr.formationparlarecherche/files/pdoc-dmpe-2020-03.pdf
Record Data:
Last Modified10:25:14, Monday, May 25, 2020

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