178 research outputs found
Perspectives on predicting and controlling turbulent flows through deep learning
The current revolution in the field of machine learning (ML) is leading to
many interesting developments in a wide range of areas, including fluid
mechanics. Here we review recent and emerging possibilities in the context of
predictions, simulations and control of fluid flows, focusing on wall-bounded
turbulence. A number of important areas are benefiting from ML, and it is
important to identify the synergies with the existing pillars of scientific
discovery, i.e. theory, experiments and simulations. It is essential to adopt a
balanced approach as a community in order to harness all the positive potential
of these novel methods
Assessment of uncertainties in hot-wire anemometry and oil-film interferometry measurements for wall-bounded turbulent flows
In this study, the sources of uncertainty of hot-wire anemometry (HWA) and
oil-film interferometry (OFI) measurements are assessed. Both statistical and
classical methods are used for the forward and inverse problems, so that the
contributions to the overall uncertainty of the measured quantities can be
evaluated. The correlations between the parameters are taken into account
through the Bayesian inference with error-in-variable (EiV) model. In the
forward problem, very small differences were found when using Monte Carlo (MC),
Polynomial Chaos Expansion (PCE) and linear perturbation methods. In flow
velocity measurements with HWA, the results indicate that the estimated
uncertainty is lower when the correlations among parameters are considered,
than when they are not taken into account. Moreover, global sensitivity
analyses with Sobol indices showed that the HWA measurements are most sensitive
to the wire voltage, and in the case of OFI the most sensitive factor is the
calculation of fringe velocity. The relative errors in wall-shear stress,
friction velocity and viscous length are 0.44%, 0.23% and 0.22%, respectively.
Note that these values are lower than the ones reported in other wall-bounded
turbulence studies. Note that in most studies of wall-bounded turbulence the
correlations among parameters are not considered, and the uncertainties from
the various parameters are directly added when determining the overall
uncertainty of the measured quantity. In the present analysis we account for
these correlations, which may lead to a lower overall uncertainty estimate due
to error cancellation. Furthermore, our results also indicate that the crucial
aspect when obtaining accurate inner-scaled velocity measurements is the
wind-tunnel flow quality, which is more critical than the accuracy in
wall-shear stress measurements
Emerging trends in machine learning for computational fluid dynamics
The renewed interest from the scientific community in machine learning (ML)
is opening many new areas of research. Here we focus on how novel trends in ML
are providing opportunities to improve the field of computational fluid
dynamics (CFD). In particular, we discuss synergies between ML and CFD that
have already shown benefits, and we also assess areas that are under
development and may produce important benefits in the coming years. We believe
that it is also important to emphasize a balanced perspective of cautious
optimism for these emerging approache
Evidence of quasi equilibrium in pressure-gradient turbulent boundary layers
Two sets of measurements utilizing hot-wire anemometry and oil film
interferometry for flat-plate turbulent boundary layers, exposed to various
controlled adverse and favorable pressure gradients, are used to evaluate
history effects of the imposed and varying freestream gradients. The results
are from the NDF wind tunnel at ILLINOIS TECH (IIT) and the MTL wind tunnel at
KTH, over the range (where is the friction
Reynolds number). The streamwise pressure-gradient parameter varied between , where is an outer length scale for boundary layers equivalent to
the half height of channel flow and the radius of pipe flow, and is estimated
for each boundary-layer profile. Extracting from each profile the three
parameters of the overlap region, following the recent work of \cite{mon23}
that led to an overlap region of combined logarithmic and linear parts, we find
minimum history effects in the overlap region. Thus, the overlap region in this
range of pressure-gradient boundary layers appears to be in ``quasi
equilibrium".Comment: 10 pages, 6 figure
A socio-technical framework for digital contact tracing
In their efforts to tackle the COVID-19 crisis, decision makers are
considering the development and use of smartphone applications for contact
tracing. Even though these applications differ in technology and methods, there
is an increasing concern about their implications for privacy and human rights.
Here we propose a framework to evaluate their suitability in terms of impact on
the users, employed technology and governance methods. We illustrate its usage
with three applications, and with the European Data Protection Board (EDPB)
guidelines, highlighting their limitations
New insight into the spectra of turbulent boundary layers with pressure gradients
With the availability of new high-Reynolds-number () databases of
turbulent boundary layers (TBLs) it has been possible to identify in detail
certain regions of the boundary layer with more complex behavior. In this study
we consider a unique database at moderately-high , with a near-constant
adverse pressure gradient (APG) (Pozuelo {\it et al.}, {\it J. Fluid Mech.},
{\bf 939}, A34, 2022), and perform spectral analysis of the Reynolds stresses,
focusing on the streamwise component. We assess different regions of the APG
TBL, comparing this case with the zero-pressure-gradient (ZPG) TBL, and
identify the relevant scaling parameters as well as the contribution of the
scales of different sizes. The small scales in the near-wall region up to the
near-wall spectral peak have been found to scale using viscous units. In APG
TBLs, the largest scales close to the wall have a better scaling with the
boundary-layer thickness (), and they are significantly affected
by the APG. In the overlap and wake regions of the boundary layer, the small
energetic scales exhibit a good scaling with the displacement thickness
() while the larger scales and the outer spectral peak are better
scaled with the boundary-layer thickness. Also note that the wall-normal
location of the spectral outer peak scales with the displacement thickness
rather than the boundary layer thickness. The various scalings exhibited by the
spectra in APG TBLs are reported here for the first time, and shed light on the
complex phenomena present in these flows of great scientific and technological
importance
The transformative potential of machine learning for experiments in fluid mechanics
The field of machine learning has rapidly advanced the state of the art in
many fields of science and engineering, including experimental fluid dynamics,
which is one of the original big-data disciplines. This perspective will
highlight several aspects of experimental fluid mechanics that stand to benefit
from progress advances in machine learning, including: 1) augmenting the
fidelity and quality of measurement techniques, 2) improving experimental
design and surrogate digital-twin models and 3) enabling real-time estimation
and control. In each case, we discuss recent success stories and ongoing
challenges, along with caveats and limitations, and outline the potential for
new avenues of ML-augmented and ML-enabled experimental fluid mechanics
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