Two models based on convolutional neural networks are trained to predict the two-dimensional instantaneous velocity-fluctuation fields at different wall-normal locations in a turbulent open-channel flow, using wall-shear-stress components and wall pressure as inputs. The first model is fully network (FCN) which directly predicts fluctuations, while second one reconstructs flow linear combination of orthonormal basis functions, obtained through proper orthogonal decomposition (POD), hence...

This work evaluates the applicability of super-resolution generative adversarial networks (SRGANs) as a methodology for reconstruction turbulent-flow quantities from coarse wall measurements. The method is applied both resolution enhancement fields and estimation wall-parallel velocity measurements shear stress pressure. analysis has been carried out with database turbulent open-channel flow friction Reynolds number $Re_{\tau}=180$ generated through direct numerical simulation. Coarse have...

We propose a novel nonlinear manifold learning from snapshot data and demonstrate its superiority over proper orthogonal decomposition (POD) for shedding-dominated shear flows. Key enablers are isometric feature mapping, Isomap, as encoder and, $K$ -nearest neighbours ( NN) algorithm decoder. The proposed technique is applied to numerical experimental datasets including the fluidic pinball, swirling jet wake behind couple of tandem cylinders. Analysing able describe pitchfork bifurcation...

In the last two decades, several techniques have been introduced that are capable to extract three-dimensional three-components velocity fields in fluid flows.Fast-paced developments both hardware and processing algorithms generated a diverse collection of methods, with growing range application flow diagnostics.The context has further enriched by an increasingly marked trend hybridization, which boundaries between different fading.In this review, we carry out survey prominent including...

A method to extract turbulent statistics from three-dimensional (3D) PIV measurements via ensemble averaging is presented. The proposed technique a 3D extension of the particle tracking velocimetry methods, which consist in summing distributions velocity vectors calculated on low image density samples and then statistical moments within sub-volumes, with size sub-volume depending desired number particles available snapshots.

This manuscripts presents a study on adverse-pressure-gradient turbulent boundary layers under different Reynolds-number and pressure-gradient conditions. In this work we performed Particle Image Velocimetry (PIV) measurements supplemented with Large-Eddy Simulations in order to have dataset covering range of displacement-thickness-based 2300 <Reδ∗< 34000 values the Clauser parameter β up 2.4. The spatial resolution limits PIV for estimation turbulence statistics been overcome via...

A comparative assessment of machine-learning (ML) methods for active flow control is performed. The chosen benchmark problem the drag reduction a two-dimensional Kármán vortex street past circular cylinder at low Reynolds number (Re = 100). manipulated with two blowing/suction actuators on upper and lower side cylinder. feedback employs several velocity sensors. Two probe configurations are evaluated: 5 11 probes located different points around in wake. laws optimized Deep Reinforcement...

The goal of this study is to present a first step towards establishing criteria aimed at assessing whether particular adverse-pressure-gradient (APG) turbulent boundary layer (TBL) can be considered well-behaved, i.e., it independent the inflow conditions and exempt numerical or experimental artifacts. To end, we analyzed several high-quality datasets, including in-house databases APG TBLs developing over flat-plates suction side wing section, five studies available in literature. Due impact...

This paper introduces a new method based on the diagnostic plot (Alfredsson et al. , Phys. Fluids vol. 23, 2011, 041702) to assess convergence towards well-behaved zero-pressure-gradient (ZPG) turbulent boundary layer (TBL). The most popular and well-understood methods state rely empirical skin-friction curves (requiring accurate measurements), shape-factor full velocity profile measurements with an wall position determination) or wake-parameter both of previous quantities). On other hand,...

This study assesses the capability of extended proper orthogonal decomposition (EPOD) and convolutional neural networks (CNNs) to reconstruct large-scale very-large-scale motions (LSMs VLSMs respectively) employing wall-shear-stress measurements in wall-bounded turbulent flows. Both techniques are used instantaneous LSM evolution flow field as a combination (POD) modes, limited set measurements. Due dominance nonlinear effects, only CNNs provide satisfying results. Being able account for...

Wall turbulence is characterized by a near-wall cycle of streaks and quasistreamwise vortices apparent as an invariant inner peak in the premultiplied energy spectra. A second, outer known to emerge this spectral view become energized with increasing Reynolds number (Re) well adverse pressure gradient (APG). An analysis experimental data sets examines how scales Re APG whether their imprint on small are different.

Abstract Advancements in machine-learning (ML) techniques are driving a paradigm shift image processing. Flow diagnostics with optical is not an exception. Considering the existing and foreseeable disruptive developments flow field measurement techniques, we elaborate this perspective, particularly focused to of particle velocimetry. The forces for advancements ML methods measurements recent years reviewed terms preprocessing, data treatment conditioning. Finally, possible routes further highlighted.

A method is proposed to obtain full-domain spatial modes based on Proper Orthogonal Decomposition (POD) of Particle Image Velocimetry (PIV) measurements performed at different (overlapping) locations. This situation occurs when large domains are covered by multiple non-simultaneous and yet the large-scale flow field organization be captured. The methodology leverages definition POD as eigenvectors correlation matrix, where local measurements, even not obtained simultaneously, provide each a...

A novel method to improve the accuracy of pressure field estimation from time-resolved Particle Image Velocimetry data is proposed. This generates several new time-series velocity by propagating in time original one using an advection-based model, which assumes that small-scale turbulence advected large-scale motions. Then smoothing performed at corresponding positions across all generated time-series. The process repeated through iterative scheme. proposed technique smears out spatial noise...