### 2019

Alessandro Corbetta; Vlado Menkovski; Roberto Benzi; Federico Toschi

Deep learning velocity signals allows to quantify turbulence intensity Journal Article

In: arXiv, 2019.

Abstract | BibTeX | Tags: cond-mat.stat-mech, cs.AI, cs.LG, physics.flu-dyn

@article{046d24a1bab542e983a477781595c64f,

title = {Deep learning velocity signals allows to quantify turbulence intensity},

author = {Alessandro Corbetta and Vlado Menkovski and Roberto Benzi and Federico Toschi},

year = {2019},

date = {2019-01-01},

journal = {arXiv},

publisher = {Cornell University Library},

abstract = {Turbulence, the ubiquitous and chaotic state of fluid motions, is characterized by strong and statistically non-trivial fluctuations of the velocity field, over a wide range of length- and time-scales, and it can be quantitatively described only in terms of statistical averages. Strong non-stationarities hinder the possibility to achieve statistical convergence, making it impossible to define the turbulence intensity and, in particular, its basic dimensionless estimator, the Reynolds number. Here we show that by employing Deep Neural Networks (DNN) we can accurately estimate the Reynolds number within $15%$ accuracy, from a statistical sample as small as two large-scale eddy-turnover times. In contrast, physics-based statistical estimators are limited by the rate of convergence of the central limit theorem, and provide, for the same statistical sample, an error at least $100$ times larger. Our findings open up new perspectives in the possibility to quantitatively define and, therefore, study highly non-stationary turbulent flows as ordinarily found in nature as well as in industrial processes.},

keywords = {cond-mat.stat-mech, cs.AI, cs.LG, physics.flu-dyn},

pubstate = {published},

tppubtype = {article}

}

Turbulence, the ubiquitous and chaotic state of fluid motions, is characterized by strong and statistically non-trivial fluctuations of the velocity field, over a wide range of length- and time-scales, and it can be quantitatively described only in terms of statistical averages. Strong non-stationarities hinder the possibility to achieve statistical convergence, making it impossible to define the turbulence intensity and, in particular, its basic dimensionless estimator, the Reynolds number. Here we show that by employing Deep Neural Networks (DNN) we can accurately estimate the Reynolds number within $15%$ accuracy, from a statistical sample as small as two large-scale eddy-turnover times. In contrast, physics-based statistical estimators are limited by the rate of convergence of the central limit theorem, and provide, for the same statistical sample, an error at least $100$ times larger. Our findings open up new perspectives in the possibility to quantitatively define and, therefore, study highly non-stationary turbulent flows as ordinarily found in nature as well as in industrial processes.

Roberto Benzi; Thibaut Divoux; Catherine Barentin; S é; Mauro Sbragaglia; Federico Toschi

Unified theoretical and experimental view on transient shear banding Journal Article

In: arXiv, 2019, (5 pages, 4 figures - supplemental 5 pages, 4 figures).

Abstract | BibTeX | Tags: cond-mat.soft, cond-mat.stat-mech, physics.flu-dyn

@article{67228bed5d894c6688f967ac3899f499,

title = {Unified theoretical and experimental view on transient shear banding},

author = {Roberto Benzi and Thibaut Divoux and Catherine Barentin and S é and Mauro Sbragaglia and Federico Toschi},

year = {2019},

date = {2019-01-01},

journal = {arXiv},

publisher = {Cornell University Library},

abstract = {Dense emulsions, colloidal gels, microgels, and foams all display a solid-like behavior at rest characterized by a yield stress, above which the material flows like a liquid. Such a fluidization transition often consists of long-lasting transient flows that involve shear-banded velocity profiles. The characteristic time for full fluidization, $tau_textf$, has been reported to decay as a power-law of the shear rate $dot gamma$ and of the shear stress $sigma$ with respective exponents $alpha$ and $beta$. Strikingly, the ratio of these exponents was empirically observed to coincide with the exponent of the Herschel-Bulkley law that describes the steady-state flow behavior of these complex fluids. Here we introduce a continuum model based on the minimization of an out-of-equilibrium free energy that captures quantitatively all the salient features associated with such textittransient shear-banding. More generally, our results provide a unified theoretical framework for describing the yielding transition and the steady-state flow properties of yield stress fluids.},

note = {5 pages, 4 figures - supplemental 5 pages, 4 figures},

keywords = {cond-mat.soft, cond-mat.stat-mech, physics.flu-dyn},

pubstate = {published},

tppubtype = {article}

}

Dense emulsions, colloidal gels, microgels, and foams all display a solid-like behavior at rest characterized by a yield stress, above which the material flows like a liquid. Such a fluidization transition often consists of long-lasting transient flows that involve shear-banded velocity profiles. The characteristic time for full fluidization, $tau_textf$, has been reported to decay as a power-law of the shear rate $dot gamma$ and of the shear stress $sigma$ with respective exponents $alpha$ and $beta$. Strikingly, the ratio of these exponents was empirically observed to coincide with the exponent of the Herschel-Bulkley law that describes the steady-state flow behavior of these complex fluids. Here we introduce a continuum model based on the minimization of an out-of-equilibrium free energy that captures quantitatively all the salient features associated with such textittransient shear-banding. More generally, our results provide a unified theoretical framework for describing the yielding transition and the steady-state flow properties of yield stress fluids.