2019 

Alessandro Corbetta; Vlado Menkovski; Roberto Benzi; Federico Toschi Deep learning velocity signals allows to quantify turbulence intensity Journal Article arXiv, 2019. Abstract  BibTeX  Tags: condmat.statmech, cs.AI, cs.LG, physics.fludyn @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 = {20190101}, journal = {arXiv}, publisher = {Cornell University Library}, abstract = {Turbulence, the ubiquitous and chaotic state of fluid motions, is characterized by strong and statistically nontrivial fluctuations of the velocity field, over a wide range of length and timescales, and it can be quantitatively described only in terms of statistical averages. Strong nonstationarities 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 largescale eddyturnover times. In contrast, physicsbased 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 nonstationary turbulent flows as ordinarily found in nature as well as in industrial processes.}, keywords = {condmat.statmech, cs.AI, cs.LG, physics.fludyn}, pubstate = {published}, tppubtype = {article} } Turbulence, the ubiquitous and chaotic state of fluid motions, is characterized by strong and statistically nontrivial fluctuations of the velocity field, over a wide range of length and timescales, and it can be quantitatively described only in terms of statistical averages. Strong nonstationarities 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 largescale eddyturnover times. In contrast, physicsbased 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 nonstationary 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 arXiv, 2019, (5 pages, 4 figures  supplemental 5 pages, 4 figures). Abstract  BibTeX  Tags: condmat.soft, condmat.statmech, physics.fludyn @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 = {20190101}, journal = {arXiv}, publisher = {Cornell University Library}, abstract = {Dense emulsions, colloidal gels, microgels, and foams all display a solidlike behavior at rest characterized by a yield stress, above which the material flows like a liquid. Such a fluidization transition often consists of longlasting transient flows that involve shearbanded velocity profiles. The characteristic time for full fluidization, $tau_textf$, has been reported to decay as a powerlaw 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 HerschelBulkley law that describes the steadystate flow behavior of these complex fluids. Here we introduce a continuum model based on the minimization of an outofequilibrium free energy that captures quantitatively all the salient features associated with such textittransient shearbanding. More generally, our results provide a unified theoretical framework for describing the yielding transition and the steadystate flow properties of yield stress fluids.}, note = {5 pages, 4 figures  supplemental 5 pages, 4 figures}, keywords = {condmat.soft, condmat.statmech, physics.fludyn}, pubstate = {published}, tppubtype = {article} } Dense emulsions, colloidal gels, microgels, and foams all display a solidlike behavior at rest characterized by a yield stress, above which the material flows like a liquid. Such a fluidization transition often consists of longlasting transient flows that involve shearbanded velocity profiles. The characteristic time for full fluidization, $tau_textf$, has been reported to decay as a powerlaw 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 HerschelBulkley law that describes the steadystate flow behavior of these complex fluids. Here we introduce a continuum model based on the minimization of an outofequilibrium free energy that captures quantitatively all the salient features associated with such textittransient shearbanding. More generally, our results provide a unified theoretical framework for describing the yielding transition and the steadystate flow properties of yield stress fluids. 
publications
2019 

Deep learning velocity signals allows to quantify turbulence intensity Journal Article arXiv, 2019.  
Unified theoretical and experimental view on transient shear banding Journal Article arXiv, 2019, (5 pages, 4 figures  supplemental 5 pages, 4 figures). 