Investigating mode competition and three-dimensional features from two-dimensional velocity fields in an open cavity flow by modal decompositions

Shear-layer driven open cavity flows are known to exhibit strong self-sustained oscillations of the shear-layer. Over some range of the control parameters, a competition between two modes of oscillations of the shear layer can occur. We apply both Proper Orthogonal Decomposition and Dynamic Mode Decomposition to experimental two-dimensional two-components time and spaced velocity fields of an incompressible open cavity flow, in a regime of mode competition. We show that, although proper orthogonal decomposition successes in identifying salient features of the flow, it fails at identifying the spatial coherent structures associated with dominant frequencies of the shear-layer oscillations. On the contrary, we show that, as dynamic mode decomposition is devoted to identify spatial coherent structures associated with clearly defined frequency channels, it is well suited for investigating coherentstructuresinintermittentregimes.Weconsiderthevelocitydivergencefield, inordertoidentifyspanwisecoherentfeaturesoftheflow.Finally,weshowthatboth coherent structures in the inner-flow and in the shear-layer exhibit strong spanwise velocitygradients,andarethereforethree-dimensiona

Caracteristiques cellulaires du regime a poches en ecoulement gaz-liquide co-courant vertical. Transition vers le regime destructure

Available from INIST (FR), Document Supply Service, under shelf-number : TD 20441 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEFRFranc

xMLC - A Toolkit for Machine Learning Control

International audiencexMLC is the second book of this `Machine Learning Tools in Fluid Mechanics' Series and focuses on Machine Learning Control (MLC). The objectives of this book are two-fold: First, provide an introduction to MLC for students, researchers, and newcomers on the field; and second, share an open-source code, xMLC, to automatically learn open- and closed-loop control laws directly in the plant with only a few executable commands. This presented MLC algorithm is based on genetic programming and highlights the learning principles (exploration and exploitation). The need for balance between these two principles is illustrated with an extensive parametric study where the explorative and exploitative forces are gradually integrated into the optimization process. The provided software xMLC is an implementation of MLC. It builds on OpenMLC (Duriez et al., 2017) but replaces tree-based genetic programming but the linear genetic programming framework (Brameier and Banzhaf, 2006). The latter representation is preferred for its easier implementation of multiple-input multiple-output control laws and of the genetic operators (mutation and crossover). The handling of the software is facilitated by a step-by-step guide that shall help new practitioners use the code within a few minutes. We also provide detailed advice on using the code for other solvers and for experiments. The code is open-source and a GitHub version is available for future updates, options, and add-ons