Streakline-based closed-loop control of a bluff body flow

A novel closed-loop control methodology is introduced to stabilize a cylinder wake flow based on images of streaklines. Passive scalar tracers are injected upstream the cylinder and their concentration is monitored downstream at certain image sectors of the wake. An AutoRegressive with eXogenous inputs mathematical model is built from these images and a Generalized Predictive Controller algorithm is used to compute the actuation required to stabilize the wake by adding momentum tangentially to the cylinder wall through plasma actuators. The methodology is new and has real-world applications. It is demonstrated on a numerical simulation and the provided results show that good performances are achieved.Fil: Roca, Pablo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; ArgentinaFil: Cammilleri, Ada. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; ArgentinaFil: Duriez, Thomas Pierre Cornil. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; ArgentinaFil: Mathelin, Lionel. Centre National de la Recherche Scientifique. Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur; FranciaFil: Artana, Guillermo Osvaldo. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Ventilator output splitting interface ‘ACRA’: Description and evaluation in lung simulators and in an experimental ARDS animal model

The current COVID-19 pandemic has led the world to an unprecedented global shortage of ventilators, and its sharing has been proposed as an alternative to meet the surge. This study outlines the performance of a preformed novel interface called’ACRA’, designed to split ventilator outflow into two breathing systems. The’ACRA’ interface was built using medical use approved components. It consists of four unidirectional valves, two adjustable flow-restrictor valves placed on the inspiratory limbs of each unit, and one adjustable PEEP valve placed on the expiratory limb of the unit that would require a greater PEEP. The interface was interposed between a ventilator and two lung units (phase I), two breathing simulators (phase II) and two live pigs with heterogeneous lung conditions (phase III). The interface and ventilator adjustments tested the ability to regulate individual pressures and the resulting tidal volumes. Data were analyzed using Friedman and Wilcoxon tests test (p < 0.05). Ventilator outflow splitting, independent pressure adjustments and individual tidal volume monitoring were feasible in all phases. In all experimental measurements, dual ventilation allowed for individual and tight adjustments of the pressure, and thus volume delivered to each paired lung unit without affecting the other unit’s ventilation—all the modifications performed on the ventilator equally affected both paired lung units. Although only suggested during a dire crisis, this experiment supports dual ventilation as an alternative worth to be considered.Fil: Otero, Pablo Ezequiel. Universidad de Buenos Aires. Facultad de Ciencias Veterinarias. Cátedra de Anestesiología y Algiología; ArgentinaFil: Tarragona, Lisa. Universidad de Buenos Aires. Facultad de Ciencias Veterinarias. Cátedra de Anestesiología y Algiología; ArgentinaFil: Zaccagnini, Andrea Silvia. Universidad de Buenos Aires. Facultad de Ciencias Veterinarias. Cátedra de Anestesiología y Algiología; ArgentinaFil: Verdier, Natali. Universidad de Buenos Aires. Facultad de Ciencias Veterinarias. Cátedra de Anestesiología y Algiología; ArgentinaFil: Ceballos, Martin. Universidad de Buenos Aires. Facultad de Ciencias Veterinarias. Cátedra de Anestesiología y Algiología; ArgentinaFil: Gogniat, Emiliano. Sociedad Argentina de Terapia Intensiva.; ArgentinaFil: Cabaleiro, Juan Martin. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; ArgentinaFil: D'adamo, Juan Gastón Leonel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; ArgentinaFil: Duriez, Thomas Pierre Cornil. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; ArgentinaFil: Garcia Eijo, Pedro Manuel. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; ArgentinaFil: Artana, Guillermo Osvaldo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; Argentin

Prediction of biodiesel physico-chemical properties from its fatty acid composition using genetic programming

This paper presents regression analysis of biodiesel physico-chemical properties as a function of fatty acid composition using an experimental database. The study is done by using 48 edible and non-edible oils-based biodiesel available data. Regression equations are presented as a function of fatty acid composition (saturated and unsaturated methyl esters). The physico-chemical properties studied are kinematic viscosity, flash point, cloud point, pour point (PP), cold filter plugging point, cetane (CN) and iodine numbers. The regression technique chosen to carry out this work is genetic programming (GP). Unlike multiple linear regression (MLR) strategies available in literature, GP provides generic, non-parametric regression among variables. For all properties analyzed, the performance of the regression is systematically better for GP than MLR. Indeed, the RSME related to the experimental database is lower for GP models, from ≈3% for CN to ≈55% for PP, in comparison to the best MLR model for each property. Particularly, most GP regression models reproduce correctly the dependence of properties on the saturated and unsaturated methyl esters.Fil: Alviso, Dario. Universidad Nacional de Asunción; Paraguay. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Artana, Guillermo Osvaldo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; ArgentinaFil: Duriez, Thomas Pierre Cornil. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; Argentina. Universidad de la Marina Mercante; Argentin

Modeling of vegetable oils cloud point, pour point, cetane number and iodine number from their composition using genetic programming

Vegetable oils (VOs) are composed of 90–98% of triglycerides, i.e. esters composed of three fatty acids and glycerol, and small amounts of mono- and di-glycerides. Due to their physico-chemical properties, VOs have been considered for uses especially in large ships, in stationary engines and low and medium speed diesel engines, in pure form or in blends with fuel oil, diesel, biodiesel and alcohols. There are about 350 VOs with potential as fuel sources, and for most of them, physico-chemical properties values have not yet been measured. In this context, regression models using only VOs fatty acid composition are very useful. In the present paper, regression analysis of VOs cloud point (CP), pour point (PP), cetane number (CN) and iodine number (IN) as a function of saturated and unsaturated fatty acids is conducted. The study is done by using 4 experimental databases including 88 different data of VOs. Concerning the regression technique, genetic programming (GP) has been chosen. The cost function of GP is defined to minimize the Mean Absolute Error (MAE) between experimental and predicted values of each property. The resulting GP models consisting of terms including saturated and unsaturated fatty acids reproduce correctly the dependencies of all four properties on those acids. And they are validated by showing that their results are in good agreement to the experimental databases. In fact, MAE values of the proposed models with respect to the databases for CP, PP, CN and IN are lower than 4.51 °C, 4.54 °C, 3.64 and 8.01, respectively.Fil: Alviso, Dario. Universidad Nacional de Asunción; Paraguay. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Zárate Evers, Cristhian Manuel. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Duriez, Thomas Pierre Cornil. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Regressions of the dielectric constant and speed of sound of vegetable oils from their composition and temperature using genetic programming

The dielectric constant (DC) and speed of sound (SoS) have been measured in many studies on vegetable oils (VOs). These measurements can be applied for quality control, for the detection of contaminants, and in works related to heated and frying VOs. There are several hundreds of VOs with potential use in the food industry, and for most of them, the DC and SoS values are not yet available. This paper proposes regression models of the DC and SoS of VOs as a function of their composition (saturated and unsaturated fatty acids) and the temperature. A regression study was conducted using available experimental databases including a total of 57 and 56 data in the range of 20−50 °C for the DC and SoS, respectively. The equations are obtained using genetic programming (GP). The goal is to minimize the mean absolute error (MAE) between the values of the measured and predicted DC and SoS for several VOs. The resulting GP regression equations reproduce correctly the dependencies of the DC and SoS of VOs on the saturated and unsaturated fatty acids. The validation of these equations is carried out by comparing their results to those of the experimental databases. The MAE values of the regression equations concerning the databases for DC and SoS of VOs are 0.02 and 1.0 m/s, respectively.Fil: Alviso, Dario. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; Argentina. Universidad María Auxiliadora; ParaguayFil: Zárate Evers, Cristhian Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; ArgentinaFil: Artana, Guillermo Osvaldo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; ArgentinaFil: Duriez, Thomas Pierre Cornil. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; Argentina. Universidad de la Marina Mercante; Argentin

A machine learning-based framework to design capillary-driven networks

We present a novel approach for the design of capillary-driven microfluidic networks using a machine learning genetic algorithm (ML-GA). This strategy relies on a user-friendly 1D numerical tool specifically developed to generate the necessary data to train the ML-GA. This 1D model was validated using analytical results issued from a Y-shaped capillary network and experimental data. For a given microfluidic network, we defined the objective of the ML-GA to obtain the set of geometric parameters that produces the closest matching results against two prescribed curves of delivered volume against time. We performed more than 20 generations of 10 000 simulations to train the ML-GA and achieved the optimal solution of the inverse design problem. The optimisation took less than 6 hours, and the results were successfully validated using experimental data. This work establishes the utility of the presented method for the fast and reliable design of complex capillary-driven devices, enabling users to optimise their designs via an easy-to-use 1D numerical tool and machine learning technique.Fil: Garcia Eijo, Pedro Manuel. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; ArgentinaFil: Duriez, Thomas Pierre Cornil. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Cabaleiro, Juan Martin. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; ArgentinaFil: Artana, Guillermo Osvaldo. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Closed-loop separation control using machine learning

We present the first closed-loop separation control experiment using a novel, model-free strategy based on genetic programming, which we call 'machine learning control'. The goal is to reduce the recirculation zone of backward-facing step flow at Reh = 1350 manipulated by a slotted jet and optically sensed by online particle image velocimetry. The feedback control law is optimized with respect to a cost functional based on the recirculation area and a penalization of the actuation. This optimization is performed employing genetic programming. After 12 generations comprised of 500 individuals, the algorithm converges to a feedback law which reduces the recirculation zone by 80 %. This machine learning control is benchmarked against the best periodic forcing which excites Kelvin-Helmholtz vortices. The machine learning control yields a new actuation mechanism resonating with the low-frequency flapping mode instability. This feedback control performs similarly to periodic forcing at the design condition but outperforms periodic forcing when the Reynolds number is varied by a factor two. The current study indicates that machine learning control can effectively explore and optimize new feedback actuation mechanisms in numerous experimental applications.Fil: Gautier, N.. École Supérieure de Physique et Chimie Industrielles de la ville de Paris; FranciaFil: Aider, J. L.. École Supérieure de Physique et Chimie Industrielles de la ville de Paris; FranciaFil: Duriez, Thomas Pierre Cornil. Université de Poitiers; Francia. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Noack, B. R.. Université de Poitiers; FranciaFil: Segond, M.. Ambrosys; AlemaniaFil: Abel, M.. Ambrosys; Alemani

Circular Cylinder Drag Reduction By Three-Electrode Plasma Symmetric Forcing

This study reports an efficient reduction of the drag exerted by a flow on a cylinder when the former is forced with a plasma actuator. A three-electrode plasma device (TED) disposed on the surface of the body is considered, and the effect of the actuation frequency and amplitude is studied. Particle image velocimetry (PIV) measurements provided a detailed information that was processed to obtain the time-averaged drag force and to compare the performances of TED actuator and the canonical dielectric discharge barrier actuator. For the Reynolds number considered (Re = 5500), excitations with the TED actuator were more efficient, achieving drag reductions that attained values close to 40% with high net energy savings. The reduction of coherent structures using the instantaneous vorticity fields and a clustering technique allowed us to gain insight into the physical mechanisms involved in these phenomena. This highlights that the symmetrical forcing of the wake flow at its resonant frequency with the TED promotes symmetrical vorticity patterns which favor drag reductions.Fil: D'adamo, Juan Gastón Leonel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; ArgentinaFil: Leonardo, Leandro. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; ArgentinaFil: Castro Hebrero, Federico Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; ArgentinaFil: Sosa, Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; ArgentinaFil: Duriez, Thomas Pierre Cornil. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; ArgentinaFil: Artana, Guillermo Osvaldo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Ingeniería Mecánica. Laboratorio de Fluidodinámica; Argentin

Mixing layer manipulation experiment: From open-loop forcing to closed-loop machine learning control

Open- and closed-loop control of a turbulent mixing layer is experimentally performed in a dedicated large scale, low speed wind-tunnel facility. The flow is manipulated by an array of fluidic micro-valve actuators integrated into the trailing edge of a splitter plate. Sensing is performed using a rake of hot-wire probes downstream of the splitter plate in the mixing layer. The control goal is the manipulation of the local fluctuating energy level. The mixing layer's response to the control is tested with open-loop forcing with a wide range of actuation frequencies. Results are discussed for different closed-loop control approaches, such as: adaptive extremum-seeking and in-time POD mode feedback control. In addition, we propose Machine Learning Control (MLC) as a model-free closed-loop control method. MLC arrives reproducibly at the near-optimal in-time control.Fil: Parezanović, Vladimir. Institut Pprime; Francia. Centre National de la Recherche Scientifique; FranciaFil: Laurentie, Jean Charles. Centre National de la Recherche Scientifique; Francia. Institut Pprime; FranciaFil: Fourment, Carine. Centre National de la Recherche Scientifique; Francia. Institut Pprime; FranciaFil: Delville, Joël. Centre National de la Recherche Scientifique; Francia. Institut Pprime; FranciaFil: Bonnet, Jean-Paul. Centre National de la Recherche Scientifique; Francia. Institut Pprime; FranciaFil: Spohn, Andreas. Institut Pprime; Francia. Centre National de la Recherche Scientifique; FranciaFil: Duriez, Thomas Pierre Cornil. Centre National de la Recherche Scientifique; Francia. Institut Pprime; Francia. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Cordier, Laurent. Centre National de la Recherche Scientifique; Francia. Institut Pprime; FranciaFil: Noack, Bernd R.. Centre National de la Recherche Scientifique; Francia. Institut Pprime; FranciaFil: Abel, Markus. Ambrosys; Alemania. University of Potsdam; Alemania. Université de Lorraine; FranciaFil: Segond, Marc. Ambrosys; Alemania. Taflia Technical University; JordaniaFil: Shaqarin, Tamir. Taflia Technical University; JordaniaFil: Brunton, Steven L.. University of Washington; Estados Unido