scholarly journals Large scale patterning of hydrogel microarrays using capillary pinning

Lab on a Chip ◽  
2015 ◽  
Vol 15 (3) ◽  
pp. 664-667 ◽  
Author(s):  
Burcu Gumuscu ◽  
Johan G. Bomer ◽  
Albert van den Berg ◽  
Jan C. T. Eijkel
Keyword(s):  
Fluid Flow ◽  
Flow Control ◽  
Large Scale ◽  
Microfluidic Systems ◽  
Capillary Barriers ◽  
Fluid Flow Control

Capillary barriers provide a simple and elegant means of autonomous fluid-flow control in microfluidic systems.

The effect of seismic deformation mechanisms on fluid migration in accumulation area of gold ore concentrations

2019 ◽  
Vol 484 (1) ◽  
pp. 87-92
Author(s):  
T. M. Zlobina ◽  
V. A. Petrov ◽  
K. Yu. Murashov ◽  
A. A. Kotov
Keyword(s):  
Fluid Flow ◽  
Flow Control ◽  
Deformation Mechanisms ◽  
Fluid Migration ◽  
Fluid Regime ◽  
Gold Ore ◽  
Fluid Flow Control ◽  
Seismic Deformation ◽  
Fluid Inflow ◽  
Main Influence

This study investigates the effect of mechanisms of paleode formations during the period of fluid inflow into the accumulation sphere of gold concentrations. Such mechanisms are believed to correspond to DC- and NDC- type seismic mechanisms, whose main influence on fluid migration lies on the formation of different, relative to fluid regime parameters, structural and hydrodynamic organizations of the ore-forming system, and fluid flow control within the area of the accumulation of ore concentrations.  

scholarly journals Fluid Flow Control: A Vision-Based Approach

2011 ◽  
Vol 3 (2-3) ◽  
pp. 133-170 ◽  
Author(s):  
Romeo Fomena ◽  
Christophe Collewet
Keyword(s):  
Fluid Flow ◽  
Flow Control ◽  
Fluid Flow Control

Anisothermal Flow Control by Using Reduced-Order Models

2012 ◽  
Author(s):  
Alexandra Tallet ◽  
Cédric Leblond ◽  
Cyrille Allery
Keyword(s):  
Fluid Flow ◽  
Flow Control ◽  
Projection Method ◽  
Control Strategies ◽  
Pollutant Concentration ◽  
Galerkin Projection ◽  
Reduced Order Models ◽  
Reduced Order ◽  
Optimal Projection ◽  
Fluid Flow Control

Despite constantly improving computer capabilities, classical numerical methods (DNS, LES,…) are still out of reach in fluid flow control strategies. To make this problem tractable almost in real-time, reduced-order models are used here. The spatial basis is obtained by POD (Proper Orthogonal Decomposition), which is the most commonly used technique in fluid mechanics. The advantage of the POD basis is its energetic optimality: few modes contain almost the totality of energy. The ROM is achieved with the recent developed optimal projection [1], unlike classical methods which use Galerkin projection. This projection method is based on the minimization of the residual equations in order to have a stabilizing effect. It enables moreover to access pressure field. Here, the projection method is slightly different from [1]: a formulation without the Poisson equation is proposed and developed. Then, the ROM obtained by optimal projection is introduced within an optimal control loop. The flow control strategy is illustrated on an isothermal square lid-driven cavity and an anisothermal square ventilated cavity. The aim is to reach a target temperature (or target pollutant concentration) in the cavity, with an interior initial temperature (or initial pollutant concentration), by adjusting the inlet fluid flow rate.

Piezoelectric actuators for fluid-flow control

1997 ◽  
Author(s):  
Louis N. Cattafesta III ◽  
Sanjay Garg ◽  
Anthony E. Washburn
Keyword(s):  
Fluid Flow ◽  
Flow Control ◽  
Piezoelectric Actuators ◽  
Fluid Flow Control

DESIGN OF A NEUROFUZZY CONTROLLER WITH SIMPLIFIED ARCHITECTURE

2001 ◽  
Vol 11 (03) ◽  
pp. 229-235
Author(s):  
MÁRIO EDUARDO BORDON ◽  
IVAN NUNES DA SILVA ◽  
EDWIN AVOLIO
Keyword(s):  
Fluid Flow ◽  
Flow Control ◽  
Industrial Application ◽  
Processing Time ◽  
Control Rules ◽  
Control Actions ◽  
Fluid Flow Control

This work presents the design of a neurofuzzy controller with simplified architecture that minimizes the processing time used in several stages associated with systems and processes modelling. The basic procedures of fuzzification and defuzzification are very simplified, whereas the inference procedures are computed in a direct way. The simplified architecture has allowed a fast and easy configuration of the neurofuzzy controller, as consequence, the control rules that define the control actions are obtained automatically. To validate the proposed approach, this neurofuzzy system is used in an industrial application for fluid flow control.

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