Nonlinear flow control strategies for connection-oriented communication networks

2006 ◽  
Vol 153 (1) ◽  
pp. 21-28 ◽  
Author(s):  
A. Bartoszewicz
Keyword(s):  
Flow Control ◽  
Communication Networks ◽  
Control Strategies ◽  
Nonlinear Flow

Control Strategies for Multistage Solar Collector Systems

1988 ◽  
Vol 110 (3) ◽  
pp. 230-232
Author(s):  
C. Saltiel
Keyword(s):  
Comparative Study ◽  
Flow Control ◽  
Numerical Simulations ◽  
Control Strategy ◽  
System Performance ◽  
Solar Collector ◽  
Control Strategies ◽  
Climatic Conditions ◽  
High Threshold ◽  
Threshold Values

A comparative study of the yearly performance of multistage solar collector systems, (comprised of more than one collector type) with a single on/off flow control strategy for all the collectors and separate on/off controls for each collector stage, is performed. Detailed numerical simulations under a range of climatic conditions showed that there is little advantage in using individual collector controls over a single on/off control strategy when the systems operate at low collector thresholds, but differences in system performance can be quite significant at high threshold values. In addition, the choice of the single control strategy (i.e., which collector the strategy is based on) at low thresholds is not critical in terms of system performance.

scholarly journals Passive flow control strategies for low Reynolds number airfoils

2020 ◽  
Vol 954 ◽  
pp. 012002
Author(s):  
T Rajesh Senthil Kumar ◽  
Mohini Priya Kolluri ◽  
V R Gopal Subramaniyan ◽  
A D Sripathi
Keyword(s):  
Reynolds Number ◽  
Flow Control ◽  
Control Strategies ◽  
Low Reynolds Number ◽  
Passive Flow Control ◽  
Passive Flow ◽  
Low Reynolds

scholarly journals Laminar and turbulent comparisons for channel flow and flow control

2007 ◽  
Vol 570 ◽  
pp. 467-477 ◽  
Author(s):  
IVAN MARUSIC ◽  
D. D. JOSEPH ◽  
KRISHNAN MAHESH
Keyword(s):  
Pressure Gradient ◽  
Flow Control ◽  
Turbulent Flows ◽  
Control Strategies ◽  
Reynolds Numbers ◽  
Volume Flux ◽  
Reynolds Number Flow ◽  
Number Flow ◽  
High Reynolds ◽  
Strategy Design

A formula is derived that shows exactly how much the discrepancy between the volume flux in laminar and in turbulent flow at the same pressure gradient increases as the pressure gradient is increased. We compare laminar and turbulent flows in channels with and without flow control. For the related problem of a fixed bulk-Reynolds-number flow, we seek the theoretical lowest bound for skin-friction drag for control schemes that use surface blowing and suction with zero-net volume-flux addition. For one such case, using a crossflow approach, we show that sustained drag below that of the laminar-Poiseuille-flow case is not possible. For more general control strategies we derive a criterion for achieving sublaminar drag and use this to consider the implications for control strategy design and the limitations at high Reynolds numbers.

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.

scholarly journals Reinforcement learning for bluff body active flow control in experiments and simulations

2020 ◽  
Vol 117 (42) ◽  
pp. 26091-26098
Author(s):  
Dixia Fan ◽  
Liu Yang ◽  
Zhicheng Wang ◽  
Michael S. Triantafyllou ◽  
George Em Karniadakis
Keyword(s):  
Reinforcement Learning ◽  
Fluid Mechanics ◽  
Drag Reduction ◽  
Flow Control ◽  
Bluff Body ◽  
Control Strategies ◽  
Active Flow Control ◽  
Reduction Process ◽  
Automatic Sequence ◽  
Active Flow

We have demonstrated the effectiveness of reinforcement learning (RL) in bluff body flow control problems both in experiments and simulations by automatically discovering active control strategies for drag reduction in turbulent flow. Specifically, we aimed to maximize the power gain efficiency by properly selecting the rotational speed of two small cylinders, located parallel to and downstream of the main cylinder. By properly defining rewards and designing noise reduction techniques, and after an automatic sequence of tens of towing experiments, the RL agent was shown to discover a control strategy that is comparable to the optimal strategy found through lengthy systematically planned control experiments. Subsequently, these results were verified by simulations that enabled us to gain insight into the physical mechanisms of the drag reduction process. While RL has been used effectively previously in idealized computer flow simulation studies, this study demonstrates its effectiveness in experimental fluid mechanics and verifies it by simulations, potentially paving the way for efficient exploration of additional active flow control strategies in other complex fluid mechanics applications.

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