Towards Practical Flow Sensing and Control via POD and LSE Based Low-Dimensional Tools (Keynote Paper)

2002 ◽  
Author(s):  
Jeffrey Taylor ◽  
M. N. Glauser
Keyword(s):  
Velocity Field ◽  
Control Strategies ◽  
Adverse Pressure Gradient ◽  
Wall Pressure ◽  
Mean Flow ◽  
Flow Sensing ◽  
Active Feedback ◽  
Low Dimensional ◽  
And Control ◽  
Proper Orthogonal

We present the application of Proper Orthogonal Decomposition (POD) and Linear Stochastic Estimation (LSE) based low-dimensional methods to the flow over a backward facing ramp with an adjustable flap above the ramp which allows for dynamic variation of the adverse pressure gradient. There is a range of flap angles where the flow is incipiently separated so that this relatively simple experiment can be used to flush out ideas for active feedback separation control strategies. The study utilized a combination of PIV and multi-point wall pressure measurements to estimate the full velocity field (mean plus fluctuating) from a modified complementary technique. Specifically we want to identify a low-dimensional mean flow to observe when the profiles are inflectionary, i.e., the incipient condition, just from wall pressure. We demonstrate via this method, that a reasonable estimate of the low dimensional full velocity field can be obtained. This is important for practical active feedback flow control strategies since from wall pressure we can estimate the state of the flow without resorting to probes in the flow.

Towards Practical Flow Sensing and Control via POD and LSE Based Low-Dimensional Tools

2004 ◽  
Vol 126 (3) ◽  
pp. 337-345 ◽  
Author(s):  
J. A. Taylor ◽  
M. N. Glauser
Keyword(s):  
Velocity Field ◽  
Control Strategies ◽  
Accurate Estimate ◽  
Wall Pressure ◽  
Flow Sensing ◽  
Image Velocimetry ◽  
Active Feedback ◽  
Low Dimensional ◽  
And Control ◽  
Proper Orthogonal

Low-dimensional methods including the Proper Orthogonal Decomposition (POD) and Linear Stochastic Estimation (LSE) have been applied to the flow between a backward facing ramp and an adjustable flap. A range of flap angles provide a flow which is incipiently separated and can be used to flesh out ideas for active feedback separation control strategies. The current study couples Particle Image Velocimetry (PIV) and multi-point wall pressure measurements using POD and LSE to estimate the full velocity field from the wall pressure alone. This technique yields a sufficiently accurate estimate of the velocity field that the incipient condition can be detected. The ability to estimate the state of the flow without inserting probes into the flow is important for the development of practical active feedback flow control strategies.

Towards Sensing and Control of Separation in Subsonic Flows

2003 ◽  
Author(s):  
Monica J. Young ◽  
Mark N. Glauser ◽  
Hiroshi Higuchi ◽  
Jeffrey Taylor
Keyword(s):  
Velocity Field ◽  
Wall Pressure ◽  
Flow State ◽  
Mean Flow ◽  
Airfoil Surface ◽  
Subsonic Flows ◽  
Image Velocimetry ◽  
Low Dimensional ◽  
And Control ◽  
Proper Orthogonal

The purpose of this study is to validate the use of Proper Orthogonal Decomposition POD and Modified Linear Stochastic Estimation mLSE based low-dimensional methods to model an external flow over a NACA 4412 airfoil. By using a combination of Particle Image Velocimetry PIV and multiple airfoil surface pressure measurements, the full velocity field (mean plus fluctuating) is estimated through implementation of a modified complementary technique. We will identify a low-dimensional mean flow just from the wall pressure, specifically observing when the profiles are at the incipient condition. This gives a reasonable estimate of the low-dimensional velocity field. The importance of this work lies in that the flow is estimated from the wall pressure only, providing a practical means for estimating the flow state. This is particularly important for flow control applications.

Analysis of Particle Image Velocimetry Measurements of Natural Convection in an Enclosure Using Proper Orthogonal Decomposition

2015 ◽  
Vol 137 (12) ◽  
Author(s):  
Nirmalendu Biswas ◽  
Souvick Chatterjee ◽  
Mithun Das ◽  
Amlan Garai ◽  
Prokash C. Roy ◽  
...  
Keyword(s):  
Particle Image Velocimetry ◽  
Natural Convection ◽  
Velocity Field ◽  
Proper Orthogonal Decomposition ◽  
Particle Image ◽  
Field Measurements ◽  
Orthogonal Decomposition ◽  
Image Velocimetry ◽  
Low Dimensional ◽  
Proper Orthogonal

This work investigates natural convection in an enclosure with localized heating on the bottom wall with a flushed or protruded heat source and cooled on the top and the side walls. Velocity field measurements are done by using 2D particle image velocimetry (PIV) technique. Proper orthogonal decomposition (POD) has been used to create low dimensional approximations of the system for predicting the flow structures. The POD-based analysis reveals the modal structure of the flow field and also allows reconstruction of velocity field at conditions other than those used in PIV study.

Effective infection prevention and control strategies in a large accredited psychiatric facility in Singapore

2020 ◽  
Author(s):  
Daniel Poremski ◽  
Sandra Henrietta Subner ◽  
Grace Lam Fong Kin ◽  
Raveen Dev Ram Dev ◽  
Mok Yee Ming ◽  
...  
Keyword(s):  
Mental Health ◽  
Prevention And Control ◽  
Infection Prevention ◽  
Control Strategies ◽  
Infection Prevention And Control ◽  
Psychiatric Facility ◽  
Essential Services ◽  
Community Transmission ◽  
And Control

The Institute of Mental Health in Singapore continues to attempt to prevent the introduction of COVID-19, despite community transmission. Essential services are maintained and quarantine measures are currently unnecessary. To help similar organizations, strategies are listed along three themes: sustaining essential services, preventing infection, and managing human and consumable resources.

Optimization and Control Petroleum Refinery Wastewater Treatment Systems — Status, Trends and Needs

1989 ◽  
Vol 24 (3) ◽  
pp. 463-477
Author(s):  
Stephen G. Nutt
Keyword(s):  
Wastewater Treatment ◽  
Process Control ◽  
Petroleum Refinery ◽  
Control Strategies ◽  
Refinery Wastewater ◽  
Statistical Process ◽  
Petroleum Refinery Wastewater ◽  
Optimization And Control ◽  
And Control ◽  
Wastewater Treatment Systems

Abstract Based on discussions in workshop sessions, several recurring themes became evident with respect to the optimization and control of petroleum refinery wastewater treatment systems to achieve effective removal of toxic contaminants. It was apparent that statistical process control (SPC) techniques are finding more widespread use and have been found to be effective. However, the implementation of real-time process control strategies in petroleum refinery wastewater treatment systems is in its infancy. Considerable effort will need to be expended to demonstrate the practicality of on-line sensors, and the utility of automated process control in petroleum refinery wastewater treatment systems. This paper provides a summary of the discussions held at the workshop.

Learning and control

2018 ◽  
Author(s):  
Ivan Herreros
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Brain Function ◽  
Control Strategies ◽  
Learning Problems ◽  
Animal Learning ◽  
Feed Forward Control ◽  
Machine Learning Applications ◽  
And Control

This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant conditioning, and between unsupervised and perceptual learning. Additionally, it interprets innate and acquired actions from the standpoint of feedback vs anticipatory and adaptive control. Finally, it argues how this framework of translating knowledge between formal and biological disciplines can serve us to not only structure and advance our understanding of brain function but also enrich engineering solutions at the level of robot learning and control with insights coming from biology.

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