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Report . 2021
License: CC BY
Data sources: Datacite
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ZENODO
Report . 2021
License: CC BY
Data sources: Datacite
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Active flow control in simulations of fluid flows based on deep reinforcement learning

Authors: Thummar, Darshan;

Active flow control in simulations of fluid flows based on deep reinforcement learning

Abstract

For active flow control, flow around a 2D cylinder is considered a generic example. The von kármán vortices are generated for flow around the 2D cylinder. These vortices impose periodic drag and lift on the cylinder. The objective of this study is to reduce drag, fluctuation of and drag, and lift in order to stabilize the cylinder. Hence, open-loop control and closed-loop control strategies are implemented to control drag and lift. The optimal open-loop control strategy is achieved by parameter study. The optimal closed-loop control strategy is achieved by deep reinforcement learning using the PPO algorithm.

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Keywords

Deep reinforcement learning, open-loop and closed-loop control, Flow around 2D cylinder, Active flow control, Proximal policy optimization (PPO) algorithm

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
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