Machine learning strategies applied to the control of a fluidic pinball

Simple item page
dc.contributor.authorRaibaudo, Cedric
dc.contributor.authorZhong, Peng
dc.contributor.authorNoack, Bernd R.
dc.contributor.authorMartinuzzi, Robert John
dc.date.accessioned2020-01-13T16:54:19Z
dc.date.available2020-01-13T16:54:19Z
dc.date.issued2020-01-10
dc.description.abstractThe wake stabilization of a triangular cluster of three rotating cylinders is investigated. Experiments are performed at Reynolds number Re ∼ 2200. Flow control is realized using rotating cylinders spanning the wind-tunnel height. The cylinders are individually connected to identical brushless DC motors. Two-component planar particle image velocimetry measurements and constant temperature hot-wire anemometry were used to characterize the flow without and with actuation. Main open-loop configurations are studied and different controlled flow topologies are identified. Machine learning control is then implemented for the optimization of the flow control performance. Linear genetic algorithms are used here as the optimization technique for the open-loop constant speed-actuators. Two different cost functions J are considered targeting either drag reduction or wake symmetrization. The functions are estimated based on the velocity from three hot-wire sensors in the wake. It is shown that the machine learning approach is an effective strategy for controlling the wake characteristics. More significantly, the results show that machine learning strategies can reveal unanticipated solutions or parameter relations, in addition to being a tool for optimizing searches in large parameter spaces.en_US
dc.description.grantingagencyNatural Sciences and Engineering Research Council (NSERC)en_US
dc.identifier.citationRaibaudo, C., Zhong, P., Noack, B. R., & Martinuzzi, R. J. (2020). Machine learning strategies applied to the control of a fluidic pinball. "Physics of Fluids", 32(1), 1–42. doi: 10.1063/1.5127202en_US
dc.identifier.doihttps://doi.org/10.1063/1.5127202en_US
dc.identifier.grantnumber04079en_US
dc.identifier.urihttp://hdl.handle.net/1880/111482
dc.identifier.urihttps://doi.org/10.11575/PRISM/46046
dc.language.isoengen_US
dc.publisherAIP Publishing LLCen_US
dc.publisher.departmentMechanical & Manufacturing Engineeringen_US
dc.publisher.facultySchulich School of Engineeringen_US
dc.publisher.institutionUniversity of Calgaryen_US
dc.publisher.policyhttps://publishing.aip.org/resources/researchers/policies-and-ethics/authors/en_US
dc.rightsUnless otherwise indicated, this material is protected by copyright and has been made available with authorization from the copyright owner. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.en_US
dc.subjectPassive and Active Control of Turbulent Flowsen_US
dc.subjectGenetic Algorithmsen_US
dc.subjectFluidic Pinballen_US
dc.titleMachine learning strategies applied to the control of a fluidic pinballen_US
dc.typejournal articleen_US
dc.typepublishedVersionen_US
ucalgary.item.requestcopytrueen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
POF19-AR-PACT2019-01878.pdf
Size:
17.42 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.92 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Schulich School of Engineering Research & Publications