Suppression of self-excited thermoacoustic oscillations using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{Yin:2020:APS,

• author = "Bo Yin and Yu Guan and Stephane Redonnet and Vikrant Gupta and Larry K. B. Li",
• title = "Suppression of self-excited thermoacoustic oscillations using genetic programming",
• booktitle = "73rd Annual Meeting of the APS Division of Fluid Dynamics",
• year = "2020",
• volume = "65",
• number = "13",
• pages = "E04.00001",
• address = "Virtual",
• month = nov # " 22-24",
• publisher = "Bulletin of the American Physical Society",
• keywords = "genetic algorithms, genetic programming",
• bibsource = "OAI-PMH server at repository.ust.hk",
• language = "English",
• oai = "oai:repository.ust.hk:1783.1-107430",
• URL = "https://meetings.aps.org/Meeting/DFD20/Session/E04.1",
• URL = "http://absimage.aps.org/image/DFD20/MWS_DFD20-2020-000312.pdf",
• URL = "http://hdl.handle.net/1783.1/107430",
• URL = "http://repository.ust.hk/ir/Record/1783.1-107430",
• broken = "http://lbdiscover.ust.hk/uresolver?url_ver=Z39.88-2004\&rft_val_fmt=info:ofi/fmt:kev:mtx:journal\&rfr_id=info:sid/HKUST:SPI\&rft.genre=article\&rft.issn=\&rft.volume=\&rft.issue=\&rft.date=2020\&rft.spage=\&rft.aulast=Yin\&rft.aufirst=Bo\&rft.atitle=Suppression+of+self-excited+thermoacoustic+oscillations+using+genetic+programming\&rft.title =",
• abstract = "Genetic programming (GP) is a powerful tool for unsupervised data-driven discovery of closed-loop control laws. In fluid mechanics, it has been used for various purposes, such as to enhance mixing in a turbulent shear layer and to delay flow separation. This model-free control framework is well suited for such complex tasks as it exploits an evolutionary mechanism to propagate the genetics of high-performing control laws from one generation to the next. Here we combine automated experiments with GP to discover model-free control laws for the suppression of self-excited thermoacoustic oscillations in a Rijke tube. Using a GP-based controller linked to a single sensor (a microphone) and a single actuator (a loudspeaker), we rank the performance of all the control laws in a given generation based on a cost function that accounts for the pressure amplitude and the actuation effort. We use a tournament process to breed further generations of control laws, and then benchmark them against conventional periodic forcing optimised via open-loop mapping. We find that, with only minimal input from the user, this GP-based control framework can identify new feedback actuation mechanisms, providing improved control laws for the suppression of self-excited thermoacoustic oscillations.",

}

Genetic Programming entries for Bo Yin Yu Guan Stephane Pierre Andre Redonnet Vikrant Gupta Larry K B Li

Citations