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Document type:
Forschungsdaten 
Publication date:
17.03.2022 
Authors:
Kulkarni, Sagar; Guo, Shuai; Silva, Camilo F.; Polifke, Wolfgang 
Author affiliation:
TUM 
Publisher:
TUM 
Title:
Temporal data of the turbulent swirl EM2C burner at different combustor back plate temperatures for the estimation of Flame Impulse Response 
Time of production:
31.01.2021 
Subject area:
MAS Maschinenbau; MTA Technische Mechanik, Technische Thermodynamik, Technische Akustik 
Resource type:
Simulationen / simulations 
Data type:
Datenbanken / data bases 
Description:
The dataset contains the statistically-steady state data (.h5) generated from fully compressible LES simulations carried out in AVBP 7.0.1 at the base combustor back plate temperature of 823 K. The dataset also contains temporal data (.h5) of the simulations carried out at different combustor back plate temperatures as described in the work of Kulkarni et al., Confidence in Flame Impulse Response Estimation From Large Eddy Simulation With Uncertain Thermal Boundary Conditions, JEGTP, GTP-21-1276, https://doi.org/10.1115/1.4052022 
Method of data assessment:
The database was generated using the fully compressible CFD code AVBP developed by CERFACS (www.cerfacs.fr/avbp7x/index.php). Large Eddy Simulations were run using AVBP 7.0.1 version at different combustor back plate temperatures according to Kulkarni et al., Confidence in Flame Impulse Response Estimation From Large Eddy Simulation With Uncertain Thermal Boundary Conditions, JEGTP, GTP-21-1276, https://doi.org/10.1115/1.4052022 on the LRZ SuperMUC-NG supercomputer 
Links:

This dataset relates to the publication: https://doi.org/10.1115/1.4052022

 
Key words:
Flame Impulse Response; AVBP; Uncertainty Quantification 
Technical remarks:
View and download (4,2 GB total, 245 Files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1651629):
rsync rsync:// m1651629@dataserv.ub.tum.de/ m1651629/ 
Language:
en 
Rights:
by, http://creativecommons.org/licenses/by/4.0 
Horizon 2020:
766264