Data-driven identification of nonlinear flame models

Ghani, Abdulla and Boxx, Isaac and Noren, Carrie (2020) Data-driven identification of nonlinear flame models. Journal of Engineering for Gas Turbines and Power, 142, p. 121015. American Society of Mechanical Engineers (ASME). doi: 10.1115/1.4049071. ISSN 0742-4795.

PDF - Postprint version (accepted manuscript)
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Official URL: https://doi.org/10.1115/1.4049071

Item URL in elib:

https://elib.dlr.de/148793/

Document Type:

Article

Title:

Data-driven identification of nonlinear flame models

Authors:

Authors	Institution or Email of Authors	Author's ORCID iD	ORCID Put Code
Ghani, Abdulla	Thermo-Fluid Dynamics Group, Department of Mechanical Engineering. Technical University of Munich, Germany	UNSPECIFIED	UNSPECIFIED
Boxx, Isaac	UNSPECIFIED	https://orcid.org/0000-0003-1916-6206	UNSPECIFIED
Noren, Carrie	DLR / Air Force Research Laboratory, Kirtland Air Force Base, Albuquerque, USA	UNSPECIFIED	UNSPECIFIED

Date:

9 November 2020

Journal or Publication Title:

Journal of Engineering for Gas Turbines and Power

Refereed publication:

Yes

Open Access:

Yes

Gold Open Access:

In SCOPUS:

Yes

In ISI Web of Science:

Yes

Volume:

142

DOI:

10.1115/1.4049071

Page Range:

p. 121015

Publisher:

American Society of Mechanical Engineers (ASME)

ISSN:

0742-4795

Status:

Published

Keywords:

nonlinear flame models, kHz, laser diagnostics

HGF - Research field:

Energy

HGF - Program:

Energy Efficiency, Materials and Resources

HGF - Program Themes:

Efficient and Flexible Power Plants

DLR - Research area:

Energy

DLR - Program:

E VS - Combustion Systems

DLR - Research theme (Project):

E - Combustion and Power Plant Systems (old), E - Reliability (old)

Location:

Stuttgart

Institutes and Institutions:

Institute of Combustion Technology > Combustion Diagnostícs

Deposited By:

Geigle, Dr. Klaus Peter

Deposited On:

16 Feb 2022 10:18

Last Modified:

05 Dec 2023 07:11

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