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An Automated Process to Create Start Values for Gas Turbine Performance Simulations Using Neural Networks and Evolutionary Algorithms

Becker, Richard-Gregor and Bolemant, Martin and Krause, Daniel and Peitsch, Dieter (2015) An Automated Process to Create Start Values for Gas Turbine Performance Simulations Using Neural Networks and Evolutionary Algorithms. International Gas Turbine Congress 2015 Tokyo, 15.-20. November 2015, Tokyo, Japan.

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Abstract

This paper presents a fully automated methodology to create start values for gas turbine performance computer programs. The methodology employs the application of evolutionary algorithms for a more robust convergence of the iterative process of a performance program as well as neural networks for a self-learning start value generation procedure. The achieved results showed that a connection of both methods for the creation of performance model start values is a feasible option. Different types of neural networks as well as several training methods have been evaluated in order to find the best equivalent model. Having found a practical approach for the structure of the neural networks, several performance models of different levels of complexity have been tested. The combination of both of the above presented steps achieved very good convergence rates in combination with a minimum effort for the creation of start values.

Item URL in elib:https://elib.dlr.de/100666/
Document Type:Conference or Workshop Item (Speech)
Title:An Automated Process to Create Start Values for Gas Turbine Performance Simulations Using Neural Networks and Evolutionary Algorithms
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Becker, Richard-GregorRichard.Becker (at) dlr.deUNSPECIFIED
Bolemant, MartinMartin.Bolemant (at) ilr.tu-berlin.deUNSPECIFIED
Krause, DanielUNSPECIFIEDUNSPECIFIED
Peitsch, Dieterdieter.peitsch (at) tu-berlin.deUNSPECIFIED
Date:November 2015
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:gas turbine performance, robust convergence, evolutionary algorithms, neural networks
Event Title:International Gas Turbine Congress 2015 Tokyo
Event Location:Tokyo, Japan
Event Type:international Conference
Event Dates:15.-20. November 2015
Organizer:Gas Turbine Society of Japan
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:propulsion systems
DLR - Research area:Aeronautics
DLR - Program:L ER - Engine Research
DLR - Research theme (Project):L - Virtual Engine and Validation methods
Location: Köln-Porz
Institutes and Institutions:Institute of Propulsion Technology > Engine
Deposited By: Becker, Richard-Gregor
Deposited On:14 Dec 2015 09:46
Last Modified:31 Jul 2019 19:57

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