Comparison of Selected Machine Learning Tools for Identification of Jet Engine Dynamics

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG43__%2F26%3A00564328" target="_blank" >RIV/60162694:G43__/26:00564328 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/11061307" target="_blank" >https://ieeexplore.ieee.org/document/11061307</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICMT65201.2025.11061307" target="_blank" >10.1109/ICMT65201.2025.11061307</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of Selected Machine Learning Tools for Identification of Jet Engine Dynamics

Popis výsledku v původním jazyce

This paper addresses an application of machine learning to identify dynamical model of a gas turbine engine. Fundamental aspects of component modeling and calculation of both the steady state and the transient operation are briefly described. Subsequently, models of spool speed dynamics were identified via the neural network, the sparse identification of nonlinear dynamics, and the Koopman operator theory. Concerning the second mentioned approach, the utilization of nonlinear parameters of basis functions for sparsity promotion is proposed. The results show similar performance of considered approaches, while their complexity and interpretability differ severely. Furthermore, control-oriented advantages of sparse identification of dynamics and Koopman model are discussed.

Název v anglickém jazyce

Comparison of Selected Machine Learning Tools for Identification of Jet Engine Dynamics

Popis výsledku anglicky

This paper addresses an application of machine learning to identify dynamical model of a gas turbine engine. Fundamental aspects of component modeling and calculation of both the steady state and the transient operation are briefly described. Subsequently, models of spool speed dynamics were identified via the neural network, the sparse identification of nonlinear dynamics, and the Koopman operator theory. Concerning the second mentioned approach, the utilization of nonlinear parameters of basis functions for sparsity promotion is proposed. The results show similar performance of considered approaches, while their complexity and interpretability differ severely. Furthermore, control-oriented advantages of sparse identification of dynamics and Koopman model are discussed.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20304 - Aerospace engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2025 10th International Conference on Military Technologies, ICMT 2025 - Proceedings

ISBN

979-8-3315-2338-1

ISSN

—

e-ISSN

2996-4474

Počet stran výsledku

7

Strana od-do

1-7

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

Brno

Místo konání akce

Brno, Czech Republic

Datum konání akce

27. 5. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001545807300049