Hardware-in-the-loop neuro-based simulation for testing gas turbine engine control system

Kumarin, Aleksei; Кузнецов, А. В.; Makaryants, Georgy M.

doi:10.1109/gfps.2018.8472379

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…For the given displacement and the applied power, the ML model predicts the rotational speed. A similar research focusing on the gas turbine engine is presented in [5]. Both of these engine models predict only one signal at a time.…”

Section: Related Workmentioning

confidence: 99%

Modeling and Simulation of a Spacecraft Payload Hardware Using Machine Learning Techniques

et al. 2020

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Space systems are complex and consist of multiple subsystems. Research and development teams of such complex systems are usually distributed among various institutions and space agencies. This affects the quality of the On-board Software (OBSW) since testing it without having all required subsystems at the software development site can be troublesome. In this paper, we present a data-driven method which can be used to synthesize parts of a system or even an entire system as a black-box model. We exploit the data collected from the real hardware to derive a model using a Machine Learning (ML) algorithm. The proposed model can easily be distributed among development teams and is dedicated to emulate the system for testing the OBSW.

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Section: Related Workmentioning

confidence: 99%

Modeling and Simulation of a Spacecraft Payload Hardware Using Machine Learning Techniques

et al. 2020

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“…Still, it extends to hybrid electro-hydraulic and pneumatic systems by Hoang et al [12], presenting a dynamic model for highperformance tracking control of rapidly changing acceleration. Many other researchers [13][14][15][16][17][18][19] modeled the electrohydraulic control system of gas turbine engines. Additionally, in some recent literature, other studies [20][21][22] have considered modeling the same electrohydraulic systems yet utilized in other applications.…”

Section: Introductionmentioning

confidence: 99%

Reduced order model for an electro-hydraulic valve of a gas turbine engine's controller

Elmayyah¹,

Samy²

2023

FME Transactions

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Gas turbine engines (GTE) are widely used in military and industrial applications. Accurate modeling is mandatory to advance GTE control. The present article investigates, experimentally and theoretically, a detailed dynamic model of an electro-hydraulic system that controls a variable geometry inlet guide vanes (VIGV) of turboshaft GTE. A parametric study for the computationally expensive and time-consuming model has been conducted considering the forces affecting the valve's spool and its relatively short settling time. A reduced mathematical model has been developed. The prediction results of the reduced and full-detailed models have been compared with the experimental results. The reduced model has decreased calculation time by 45% to 50 % while keeping the RMSE of the model within 1-2 % away from the actual system's experimental results for the complete operating range. An improvement allows future studies to integrate the whole subsystems of the GTE in a single computationally affordable model.

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“…Model-based gas path analysis systems make it possible to monitor engine performance parameters for fault diagnosis and manage component deterioration [48,49]. However, data-driven approaches are more and more common recently, not only in full-scale engines but also in microturbines [50,51]. Often, engine parameters are estimated or predicted by machine learning techniques which are trained with data obtained from simplified aeroengine gas-path performance prognostic models.…”

Section: Introductionmentioning

confidence: 99%

Model-Based Dynamic Performance Simulation of a Microturbine Using Flight Test Data

2022

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Microturbines can be used not only in models and education but also to propel UAVs. However, their wider adoption is limited by their relatively low efficiency and durability. Validated simulation models are required to monitor their performance, improve their lifetime, and to design engine control systems. This study aims at developing a numerical model of a micro gas turbine intended for prediction and prognostics of engine performance. To build a reliable zero-dimensional model, the available compressor and turbine maps were scaled to the available test bench data with the least squares method, to meet the performance of the engine achieved during bench and flight tests. A steady-state aeroengine model was implemented in the Gas turbine Simulation Program (GSP) and was compared with experimental operating points. The selected flight data were then used as input for the transient engine model. The exhaust gas temperature (EGT) and fuel flow were chosen as the two key parameters to validate the model, comparing the numerical predicted values with the experimental ones. The observed difference between the model and the flight data was lower than 3% for both EGT and fuel flow.

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Hardware-in-the-loop neuro-based simulation for testing gas turbine engine control system

Cited by 8 publications

References 10 publications

Modeling and Simulation of a Spacecraft Payload Hardware Using Machine Learning Techniques

Modeling and Simulation of a Spacecraft Payload Hardware Using Machine Learning Techniques

Reduced order model for an electro-hydraulic valve of a gas turbine engine's controller

Model-Based Dynamic Performance Simulation of a Microturbine Using Flight Test Data

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