Non-linear model calibration for off-design performance prediction of gas turbines with experimental data

Tsoutsanis, Elias; Li, Yi-Guang; Pilidis, Pericles; Newby, Mike

doi:10.1017/aer.2017.96

Cited by 23 publications

(20 citation statements)

References 29 publications

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“…The review emphasises the importance of the development of condition monitoring systems based on transient data that can capture the fast-nonlinear dynamics of the engine. An advanced performance adaptation system to estimate the steady state performance of gas turbine models at off-design conditions is presented by Tsoutsanis et al [19]. The method consists of a novel method for compressor map generation and a genetic algorithm to match gas path measurements of an engine at off-design conditions.…”

Section: Introductionmentioning

confidence: 99%

Gas Path Fault and Degradation Modelling in Twin-Shaft Gas Turbines

2018

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In this study, an assessment of degradation and failure modes in the gas-path components of twin-shaft industrial gas turbines (IGTs) has been carried out through a model-based analysis. Measurements from twin-shaft IGTs operated in the field and denoting reduction in engine performance attributed to compressor fouling conditions, hot-end blade turbine damage, and failure in the variable stator guide vane (VSGV) mechanism of the compressor have been considered for the analysis. The measurements were compared with simulated data from a thermodynamic model constructed in a Simulink environment, which predicts the physical parameters (pressure and temperature) across the different stations of the IGT. The model predicts engine health parameters, e.g., component efficiencies and flow capacities, which are not available in the engine field data. The results show that it is possible to simulate the change in physical parameters across the IGT during degradation and failure in the components by varying component efficiencies and flow capacities during IGT simulation. The results also demonstrate that the model can predict the measured field data attributed to failure in the gas-path components of twin-shaft IGTs. The estimated health parameters during degradation or failure in the gas-path components can assist the development of health-index prognostic methods for operational engine performance prediction.

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Section: Introductionmentioning

confidence: 99%

Gas Path Fault and Degradation Modelling in Twin-Shaft Gas Turbines

2018

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“…The validation process carried out here can be further improved by implementing inverse modeling adaptation methods [15,57,56] in order to minimize any deviations between Simulink and PROOSIS.…”

Section: Case Study 5: Validation With Proosis For Fuel Schedulementioning

confidence: 99%

Dynamic performance simulation and control of gas turbines used for hybrid gas/wind energy applications

Tsoutsanis

Meskin

2019

Applied Thermal Engineering

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Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document. When citing, please reference the published version. Take down policy While the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.

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“…The non-linear thermo-gas-dynamic models of turbine engines [6] and identification procedures have been applied in diagnostics for more than 40 years. Identification adjusts the model to make its output parameters as close as possible to the experimental data [7][8][9][10][11][12][13][14]. Besides the significant improvement in the gas path simulation, the estimated parameters contain information about the health of each component.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Performance Parameters of Turbine Engine Components Using Experimental Data in Parametric Uncertainty Conditions

et al. 2020

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Zero-dimensional models based on the description of the thermo-gas-dynamic process are widely used in the design of engines and their control and diagnostic systems. The models are subjected to an identification procedure to bring their outputs as close as possible to experimental data and assess engine health. This paper aims to improve the stability of engine model identification when the number of measured parameters is small, and their measurement error is not negligible. The proposed method for the estimation of engine components’ parameters, based on multi-criteria identification, provides stable estimations and their confidence intervals within known measurement errors. A priori information about the engine, its parameters and performance is used directly in the regularized identification procedure. The mathematical basis for this approach is the fuzzy sets theory. Synthesis of objective functions and subsequent scalar convolutions of these functions are used to estimate gas-path components’ parameters. A comparison with traditional methods showed that the main advantage of the proposed approach is the high stability of estimation in the parametric uncertainty conditions. Regularization reduces scattering, excludes incorrect solutions that do not correspond to a priori assumptions and also helps to implement the gas path analysis with a limited number of measured parameters. The method can be used for matching thermodynamic models to experimental data, gas path analysis and adapting dynamic models to the needs of the engine control system.

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Non-linear model calibration for off-design performance prediction of gas turbines with experimental data

Cited by 23 publications

References 29 publications

Gas Path Fault and Degradation Modelling in Twin-Shaft Gas Turbines

Gas Path Fault and Degradation Modelling in Twin-Shaft Gas Turbines

Dynamic performance simulation and control of gas turbines used for hybrid gas/wind energy applications

Estimation of Performance Parameters of Turbine Engine Components Using Experimental Data in Parametric Uncertainty Conditions

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