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Research Papers: Gas Turbines: Aircraft Engine

Transient Gas Turbine Performance Diagnostics Through Nonlinear Adaptation of Compressor and Turbine Maps

[+] Author and Article Information
Elias Tsoutsanis

Department of Electrical Engineering,
College of Engineering,
Qatar University,
P.O. Box 2713,
Doha, Qatar
e-mail: elias.tsoutsanis@qu.edu.qa

Nader Meskin

Department of Electrical Engineering,
College of Engineering,
Qatar University,
P.O. Box 2713,
Doha, Qatar
e-mail: nader.meskin@qu.edu.qa

Mohieddine Benammar

Department of Electrical Engineering,
College of Engineering,
Qatar University,
P.O. Box 2713,
Doha, Qatar
e-mail: mbenammar@qu.edu.qa

Khashayar Khorasani

Department of Electrical and
Computer Engineering,
Concordia University,
Montreal QC H3G 1M8, Canada
e-mail: kash@ece.concordia.ca

Contributed by the Aircraft Engine Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received November 18, 2014; final manuscript received January 27, 2015; published online February 25, 2015. Editor: David Wisler.

J. Eng. Gas Turbines Power 137(9), 091201 (Sep 01, 2015) (12 pages) Paper No: GTP-14-1630; doi: 10.1115/1.4029710 History: Received November 18, 2014; Revised January 27, 2015; Online February 25, 2015

Gas turbines are faced with new challenges of increasing flexibility in their operation while reducing their life cycle costs, leading to new research priorities and challenges. One of these challenges involves the establishment of high fidelity, accurate, and computationally efficient engine performance simulation, diagnosis, and prognosis schemes, which will be able to handle and address the gas turbine's ever-growing flexible and dynamic operational characteristics. Predicting accurately the performance of gas turbines depends on detailed understanding of the engine components behavior that is captured by component performance maps. The limited availability of these maps due to their proprietary nature has been commonly managed by adapting default generic maps in order to match the targeted off-design or engine degraded measurements. Although these approaches might be suitable in small range of operating conditions, further investigation is required to assess the capabilities of such methods for use in gas turbine diagnosis under dynamic transient conditions. The diversification of energy portfolio and introduction of distributed generation in electrical energy production have created need for such studies. The reason is not only the fluctuation in energy demand but also more importantly the fact that renewable energy sources, which work with conventional fossil fuel based sources, supply the grid with varying power that depend, for example, on solar irradiation. In this paper, modeling methods for the compressor and turbine maps are presented for improving the accuracy and fidelity of the engine performance prediction and diagnosis. The proposed component map fitting methods simultaneously determine the best set of equations for matching the compressor and the turbine map data. The coefficients that determine the shape of the component map curves have been analyzed and tuned through a nonlinear multi-objective optimization scheme in order to meet the targeted set of engine measurements. The proposed component map modeling methods are developed in the object oriented matlab/simulink environment and integrated with a dynamic gas turbine engine model. The accuracy of the methods is evaluated for predicting multiple component degradations of an engine at transient operating conditions. The proposed adaptive diagnostics method has the capability to generalize current gas turbine performance prediction approaches and to improve performance-based diagnostic techniques.

Copyright © 2015 by ASME
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References

Figures

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Fig. 1

Flexible gas turbine operating profile [1] (courtesy of Siemens AG)

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Fig. 2

Map fitting approaches that are applied to typical component maps available from proosis. (a) Compressor πc versus mc-fitting, (b) compressor ηc versus mc-fitting, (c) turbine mt versus πt-fitting, (d) turbine ηt versus πt-fitting, (e) power turbine mpt versus πpt-fitting, and (f) power turbine ηpt versus πpt-fitting.

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Fig. 3

Variation of the compressor turbine efficiency map coefficients with respect to the corrected speed N

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Fig. 4

Flow charts of the proposed map modeling methods for various engine components. (a) Compressor map modeling, (b) compressor turbine map modeling, and (c) power turbine map modeling.

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Fig. 5

Engine model layout in simulink (for definition of the variables, refer to the Nomenclature section)

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Fig. 6

Flow chart of the engine model adaptive diagnostics

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Fig. 15

The diagnostic error of the isentropic efficiency deviation parameters during the transient response

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Fig. 16

The average diagnostic error of the component parameters during the transient response

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Fig. 14

The diagnostic error of the mass flow capacity deviation parameters during the transient response

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Fig. 13

The tuning range of the component map sub-coefficients. Initial values of subcoefficients are normalized and the tuned values are expressed with respect to the initial ones.

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Fig. 12

The average prediction error of the targeted engine performance measurements

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Fig. 11

The compressor map trajectories during the transient response for the reference engine and the engine model

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Fig. 10

The gas path temperatures during the transient response

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Fig. 9

The power turbine output and the shaft rotational speed during the transient response

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Fig. 8

The fuel flow schedule during the transient response

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Fig. 7

The reference engine and the engine model representations. (a) Reference engine with proosis maps as lookup tables and (b) engine model with proposed map modeling process.

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Fig. 17

The performance of the proposed optimization algorithm for the diagnostic case study

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Fig. 18

The corrected mass flow parameters for the reference engine and the engine model during the untrained off-design steady state operation

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Fig. 19

The isentropic efficiency parameters for the reference engine and the engine model during the untrained off-design steady state operation

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Fig. 20

The operating points of the reference engine and the engine model for the steady state and transient conditions

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