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Research Papers: Gas Turbines: Turbomachinery

Effects of Humidity Condensation on the Trend of Gas Turbine Performance Deterioration

[+] Author and Article Information
Houman Hanachi

Mem. ASME
Department of Mechanical and
Aerospace Engineering,
Carleton University,
1125 Colonel By Drive,
Ottawa, ON K1S 5B6, Canada
e-mail: houman.hanachi@carleton.ca

Jie Liu

Department of Mechanical and
Aerospace Engineering,
Carleton University,
1125 Colonel By Drive,
Ottawa, ON K1S 5B6, Canada
e-mail: jie.liu@carleton.ca

Avisekh Banerjee

Life Prediction Technologies, Inc.,
Unit 23, 1010 Polytek Street,
Ottawa, ON K1J 9J1, Canada
e-mail: banerjeea@lifepredictiontech.com

Ying Chen

Life Prediction Technologies, Inc.,
Unit 23, 1010 Polytek Street,
Ottawa, ON K1J 9J1, Canada
e-mail: cheny@lifepredictiontech.com

1Corresponding author.

Contributed by the Turbomachinery Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received February 23, 2015; final manuscript received June 4, 2015; published online June 30, 2015. Assoc. Editor: Klaus Brun.

J. Eng. Gas Turbines Power 137(12), 122604 (Jun 30, 2015) (11 pages) Paper No: GTP-15-1081; doi: 10.1115/1.4030815 History: Received February 23, 2015

Performance deterioration in gas turbine engines (GTEs) depends on various factors in the ambient and the operating conditions. For example, humidity condensation at the inlet duct of a GTE creates water mist, which affects the fouling phenomena in the compressor and varies the performance. In this paper, the effective factors on the short-term performance deterioration of a GTE are identified and studied. GTE performance level is quantified with two physics-based performance indicators, calculated from the recorded operating data from the control system of a GTE over a full time between overhaul (TBO) period. A regularized particle filtering (RPF) framework is developed for filtering the indicator signals, and an adaptive neuro-fuzzy inference system (ANFIS) is then trained with the filtered signals and the effective ambient and the operating conditions, i.e., the power, the air mass flow, and the humidity condensation rate. The trained ANFIS model is then run to simulate the GTE performance deterioration in different conditions for system identification. The extracted behavior of the system clearly shows the dependency of the trend of performance deterioration on the operating conditions, especially the humidity condensation rate. The developed technique and the results can be utilized for GTE performance prediction, as well as for suggesting the optimum humidity supply at the GTE intake to control the performance deterioration rate.

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Figures

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Fig. 1

Performance deterioration during the operating life: (a) EH performance indicator and (b) ER performance indicator

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Fig. 3

RPF state estimation on the performance indicator signals: (a) EH performance indicator and (b) ER performance indicator

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Fig. 2

Humidity in inlet air: (a) total humidity, (b) condensed humidity, and (c) comparison in selected time window

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Fig. 4

Variation rates of the performance indicators do not constitute functions over PW–Wcon space: (a) variation rate of EH and (b) variation rate of ER

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Fig. 10

Lack of training data points for ER simulation with WCiWcon data

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Fig. 6

Training data points and the result of ANFIS model prediction: (a) EH data and prediction, (b) ER data and prediction, and (c) scarcity of data points in some regions of the domain

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Fig. 5

ANFIS structure for system identification. Dashed line shows the use of output as input of the next step for profile simulation.

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Fig. 7

ANFIS model simulated profiles for EH: (a) EH profiles at constant power and humidity condensation rates and (b) EH profiles at PW = 2400 kW

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Fig. 8

ER simulated profiles at PW = 3500 kW

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Fig. 9

EH simulated profiles at Wcon = 16.75 kg/s

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