Development and validation of a full-range performance analysis model for a three-spool gas turbine with turbine cooling

Song, Yin; Gu, Chen; Ji, Xing-xing

doi:10.1016/j.energy.2015.06.015

Cited by 20 publications

(9 citation statements)

References 32 publications

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“…The prediction accuracy of the algorithm for part-load performance remained unexplored because it had only been validated under full-load conditions. Using thorough compressor and turbine maps, Song et al [ 33 ] created a performance analysis model for a three-spool gas turbine and showed that the model was accurate in forecasting gas turbine performance. All these models require precise performance maps of the air compressor and turbine to predict gas turbine performance [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

The Effect of Physical Faults on a Three-Shaft Gas Turbine Performance at Full- and Part-Load Operation

Salilew

Karim

Lemma

et al. 2022

Sensors

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A gas path analysis approach of dynamic modelling was used to examine the gas turbine performance. This study presents an investigation of the effect of physical faults on the performance of a three-shaft gas turbine at full-load and part-load operation. A nonlinear steady state performance model was developed and validated. The datasheet from the engine manufacturer was used to gather the input and validation data. Some engineering judgement and optimization were used. Following validation of the engine performance model with the engine manufacturer data using physical fault and component health parameter relationships, physical faults were implanted into the performance model to evaluate the performance characteristics of the gas turbine at degradation state at full- and part-load operation. The impact of erosion and fouling on the gas turbine output parameters, component measurement parameters, and the impact of degraded components on another primary component of the engine have been investigated. The simulation results show that the deviation in the output parameters and component isentropic efficiency due to compressor fouling and erosion is linear with the load variation, but it is almost nonlinear for the downstream components. The results are discussed following the plots.

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Section: Introductionmentioning

confidence: 99%

The Effect of Physical Faults on a Three-Shaft Gas Turbine Performance at Full- and Part-Load Operation

Salilew

Karim

Lemma

et al. 2022

Sensors

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“…Haglind and Elmegaard [8] developed two models for predicting engine part-load performance, which can well predict exhaust temperatures. Song et al [9]. developed an engine performance prediction model and verified the accuracy of the model in predicting engine gas path state parameters.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Exhaust Gas Temperature Baseline Predictions for Aeroengine Based on Machine Learning Algorithms

Wang

Zhao

2022

Aerospace

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The exhaust gas temperature (EGT) baseline of an aeroengine is key to accurately analyzing engine health, formulating maintenance decisions and ensuring flight safety. However, due to the complex performance characteristics of aeroengine and the constraints of many external factors, it is difficult to obtain accurate non-linear features between various operating factors and EGT. In order to diagnose and forecast aeroengine performance quickly and accurately, four data-driven baseline prediction frameworks for EGT are proposed. These baseline frameworks took engine operating conditions and operating state control parameters as input variables and EGT as predicted output variables. The original data were collected from CFM56-5B engine ACARS flight data. Four typical machine learning methods, including Generalized Regression Neural Network (GRNN), Radial Basis Neural Network (RBF), Support Vector Regression (SVR) and Random Forest (RF) are trained to develop the models. Four aeroengine EGT baseline models were validated by comparing the after-flight data of another engine. The results show that the developed GRNN models have the best accuracy and computational efficiency compared with other models, and their RE and CPU calculation time on the verification set are 1.132 × 10−3 and 3.512 × 10−3 s, respectively. The developed baseline prediction frameworks can meet the needs of practical engineering applications for airlines. The methodologies developed can be employed by airlines to predict the EGT baseline for the purpose of engine performance monitoring and health management.

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“…In the absence of sufficient data, the estimation results will not be accurate. It is seen that this method is frequently used in the literature due to the advantage that the characteristics and performance factors of the power plant can be evaluated simultaneously [31][32][33]. One of the methods for estimating the desired values is the artificial intelligence (AI) thanks to the algorithms developed using the obtained data.…”

Section: Introductionmentioning

confidence: 99%

Experimental study for artificial neural network (ANN) based prediction of electric energy production of diesel engine based cogeneration power plant

Çolak¹

2021

Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi

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In this study artificial neural network (ANN) has been developed in order to estimate the electricity production of cogeneration power plant, which produces a total of 11.52 MW electric power, consisting of two V type and 12 cylinders each of which is 5.760 kW diesel engines running with heavy fuel oil no 6. In the ANN which was developed for the estimation of electric power generation of cogeneration, power plant(W), Time period (t), working hours (h), fuel consumption (m) and internal power consumption (Wp) values were used as input variables. After evaluating the performance of different ANNs, an ANN, consisting of one hidden layer and 10 neurons, was considered to be the most ideal one. As a result of the comparison with experimental data, it is concluded that this model estimates the electricity generation values of the cogeneration power plant with an R-value of 0,99073 and mean square error 4.734e-8 Bu çalışmada iki adet V tipi 12 silindirli dizel motordan oluşan ve her biri 5.760 kW olmak üzere toplam 11.52 MW elektrik enerjisi üreten kojenerasyon enerji santralinin elektrik üretiminin tahmin edilmesi için yapay sinir ağı (YSA) geliştirilmiştir. 6 no'lu fuel oil ile çalışan kojenerasyon enerji santralinin elektrik enerjisi üretiminin (W) tahmini için geliştirilen YSA'da, zaman (t), çalışma saatleri (h), yakıt tüketimi (m) ve iç tüketim (Wp) değerleri giriş değişkenleri olarak kullanılmıştır. Farklı YSA'ların performansı değerlendirildikten sonra, bir gizli katman ve 10 nörondan oluşan YSA en ideal model olarak değerlendirilmiştir. Deneysel verilerle yapılan karşılaştırma sonucunda, bu modelin kojenerasyon enerji santralinin elektrik üretim değerlerini 0,99073 R değeri ve 4.734e-8 MSE ile tahmin edebileceği sonucuna varılmıştır.

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Development and validation of a full-range performance analysis model for a three-spool gas turbine with turbine cooling

Cited by 20 publications

References 32 publications

The Effect of Physical Faults on a Three-Shaft Gas Turbine Performance at Full- and Part-Load Operation

The Effect of Physical Faults on a Three-Shaft Gas Turbine Performance at Full- and Part-Load Operation

Data-Driven Exhaust Gas Temperature Baseline Predictions for Aeroengine Based on Machine Learning Algorithms

Experimental study for artificial neural network (ANN) based prediction of electric energy production of diesel engine based cogeneration power plant

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