Enhanced Component Analytical Solution for Performance Adaptation and Diagnostics of Gas Turbines

Yan, Binbin; Hu, Minghui; Feng, Kun; Jiang, Zhinong

doi:10.3390/en14144356

Cited by 6 publications

(3 citation statements)

References 32 publications

(50 reference statements)

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“…Scaling the maps based on the least squares regression was sufficient and much simpler and faster than the adaptation of maps using the nonlinear optimization method [68,69]. Adaptive modelling methods [70][71][72] will, however, be necessary for monitoring a fleet of engines with significant manufacturing tolerances or varying degrees of wear.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

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

2022

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“…To overcome the zonal distribution effect existing in field data-gas turbine performance adaption, Li et al [24] proposed a new metric to evaluate the adaption accuracy and changed the performance parameters at the design point. Yan et al [25] proposed an enhanced component analytical solution and utilized sensitivity analysis to set the weight coefficient for the tuning factors.…”

Section: Introductionmentioning

confidence: 99%

Gas Turbine Off-Design Performance Adaption Based on Cluster Sampling

Kong

Chen

et al. 2023

Applied Sciences

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An accurate gas turbine performance model is important for system performance evaluation. To minimize the simulated performance error, an adaption method using coefficients of scaling factors is applied to tune the component characteristic maps and make the gas turbine model meet measurements of a few randomly sampled points. However, the field data are non-homogeneously distributed. In this situation, randomly selecting a few sampling may lead to the inappropriate correction of the component characteristic maps and lower the prediction accuracy of model. Firstly, the coefficients of scaling factors are introduced to construct the performance adaption optimization problem of the gas turbine model. Secondly, the k-means clustering algorithm is applied to divide the 146 field data points into 10 different categories, and then the points closest to the cluster centers are selected to form the sampling set. Thirdly, a particle swarm optimization algorithm is used to search the optimal scaling factors. As a result, the model error decreases from 2.947% to 0.610%. Finally, the proposed method is validated with the remaining field data of a real E-class gas turbine. The average predicted error is 0.466%. Compared with the performance results obtained by random sample, the model based on cluster sampling shows a better accuracy.

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“…Scaling the maps based on the least squares regression was sufficient and much 270 simpler and faster than the adaptation of maps using the nonlinear optimization method 271 [64,65]. Adaptive modelling methods [66][67][68] will, however, be necessary for monitoring 272 a fleet of engines with significant manufacturing tolerances or varying degrees of wear.…”

mentioning

confidence: 99%

Model-Based Dynamic Performance Simulation of a Microturbine

Erario¹,

Giorgi²,

Przysowa³

2021

Preprint

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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 design engine control systems. This study aims at developing a numerical model of a micro gas turbine 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 GSP and compared with experimental operating points. The selected flight data was 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.

show abstract

Enhanced Component Analytical Solution for Performance Adaptation and Diagnostics of Gas Turbines

Cited by 6 publications

References 32 publications

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

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

Gas Turbine Off-Design Performance Adaption Based on Cluster Sampling

Model-Based Dynamic Performance Simulation of a Microturbine

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