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Research Papers: Gas Turbines: Turbomachinery

Real-Time Variable Geometry Triaxial Gas Turbine Model for Hardware-in-the-Loop Simulation Experiments

[+] Author and Article Information
Tao Wang

Institute of Engineering Thermophysics,
Chinese Academy of Sciences,
University of Chinese Academy of Sciences,
Beijing 100190, China
e-mail: wangtao15@iet.cn

Yong-Sheng Tian

Institute of Engineering Thermophysics,
Chinese Academy of Sciences,
Beijing 100190, China
e-mail: tianyongsheng@iet.cn

Zhao Yin

Institute of Engineering Thermophysics,
Chinese Academy of Sciences,
Beijing 100190, China
e-mail: yinzhao@iet.cn

Da-Yue Zhang

Institute of Engineering Thermophysics,
Chinese Academy of Sciences,
Beijing 100190, China
e-mail: zhangdayue@iet.cn

Ming-Ze Ma

Energy and Power Engineering College,
Inner Mongolia University of Technology,
Huhhot 010080, China
e-mail: 540634964@qq.com

Qing Gao

Institute of Engineering Thermophysics,
Chinese Academy of Sciences,
University of Chinese Academy of Sciences,
Beijing 100190, China
e-mail: gaoqing@iet.cn

Chun-Qing Tan

Institute of Engineering Thermophysics,
Chinese Academy of Sciences,
University of Chinese Academy of Sciences,
Beijing 100190, China
e-mail: tan@iet.cn

1Corresponding author.

Contributed by the Turbomachinery Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received August 25, 2017; final manuscript received December 27, 2017; published online May 24, 2018. Assoc. Editor: Liang Tang.

J. Eng. Gas Turbines Power 140(9), 092603 (May 24, 2018) (10 pages) Paper No: GTP-17-1477; doi: 10.1115/1.4038992 History: Received August 25, 2017; Revised December 27, 2017

This paper proposes a hybrid method (HMRC) comprised of a radial basis function (RBF) neural net algorithm and component-level modeling method (CMM) as a real-time simulation model for triaxial gas turbines with variable power turbine guide vanes in matlab/simulink. The sample size is decreased substantially after analyzing the relationship between high and low pressure shaft rotational speeds under dynamic working conditions, which reduces the computational burden of the simulation. The effects of the power turbine rotational speed on overall performance are also properly accounted for in the model. The RBF neural net algorithm and CMM are used to simulate the gas generator and power turbine working conditions, respectively, in the HMRC. The reliability and accuracy of both the traditional single CMM model (SCMM) and HMRC model are verified using gas turbine experiment data. The simulation models serve as a controlled object to replace the real gas turbine in a hardware-in-the-loop simulation experiment. The HMRC model shows better real-time performance than the traditional SCMM model, suggesting that it can be readily applied to hardware-in-the-loop simulation experiments.

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Figures

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Fig. 1

Structure schematic of triaxial gas turbine with variable power turbine guide vanes

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Fig. 2

Relationship between NH and NL for different fuel supply control laws: (a) control law of fuel and (b) corresponding relationship between NH and NL

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Fig. 3

Relationship between NH and NL for different angles of power turbine guide vanes

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Fig. 4

Operating line for different power turbine rotational speeds: (a) low pressure compressor and (b) high pressure compressor

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Fig. 5

Relationship between ηPT and NPT

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Fig. 6

Structure diagram of HMRC model

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Fig. 7

Comparison diagrams of the experiment data and SCMM model computation results: (a) output power and fuel flow, (b) discharge total temperature and fuel flow, (c) high pressure shaft rotational speed and fuel flow, and (d) low pressure shaft rotational speed and fuel flow

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Fig. 8

Comparison diagrams of the experiment data and simulation models for dynamic working conditions: (a) control law, (b) high pressure shaft rotational speed, and (c) low pressure shaft rotational speed

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Fig. 9

Comparison of computational results between HMRC model and SCMM model: (a) control law, (b) high pressure shaft rotational speed, (c) low pressure shaft rotational speed, (d) low pressure turbine exit pressure, and (e) high pressure compressor exit total temperature

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Fig. 10

Comparison of simulation results between HMRC model and SCMM model

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Fig. 11

Schematic diagram of hardware-in-the-loop simulation experiment

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Fig. 12

The actuator of fuel and guide vanes regulation

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Fig. 13

The first hardware-in-the-loop simulation experiment result: (a) the SCMM model and (b) the HMRC model

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Fig. 14

The second hardware-in-the-loop simulation experiment result: (a) the SCMM model and (b) the HMRC model

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