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Yuefeng Yu
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China

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Journal article
Published: 13 March 2020 in Fuel
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This study proposes a diagnostic method for gas-fired combustion based on the image processing technology, for identifying an abnormal combustion situation in a gaseous flame. The proposed algorithm is divided into four aspects: (1) a logarithmic entropy multi-threshold segmentation method for segmenting the flame region utilized to extract image features; (2) 12 typical characteristic parameters representing gas-fired flame images, with five of them extracted for identification; (3) a fuzzy pattern recognition algorithm using an S-type membership function and a maximum-minimum distance function to distinguish between variable flame states; and (4) two statistics, Q and T2, used to evaluate the decision-making results of the fuzzy pattern recognition. The results are also compared to those from several other algorithms, including the self-organizing map, neural network, and support vector machine methods. The experimental results indicate that the proposed method has better performance in identifying different combustion situations in a gaseous flame and is superior to the other algorithms. Through a two-parameter adjustment, normal gas-fired combustion state can be accurately identified; for abnormal combustion, the prediction accuracy can become more than 90%. There can be a slight misjudgment; this may be owing to the relatively less training data for abnormal flame states.

ACS Style

Yu Wang; Yuefeng Yu; Xiaolei Zhu; Zhongxiao Zhang. Pattern recognition for measuring the flame stability of gas-fired combustion based on the image processing technology. Fuel 2020, 270, 117486 .

AMA Style

Yu Wang, Yuefeng Yu, Xiaolei Zhu, Zhongxiao Zhang. Pattern recognition for measuring the flame stability of gas-fired combustion based on the image processing technology. Fuel. 2020; 270 ():117486.

Chicago/Turabian Style

Yu Wang; Yuefeng Yu; Xiaolei Zhu; Zhongxiao Zhang. 2020. "Pattern recognition for measuring the flame stability of gas-fired combustion based on the image processing technology." Fuel 270, no. : 117486.

Journal article
Published: 31 August 2016 in Energies
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A three-dimensional (3D) geometry model of twin screw expander has been developed in this paper to measure and analyze geometric parameters such as groove volume, suction port area, and leakage area, which can be described as functions of rotation angle of male rotor. Taking the suction loss, leakage loss, and real gas effect into consideration, a thermodynamic model is developed using continuity and energy conservation equation. The developed model is verified by comparing predicted results of power output and internal efficiency with experimental data. Based on the model, the relationship between mass flow rate through inlet port and leakage path with rotation angle of male rotor as well as effects of the inlet parameter and operating parameter on the performance of the expander are analyzed.

ACS Style

Yuanqu Qi; Yuefeng Yu. Thermodynamic Simulation on the Performance of Twin Screw Expander Applied in Geothermal Power Generation. Energies 2016, 9, 694 .

AMA Style

Yuanqu Qi, Yuefeng Yu. Thermodynamic Simulation on the Performance of Twin Screw Expander Applied in Geothermal Power Generation. Energies. 2016; 9 (9):694.

Chicago/Turabian Style

Yuanqu Qi; Yuefeng Yu. 2016. "Thermodynamic Simulation on the Performance of Twin Screw Expander Applied in Geothermal Power Generation." Energies 9, no. 9: 694.