Liansuo An
North China Electric Power University
Network
Latest external collaboration on country level. Dive into details by clicking on the dots.
Publication
Featured researches published by Liansuo An.
Combustion Science and Technology | 2018
Weilong Xu; Genshan Jiang; Liansuo An; Yuechao Liu
ABSTRACT To investigate the heat transfer of pulverized coal particles under the effect of an acoustic field (without a superposed steady component), we derived and solved the two-dimensional, unsteady, laminar conservation equations for mass, momentum, and energy transport in the flue gas phase of a power plant boiler. The local, space-averaged, and space- and time-averaged Nusselt (Nu) numbers for the coal particles are discussed for the full audible frequency range. Experiments were also conducted to study the heat transfer, in particular the heat exchange at the surface, considering the effect of the acoustic wave around a stationary copper sphere. The sphere was exposed to different sound pressure levels of acoustic waves in air, and the temperature of the sphere was recorded. The range of the studied sound frequencies is between 500 Hz and 3 kHz. Both, numerical and experimental investigations indicate that the acoustic field can increase the heat transfer significantly. Higher sound pressure levels cause higher heat-transfer rate. However, there is a special frequency, at which Nu reaches a maximum, due to the flow separation caused by flow acceleration. This special frequency increases with increasing sound pressure level.
ASME 2011 Power Conference collocated with JSME ICOPE 2011 | 2011
Liansuo An; Peng Wang; Guoqing Shen; Jie Shi
The inference of strong background noise and reflected by the wall and tube rows surface makes it impossible that justify accurately leakage position employing the characteristic received by multi-channel sensors. It is the ‘bottleneck’ for promoting the accuracy of boiler tube leakage location. The 600MW supercritical boiler model was established, the leakage source propagation process of reflection and attenuation in boiler furnace was simulated by EASE. The approximate signal to noise ratio (SNR) was obtained and the reverberation time was calculated with the squared impulse response integration method on the foundation of simulation. The time delay estimation algorithm PTN, SWITCH derived from PHAT and ML, respectively, are proposed and experiments results revealed the superiority over the classical generalized cross correlation (GCC) method in reverberant and noisy boiler background. Although SWITCH is outperformed by PTN slightly, but the prior knowledge of reverberant energy to direct energy ratio may be hard to obtain in practice and frequencies onset detection is required in PTN method, so the implementation of SWITCH is much easier.© 2011 ASME
world congress on intelligent control and automation | 2010
Peng Wang; Liansuo An; Guoqing Shen; Jie Shi; Yongxing Lu
Efficient and reliable operation is the main requirement of the modern power plant,hence its significant that the boiler tube leakage source is localized precisely. The four-element acoustic array was exploited in the furnace and the schematic of sensors distributed for capture the leakage signal were given. A set of hyperbolic equations were established and Accelerating genetic algorithm (AGA) was proposed to give an iterative solution avoiding initial guess and far-field assumption. The stable and sharp peak can be obtained by an approximation of the maximum likelihood (ML) estimator, which was verified via the experiment on localization of leakage. The time differences of arrival (TDOA) error level was 0.1us, the array failed to fix position as the remarkable coordinate error, while the error level was 0.01us, the coordinates error was linearized and reduced to the permitted range (less than 1m).
world congress on intelligent control and automation | 2010
Liansuo An; Peng Wang; Guoqing Shen; Jie Shi; Qiang Feng
Efficient and reliable operation is the main requirement of the modern power plant, hence its significant that the boiler tube leakage source is localized precisely. The four-element acoustic array was exploited in the furnace, a set of hyperboloidal equations are established and the sensors distributed for capture the leakage signal is given. In order to avoiding initial guess and distant source assumption, adaptive Guassian mutation operator implemented in GA is employed to give an explicit solution. Comparing GA to quasi-Newton method, it indicates that GA is more robust and accuracy, but cost more time to converge. Time delay estimator error has great influence on localization accuracy, the conclusion that error level is 0.01us,the four-element array localization error is in the permitted range (smaller than 1m) is draw by numerical calculation.
ieee pes asia-pacific power and energy engineering conference | 2010
Peng Wang; Liansuo An; Genshan Jiang; Guoqing Shen; Yongxing Lu
Efficient and reliable operation is the main requirement of the modern power plant, hence its significant that the boiler tube leakage source is localized precisely. The four-element acoustic array was exploited in the furnace, a set of hyperbolic equations were established and the sensors distributed for capture the leakage signal were given. The stable and sharp peak can be obtained by an approximation of the maximum likelihood (ML) estimator, which was verified via the experiment on localization of leakage. The time differences of arrival (TDOA) error level was 0.1us, the array failed to fix position as the remarkable coordinate error, while the error level was 0.01us, the coordinates error was linearized and reduced to the permitted range (less than 1m).
Applied Thermal Engineering | 2015
Shiping Zhang; Guoqing Shen; Liansuo An; Yuguang Niu
Applied Thermal Engineering | 2017
Haiping Chen; Yanan Zhou; Sutian Cao; Xiang Li; Xin Su; Liansuo An; Dan Gao
Applied Thermal Engineering | 2015
Shiping Zhang; Guoqing Shen; Liansuo An; Xianbo Gao
Applied Thermal Engineering | 2015
Shiping Zhang; Guoqing Shen; Liansuo An; Gengsheng Li
Applied Thermal Engineering | 2016
Genshan Jiang; Yuechao Liu; Qian Kong; Weilong Xu; Liansuo An