Simulating and Designing Brillouin Gain Spectrum in Single-Mode Fibers

2004 ◽  
Vol 22 (2) ◽  
pp. 631-639 ◽  
Author(s):  
Y. Koyamada ◽  
S. Sato ◽  
S. Nakamura ◽  
H. Sotobayashi ◽  
W. Chujo
Keyword(s):  
Single Mode ◽  
Gain Spectrum ◽  
Brillouin Gain

Importance of residual stresses in the Brillouin Gain Spectrum of single mode optical fibers

2011 ◽  
Author(s):  
Yolande Sikali Mamdem ◽  
Ekaterina Burov ◽  
Louis-Anne De Montmorillon ◽  
Frédéric Taillade ◽  
Yves Jaouen ◽  
...  
Keyword(s):  
Residual Stresses ◽  
Optical Fibers ◽  
Single Mode ◽  
Gain Spectrum ◽  
Brillouin Gain

scholarly journals Importance of residual stresses in the Brillouin gain spectrum of single mode optical fibers

2012 ◽  
Vol 20 (2) ◽  
pp. 1790 ◽  
Author(s):  
Y. Sikali Mamdem ◽  
E. Burov ◽  
L-A. de Montmorillon ◽  
Y. Jaouën ◽  
G. Moreau ◽  
...  
Keyword(s):  
Residual Stresses ◽  
Optical Fibers ◽  
Single Mode ◽  
Gain Spectrum ◽  
Brillouin Gain

scholarly journals Experimental analysis of the temperature dependence of the Brillouin gain spectrum in short-length single-mode fiber

2017 ◽  
Vol 25 ◽  
pp. 3881-3891 ◽  
Author(s):  
Murat YÜCEL ◽  
Halim Haldun GÖKTAŞ ◽  
Murat YÜCEL ◽  
Nail Ferhat ÖZTÜRK ◽  
Abdullah Erkam GÜNDÜZ
Keyword(s):  
Temperature Dependence ◽  
Experimental Analysis ◽  
Single Mode ◽  
Single Mode Fiber ◽  
Short Length ◽  
Gain Spectrum ◽  
Brillouin Gain

scholarly journals Brillouin gain spectrum characterization in single-mode optical fibers

1997 ◽  
Vol 15 (10) ◽  
pp. 1842-1851 ◽  
Author(s):  
M. Nikles ◽  
L. Thevenaz ◽  
P.A. Robert
Keyword(s):  
Optical Fibers ◽  
Single Mode ◽  
Gain Spectrum ◽  
Brillouin Gain

scholarly journals Measurement Improvement of Distributed Optical Fiber Sensor via Lorenz Local Single Peak Fitting Algorithm

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1166
Author(s):  
Bin Liu ◽  
Jianping He ◽  
Shihai Zhang ◽  
Yinping Zhang ◽  
Jianan Yu ◽  
...  
Keyword(s):  
Optical Fiber ◽  
Frequency Shift ◽  
Optical Fiber Sensor ◽  
Gain Spectrum ◽  
Fiber Sensor ◽  
Peak Fitting ◽  
Fitting Algorithm ◽  
Distributed Optical Fiber Sensor ◽  
Brillouin Gain ◽  
Distributed Optical Fiber

Brillouin frequency shift (BFS) of distributed optical fiber sensor is extracted from the Brillouin gain spectrum (BGS), which is often characterized by Lorenz type. However, in the case of complex stress and optical fiber self damage, the BGS will deviate from Lorenz type and be asymmetric, which leads to the extraction error of BFS. In order to enhance the extraction accuracy of BFS, the Lorenz local single peak fitting algorithm was developed to fit the Brillouin gain spectrum curve, which can make the BSG symmetrical with respect to the Brillouin center frequency shift. One temperature test of a fiber-reinforced polymer (FRP) packaged sensor whose BSG curve is asymmetric was conducted to verify the idea. The results show that the local region curve of BSG processed by the developed algorithm has good symmetry, and the temperature measurement accuracy obtained by the developed algorithm is higher than that directly measured by demodulation equipment. Comparison with the reference temperature, the relative measurement error measured by the developed algorithm and BOTDA are within 4% and 8%, respectively.

scholarly journals Fast Measurement of Brillouin Frequency Shift in Optical Fiber Based on a Novel Feedforward Neural Network

Photonics ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 474
Author(s):  
Fen Xiao ◽  
Mingxing Lv ◽  
Xinwan Li
Keyword(s):  
Neural Network ◽  
Optical Fiber ◽  
Frequency Shift ◽  
Brillouin Scattering ◽  
Principal Component ◽  
Feedforward Neural Network ◽  
Gain Spectrum ◽  
Fiber Sensors ◽  
Brillouin Gain ◽  
Distributed Optical Fiber

Brillouin scattering-based distributed optical fiber sensors have been successfully employed in various applications in recent decades, because of benefits such as small size, light weight, electromagnetic immunity, and continuous monitoring of temperature and strain. However, the data processing requirements for the Brillouin Gain Spectrum (BGS) restrict further improvement of monitoring performance and limit the application of real-time measurements. Studies using Feedforward Neural Network (FNN) to measure Brillouin Frequency Shift (BFS) have been performed in recent years to validate the possibility of improving measurement performance. In this work, a novel FNN that is 3 times faster than previous FNNs is proposed to improve BFS measurement performance. More specifically, after the original Brillouin Gain Spectrum (BGS) is preprocessed by Principal Component Analysis (PCA), the data are fed into the Feedforward Neural Network (FNN) to predict BFS.

Distributed temperature and strain measurement based on Brillouin gain spectrum and Brillouin beat spectrum

2021 ◽  
pp. 1-1
Author(s):  
Jianqin Peng ◽  
Yuangang Lu ◽  
Zelin Zhang ◽  
Zhengnan Wu ◽  
Yuyang Zhang
Keyword(s):  
Strain Measurement ◽  
Gain Spectrum ◽  
Brillouin Gain
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