GPU-Based Real-Time Distributed Dynamic Strain Sensing in Optical Frequency Domain Reflectometry
Chenhuan Wang, Kun Liu, Zhenyang Ding, Yuanyao Li, Dongfang Zhu, Ming Pan, Zeen Chen, Haohan Guo, Sheng Li, Junfeng Jiang, Yin Yu, Tiegen Liu
Abstract
We present a graphics processing unit (GPU)-based real time distributed dynamic strain sensing in optical frequency domain reflectometry (OFDR). GPU can accelerate the data processing rate distributed sensing in OFDR because it contains many parallel processing steps. We analyze parameters and performance of tunable laser source (TLS), data acquisition card (DAQ) and GPU. We present a choice method of the thread number in a block based on the GPU streaming multiprocessor utilization efficiency. We construct several kernel functions based on process steps of strain sensing and discuss the selection of blocks number for each kernel function. In the experiment, we find that total process time by the parallel computing in GPU is enhanced about 81 times compared with the serial computing in CPU. The measurement rate of this system is up to 60 Hz for real time distributed dynamic strain sensing. We achieve a dynamic strain sensing with a peak-to-peak variation of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.5~\mu \varepsilon $ </tex-math></inline-formula> , a vibration frequency of 20 Hz and a sensing spatial resolution of 20 cm at a sensing range of 200 m. More importantly, two strain areas simultaneously loaded with a distance interval of 20 cm can be distinguished clearly.