Abstract
This paper reports in-pile testing results of radiation-resistant fiber Bragg grating (FBG) sensors at high temperatures, intense neutron irradiation environments, and machine learning methods for radiation-induced sensor drift mitigation and reactor anomaly identification. The in-pile testing of fiber sensors was carried out in an MIT test reactor for 180 days at a nominal operational temperature of 640°C and high neutron flux. The test results show that FBG sensors inscribed by a femtosecond laser in random airline pure silica fiber can withstand harsh environments in the reactor core but exhibit significant radiation-induced drifts. Machine learning algorithms based on long short-term memory (LSTM) networks have been used to detect reactor anomaly events and mitigate sensor drifts over a duration of up to 85 days. Through progressive supervised learning, the LSTM neural network can achieve FBG wavelength-to-temperature mapping within ±0.95°C, ±2.63°C and ±6.49°C with over 80.2%, 90%, and 95% levels of accuracy confidence, respectively. The LSTM can also identify reactor anomaly samples with an accuracy of over 94%. The results presented in this paper show that despite sensor drifts and anomaly interruptions, the LSTM-based method can effectively elucidate data harnessed by fiber sensors. Machine learning algorithms have the potential to improve situational awareness and control for a wide range of harsh environment applications, including nuclear power generation.
Highlights
Fiber optical sensors have been extensively utilized as versatile and highly multiplexable sensing devices to perform a wide array of measurements [1]
In 2017, a number of in-pile tests were performed at the MIT test reactor (MITR) to evaluate the radiation resilience of fiber Bragg grating (FBG) sensors exposed to extremely high neutron flux (>2 1014 fast neutrons per second per square centimeter) at a nominal operational temperature of 640oC for an entire testing period of 180 days [2]
We report a machine learning approach based on a long short-term memory (LSTM) neural network [18] to mitigate sensor drifts induced by harsh neutron and gamma irradiation and identify reactor anomaly events
Summary
Fiber optical sensors have been extensively utilized as versatile and highly multiplexable sensing devices to perform a wide array of measurements [1]. In 2017, a number of in-pile tests were performed at the MIT test reactor (MITR) to evaluate the radiation resilience of fiber Bragg grating (FBG) sensors exposed to extremely high neutron flux (>2 1014 fast neutrons per second per square centimeter) at a nominal operational temperature of 640oC for an entire testing period of 180 days [2]. These encouraging results suggest that fiber sensors have the potential to perform high spatial resolution monitoring to improve the safety and operational efficiency of nuclear energy systems. The results presented in this paper show that the continuous data harness and training of the LSTM neural network can effectively mitigate the adverse effects of sensor drifts, leading to accurate temperature measurements and anomaly event identification
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