The femtosecond laser-induced fiber Bragg grating is an effective sensor technology that can be deployed in harsh environments. Depending on the optical fiber chosen and the inscription parameters that are used, devices suitable for high temperature, pressure, ionizing radiation and strain sensor applications are possible. Such devices are appropriate for aerospace or energy production applications where there is a need for components, instrumentation and controls that can function in harsh environments. This paper will present a review of some of the more recent developments in this field.

Periodic planar nanostructures are found in Type II-IR Bragg gratings produced in SMF-28 fiber by side-illuminating it with infrared femtosecond-laser pulses through a phase mask. The planar nanostructures are aligned perpendicular to the laser polarization, as demonstrated using scanning electron microscopy analysis of cleaved fiber samples. Dark field optical microscopy is employed for real-time monitoring of structural changes occurring inside the fiber during the inscription process.

Nonlinear photoluminescence imaging is used to visualize the intensity distribution of femtosecond laser pulses inside the optical fiber during Bragg grating inscription based on side illumination through a phase mask. This technique, which results in direct imaging of the inscription laser field inside the optical fiber, facilitates i) the characterization of the laser focus in the vicinity of the fiber core and ii) the optimization of the fiber alignment with respect to the laser focus while using pulses with energies several times lower than those used during the actual inscription process. The applicability of this imaging technique is demonstrated for Bragg grating inscription in different optical fibers, including direct inscription through the fiber coating.

Bragg gratings with ultrastrong cladding modes are inscribed in a standard telecom optical fiber (i.e., SMF-28) using the phase mask technique and an infrared femtosecond laser. 1000°C-resistant fiber Bragg gratings (FBGs) whose cladding modes exceed 30 dB in transmission and spectrally span more than 250 nm can be produced with only a few (i.e., less than five) laser pulses, provided that the light intensity in the fiber core is kept at the level of ∼<!-- ∼ --> 6 ×<!-- × --> 10 13 W / c m 2 . The use of FBGs fabricated under the above conditions for refractometry is demonstrated.

Type II π-phase-shifted Bragg gratings stable up to ~1000°C are written inside a standard single mode silica optical fiber (SMF-28) with infrared femtosecond pulses and a special phase mask. Inscription through the protective polyimide fiber coating is also demonstrated. The birefringence of the Bragg gratings and, as a result, the polarization dependence of their spectra are strongly affected by the femtosecond laser polarization. Using optimized writing conditions, the full width at half maximum of the π-phase-shifted passband feature can be ~30 pm in transmission, while the polarization-dependent shift of its central wavelength can be less than 8 pm, for a 7 mm long grating structure. This makes such gratings a unique tool for high-resolution measurements of temperature, load and vibration in extreme temperature environments.

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.