An Intelligent Sensor Array Network System Technology for Spent Nuclear Fuel Dry Storage Canister Helium Leakage Detection

Statement of the Problem: The spent nuclear fuel (SNF) ultimately needs to be placed in a helium pressurized stainless steel dry storage canister (DSC). Monitoring the internal conditions of DSC becomes crucially important to DOE for public safety. As such, it is crucial to develop a practical, robust, sensor system technology to effectively monitor internal pressure and temperature variation, and gas composition and related helium leakage on SNF-DSC. The DOE is therefore interested in developing innovative internal conditions monitoring technology on SNF-DSC to provide assurance that the safety functions continue to be met. The developed technology needs to satisfy the geometric limitation and survive in radiation and harsh environment. Proposed Solution: X-wave Innovations, Inc., teaming up with ORANO proposes an intelligent sensor array network system (ISAN) for long-term internal pressure and temperature variation, and Xe/Kr gas composition monitoring and related helium leakage detection on SNF-DSCs. The ISAN system includes active monitoring module which integrate guided wave with an improved time of arrival (TOA) analysis and signal processing algorithm for continuous internal pressure, temperature and fission gas composition monitoring and related helium leakage detection. The ISAN system works in a radiation environment and will be installed on the rails located external to the canister and attached to the shield wall internal to the canister. The ISAN system integrates sensor network hardware with a control software package for real-time pressure and temperature variation, and fission gas composition monitoring and accurate helium leakage detection. Phase I work: During the Phase I program, we will build an ISAN prototype and demonstrate the feasibility of the proposed approach. The prototype ISAN system includes sensor system hardware, accompanying software tools to control and operate the hardware, and data analysis algorithms to detect internal pressure and temperature variation, fission gas composition, and potential helium leakage area using acquired data. We will build a machine learning classification algorithm to identify the wave signal from environment noise for robust prototype performance. Commercial Applications: The proposed technology is capable of monitor internal conditions on SNF-DSC during long-term storage and transportation. The unique advantages are radiation endurance sensor array and acoustic gas sensor hardware system, which is able to monitor several factors including pressure, temperature, fission gas composition and helium leakage, and the system is easy to operate and provide advanced signal process algorithm software package. This technology is interested by ORANO/TN America, and NAC International, and will be field tested on real canister. This technology also can be used in industrial pressure vessel health monitoring. We therefore envision that the proposed ISAN technology will be widely applicable in different scenarios.