Currently in the United States, periodic sensor recalibration is required for all safety-related sensors, typically occurring at every refueling outage, and it has emerged as a critical path item for shortening outage duration in some plants. Online monitoring can be employed to identify those sensors that require calibration, allowing for calibration of only those sensors that need it. International application of calibration monitoring has shown that sensors may operate for longer periods within calibration tolerances. This issue is expected to also be important as the United States looks to the next generation of reactor designs (such as small modular reactors and advanced concepts), given the anticipated longer refueling cycles, proposed advanced sensors, and digital instrumentation and control systems. The U.S. Nuclear Regulatory Commission (NRC) accepted the general concept of online monitoring for sensor calibration monitoring in 2000, but no U.S. plants have been granted the necessary license amendment to apply it. This report presents a state-of-the-art assessment of online calibration monitoring in the nuclear power industry, including sensors, calibration practice, and online monitoring algorithms. This assessment identifies key research needs and gaps that prohibit integration of the NRC-approved online calibration monitoring system in the U.S. nuclear industry. Several needs are identified, including an understanding of the impacts of sensor degradation on measurements for both conventional and emerging sensors; the quantification of uncertainty in online calibration assessment; determination of calibration acceptance criteria and quantification of the effect of acceptance criteria variability on system performance; and assessment of the feasibility of using virtual sensor estimates to replace identified faulty sensors in order to extend operation to the next convenient maintenance opportunity. v Summary Transmission of accurate and reliable measurements is central to safe, efficient, and economic operation of nuclear power plants (NPPs). Current instrument channel calibration practice in the United States utilizes periodic assessment and adjustment, if necessary, of sensors to maintain sensor calibration within some prescribed tolerance. In performing calibration, intrusive techniques are used to determine the calibration condition-instruments are isolated from the system, sometimes through physical removal, and exercised through a series of known inputs. This sensor performance assessment is performed periodically, as required by the plant technical specifications (TS). Non-safety-related sensors also undergo recalibration, although not as frequently. Typically, calibration occurs during refueling outages (about every two years). The current approach to sensor calibration in operating light water reactors is expensive and time consuming, resulting in longer outages, increased maintenance cost, and additional radiation exposure to maintenance personnel, and it can be counterproductive, introducing e...

No abstract

Executive SummarySafe, efficient, and economic operation of nuclear systems (nuclear power plants, fuel fabrication and storage, used fuel processing, etc.) relies on accurate and reliable measurements. Newer types of sensors, and sensors to monitor non-traditional parameters, are expected in next-generation nuclear power plant (NPP) and fuel-cycle environments. A number of factors (besides changes in the monitored variable) affect the measured signals, resulting in effects such as signal drift and response time changes, requiring techniques to distinguish between signal changes from plant or subsystem performance deviations and those from sensor or instrumentation issues. Advanced algorithms that continuously monitor sensor responses can address this issue and facilitate automated monitoring and control of plant and subsystem performance.Currently, periodic sensor recalibration is performed to avoid problems with signal drift and sensor performance degradation. Periodic sensor calibration involves (1) isolating the sensor from the system, (2) applying an artificial load and recording the result, and (3) comparing this "As Found" result with the recorded "As Left" condition from the previous recalibration to evaluate the drift at several input values in the range of the sensor. If the sensor output is found to have drifted from the previous condition, then the sensor is adjusted to meet the prescribed "As Left" tolerances. However, this approach is expensive and time-consuming, and unnecessary maintenance actions can potentially damage sensors and sensing lines. Online monitoring (OLM) can help mitigate many of these issues, while providing a more frequent assessment of calibration and signal validation. However, widespread utilization of traditional OLM approaches is lacking with the need to better quantify OLM uncertainty a key factor in this.Sources of uncertainty in OLM can be roughly categorized as (1) process noise, (2) measurement uncertainty, (3) electronic noise, and (4) modeling uncertainty. Approaches to uncertainty quantification (UQ) that are data-driven may be capable of providing estimates of uncertainty that are time-varying as the quantities being measured vary with time. Such a capability provides the option of adjusting acceptance criteria and, potentially, setpoints in a time-varying fashion to meet the needs of the nuclear power system.A Gaussian Process (GP) model is proposed in this study for addressing the UQ issue. The advantage of this approach is the ability to account for spatial and temporal correlations among the sensor measurements that are used in OLM. The GP model, as proposed, may be considered an extension of a commonly used OLM model and, therefore, the hypothesis is that the UQ methodology may be readily extended to accommodate commonly used OLM models.Two approaches were taken for generating the data sets needed for evaluating the proposed model. Experimental data was acquired using an instrumented flow loop, with varying test conditions. In addition, a simulation model of a ...

No abstract

No abstract