Richard Skifton

Nuclear power plants (NPPs) require continuous monitoring of various systems, structures, and components to ensure safe and efficient operations. The critical safety testing of new fuel compositions and the analysis of the effects of power transients on core temperatures can be achieved through modeling and simulations. They capture the dynamics of the physical phenomenon associated with failure modes and facilitate the creation of digital twins (DTs). Accurate reconstruction of fields of interest (e.g., temperature, pressure, velocity) from sensor measurements is crucial to establish a two-way communication between physical experiments and models. Sensor placement is highly constrained in most nuclear subsystems due to challenging operating conditions and inherent spatial limitations. This study develops optimized data-driven sensor placements for full-field reconstruction within reactor and steam generator subsystems of NPPs. Optimized constrained sensors reconstruct field of interest within a tri-structural isotropic (TRISO) fuel irradiation experiment, a lumped parameter model of a nuclear fuel test rod and a steam generator. The optimization procedure leverages reduced-order models of flow physics to provide a highly accurate full-field reconstruction of responses of interest, noise-induced uncertainty quantification and physically feasible sensor locations. Accurate sensor-based reconstructions establish a foundation for the digital twinning of subsystems, culminating in a comprehensive DT aggregate of an NPP.

Passive monitoring techniques have been used for peak temperature measurements during irradiation tests by exploiting the melting point of well-characterized materials. Recent efforts to expand the capabilities of such peak temperature detection instrumentation include the development and testing of additively manufactured (AM) melt wires. In an effort to demonstrate and benchmark the performance and reliability of AM melt wires, we conducted a study to compare prototypical standard melt wires to an AM melt wire capsule, composed of printed aluminum, zinc, and tin melt wires. The lowest melting-point material used was Sn, with a melting point of approximately 230 °C, Zn melts at approximately 420 °C, and the high melting-point material was aluminum, with an approximate melting point of 660 °C. Through differential scanning calorimetry and furnace testing we show that the performance of our AM melt wire capsule was consistent with that of the standard melt-wire capsule, highlighting a path towards miniaturized peak-temperature sensors for in-pile sensor applications.

The sublimation temperature sensor (or “sublime sensor”) provides a continuum of measurement locations in which certain maximum temperatures are achieved during a heat up/cool down cycle. A predetermined material is encapsulated within a vacuum-sealed, non-volatile long tube (i.e., both ends capped and L ≫ D). This assembly is then inserted and centered into a heated zone, such as a furnace, exhaust pipe, or reactor. As the temperature increases, the material will sublimate (i.e., a process of having both the solid and gaseous states of matter simultaneously present) and will begin to fill the void—moving outward in both directions toward the ends of the tube. Once beyond the elevated temperatures, the gas will de-sublimate (i.e., deposition) onto the inner wall of the tube. The desired result of the sensor is the ring of material that develops over a relatively short period of time. This material deposit can be equated with temperature at an exact location. There is no need to interpolate and/or extrapolate for the desired measurement. Accuracy has been recorded for temperature locations on the range of ±2 mm over a 1 m span. Likewise, the precision of the measurement is ±0.2% the overall sensor domain. Furthermore, individual tubes with unique materials and pressures can be bundled together to provide a complete temperature profile of the heated zone.