Analysis of Filtered Thermal-Fluid Video Data From Downward Facing Boiling Experiments

Abstract

During severe accidents in a nuclear power plant, in-vessel cooling may be required to mitigate the risk of vessel failure in the event of core meltdown and subsequent corium contamination. This cooling technique, known as in-vessel retention (IVR), entails flooding the reactor cavity with water. If the temperatures are sufficiently high, IVR may cause downward facing boiling (DFB) on the outer surface of the reactor pressure vessel (RPV), which gives rise to two-phase thermal-hydraulic phenomena. The regimes in DFB may range from film boiling to nucleate boiling, where the efficiency of cooling varies immensely between these two. In the DFB geometry under consideration (i.e., a hemispherical vessel), the collected signals/images are heavily contaminated by unavoidable noise and spurious disturbances, which hinder the extraction of pertinent information, such as film thickness and the boiling cycle. This paper proposes a wavelet-based filtering of sensor measurements for denoising of the nonstationary signals with the future objective of estimating the thickness of vapor films in real time, as needed for process monitoring and control. The proposed concept has been validated with experimental data recorded from a pool boiling apparatus for physics-based understanding of the associated phenomena.