Seismic and acoustic data recorded 50 meters away from a research nuclear reactor could predict whether the reactor was in an on or off state with 98% accuracy. This is according to a new study published in Seismological Research Letters.
Scientists applied several machine learning models to the data. Oak Ridge National Laboratory scientists can also predict when the reactor was transitioning between on and off. They can also estimate its power levels with about 66% accuracy.
The findings provide another tool for the international community to cooperatively verify and monitor nuclear reactor operations in a minimally invasive way.
Seismic and acoustic data have long been used to monitor earthquakes. Scientists now use the data to take a closer look at the movements associated with industrial processes. They deployed seismic and acoustic sensors around the High Flux Isotope Reactor at Oak Ridge. A research reactor used to produce neutrons for studies in physics, chemistry, biology, engineering and materials science.
The reactor’s power status is a thermal process with a cooling tower that dissipates heat. Scientists then compared a number of machine learning algorithms to discover which were best at estimating the reactor’s power state from specific seismo-acoustic signals. The algorithms were trained with seismic-only, acoustic-only and both types of data collected over a year. The combined data produced the best results.
Scientists detected some interesting signals during the course of their study. This includes the vibrations of a noisy pump in the reactors off state. It disappeared when the pump was replaced. Scientists said it is a long-term and challenging goal to associate seismic and acoustic signatures with different industrial activities and equipment. Preliminary research shows that fans and pumps have different seismo-acoustic fingerprints, for the High Flux Isotope Reactor. Different fan speeds have their own unique signatures.