Nuclear reactor power levels can be monitored using seismic and acoustic
data

Date:
March 16, 2022
Source:
Seismological Society of America
Summary:
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, according to a new study.



FULL STORY ========================================================================== 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, according to a new study published in Seismological
Research Letters.


==========================================================================
By applying several machine learning models to the data, researchers
at Oak Ridge National Laboratory could also predict when the reactor
was transitioning between on and off, and 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, said the study's lead author Chengping Chai, a geophysicist at Oak Ridge. "Nuclear reactors can be used for both benign
and nefarious activities. Therefore, verifying that a nuclear reactor
is operating as declared is of interest to the nuclear nonproliferation community." Although seismic and acoustic data have long been used to
monitor earthquakes and the structural properties of infrastructure such
as buildings and bridges, some researchers now use the data to take a
closer look at the movements associated with industrial processes. In
this case, Chai and colleagues deployed seismic and acoustic sensors
around the High Flux Isotope Reactor at Oak Ridge, a research reactor
used to produced 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. "We found that seismo-acoustic sensors can record
the mechanical signatures of vibrating equipment such as fans and pumps
at the cooling tower at an accuracy enough to shed light into operational questions," Chai said.

The researchers 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, they found.

"The seismo-acoustic signals associated with different power levels
show complicated patterns that are difficult to analyze with traditional techniques," Chai explained. "The machine learning approaches are able
to infer the complex relationship between different reactor systems and
their seismo- acoustic fingerprint and use it to predict power levels."
Chai and colleagues detected some interesting signals during the course
of their study, including the vibrations of a noisy pump in the reactor's
off state, which disappeared when the pump was replaced.

Chai said it is a long-term and challenging goal to associate seismic
and acoustic signatures with different industrial activities and
equipment. For the High Flux Isotope Reactor, preliminary research
shows that fans and pumps have different seismo-acoustic fingerprints,
and that different fan speeds have their own unique signatures.

"Some normal but less frequent activities such as yearly or incidental maintenance need to be distinguished in seismic and acoustic data,"
Chai said.

To better understand how these signatures relate to specific operations,
"we need to study both the seismic and acoustic signatures of instruments
and the background noise at various industrial facilities."

========================================================================== Story Source: Materials provided by
Seismological_Society_of_America. Note: Content may be edited for style
and length.


========================================================================== Journal Reference:
1. Chengping Chai, Camila Ramirez, Monica Maceira, Omar
Marcillo. Monitoring
Operational States of a Nuclear Reactor Using Seismoacoustic
Signatures and Machine Learning. Seismological Research Letters,
2022; DOI: 10.1785/ 0220210294 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/03/220316115003.htm

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