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July 28, 2026

It’s boiling over! Acoustic AI hears what cameras can’t see

A new method uses sound to predict unsafe conditions

AI Acoustics
A schematic of a proton beam hitting isotope production targets (left) at the Isotope Production Facility. The sound of boiling bubbles from the target cooling water are converted into diagnostic data about thermal conditions, providing critical monitoring capabilities in radiation-heavy environments where cameras cannot operate. Credit to: Los Alamos National Laboratory

In a Nature Scientific Reports study, Los Alamos researchers showed they can “listen” to boiling sounds with underwater microphones and use AI to estimate what’s happening at a heated surface. The new method could be broadly applied to nuclear reactors, particle accelerators and other high-power thermal technologies where conventional monitoring techniques such as cameras are constrained.

Read the paper

Why this matters: If conditions get too extreme, water used to cool systems can suddenly stop carrying heat away effectively, leading to rapid damage. Better real-time boiling diagnostics would help protect high-power targets and other heat-loaded systems from critical heat flux events by giving operators a radiation-tolerant way to spot unsafe thermal conditions before damage occurs.

What they did:

  • Turned boiling sounds into visual patterns a computer could read, then used AI to estimate how intense the boiling was and how the bubbles were behaving.
  • Plugged those results into a physics-based simulation and showed the method still worked even with background noise and modest changes in operating conditions.
  • Tested their method in an experimental facility where radiation levels prevent the use of cameras and other monitoring techniques.

What’s next:

  • The team will test their method in the Isotope Production Facility at the Los Alamos Neutron Science Center, measuring the boiling sound signals, and then use AI to predict the thermal and boiling bubble behaviors.
  • They will then integrate these predictions into a physics-based simulation and validate the approach by comparing the AI-predicted thermal characteristics with simulation results. 

Funding: U.S. Department of Energy Isotope Program, managed by the Office of Science for Isotope R&D and Production.

AI tools were used to develop this content with human oversight and review.

LA-UR-26-25205

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