Noninvasive acoustic time-of-flight measurements in heated, hermetically-sealed high explosives using a convolutional neural network

Journal Article · Tue Aug 02 00:00:00 EDT 2022 · Machine Learning with Applications

DOI:https://doi.org/10.1016/j.mlwa.2022.100391· OSTI ID:1881818

Greenhall, John James ^[1]; Zerkle, David Karl ^[1]; Davis, Eric Sean ^[1]; Broilo, Robert M. ^[1]; Pantea, Cristian ^[1]

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1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

In this work, we present a data-driven technique for measuring the time-of-flight through material sealed within a container. Time-of-flight measurement provides a noninvasive means of quantifying the sound speed profile within a material by transmitting an acoustic burst and then measuring the time required for the burst to arrive at an opposing receiver. In a hermetically-sealed cylindrical container, a portion of the acoustic energy propagates through the material as a bulk wave, while the remainder of the acoustic energy propagates around the container walls as guided waves. As a result, interference from the guided waves obscures the bulk arrival, inhibiting measurement of the sound speed. The technique uses a Convolutional Neural Network (CNN) to identify critical features in the measured waveforms and identify bulk wave arrivals. We demonstrate this time-of-flight measurement technique on high explosive-filled containers as they are heated from room temperature to detonation. This is a particularly challenging application for acoustic time-of-flight measurements as the high explosives have significant sound speed gradients as they undergo heating, and they lead to significant attenuation of the bulk wave, as opposed to the guided waves, which do not suffer significant attenuation. We characterize the performance of the CNN as a function of the high explosive temperature and as a function of the CNN hyperparameters. We then provide physical insight into the error trends.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 1881818
• Report Number(s): LA-UR-21-32269
• Journal Information: Machine Learning with Applications, Journal Name: Machine Learning with Applications Vol. 9; ISSN 2666-8270
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

47 OTHER INSTRUMENTATION
Acoustic measurement in high explosives
Acoustic time-of-flight
Convolutional Neural Networks
Nondestructive evaluation
Wave interference