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Title: PREDICTIVE MODELING OF ACOUSTIC SIGNALS FROM THERMOACOUSTIC POWER SENSORS (TAPS)

Abstract

Thermoacoustic Power Sensor (TAPS) technology offers the potential for self-powered, wireless measurement of nuclear reactor core operating conditions. TAPS are based on thermoacoustic engines, which harness thermal energy from fission reactions to generate acoustic waves by virtue of gas motion through a porous stack of thermally nonconductive material. TAPS can be placed in the core, where they generate acoustic waves whose frequency and amplitude are proportional to the local temperature and radiation flux, respectively. TAPS acoustic signals are not measured directly at the TAPS; rather, they propagate wirelessly from an individual TAPS through the reactor, and ultimately to a low-power receiver network on the vessel’s exterior. In order to rely on TAPS as primary instrumentation, reactor-specific models which account for geometric/acoustic complexities in the signal propagation environment must be used to predict the amplitude and frequency of TAPS signals at receiver locations. The reactor state may then be derived by comparing receiver signals to the reference levels established by predictive modeling. In this paper, we develop and experimentally benchmark a methodology for predictive modeling of the signals generated by a TAPS system, with the intent of subsequently extending these efforts to modeling of TAPS in a liquid sodium environmen

Authors:
;
Publication Date:
Research Org.:
University of Tulsa
Sponsoring Org.:
USDOE Office of Nuclear Energy (NE)
OSTI Identifier:
1333922
Report Number(s):
61034
61034
DOE Contract Number:
NE0008422
Resource Type:
Conference
Resource Relation:
Conference: Proceedings of the 2016 24th International Conference on Nuclear Engineering ICONE24
Country of Publication:
United States
Language:
English
Subject:
thermoacoustics, wireless in-core radiation detection, wireless in-core temperature sensing, vibro-acoustic measureme

Citation Formats

Dumm, Christopher M., and Vipperman, Jeffrey S.. PREDICTIVE MODELING OF ACOUSTIC SIGNALS FROM THERMOACOUSTIC POWER SENSORS (TAPS). United States: N. p., 2016. Web. doi:10.1115/ICONE24-61034.
Dumm, Christopher M., & Vipperman, Jeffrey S.. PREDICTIVE MODELING OF ACOUSTIC SIGNALS FROM THERMOACOUSTIC POWER SENSORS (TAPS). United States. doi:10.1115/ICONE24-61034.
Dumm, Christopher M., and Vipperman, Jeffrey S.. Thu . "PREDICTIVE MODELING OF ACOUSTIC SIGNALS FROM THERMOACOUSTIC POWER SENSORS (TAPS)". United States. doi:10.1115/ICONE24-61034.
@article{osti_1333922,
title = {PREDICTIVE MODELING OF ACOUSTIC SIGNALS FROM THERMOACOUSTIC POWER SENSORS (TAPS)},
author = {Dumm, Christopher M. and Vipperman, Jeffrey S.},
abstractNote = {Thermoacoustic Power Sensor (TAPS) technology offers the potential for self-powered, wireless measurement of nuclear reactor core operating conditions. TAPS are based on thermoacoustic engines, which harness thermal energy from fission reactions to generate acoustic waves by virtue of gas motion through a porous stack of thermally nonconductive material. TAPS can be placed in the core, where they generate acoustic waves whose frequency and amplitude are proportional to the local temperature and radiation flux, respectively. TAPS acoustic signals are not measured directly at the TAPS; rather, they propagate wirelessly from an individual TAPS through the reactor, and ultimately to a low-power receiver network on the vessel’s exterior. In order to rely on TAPS as primary instrumentation, reactor-specific models which account for geometric/acoustic complexities in the signal propagation environment must be used to predict the amplitude and frequency of TAPS signals at receiver locations. The reactor state may then be derived by comparing receiver signals to the reference levels established by predictive modeling. In this paper, we develop and experimentally benchmark a methodology for predictive modeling of the signals generated by a TAPS system, with the intent of subsequently extending these efforts to modeling of TAPS in a liquid sodium environmen},
doi = {10.1115/ICONE24-61034},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Jun 30 00:00:00 EDT 2016},
month = {Thu Jun 30 00:00:00 EDT 2016}
}

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