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Predictive Modeling of Acoustic Signals From Thermoacoustic Power Sensors (TAPS)

[+] Author Affiliations
Christopher M. Dumm, Jeffrey S. Vipperman

University of Pittsburgh, Pittsburgh, PA

Jorge V. Carvajal, Melissa M. Walter, Luke Czerniak, Amy S. Ruane, Paolo Ferroni, Michael D. Heibel

Westinghouse Electric Company, Pittsburgh, PA

Paper No. ICONE24-61034, pp. V001T04A019; 12 pages
doi:10.1115/ICONE24-61034
From:
  • 2016 24th International Conference on Nuclear Engineering
  • Volume 1: Operations and Maintenance, Aging Management and Plant Upgrades; Nuclear Fuel, Fuel Cycle, Reactor Physics and Transport Theory; Plant Systems, Structures, Components and Materials; I&C, Digital Controls, and Influence of Human Factors
  • Charlotte, North Carolina, USA, June 26–30, 2016
  • Conference Sponsors: Nuclear Engineering Division
  • ISBN: 978-0-7918-5001-5
  • Copyright © 2016 by ASME

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 environment.

Copyright © 2016 by ASME

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