An algorithmic approach to predicting mechanical draft cooling tower fan speeds from infrasound signals

Eaton, Samuel W.; Cardenas, Edna S; Hix, Jay D.; Johnson, James Turner; Watson, Scott M.; Chichester, David L.; Garcés, Milton; Magaña-Zook, Steven A.; Maceira, Mónica; Marcillo, Omar; Chai, Chengping; d’Entremont, Brian; Reichardt, Thomas A.

doi:10.1016/j.apacoust.2022.109015

Cited by 3 publications

(8 citation statements)

References 20 publications

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“…The results presented in Section 3.3 focused on isolating the 21.4 Hz fundamental frequency and related harmonics that occur during HFIR full power operation. The paper by Eaton et al discussed the relationship of additional acoustic signals related to operation of the dual-speed and variable-speed fans [33]. These additional acoustic signals were observed in this investigation and are discussed here for completeness.…”

Section: Observation Of Additional Frequencies Related To Fan Operationsmentioning

confidence: 51%

Section: Observation Of Additional Frequencies Related To Fan Operationsmentioning

confidence: 93%

“…This frequency and its related harmonics were discussed in Section 3.3 and in the study by Marcillo et al [30]. In addition to the 21.4 Hz fundamental frequency, features at 10.8 Hz and 15.0 Hz were identified by [33] as the dual-speed fan's blade passing and motor frequencies, respectively, when the 1800 RPM motor is operating at half speed. These additional peaks are clearly observed in this examination in the PDR calculated with data collected from Phone 33 and 35 and presented in Figure 10a,b, respectively.…”

Section: Observation Of Additional Frequencies Related To Fan Operationsmentioning

confidence: 95%

“…In [33], the authors showed two time-averaged frequency spectra-one that showed all frequencies emitted during power generation, within the range of frequencies presented, and one where steady-state operations are background subtracted from the total. The total spectrum showed unchanging frequencies (e.g., those related to the cooling tower dual-speed fans), while changing features (e.g., those related to variable-speed fans) remained in the background subtracted spectrum.…”

Section: Observation Of Additional Frequencies Related To Fan Operationsmentioning

confidence: 99%

“…The implementation of monitoring techniques at nuclear facilities has been an ongoing topic of research in areas concerned with diagnostic evaluations and safeguards. Recent research, performed under the Multi-Informatics for Nuclear Operating Scenarios (MINOS) venture, have reported infrasound and low-frequency acoustic detection during power generation of the High Flux Isotope Reactor (HFIR) facility at Oak Ridge National Laboratory (ORNL) [30][31][32][33]. The ORNL campus serves as a testbed for multi-sensor deployment and data fusion research.…”

Section: Introductionmentioning

confidence: 99%

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An Assessment of Persistent Acoustic Monitoring of a Nuclear Reactor during Full Power Generation

et al. 2023

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Persistent low-frequency (<180 Hz) acoustic detection took place within the boundaries of Oak Ridge National Laboratory to monitor full power operations of the High Flux Isotope Reactor. Three acoustic sensors were installed at distances of 69, 101, and 914 m from the northeast corner of the cooling towers to monitor and assess four reactor power generation cycles. Features were extracted from power spectral density calculations where data were collected during reactor on and off operations. Diverse spectral features were present during full reactor power, including a 21.4 Hz fundamental frequency and ascending harmonics. Using bandpass filters, these related frequencies were isolated and summed, and the root mean square energy was calculated. The method of isolating and summing characteristic features provided a significant improvement in identifying acoustic behavior related to reactor power when the raw signals were obscured by noise.

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Section: Observation Of Additional Frequencies Related To Fan Operationsmentioning

confidence: 51%

Section: Observation Of Additional Frequencies Related To Fan Operationsmentioning

confidence: 93%

Section: Observation Of Additional Frequencies Related To Fan Operationsmentioning

confidence: 95%

Section: Observation Of Additional Frequencies Related To Fan Operationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Assessment of Persistent Acoustic Monitoring of a Nuclear Reactor during Full Power Generation

et al. 2023

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Application of Machine Learning for Classification of Nuclear Reactor Operational Status Using Magnetic Field Sensors

Burt,

Borghetti,

Franz

et al. 2023

JNE

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The nuclear fuel cycle forms the basis for producing special nuclear materials used in nuclear weapons via a series of interdependent industrial operations. These industrial operations each produce characteristic emanations that can be gathered to ascertain signatures of facility operations. Machine learning and deep learning techniques were applied to time series magnetic field sensor data collected at the High Flux Isotope Reactor (HFIR) to assess the feasibility of determining the ON/OFF operational state of the reactor. When data collected by the sensor near the cooling fans, position 9, are transformed to the frequency domain, it was found that both machine and deep learning methods were able to classify the operational state of the reactor with a balanced accuracy of over 90%. This result suggests that the utilized methods show promise for application as techniques to verify declared activities involving nuclear reactors. Additional effort is recommended to develop models and architectures that will more fully capitalize on the data’s temporal nature by incorporating the magnetic field’s time dependence to improve the model’s robustness and classification performance.

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Objective identification of pressure wave events from networks of 1 Hz, high-precision sensors

Allen,

Yuter,

Miller

et al. 2024

Atmos. Meas. Tech.

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Abstract. Mesoscale pressure waves, including atmospheric gravity waves, outflow and frontal passages, and wake lows, are outputs of and can potentially modify clouds and precipitation. The vertical motions associated with these waves can modify the temperature and relative humidity of air parcels and thus yield potentially irreversible changes to the cloud and precipitation content of those parcels. A wavelet-based method for identifying and tracking these types of wave signals in time series data from networks of low-cost, high-precision (0.8 Pa noise floor, 1 Hz recording frequency) pressure sensors is demonstrated. Strong wavelet signals are identified using a wave-period-dependent (i.e., frequency-dependent) threshold, and then those signals are extracted by inverting the wavelet transform. Wave periods between 1 and 120 min were analyzed – a range which could capture acoustic, acoustic-gravity, and gravity wave modes. After extracting the signals from a network of pressure sensors, the cross-correlation function is used to estimate the time difference between the wave passage at each pressure sensor. From those time differences, the wave phase velocity vector is calculated using a least-squares fit. If the fitting error is sufficiently small (thresholds of RMSE < 90 s and NRMSE < 0.1 were used), then a wave event is considered robust and trackable. We present examples of tracked wave events, including a Lamb wave caused by the Hunga Tonga volcanic eruption in January 2020, a gravity wave train, an outflow boundary passage, a frontal passage, and a cold front passage. The data and processing techniques presented here can have research applications in wave climatology and testing associations between waves and atmospheric phenomena.

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An algorithmic approach to predicting mechanical draft cooling tower fan speeds from infrasound signals

Cited by 3 publications

References 20 publications

An Assessment of Persistent Acoustic Monitoring of a Nuclear Reactor during Full Power Generation

An Assessment of Persistent Acoustic Monitoring of a Nuclear Reactor during Full Power Generation

Application of Machine Learning for Classification of Nuclear Reactor Operational Status Using Magnetic Field Sensors

Objective identification of pressure wave events from networks of 1 Hz, high-precision sensors

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