Performance evaluation of eddy current flowmeter in Monju

Aizawa, Kosuke; Chikazawa, Yoshitaka; Morohashi, Yuko

doi:10.1080/00223131.2018.1512425

Cited by 2 publications

(3 citation statements)

References 5 publications

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“…While this problem could have been cast in many different ways we chose to use this approach because it allowed us to easily and quickly generate a large data set to train the DNN with. Simple inverse methods or optimization algorithms could also be used to solve equation (4).…”

Section: Problem Descriptionmentioning

confidence: 99%

“…This makes these devices particularly well suited for application in high temperature, corrosive and nuclear environments. ECFMs have been used in fission reactors in the past to measure average fluid velocity and to detect voids [2][3][4][5][6]. ECFMs may also be relevant in fusion reactor systems such as liquid metal breeder blankets [7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Measurement of a radial flow profile with eddy current flow meters and deep neural networks ^*

Gall¹,

Lau²,

Varma³

et al. 2023

Meas. Sci. Technol.

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Eddy Current Flow Meters (ECFMs) measure flows of conductive fluids. Recent interest in ECFMs has increased due to applications in advanced nuclear reactors. ECFMs are well suited for such applications, as they can provide non-invasive measurements of flow in fluids that are often difficult to measure. Traditionally, ECFMs are operated using an AC current at a single frequency, limiting ECFMs to measure average fluid velocities, blockages, or voids. We expand the capabilities of ECFMs by measuring the fluid radial velocity profile of liquid mercury. To accomplish this, we made several ECFM sensitivity measurements at a range of frequencies. Different frequencies vary the electromagnetic skin depth of the device. By adjusting frequencies, we probed the fluid velocity at various radial locations and constructed a flow-velocity profile. The relationship between the ECFM measurements and velocity profile is nonlinear and requires solving an inverse problem. Using electromagnetic finite-element simulations to train a Deep Neural Network, we created a model that provides a stable general relationship between the sensitivity measurements of an ECFM and the fluid velocity profile. Using ECFM measurements of liquid mercury, our Deep Neural Network (DNN) model calculates a flow profile that agrees well with computational fluid dynamics (CFD) simulations. This technique has potential to improve flow monitoring for optimization, safe operation of conductive fluid loops, and/or validating complex CFD models.

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Section: Problem Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Measurement of a radial flow profile with eddy current flow meters and deep neural networks ^*

Gall¹,

Lau²,

Varma³

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Measurement of these flows in reactor cells [1,2] can greatly improve the accuracy of the empirical formulas used for fluid dynamics near the reactor core and aid in mapping any non-uniformities in fluid flow such as estimates of temperature and phase. The ECFM was orginally developed in 1948 [3] and has been used to measure flows in the Fast Flux Test Facility reactor in the United States [4], Monju Liquid Metal Fast Breeder Reactor in Japan [5], and Protype Fast Breeder Reactor [6] in India. ECFM has also been used to detect voids in the Liquid Metal Fast Breeder Reactor in Japan [7] and to measure flows on various liquid metal loops around the world [8,9].…”

Section: Introductionmentioning

confidence: 99%

Eddy current flow meter model validation with a moving solid rod ^*

Lau¹,

Oleksak²,

Çetiner³

et al. 2022

Meas. Sci. Technol.

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The eddy current flow meter (ECFM) has been used to measure velocities and temperatures of conductive flows, such as liquid metal flows in a nuclear fission reactor. The goal of this paper is to develop a finite element electromagnetic model that can characterize the ECFM sensor performance and validate this finite element model with detailed velocity measurements of a controlled, well-characterized moving conductive solid rod. Both measurements and modeling were performed for various parameters that are important for ECFM performance such as rod velocity, rod material, ECFM sensor coil length, number of sensor coils, applied alternating current (AC) current amplitude, and applied ECFM AC frequency. For all parametric scans, the measurement and modeling agree well in both magnitude and trend. The normalized root-mean-square error between measurement and modeling is less than 10% for all cases. These results suggest that electromagnetic modeling could eventually be used to cost-effectively design future ECFM sensors in arbitrary geometry for more challenging applications such as liquid metal nuclear fission reactors.

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Performance evaluation of eddy current flowmeter in Monju

Cited by 2 publications

References 5 publications

Measurement of a radial flow profile with eddy current flow meters and deep neural networks ^*

Measurement of a radial flow profile with eddy current flow meters and deep neural networks ^*

Eddy current flow meter model validation with a moving solid rod ^*

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Performance evaluation of eddy current flowmeter in Monju

Cited by 2 publications

References 5 publications

Measurement of a radial flow profile with eddy current flow meters and deep neural networks *

Measurement of a radial flow profile with eddy current flow meters and deep neural networks *

Eddy current flow meter model validation with a moving solid rod *

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Product

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Measurement of a radial flow profile with eddy current flow meters and deep neural networks ^*

Measurement of a radial flow profile with eddy current flow meters and deep neural networks ^*

Eddy current flow meter model validation with a moving solid rod ^*