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三上 奈生; 相澤 康介; 栗原 成計; 植木 祥高*
AI Thermal Fluids (Internet), 5, p.100029_1 - 100029_15, 2026/03
Early detection of water/steam leakage is important in the prevention of failure propagation of heat transfer tubes in a steam generator of a sodium-cooled fast reactor. This study proposes an unsupervised learning-based acoustic method to detect gas leakage in liquid and evaluates its noise resistance based on parametric receiver operating characteristic (ROC) analysis. An autoencoder is trained, validated, and tested on time-frequency representations of simulated noise and leak signals for various signal-to-noise ratios (SNRs). To calculate a false positive rate and a true positive rate, the probability density function is assumed to be either as a normal distribution, a power transformed normal distribution, or a power normal distribution. As a result, the power normal distribution that shows the best goodness-of-fit was used as the probability density function to draw an ROC curve. The predictive ability of the autoencoder is evaluated as excellent for 
, 
, 
, and 
dB, good for 
dB, and poor for 
dB. The autoencoder can detect leakage at relatively low-noise levels and has the potential to detect leakage at relatively high-noise levels equivalent to actual noise levels. Segmentation of the noise and leak signals can also be achieved from input, reconstructed, and residual images. These results suggest that the proposed method contributes to laying the foundation for detection and accident analysis of water/steam leakage in a steam generator of a sodium-cooled fast reactor.
植木 祥高*; 平子 樹*; 手塚 晃輔*; 相澤 康介; 荒 邦章*
AI Thermal Fluids (Internet), 4, p.100021_1 - 100021_12, 2025/12
With a final goal of early detection and understanding of the transition of coolant boiling events in the core of sodium-cooled fast reactors, our present aim is to obtain and maintain the basic knowledge necessary for developing anomaly detection technology associated with local anomalies in the core and to demonstrate basic feasibility. We constructed a deep learning method and evaluated its performance to detect the occurrence and understand the transition of subcooled boiling using acoustic identification. In this research, we aim to acquire acoustic data during subcooled boiling of ultrapure water and learn feature quantities of the boiling in time-frequency expression. A deep learning model of a convolutional neural network for label classification was constructed. In addition to being able to identify the occurrence of boiling with high accuracy, the visualization of the identification basis using the gradient-weighted class activation mapping (Grad-CAM) method revealed the acoustic frequency bands that the deep learning model determined to be of high importance. We also constructed a regression analysis-type deep learning model and demonstrated that boiling heat flux values can be predicted with high accuracy.
粉川 広行; 涌井 隆; 二川 正敏
Fluids (Internet), 10(1), p.3_1 - 3_15, 2025/01
マイクロバブルはさまざまな分野で応用されている。核破砕中性子源の水銀ターゲットでは、キャビテーション損傷が寿命評価上重要な問題であり、マイクロバブルを水銀中に注入することにより、パルス陽子ビームの入射による水銀の熱膨張を吸収し、巨視的な圧力波を低減し、損傷を低減することができる。近年、陽子ビームパワーを増加させ、ガスバブルの注入量を増加させたところ、固液界面に特異な損傷形態が観察された。詳細な観察と数値解析により、ガス気泡が収縮する際に発生する微視的な圧力がピット損傷を形成するのに十分であること、すなわち、ピット損傷を連結して形成される筋状の欠陥の方向がガス気泡の軌道方向と一致していること、また、ピット間の距離がガス気泡の固有振動周期を考慮して理解できることが明らかになった。このことは、マクロな圧力波を低減するガスマイクロバブルは、ガスバブルから放出されるミクロな圧力によるキャビテーション損傷を抑制する可能性があることを示している。損傷を完全に緩和するためには、ガス気泡注入による巨視的圧力波の低減と、気泡ダイナミクスによる微視的圧力の低減という2つの効果を考慮する必要がある。
石垣 将宏*; 廣瀬 意育; 安部 諭; 永井 亨*; 渡辺 正*
Fluids (Internet), 7(7), p.237_1 - 237_18, 2022/07
To estimate thermal flow in a nuclear reactor during an accident, it is important to improve the accuracy of computational fluid dynamics simulation. Temperature and flow velocity are not homogeneous and have large variations in a reactor containment vessel because of its very large volume. In addition, Kelm et al (2016) pointed out that the influence of variations of initial and boundary conditions was important. Therefore, it is necessary to set the initial and boundary conditions taking into account the variations of these physical quantities. However, it is a difficult subject to set such complicated initial and boundary conditions. Then, we can obtain realistic initial and boundary conditions by the data assimilation technique, and we can improve the accuracy of the simulation result. In this study, we applied the data assimilation by local ensemble transform Kalman filter (Hunt et al., 2007) to the simulation of natural convection behavior in density stratification, and we performed a twin model experiment. We succeeded in the estimation of the flow fields and improving the simulation accuracy by the data assimilation, even if we applied the boundary condition with error for the true condition.
岡垣 百合亜; 与能本 泰介; 石垣 将宏; 廣瀬 意育
Fluids (Internet), 6(2), p.80_1 - 80_17, 2021/02
Many thermohydraulic issues about the safety of light water reactors are related to complicated two-phase flow phenomena. In these phenomena, computational fluid dynamics (CFD) analysis using the volume of fluid (VOF) method causes numerical diffusion generated by the first-order upwind scheme used in the convection term of the volume fraction equation. Thus, in this study, we focused on an interface compression (IC) method for such a VOF approach; this technique prevents numerical diffusion issues and maintains boundedness and conservation with negative diffusion. First, on a sufficiently high mesh resolution and without the IC method, the validation process was considered by comparing the amplitude growth of the interfacial wave between a two-dimensional gas sheet and a quiescent liquid using the linear theory. The disturbance growth rates were consistent with the linear theory, and the validation process was considered appropriate. Then, this validation process confirmed the effects of the IC method on numerical diffusion, and we derived the optimum value of the IC coefficient, which is the parameter that controls the numerical diffusion.
