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Title Page

Abstract

Contents

Nomenclature 15

Chapter 1. Introduction 16

1.1. Background and Motivation 16

1.2. Objectives and Scope 18

1.3. Dissertation Overview 19

Chapter 2. Literature Reviews 23

2.1. Acoustic Emission Signal in Boiling System 23

2.2. Deep Learning Application in Acoustic Emission Signal 29

2.3. Gaps in the Current Literature 31

Chapter 3. Boiling and Acoustic Characteristics 34

3.1. Introduction 34

3.2. Pool Boiling Experiment and AE Analysis 36

3.2.1. Experimental Apparatus and Procedure 36

3.2.2. Acoustic Signal Measurement Method 37

3.2.3. Results and Discussion 39

3.3. Flow Boiling Experiment and AE Analysis 50

3.3.1. Experimental Apparatus and Procedure 50

3.3.2. Results and Discussions 51

3.4. Quenching experiment and AE Analysis 55

3.4.1. Experimental Apparatus and Procedure 55

3.4.2. Results and Discussions 56

3.5. Comprehensive Acoustic Characteristic in Boiling Systems 58

Chapter 4. Deep Learning and AE Based-Boiling Monitoring System 86

4.1. Deep Learning for Boiling Regime Classification 86

4.1.1. Boiling Regime Classification for Each Boiling Experimental Condition 86

4.1.2. Test Matrix for Deep Learning 87

4.1.3. Results of Deep Learning 88

4.2. Deep Learning for Heat Flux and HTC Prediction 89

4.2.1. Heat flux and HTC Regression with Boiling Regime Classification 89

Chapter 5. Feasibility Study of AE Based Boiling Monitoring 106

5.1. Case 1. RPV of URILO 106

5.1.1. Introduction 106

5.1.2. Experimental Procedure 107

5.1.3. Results and Discussion 108

5.2. Case 2. High Pressure Flow Boiling Facility 110

5.2.1. Introduction 110

5.2.2. Results and Discussion 110

Chapter 6. Conclusions and Recommendations 120

6.1. Summary and Conclusions 120

6.2. Recommendations and Future Works 122

References 124

List of Tables

Table 2.1. Recent research on AE signal sensing and analysis in boiling or two-phase flow system 33

Table 3.1. The parameters, instrumentation method, measurement range, and calculated uncertainties for pool boiling experiment. 59

Table 3.2. Comparison of AE signal waveform, spectral density, and spectrogram characteristics by pool boiling heat transfer regimes. 60

Table 3.3. Comparison of AE waveform and spectrogram characteristics in flow boiling regimes. 61

Table 3.4. Phenomena images and acoustic waveforms from quenching, pool boiling, and flow boiling experiments. 62

Table 3.5. Boiling images and AE spectrogram of quenching, pool boiling, and flow boiling experiments. 63

Table 4.1. Acoustic emission signal dataset for deep learning model training. 92

Table 4.2. Test matrix for boiling regime classification 93

Table 4.3. Deep learning model performance comparison 94

Table 5.1. Experimental conditions for acoustic emission signal measurement in URI-LO. 112

Table 5.2. Results of single-phase and two-phase heat transfer prediction in URI-LO. 112

List of Figures

Figure 1.1. The research motivation and chapter configuration. 21

Figure 1.2. The objectives and scopes 22

Figure 3.1. Transparent pool boiling experiment for acoustic signal measurement. 64

Figure 3.2. Heater surface coating methods for bare case and hydrophobic case. 65

Figure 3.3. Illustrative methodologies for acoustic signal evaluation using (a) Fast Fourier Transformation and (b) Short Time Fourier Transformation. 65

Figure 3.4. (a) HSV images and (b) boiling curve with heater temperature profile across different heat fluxes, and (c) heat transfer coefficient from natural convection to CHF. 66

Figure 3.5. Analysis of acoustic signals for individual nucleate bubbles: (a) acoustic voltage paired with HSV visual representations, (b) spectrogram image by STFT conversion showing frequency,... 67

Figure 3.6. Schematic mechanism of acoustic emission signal generation and transmission by nucleation bubble. 68

Figure 3.7. (a) Evolution of bubble size over time with associated images for the scenarios of 40 kW/m² and 45 kW/m², juxtaposition of recorded AE signals with deduced pressure waves for (b) 40... 69

Figure 3.8. Estimation of bubble size via AE signal for (a) 40 kW/m², (b) 45 kW/m², and (c) elucidation of the bubble's expansion behavior immediately post-nucleation. 70

Figure 3.9. STFT visualizations of AE signals during pool boiling across key heat fluxes ranging from 20 kW/m² up to CHF. 71

Figure 3.10. Analysis of AE signal's amplitude and dominant frequency in relation to varying heat fluxes. 72

Figure 3.11. Analysis of AE count versus heat transfer coefficient as functions of heat flux, with a derived heat transfer coefficient based on AE count showcased in the inset. 73

Figure 3.12. High-speed video visualization images at each heat flux for plain and hydrophobic coated heater 74

Figure 3.13. AE signal and power spectrum density at film boiling regime in hydrophobic heater. 75

Figure 3.14. Schematic flow boiling experimental setup. 76

Figure 3.15. Flow boiling test section and visualizing window. 77

Figure 3.16. Heat flux vs. wall superheat boiling curve graph (top) and heat transfer coefficient vs. heat flux graph (bottom). 78

Figure 3.17. Flow boiling heat transfer visualization images by heat flux. 79

Figure 3.18. Analysis of acoustic emission signal characteristics by heat flux: (a) counts, (b) energy, (c) amplitude, (d) peak frequency. 80

Figure 3.19. AE signals and FFT conversion frequency characteristics graph at single-phase convection regime. 81

Figure 3.20. AE signals and FT conversion frequency characteristics graph at nucleate boiling regime. 82

Figure 3.21. AE signals and FT conversion frequency characteristics graph at nucleate and CHF regime. 83

Figure 3.22. AE spectrogram analysis from single phase convection to CHF. 84

Figure 3.23. Experimental apparatus for quenching. 85

Figure 4.1. Representative ANN model and boiling regime classification concept 95

Figure 4.2. Representative CNN model and boiling regime classification concept 95

Figure 4.3. Flow chart for AE signal data preprocessing and boiling regime diagnosis method with optimization. 96

Figure 4.4. Input image samples used for deep learning learning: original AE signal and spectrogram (left), AE signal with noise and corresponding spectrogram (right) 97

Figure 4.5. Developed ResNet-based boiling regimes, heat flux, and HTC prediction model. 98

Figure 4.6. Confusion matrix for boiling regime classification accuracy (whole boiling experimental type). 99

Figure 4.7. Predicted heat flux from Resnet-based deep learning model for total data (top) and pool boiling data (bottom). 100

Figure 4.8. Predicted heat flux from Resnet-based deep learning model for flow boiling data (top) and quenching data (bottom). 101

Figure 4.9. Predicted HTC from Resnet-based deep learning model for total data (top) and pool boiling data (bottom). 102

Figure 4.10. Predicted HTC from Resnet-based deep learning model for flow boiling data (top) and quenching data (bottom). 103

Figure 4.11. Data distribution and comparison of true and predicted heat flux data. 104

Figure 4.12. Data distribution and comparison of true and predicted HTC data. 105

Figure 5.1. Overview of the URILO Experimental Setup. 113

Figure 5.2. Experimental setup for measuring acoustic signals of two-phase states with a flow loop. 114

Figure 5.3. Single-phase flow and two-phase heat transfer flow visualization results according to URI-LO output and flow rate. 115

Figure 5.4. Spectrogram differences under single-phase and two-phase forced convection conditions at 0W with flow rates of 4.8 kg/s in a URI-LO (case 1). 116

Figure 5.5. Spectrogram differences under single-phase and two-phase forced convection conditions at 0W with flow rates of 6.0 kg/s in a URI-LO (case 1). 116

Figure 5.6. Spectrogram image of two-phase heat transfer flow in URI-LO under natural circulation conditions at 198 kW, 0 kg/s (case 2). 117

Figure 5.7. Spectrogram image of two-phase heat transer flow in URI-LO under forced circulation conditions at 198 kW, 4.8 kg/s (case 2). 117

Figure 5.8. Test section of DISNY facility 5 and configuration attached Two AE Sensors. 118

Figure 5.9. AE signal and spectrogram trend during subcooled flow boiling experiment. 119

초록보기

 This dissertation addresses the crucial challenge of enhancing nuclear power plant safety and efficiency by advancing the monitoring of boiling phenomena within reactors. Boiling, a fundamental process in nuclear reactors, plays a vital role in heat transfer and is essential for maintaining safe operational temperatures. However, traditional methods like temperature, pressure, and flow rate measurements often fall short in providing direct insights into reactor core conditions during boiling. The urgency of this research is driven by the need for more accurate and efficient monitoring methods to prevent critical issues such as Departure from Nucleate Boiling (DNB), which can lead to fuel damage and, in extreme cases, reactor meltdown. Understanding and controlling the boiling process is essential, as it directly influences heat and hydraulic conditions within the reactor core.

The research comprises a series of experimental investigations in pool boiling, flow boiling, and quenching. In pool boiling experiments, meticulous analysis of AE signals during the nucleation and growth of boiling bubbles is conducted. Techniques like the Fast Fourier Transform (FFT) and Short-Time Fourier Transform (STFT) are used to interpret signal frequency and amplitude characteristics, revealing distinct AE signal patterns at various boiling stages. This analysis notes changes in AE signal characteristics with varying heat fluxes and identifies unique patterns at critical heat flux (CHF) phases, highlighting AE signals' potential as a diagnostic tool for different boiling regimes.

In flow boiling experiments, a specialized experimental loop is employed to investigate different flow regimes and correlate AE signals with various boiling stages. This analysis provides novel insights into flow boiling phenomena, enhancing the understanding of two-phase heat transfer and its acoustic signatures.

Quenching experiments are extended to observe the complete boiling regime, including film boiling, transient boiling, CHF, and nucleate boiling. The focus is on how subcooling influences vapor film collapse during quenching, with findings indicating that higher subcooling correlates with rapid vapor film collapse and intense AE signals, offering key insights into boiling dynamics and AE signal characteristics.

This research introduces an approach in boiling heat transfer analysis using deep learning techniques, primarily employing Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs), such as ResNet models, to classify boiling regimes from AE signals. The effectiveness of a ResNetbased model in analyzing and predicting complex, nonlinear data patterns typical of boiling phenomena is demonstrated. Models are trained with extensive datasets from various boiling experiments, achieving high accuracy in predicting boiling heat transfer regimes, heat flux, and Heat Transfer Coefficients (HTCs).

This study also explores the practical application of the developed AE-based boiling monitoring technology in two distinct case studies: the thermal-hydraulic integral effect test facility (ITE) and the high-pressure flow boiling facility. The technology's effectiveness in diagnosing two-phase fluid flow phenomena within these complex systems is assessed. In ITE, AE signal analysis combined with deep learning's capability to accurately distinguish between single-phase and two-phase flows is demonstrated. The high-pressure flow boiling facility case further validates the method's applicability in high-pressure environments, successfully detecting nucleate boiling at higher heat fluxes. These investigations highlight AE-based monitoring's potential as a tool for real-time analysis and safety assurance in nuclear power plants and other critical industrial applications.

In summary, this research represents a significant advancement in nuclear reactor monitoring, merging Acoustic Emission (AE) signal analysis with deep learning. Key achievements include the development of precise deep learning models for boiling regime classification and heat flux prediction, and the effective application of AE signal analysis in complex environments. The study overcomes challenges in real-world applications, demonstrating this technology's potential in nuclear contexts. Future directions involve enhancing AE sensor capabilities, implementing robust noise cancellation, expanding deep learning datasets, exploring new deep learning architectures, conducting real-field testing, fostering interdisciplinary collaboration, and engaging with regulatory bodies. This work lays the groundwork for future exploration in AE signal analysis and deep learning in nuclear reactor monitoring, aiming to enhance the safety and efficiency of nuclear power plants.

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