本研究首先利用計算流體力學 模擬單相流在收縮-擴張噴嘴內的流動特性, 並比較純水與純蒸汽條件下,不同流量對流體行為的影響。研究的主要目標是探討過冷水通過噴嘴時發生的氣蝕現象,重點關注蒸汽生成效率與噴嘴內的壓力場。結果顯示,進出口壓力差 是影響氣蝕效率的關鍵因素。此外,透過分析不同幾何參數對相變與流體特性的影響,發現出口半徑與喉部面積 分別是影響空蝕效率與質量流率的主要因素。
鑑於機器學習的最新發展,本研究進一步結合數值模擬結果,進行創新性的分析。透過 CFD 模擬不同流量與幾何條件,建立數據庫,並使用人工神經網路(ANN)與支持向量機(SVM) 預測壓力差與其他流體特性。經過準確度評估,結果顯示 ANN 在本研究中表現更為優異,在大多數情況下決定係數(COR2)超過
0.95。

In this study, we begin by using Computational Fluid Dynamics (CFD) to simulate the flow characteristics of single-phase flow through a converging-diverging nozzle (CD nozzle). Compare fluid behavior changes with different flow rates in pure water and pure steam conditions. The primary objective of this research is to investigate the cavitation that occurs as subcooled water passes through the nozzle. The focus is on the efficiency of steam generation and the pressure field within the nozzle. It was found that the pressure
difference between the inlet and outlet is a key factor influencing cavitation efficiency. Additionally, through a comprehensive analysis of the effects of various geometric parameters on phase change and other fluid properties, it was found that the outlet radius and throat area are the main factors affecting cavitation efficiency and mass flow rate, respectively.

Given the recent advancements in machine learning, this study integrates numerical simulation results to conduct an innovative investigation. Different flow rates and geometries were simulated using CFD to establish a database, and ANN and SVM were employed to predict pressure differences and other fluid properties. After evaluating their accuracy, it was found that ANN was significantly more suitable for this study, achieving determination coefficients exceeding 95% in most cases.

論文審定書 i
誌謝 ii
摘要 iii
ABSTRACT iv
TABLE OF CONTENTS v
LIST OF FIGURES viii
LIST OF TABLES xii
NOMENCLATURE xiv
1. INTRODUCTION 1
1.1. Background and Motivations 1
1.2. Cavitation, Flash Boiling as Flow Control 2
1.3. Numerical Simulations for Cavitating Flows 6
1.4. Machine Learning 11
1.5. Scope of Research and Thesis Overview 14
2. MATHEMATICAL MODELING 17
2.1. Geometry Description 17
2.2. Mathematical Modelling for Fluid Flows with Phase Change 19
2.2.1. Principal Governing Equations: Conservation Laws 19
2.2.2. Turbulence Model 21
2.2.3. Volume-of-Fluid (VOF) Method 22
2.2.4. Phase Change Model for Cavitation 23
2.3. Machine Learning Model 24
2.2.1. Artificial Neural Network 24
2.2.2. Backpropagation Neural Network (BPNN) 25
2.2.3. Training Algorithms 27
3. NUMERICAL MODELING 37
3.1. Computational Grid 37
3.2. Mesh Independence 38
3.3. Mesh Independence 42
3.4. Numerical Setup 52
4. CHARACTERIZATION OF EVAPORATING FLOW THROUGH A CONVERGING-DIVERGING NOZZLE 53
4.1. Pure Water Flow through the Converging-Diverging Nozzle 53
4.2. Pure Steam Flow through the Converging-Diverging Nozzle 57
4.3. Calibration of the Cavitation Model 60
4.5. Evaporative Flow through the Nozzle 68
4.6. Effects of Inlet and Outlet Radius 74
4.7. Effect of Throat Radius 78
4.8. Sensitivity Study for Geometric Parameters 80
5. PREDICT THE CAVITATION FLOW BY MACHINE LEARNING BASE ON CFD RESULT 86
5.1. Training Steps 86
5.2. Training Data 87
5.3. Back Propagation Neural Network (BPNN) 89
5.4. Analysis the Training Data 93
5.5. Testing the ANN Model 97
5.6. Application of Machine Learning Model to Cavitation Flow Prediction in constant mass flow rate 100
5.7. Application the Machine Learning Model and Cavitation Flow Prediction in Different Flow Rate. 104
5.8. Predict the Steam Volume Fraction and Mass Flow Rate 107
6. CONCLUSIONS 110
6.1. Cavitation Flow Characterization and Analysis using CFD 110
6.2. Use of Machine Learning to Predict Cavitation Flow through a CD Nozzle 111
6.3. Future Works 112
BIBLIOGRAPHY 113
APPENDIX 127

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