Describe the I3D (Inflated 3D) training on the dataset. Results: Present the accuracy of heat flux estimation. Discussion: Analyze how the model performs on the video. 5. Conclusion
Propose a 3D Convolutional Neural Network (3D CNN) to extract spatial-temporal features. 4. Training & Evaluation
Solid Paper Structure: Machine Learning-Based Pool Boiling Analysis pool.mp4
Highlight the difficulty in measuring instantaneous heat flux in real-time.
Review existing CNN applications. 3. Dataset & Methodology ( pool.mp4 analysis) Describe the I3D (Inflated 3D) training on the dataset
Explain how the video is converted into a 3D dataset (height, width, time).
Based on the prompt "pool.mp4" and the request to "put together a solid paper," the search results suggest a strong connection to , according to a recent Feb 2026 study. The video likely demonstrates the 3D modelling of bubble dynamics or the experimental setup described in that paper. referencing Chun & Kang.
Discuss previous work on nucleate pool boiling, referencing Chun & Kang.