研究業績リスト
ジャーナル論文 - rm_published_papers: International Conference Proceedings
公開済 08/2025
International Symposium on Advances and Innovation in Mechanical Engineering 2024, 35
ジャーナル論文 - rm_published_papers: Scientific Journal
公開済 07/2025
Applied Sciences, 15, 14, 7892
ジャーナル論文 - rm_published_papers: Scientific Journal
Measures to improve measurement accuracy of emulator-type load test method for air conditioners
公開済 06/2025
日本冷凍空調学会論文集, 42, 2, 47 - 59
ジャーナル論文 - rm_published_papers: Others
Analysis and Validation of Genetic Programming for Circuitry Optimization
公開済 02/2025
Proceedings of ASHRAE Winter Conference, 337, 345
ジャーナル論文 - rm_published_papers: International Conference Proceedings
公開済 2025
International Symposium on Advances and Innovation in Mechanical Engineering 2024 (ISAIME 2024), In Press
ジャーナル論文 - rm_published_papers: International Conference Proceedings
Experimental Study of Flow Boiling Characteristics in Wavy- Shaped Mini-Channel
公開済 12/2024
The 3rd Pacific Rim Thermal Engineering Conference, 24062
ジャーナル論文 - rm_published_papers: International Conference Proceedings
Enhancement of Pool Boiling Heat Transfer Using 3D-printed Metal Porous Structure
公開済 12/2024
The 3rd Pacific Rim Thermal Engineering Conference, 24025
ジャーナル論文 - rm_published_papers: Scientific Journal
Prediction of Boiling Heat Transfer Coefficient for Micro-Fin Using Mini-Channel
公開済 02/08/2024
Applied Sciences, 14, 15, 6777 - 6777
The prediction of the heat transfer coefficient commonly relies on the development of new empirical prediction equations when operating conditions and refrigerants change from the existing equations. Creating new prediction equations is expensive and time-consuming; therefore, recent attention has been given to machine learning approaches. However, machine learning requires a large amount of data, and insufficient data can result in inadequate accuracy and applicability. This study showed that using mini-channel data as highly relevant data for the micro-fin heat transfer coefficient yields high prediction accuracy, even when the experimental dataset of interest is limited. In the proposed method, we added dimensionless numbers assumed to significantly influence heat transfer coefficients calculated from experimental data to the training dataset. This allowed efficient learning of the characteristics of thin liquid films present in mini-channels and micro-fins. By combining distinctive physical mechanisms related to heat transfer coefficients with DNN/GPR/Fine-tuning, the proposed method can predict 96.7% of the data points within ±30% deviation. In addition, it has been confirmed that the dryout quality and post-dryout heat transfer coefficients were predicted with high accuracy. Additionally, we utilized visualization techniques to investigate the contents of the black-box machine learning models.
ジャーナル論文 - rm_published_papers: Others
公開済 08/2024
10th World Conference on Experimental Heat Transfer, Fluid Mechanics and Thermodynamics (ExHFT)
ジャーナル論文 - rm_published_papers: Scientific Journal
Feed-Forward Compensation for Emulator-Type Testing Facilities
公開済 07/2024
International Journal of Refrigeration, in press