Development of machine learning framework for interface force closures based on bubble tracking data

Author:

Tai Cheng-Kai,Evdokimov Ilya,Schlegel Fabian,Bolotnov Igor A.,Lucas Dirk

Publisher

Elsevier BV

Subject

Mechanical Engineering,Waste Management and Disposal,Safety, Risk, Reliability and Quality,General Materials Science,Nuclear Energy and Engineering,Nuclear and High Energy Physics

Reference33 articles.

1. M. Abadi, A. Agarwal, P. Barham, E. Brevdo, C. C. Zhifeng Chen, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, R. Jozefowicz and a. others, “TensorFlow: Large-scale machine learning on heterogeneous systems, Software available from tensorflow.org.,” 2015.

2. Computationally efficient CFD prediction of bubbly flow using physics-guided deep learning;Bao;International Journal of Multiphase Flow,2020

3. Using deep learning to explore local physical similarity for global-scale bridging in thermal-hydraulic simulation;Bao;Annals of Nuclear Energy,2020

4. Experiments on the effect of acceleration on the drag of tapwater bubbles;Geld;Experiments in Fluids,2001

5. Interface capturing simulations of bubble population effects in PWR subchannels;Cambareri;Nuclear Engineering and Design,2020

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