Development of machine learning framework for interface force closures based on bubble tracking data
| Autor: | Tai, C.-K., Bolotnov, I., Evdokimov, I., Schlegel, F., Lucas, D. |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | 19th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-19), 06.-11.03.2022, Brüssel, BelgienProceedings of NURETH-19 Nuclear Engineering and Design 399(2022), 112032 |
| Popis: | Understanding the liquid-water interaction serves as the basis to credibility of two-phase flow models and safety of light water reactors. The topic is of researchers’ long interest due to the complexity of underlying physics. Recently, with growing availability to high performance computing resources, interface tracking direct numerical simulation becomes an advantage measure to probe the two-phase flow. Resulting accumulation of high-fidelity numerical data also makes data-driven modeling with machine learning methods an attractive option to gain insight to the phenomena. This work presents an interfacial force data-driven modeling framework aims to develop a bubble tracking direct numerical simulation data-based machine learning drag model for application in Euler-Euler simulations of bubbly flows. Besides technical demonstration, this work also provides a guidance for DNS data generation for relevant applications. The data-driven modeling framework is firstly verified by a benchmark problem, where artificial data is utilized to make feedforward neural network assimilate drag correlation by Tomiyama et al. (1998). The obtained model is utilized in a Euler-Euler solver for on-the-fly drag coefficient query. In the test case, resulting velocity and void fraction distribution by machine learning model is consistent with the reference model. Secondly, this work utilized direct numerical simulation bubble tracking data set to form machine learning drag model for bubbly flow based on Reynolds and Eötvös number. Pseudo-steady state filtering in Frenet frame is carried out to obtain bubble drag coefficient. The machine learning drag model is examined in a test case by Wang et al. (1987). Results and suggestions for future works are discussed. |
| Databáze: | OpenAIRE |
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