Application of data-driven CFD to turbulent bubbly flows using machine learning
- 주제(키워드) Turbulent bubbly flows , Bubble size , Two-fluid model , Artificial neural network
- 발행기관 서강대학교 일반대학원
- 지도교수 강성원
- 발행년도 2020
- 학위수여년월 2020. 2
- 학위명 석사
- 학과 및 전공 일반대학원 기계공학과
- UCI I804:11029-000000064829
- 본문언어 영어
- 저작권 서강대학교 논문은 저작권보호를 받습니다.
초록/요약
In the present study, we consider a new reliable model of the bubble size based on multi-layer artificial neural networks (ANN). A multi-layer ANN is used to establish a function for the bubble size without any assumption on the form. In the training procedure, the proposed ANN is trained using data sets collected from open literature and experiments performed in the present study. An excellent agreement was obtained between the trained ANN and experimental data in the bubble size. Also, sensitivity analyses along with principal component analysis and random forest method provide important physical parameters for the bubble size. Next, in order to rigorously evaluate the prediction capability of the present model, flow simulations were conducted for turbulent bubbly flows, for which experimental data are available. The present validation results show that a regime-adaptive data-driven model for the bubble size achieves successful estimation for both wall and core peaking regimes.
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