Bulletin of the American Physical Society
71st Annual Meeting of the APS Division of Fluid Dynamics
Volume 63, Number 13
Sunday–Tuesday, November 18–20, 2018; Atlanta, Georgia
Session G32: Machine Learning and Data Driven Models II
10:35 AM–12:45 PM,
Monday, November 19, 2018
Georgia World Congress Center
Room: B404
Chair: Alireza Yazdani, Brown University
Abstract ID: BAPS.2018.DFD.G32.1
Abstract: G32.00001 : Will it flood? Classifying entrainment outcomes via machine learning*
10:35 AM–10:48 AM
Presenter:
Lachlan R Mason
(Imperial College London)
Authors:
Lachlan R Mason
(Imperial College London)
Indranil Pan
(Imperial College London)
Aditya Karnik
(Imperial College London)
Assen Batchvarov
(Imperial College London)
Richard V Craster
(Imperial College London)
Omar K Matar
(Imperial College London)
Multiphase flow simulations are advancing to the extent at which high-fidelity results can guide engineering decision-making. Accurate simulations, however, carry a high computational cost, often due to resolution constraints and the inclusion of complex, physics-driven models: question-to-answer iteration times are routinely on the order of weeks. In this study, we accelerate an engineering analysis of a benchmark flow: that of a falling film reactor. We investigate how an injected gas stream drives droplet entrainment, with the intent of predicting the certainty at which this harmful process occurs. We first train a machine learning (ML) classifier via a low-fidelity, though sufficiently representative, volume-of-fluid solver, thus mapping the class boundary demarcating flooding. Additional high-fidelity simulations along the class boundary are then used to improve the ML classifier. We quantify the savings in computational time versus prediction accuracy using this two-step ML-augmented approach, as opposed to a conventional full parameter sweep with the high-fidelity model.
*Royal Academy of Engineering; PETRONAS; EPSRC, UK; Lloyds Register Foundation (Data-Centric Engineering Programme, Alan Turing Institute, UK), Imperial College Research Fellowship (for IP).
To cite this abstract, use the following reference: http://meetings.aps.org/link/BAPS.2018.DFD.G32.1
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