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Mechanical Engineering MS Thesis Defense by Mr. Aaron Mak

When: Thursday, May 19, 2022
1:00 PM - 3:00 PM
Where: > See description for location
Description: Mechanical Engineering MS Thesis Defense by Mr. Aaron Mak

DATE:
May 19, 2022

TIME:
1:00 p.m. - 3:00 p.m.

LOCATION:
Virtual (Contact Dr. Mehdi Raessi: mraessi@umassd.edu for Zoom link)

TOPIC:
A Machine Learning Approach to Volume Tracking in Multiphase Flow Simulations

ABSTRACT:
Multiphase flow is referred to flow of two or more immiscible phases (liquid, gas and solid). It is encountered in many essential natural phenomena and industrial processes. Computational simulations have emerged as a powerful and reliable tool for multiphase flow research to further current understanding and uncover new insights. They complement and are often strong alternatives to experimental methods, especially in studies where experiments are unfeasible or prohibitively expensive, due to, for example, short length or time scales or complex geometries. An essential component of multiphase flow simulations is capturing the dynamics of the interface separating the immiscible phases and tracking the phase volumes. Various methods have been proposed to achieve this, including the front tracking, level-set, and volume-of-fluid (VOF) methods. The VOF method has become one of the most commonly used approaches to volume tracking and is the focus of this thesis. In VOF, the most common solutions are performed in two steps: interface reconstruction followed by flux calculation for volume advection. They represent a significant computational cost in VOF-based multiphase flow simulations. In this work, a new approach using machine learning (ML) is used to generate a general advection function in a two-dimensional VOF scheme, which bypasses interface reconstruction and flux calculation. Although ML functions require a larger upfront cost to train, the resulting functions may be less computationally expensive to use when compared to traditional VOF methods. The data set in this work was generated from translation and rotation of a circle under various spatial and temporal resolutions. The ML training was performed using MATLAB's Deep Learning Toolbox. To find an optimal neural network configuration, a grid search method based on the validation performance was used. Additionally, a rating system was developed to assess the overall performance of each function, as a potential alternative to solely relying on validation performance. This thesis presents results from commonly used advection tests to evaluate performance of volume tracking methods. The ML functions developed in this work show good performance on a variety of conditions. Their computation time is a fraction of that of the conventional VOF method; however, in terms of accuracy, the VOF method is superior to the ML functions.

ADVISOR:
Dr. Mehdi Raessi, Associate Professor of Mechanical Engineering, UMass Dartmouth

COMMITTEE MEMBERS:
-Dr. Geoffrey Cowles, Associate Professor, UMass Dartmouth
-Dr. Ming (Daniel) Shao, Assistant Professor, UMass Dartmouth

Open to the public. All MNE students are encouraged to attend.

For more information, please contact Dr. Mehdi Raessi (mraessi@umassd.edu).
Topical Areas: Faculty, General Public, Staff and Administrators, Students, Students, Graduate, Students, Undergraduate, University Community, College of Engineering, Mechanical Engineering, Lectures and Seminars, STEM