|
1:00 PM
-
3:00 PM
|
-
Mechanical Engineering MS Thesis Defense by Mr. Aaron Mak
- Location: > See description for location
- Contact: Mechanical Engineering Department
- 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
|
|
1:30 PM
-
3:30 PM
|
-
Financial Planning and Retirement Info Session
- Location: Online
- Contact: Benefits
- Description: Human Resources will be hosting an information session on Thursday, May 19 from 1:30pm to 3:30pm on financial planning and retirement.
This session is for individuals who are retiring soon and also for those who do not have immediate retirement plans but want to know more about preparing for retirement and financial planning.
In this session, we will discuss:
-When you should start saving for retirement
-Are you saving enough to retire comfortably?
-Are you approaching retirement and unsure what steps you need to take?
-Do you have questions/concerns about your benefits and what is available to you after retirement?
-How a pension impacts social security
The first half of the session will be facilitated by Fidelity Retirement Planner, Michael Fraser who will focus on saving and planning for retirement. Michael will also review the Windfall Elimination Provision and how it will impact your social security when retiring with a Pension or ORP account.
The second half of the session will be facilitated by UMass Dartmouth's Benefits Manager, Sandra Escaleira who will focus on what benefits are available to retirees and what steps you will need to complete for a successful retirement.
*REGISTRATION IS REQUIRED
- Link: https://umassd.zoom.us/webinar/register/WN_bIUExivPSXWxTJcyvPLDiw
- Topical Areas: Faculty, SMAST, Staff and Administrators, Human Resources
|
