04/20/2023
By Danielle Fretwell
The Francis College of Engineering, Department of Mechanical and Industrial Engineering, invites you to attend the doctoral dissertation proposal defense by Ahmed Almeldein on “Accelerating Chemical Kinetics Calculations of Methane/Air Combustion Using Artificial Neural Networks.”
Candidate Name: Ahmed Z. Almeldein
Degree: Doctoral
Defense Date: Thursday, May 4, 2023
Time: : noon to 2 p.m.
Location: Southwick 240, the proposal defense will be available via Zoom. Those interested in attending should contact the student (Ahmed_Almeldein@student.uml.edu) and committee advisor (Noah_VanDam@uml.edu) at least 24 hours prior to the defense to request access to the meeting.
Committee:
- Advisor Noah Van Dam, Assistant Professor, Department of Mechanical and Industrial Engineering, University of Massachusetts Lowell
- J. Hunter Mack, Associate Professor, Department of Mechanical and Industrial Engineering, University of Massachusetts Lowell
- Ruizhe Ma, Assistant Professor, Miner School of Computer & Information Sciences, University of Massachusetts Lowell
- Pinaki Pal, Senior Research Scientist, Argonne National Laboratory
Brief Abstract:
Detailed chemical kinetics calculations can be very computationally expensive, so various approaches have been used to speed up combustion calculations. Artificial Neural Networks (ANNs) are one promising approach that has seen significant development recently. Despite the increasing effort placed in this area, there are still uncertainties about how best to construct an ANN to reproduce the highly non-linear and numerically stiff system representing chemical kinetics calculations. This study explores different ANN architectures to increase ANN's accuracy while maintaining a moderately small network and dataset sizes. Two different ANN concepts have been implemented to create an accurate ANN for chemical kinetics applications: Mixture-of-Experts (MoE), and Physics Informed Neural Network (PINN).