Generative design utilizes topology optimization suggesting novel geometric forms, which are then evaluated for effectiveness against the design criteria (in this case heat transferability, cost, operational life, etc.) and then developed into feasible design solutions. Students on the GM team will develop a generative design process to determine the optimal geometry for a cold-plate cooling design for an electric vehicle.
Generative design utilizes topology optimization to suggest novel geometric forms, which are then evaluated for effectiveness against the design criteria (in this case heat transferability, cost, operational life, etc.) and then developed into feasible design solutions. The team will leverage open source topological optimization engines and generative design functions to create their generative design engine. They can leverage commercially available or open source Computational Fluid Dynamics (CFD) evaluation packages to solve the flow and heat transfer problems.
Thermal management is critical for vehicle electrified propulsion systems. For example, a proper battery cooling design in a thermal management system can maintain the battery cell temperature in the optimal temperature range for desired battery performance and usable life. A cooling design process powered by generative algorithms will allow us to study the unexplored design space and accelerate development of high performance and high efficiency thermal management systems for electric vehicles.
General Programming (3 – 4 Students)
Specific Skills: Strong interest in developing new end-to-end systems requiring high computation. General skills in programming. Must have completed EECS 281 or equivalent and be willing to learn.
Likely Majors: CS, MICDE
Thermal/Flow Analysis (2 students)
Specific Skills: Computational modeling of thermal problems. Good understanding of heat transfer and fluids theory. Must have basic coding skills and be willing to learn new material.
Likely Majors: Chemical Engineering, Mechanical Engineering, Aeerospace, MICDE
Topological Optimization (1-2 Students)
Specific Skills: Topological optimization, generative design, evolutionary algorithms
Likely Majors: IOE, Mathematics, CS
Dr. Erik Yen is currently a Staff Researcher at Propulsion Systems Research Lab, General Motors Global R&D. His research focus is on thermal management solutions for vehicle electrification. Currently, he is working on exploring various technologies to meet thermal management challenges for future EV battery packs, power electronics, and automotive electronics. Erik received his Ph.D. in Mechanical Engineering at Carnegie Mellon University. Erik is a returning MDP Sponsor Mentor.
Professor Greg Hulbert
Professor Hulbert’s research interests include computational mechanics, finite element methods, structural dynamics, flexible multibody dynamics, dynamic response of composites, vehicle dynamics, and engineering mechanics education.
Course Substitutions: Honors, ChE Elective, CS MDE/Capstone, CE MDE, EE MDE, IOE Senior Design, IOE Grad Cognate, ISD AUTO 503, ISD GAME, MECHENG 490, MECHENG 590, SI Elective, SI Grad Cognate
Citizenship Requirements: This project is open to all students.
IP/NDA: Students will sign standard University of Michigan IP/NDA documents.
In Person/Remote Participation Options:
Most work will take place on campus in Ann Arbor, MI.
Students who are approved to attend classes remotely for Winter (and Fall if necessary) 2022 may participate on this project.
Internship/Summer Project Activities: No summer activity will take place on the project.