Mechanical Engineering Seminar by Dr. Hamidreza Karbasian of MIT
When: Friday,
January 26, 2024
2:00 PM
-
3:00 PM
Where: Science and Engineering Building 285 Old Westport Road, Dartmouth, MA
Description: Mechanical Engineering (MNE) SEMINAR
DATE:
January 26, 2024
TIME:
2:00 p.m. - 3:00 p.m.
LOCATION:
Science and Engineering Building (SENG) 110
and Zoom: https://umassd.zoom.us/j/91640406955?pwd=eklBZWVDOXVDa2VwUFMra1kwNWhjdz09 (Passcode: 500)
SPEAKER:
Dr. Hamidreza Karbasian, Postdoctoral Research Associate, Mechanical Engineering, MIT
TOPIC:
AI for Design: Searching for the Ways to Accelerate Designs
ABSTRACT:
A new physics-constrained data-driven approach is proposed for high-fidelity aerodynamic optimization in the presence of chaotic turbulent flows. Unlike conventional optimization methods, this approach can solve the unsteady sensitivity function for PDE-constrained optimizations. In this new approach, high-dimensional governing equations, such as Navier-Stokes, from physical space are transformed into the Hilbert space to develop a closure model in the form of a Reduced-Order Model (ROM). It is shown that the proposed approach can perform unsteady optimizations for large-scale systems, where conventional methods fail. Therefore, this AI-powered framework can be applied to complex and unresolved problems, including turbulent flow control, noise reduction, and aerodynamic performance improvement.
BIO:
Dr. Hamid R. Karbasian is currently a Postdoctoral Associate in the Department of Mechanical Engineering at the Massachusetts Institute of Technology (MIT). His research work focuses on developing mathematical algorithms and leveraging digital twins for fluid mechanics and aerospace applications. Before joining MIT, he was awarded the Fields CQAM Postdoctoral Fellowship at the Fields Institute at the University of Toronto, where he developed a deep-learning-based model for fluid dynamics in a Lagrangian system. He was also a Lead Aerospace Engineer at Limosa Inc., where he lead the aerodynamics division toward developing a new concept of air taxi for urban air mobility. He has a Ph.D. in Mechanical Engineering from Concordia University in Canada with a focus on computational science. He has more than 30 publications in well-known journals and international conference presentations. Dr. Karbasian is also the founder of the OPtimization Toolkit for Highly NOn-linear Systems (OPTHiNOS) scientific software package, a specialized framework to perform sensitivity analysis and
optimization of large-scale engineering problems.
For more information please contact Dr. Hangjian Ling, MNE Seminar Coordinator (hling1@umassd.edu).
All are welcome!
Students taking MNE-500 are REQUIRED to attend!
All other MNE BS and MS students are encouraged to attend. EAS students are also encouraged to attend.
DATE:
January 26, 2024
TIME:
2:00 p.m. - 3:00 p.m.
LOCATION:
Science and Engineering Building (SENG) 110
and Zoom: https://umassd.zoom.us/j/91640406955?pwd=eklBZWVDOXVDa2VwUFMra1kwNWhjdz09 (Passcode: 500)
SPEAKER:
Dr. Hamidreza Karbasian, Postdoctoral Research Associate, Mechanical Engineering, MIT
TOPIC:
AI for Design: Searching for the Ways to Accelerate Designs
ABSTRACT:
A new physics-constrained data-driven approach is proposed for high-fidelity aerodynamic optimization in the presence of chaotic turbulent flows. Unlike conventional optimization methods, this approach can solve the unsteady sensitivity function for PDE-constrained optimizations. In this new approach, high-dimensional governing equations, such as Navier-Stokes, from physical space are transformed into the Hilbert space to develop a closure model in the form of a Reduced-Order Model (ROM). It is shown that the proposed approach can perform unsteady optimizations for large-scale systems, where conventional methods fail. Therefore, this AI-powered framework can be applied to complex and unresolved problems, including turbulent flow control, noise reduction, and aerodynamic performance improvement.
BIO:
Dr. Hamid R. Karbasian is currently a Postdoctoral Associate in the Department of Mechanical Engineering at the Massachusetts Institute of Technology (MIT). His research work focuses on developing mathematical algorithms and leveraging digital twins for fluid mechanics and aerospace applications. Before joining MIT, he was awarded the Fields CQAM Postdoctoral Fellowship at the Fields Institute at the University of Toronto, where he developed a deep-learning-based model for fluid dynamics in a Lagrangian system. He was also a Lead Aerospace Engineer at Limosa Inc., where he lead the aerodynamics division toward developing a new concept of air taxi for urban air mobility. He has a Ph.D. in Mechanical Engineering from Concordia University in Canada with a focus on computational science. He has more than 30 publications in well-known journals and international conference presentations. Dr. Karbasian is also the founder of the OPtimization Toolkit for Highly NOn-linear Systems (OPTHiNOS) scientific software package, a specialized framework to perform sensitivity analysis and
optimization of large-scale engineering problems.
For more information please contact Dr. Hangjian Ling, MNE Seminar Coordinator (hling1@umassd.edu).
All are welcome!
Students taking MNE-500 are REQUIRED to attend!
All other MNE BS and MS students are encouraged to attend. EAS students are also encouraged to attend.
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