Page 40 - ASME_IMECE_2019_Program

He was a Research Associate (2004–2007) and then a
Senior Research Associate (2007–2011) in the E. L. Ginzton
Laboratory at Stanford University. In 2012, he joined North
Carolina State University, Raleigh, where he is now a Professor
of Electrical and Computer Engineering. His current research
focuses on developing devices and systems for ultrasound
imaging, photoacoustic imaging, image-guided therapy,
biological and chemical sensing, and ultrasound neural
stimulation.
Dr. Oralkan is an Associate Editor for the IEEE Transactions
on Ultrasonics, Ferroelectrics and Frequency Control and
serves on the Technical Program Committee of the IEEE
International Ultrasonics Symposium. He received the 2016
William F. Lane Outstanding Teacher Award at NC State, 2013
DARPA Young Faculty Award, and 2002 Outstanding Paper
Award of the IEEE Ultrasonics, Ferroelectrics, and Frequency
Control Society.
Track 5: Dynamics, Vibration, and Control
5-1-1: DYNAMICS, VIBRATION, AND CONTROL
Monday, November 11, 9:45AM–10:30AM
Room 155E,
Calvin L. Rampton Salt Palace Convention Center
Data-Driven Model Reduction and Probabilistic Learning
for Digital Twins
(IMECE2019-13995)
Charbel Farhat
Stanford University
Abstract: A digital twin refers to a digital replica of a physical
asset — whether a platform or a process — that can be used,
for example, to control in real time the operation of this asset,
or optimize in near real time its maintenance. This lecture,
however, will assert that, in the context of computational
mechanics, a more rigorous realization of a digital twin can be
grounded in recent advances in the data-driven reduction of
the dimensionality of high-fidelity models, and the data-driven
probabilistic modeling and quantification of the model-form
uncertainties associated with the resulting reduced-order
models. The lecture will also illustrate the aforementioned
assertions with two sample digital twins constructed for this
purpose — one for a UAV in order to control is automatic
landing on a carrier using a real-time model predictive control
(MPC) algorithm and one for a small-scale replica of an X-56
type aircraft in order to optimize in near real-time its
maintenance — and will highlight their performance.
xxxviii Bio: Charbel Farhat is the Vivian Church Hoff Professor of
Aircraft Structures, Chairman of the Department of Aeronautics
and Astronautics, Director of the Army High Performance
Computing Research Center, and Director of the of the King
Abdullah City of Science and Technology Center of Excellence
for Aeronautics and Astronautics at Stanford University. His
research interests focus on the development of mathematical
models, advanced computational algorithms, and high-
performance software for the design and analysis of
complex systems in aerospace, marine, mechanical, and
naval engineering. He is a member of the National Academy of
engineering; a member of the Royal Academy of Engineering
(UK); a Fellow of AIAA, ASME, IACM, SIAM, and USACM;
and an ISI Highly Cited Author in Engineering. He is also the
recipient of many other professional and academic distinctions,
including the Spirit of Saint Louis Medal and Lifetime
Achievement Award from ASME; the Ashley Award for
Aeroelasticity and the Structures, Structural Dynamics and
Materials Award from AIAA; the Gordon Bell Prize and Sidney
Fernbach Award from IEEE; the John von Neumann Medal
from USACM; the Grand Prize from the Japan Society for
Computational Engineering and Science; and the Gauss-
Newton Medal from IACM. He was selected by the U.S. Navy
as a Primary Key-Influencer, flown by the Blue Angels during
Fleet Week 2014, and appointed to the Air Force Science
Advisory Board.
Track 5: Dynamics, Vibration, and Control
5-1-2: DYNAMICS, VIBRATION, AND CONTROL
Tuesday, November 12, 9:45AM–10:30AM
Room 155F,
Calvin L. Rampton Salt Palace Convention Center
The Interplay of Nonlinearity and Noise in Tiny Resonators
(IMECE2019-13996)
Steve Shaw
Florida Institute of Technology
Abstract: Vibrating structures with dimensions on the scale
of micro-meters are playing increasingly important roles in
sensors and frequency sources (i.e., clocks) that are widely
used in commercial devices, including smart phones. Some
basic differences exist between such small structures and their
macro-scale counterparts, the most important of which are
their relatively high frequencies and small damping. These
features provide many practical benefits that include resonant
operation in the radio frequency range, the ability to utilize
electrostatics for actuation and readout, and the on-chip
integration of mechanical and electronic elements. However,
microelectromechanical system (MEMS) resonators are highly
susceptible to noise and nonlinearity and one of the basic
challenges in their design is maintaining a good signal to noise
ratio without driving them into nonlinearity. This presentation
will provide an overview of the roles of nonlinearity and noise
in MEMS resonators and describe how a fundamental
understanding of these effects can play an important role
in improving their performance. Specific examples will be
taken from time-keeping applications, where it has been
demonstrated that nonlinear operation can reduce phase noise
in MEMS-based clocks, and from resonant sensors, where it is
shown that the input-output gain in rotational rate vibratory
gyros can be increased by exploiting nonlinear mode coupling.