ARC Collaborative Research Seminar Series
Fall 2018

ARC seminars are free and open to the general public. Center members can download the presentation files on our password-access online portal iARC. Non-ARC attendees please email with your requests.

Parking & directions inquires: Contact by 2:00 p.m. the day before the seminar.

Remote attendance via tele/video conference: Contact William Lim

Refreshments will be served 9:15-9:30am. The talks will begin at 9:30 a.m. sharp.
Please note that event venue alternates between University of Michigan (Ann Arbor) and U.S. Army TARDEC (Warren).

October 12, Friday (9:30a.m. - 11a.m.)
University of Michigan, North Campus, Duderstadt Center, room 1180

1. ARC Project: Ignition Studies for Kinetic Mechanism Development and Validation
Dr. André Boehman, Prof. Mechanical Engineering, University of Michigan
Shuqi Cheng (presenter), Graduate Student Research Assistant, University of Michigan

Abstract         The research objectives are to generate ignition data for the purpose of quantifying fuel reactivity and providing high fidelity data for developing reaction theory and reaction mechanisms, and for developing and validating reduced kinetic mechanisms for use in engine simulations. To achieve these objectives, an experimental approach will be used with combines the strengths of two existing experimental systems. The first is a modified CFR Octane Rating Engine (used in a previous ARC project) with added instrumentation to generate the data needed for the validation of kinetic mechanisms and optimization of the reduced mechanisms. The second is The University of Michigan rapid compression facility which is a unique system that will be used to generate ignition and reaction pathway data at kinetically limited conditions relevant to engine conditions. This presentation will summarize recent progress on matching the ignition behavior of surrogate mixtures and model compounds using a multi-zone model of the ignition process in modified Octane Rating Engine.

2. ARC Project: Boundary Conditions for Predictive Combustion Simulation
Dr. Marcis Jansons, Assoc. Prof. Mechanical Engineering, Wayne State University

Abstract         Boundary conditions significantly impact the combustion processes occurring in reciprocating piston engines used in most military vehicles. Surface temperatures and heat flux rates affect ignition delay, combustion phasing and duration, and emissions. Values of boundary conditions representative of real combustion systems are thus essential to high fidelity, predictive combustion simulations. In direct-injection compression-ignition systems, fuel and fuel vapor distribution is influenced by the liquid fuel temperature and the physical properties of the fuel used. Although fuels such as alcohol-to-jet (ATJ) and hydroprocessed renewable jet (HRJ) have been examined by the Alternative Fuels Certification Program, their spray behavior in reciprocating piston engines is not well documented. Optical diagnostics including infrared imaging are applied to examine the effect of fuel temperatures on the sprays of alternative military fuels.

November 9, Friday (9:30a.m. - 11a.m.)
University of Michigan, North Campus, Duderstadt Center, room 1180

1. Overview of the Ground Vehicle Robotics Research Roadmap
Dr. Jon Smereka, Ground Vehicle Robotics, U.S. Army TARDEC

Abstract & Biography         Brief overview of TARDEC GVR programs and software approach followed by areas of research which are the necessary to employing a robotic vehicle in a combat zone.
        Dr. Jonathon Smereka is a researcher within the Ground Vehicle Robotics (GVR) team at the U.S. Army Tank Automotive Research, Development, & Engineering Center (TARDEC) in Warren, Michigan. Currently, Dr. Smereka is responsible for guiding in-house research and future development activities contributing to the TARDEC Robotics Technology Kernel (RTK) software. His own research focuses on machine learning and artificial intelligence related to robotic behaviors, vehicle perception, and scene understanding. He earned a PhD in Computer and Electrical Engineering from Carnegie Mellon University in 2016 under the direction of Prof. Vijayakumar Bhagavatula.

2. Learning to satisfy constraints/limits in powertrain and vehicular systems operating in uncertain environments: A learning reference governor
Dr. Ilya Kolmanovsky, Prof. Aerospace Engineering, University of Michigan

Abstract         As referenced in the 2018 National Defense Strategy, delivering autonomous systems along with the required advanced controls to fully realize the capabilities associated with these autonomous systems to the warfighter will be vital to reach the U.S. military goal of modernization. These systems could be operating in unknown environments with constraints limiting their performance. The presentation will describe the ongoing research to develop model-free learning algorithms that over time modify the parameters of a reference governor scheme so that violations of constraints are avoided after a sufficiently informative learning phase. The reference governor is an add-on scheme which can be integrated with the legacy control systems to modify their commands as/if needed to be able to enforce the constraints. The learning reference governor will be described along with simulation studies illustrating its operation for battery electric vehicle powertrain, vehicle rollover, and an agile positioning system.

November 30, Friday (9:30a.m. - 11a.m.)
U.S. Army TARDEC, 6501 E. 11 Mile Road, Warren, MI 48397-5000
Building 200B TARDEC University Class Rooms A&B

1. ARC Project: Manned-Unmanned Teaming Mission Integration
Dr. Ed Durfee, Prof. Computer Science and Engineering, University of Michigan

Abstract         To be announced.

2. ARC Project: AI-Based Attacker-Defender Dynamics of Adaptable Fleets of Autonomous Vehicles
Dr. Bogdan Epureanu, Prof. Mechanical Engineering, University of Michigan

Abstract         To be announced.

ARC members can download the presentation files on our password-access online portal iARC.
Non-ARC members please email with your requests.