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X-WR-CALNAME:Bridging First-Principle Models and Model-Based Control of Adv
 anced Powertrains
X-WR-TIMEZONE:Eastern Time (US & Canada)
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DTSTAMP:20260912T074725Z
UID:tag:localist.com\,2008:EventInstance_3033420
DTSTART:20170921T200000Z
DTEND:20170921T210000Z
DESCRIPTION:An Introduction to Model Order Reduction\n\nME-EM Graduate Semi
 nar Speaker Series\n\nproudly presents:\n\nDr. Marcello Canova\nAssistant 
 Professor\, Department of Mechanical and Aerospace Engineering\nAssociate 
 Fellow\, Center for Automotive Research\nThe Ohio State University\n\nAbst
 ract: With the advancements in internal combustion engines and powertrain 
 technology\, the automotive industry is seeking model-based control and op
 timization methods that exploit the physical consistency and high accuracy
  of first-principle models. This is especially true for downsized boosted 
 engines\, where the breathing and combustion processes are characterized b
 y highly nonlinear behavior and interactions between many actuators and su
 bsystems. On the other hand\, the state of the art in engine and powertrai
 n control design methods relies almost exclusively on heuristic\, low-fide
 lity plant models that oversimplify the physical system\, leading to high 
 calibration efforts and a loss of fidelity that limits the potential benef
 its achievable from technology advancements.\n\nThis seminar introduces a 
 novel framework that allows for direct synthesis of control-oriented model
 s from first-principle models. The approach is based upon a projection-bas
 ed Model Order Reduction (MOR) that analytically generates reduced order m
 odels from conservation laws in nonlinear Partial Differential Equation (P
 DE) form. This approach systematically transfers the accuracy and fidelity
  of physics-based models into low-order models suitable for control design
 \, virtually eliminating the need for calibration.\n\nThe use of Model Ord
 er Reduction enables engineers to apply control and estimation methods to 
 new classes of physical systems. Applications of the proposed approach wil
 l be illustrated for industry-relevant problems\, such as the real-time si
 mulation and estimation of wave action dynamics in the manifolds of downsi
 zed boosted IC engines.\n\nBio: Marcello Canova is Associate Professor in 
 Mechanical Engineering and Associate Fellow of the Center for Automotive R
 esearch\, at The Ohio State University. He earned his Diploma di Laurea 
 “Summa Cum Laude” and his Ph.D. in Mechanical Engineering from the Uni
 versity of Parma (Italy) in 2002 and 2006\, respectively.\n\nDr. Canova co
 nducts research in the area of thermal sciences and energy systems\, with 
 emphasis on modeling\, optimization and associated control problems. His r
 esearch has been funded by\, among others\, Ford\, General Motors\, Fiat C
 hrysler Automobiles\, Cummins\, the National Science\nFoundation\, the US 
 Department of Energy and ARPA-E.\n\nDr. Canova is a 2016 NSF CAREER Award 
 recipient\, and he has earned the Kappa Delta Distinguished Faculty Award 
 (2011)\, the SAE Vincent Bendix Automotive Electronics Engineering Award (
 2011)\, the Lumley Interdisciplinary Research Award (2012)\, and the SAE R
 alph Teetor Educational Award (2016). He has published over 120 articles i
 n journals and confrence proceedings.
GEO:47.119377;-88.547073
LOCATION:Electrical Energy Resources Center (EERC)\, 103
SUMMARY:Bridging First-Principle Models and Model-Based Control of Advanced
  Powertrains
URL;VALUE=URI:https://events.mtu.edu/event/bridging_first-principle_models_
 and_model-based_control_of_advanced_powertrains
CATEGORIES:Academics
CATEGORIES:Lectures/Seminars
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