A Polynomial-Chaos-Based Multifidelity Approach to the Efficient Uncertainty Quantification of Online Simulations of Automotive Propulsion Systems
Author:
Affiliation:
1. Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109
2. Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI 48109
3. Research and Advanced Engineering Ford Motor Company, Dearborn, MI 48124
Abstract
Publisher
ASME International
Subject
Applied Mathematics,Mechanical Engineering,Control and Systems Engineering,Applied Mathematics,Mechanical Engineering,Control and Systems Engineering
Link
https://asmedigitalcollection.asme.org/computationalnonlinear/article-pdf/17/5/051012/6864473/cnd_017_05_051012.pdf
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3. A Novel Drivetrain Modelling Approach for Real-Time Simulation;Mechatronics,2015
4. Machine Learning Approach for Constructing Wet Clutch Torque Transfer Function,2021
5. Uncertainty Quantification Using Generalized Polynomial Chaos for Online Simulations of Automotive Propulsion Systems,2020
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1. CONTROL VARIATE POLYNOMIAL CHAOS: OPTIMAL FUSION OF SAMPLING AND SURROGATES FOR MULTIFIDELITY UNCERTAINTY QUANTIFICATION;International Journal for Uncertainty Quantification;2023
2. Uncertainty Quantification of Wet Clutch Actuator Behaviors in P2 Hybrid Engine Start Process;SAE Technical Paper Series;2022-03-29
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