Uncertainty Quantification for Mechanical Systems Reliability Management
日期：2014-10-11
来源：学术报告
阅读：2655
报告人: Professor Sankaran Mahadevan(Vanderbilt University, Nashville, TN, USA)
时 间: 2014-10-14 16:30:00
地 点: 澳门太阳网城官网木兰楼A1006室
主 办:
联系人: 黄淑萍 18616091156 sphuang@sjtu.edu.cn
About the Speaker
Professor Sankaran Mahadevan has more than twenty-five years of research and teaching experience in reliability and risk analysis methods, design optimization, structural health monitoring, and model verification, validation and uncertainty quantification (V&V and UQ) methods. His research has been extensively funded by NSF, NASA, FAA, DOE, DOD, DOT, General Motors, Chrysler, Union Pacific, American Railroad Association, and Sandia, Idaho, Los Alamos and Oak Ridge National Laboratories. His research contributions are documented in more than 400 technical publications, including two books and 170 journal articles. He has directed 38 Ph.D. dissertations and 24 M. S. theses, and has taught many industry short courses on reliability and risk analysis methods. He has served as chair of several technical committees and conferences in ASCE and AIAA, as Associate Editor and Editorial Board Member for several journals, and as keynote speaker in several conferences. He has received awards for research, teaching and service from several organizations such as ASCE, AIAA, ASME, SAE, and NASA.
Professor Mahadevan obtained his B.Tech from Indian Institute of Technology, Kanpur, M.S. from Rensselaer Polytechnic Institute, Troy, NY, and Ph.D. from Georgia Institute of Technology, Atlanta, GA. Professor Mahadevan is Director of NSF-IGERT Program in Reliability and Risk Engineering and Management, John R. Murray Sr. Professor of Engineering, Professor of Civil and Environmental Engineering and Mechanical Engineering.
This talk will focus on uncertainty quantification (UQ) in performance prediction and risk assessment/management of engineered systems. Model-based simulation becomes attractive for systems that are too large and complex for full-scale testing. However, model-based simulation involves many approximations and assumptions, and thus confidence in the simulation result is an important consideration in risk-informed decision-making. Sources of uncertainty are both aleatory and epistemic, stemming from natural variability, information uncertainty, and modeling approximations. The presentation will draw on illustrative problems in aerospace, mechanical, civil, and environmental engineering disciplines to discuss recent research on (1) quantification of various types of errors and uncertainties, particularly focusing on data uncertainty and model uncertainty (both due to model form assumptions and solution approximations); (2) information fusion from multiple sources (models, tests, experts), multiple model development activities (calibration, verification, validation), and multiple formats; and (3) use of UQ for risk-informed decision-making throughout the life cycle of engineered systems, such as testing, design, operations, health and risk assessment, and risk management.
