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Enhancing Uncertainty Analysis for Engineering Students

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Conference

2019 ASEE Annual Conference & Exposition

Location

Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

October 19, 2019

Conference Session

Multidisciplinary Engineering Courses

Tagged Division

Multidisciplinary Engineering

Page Count

15

DOI

10.18260/1-2--32753

Permanent URL

https://peer.asee.org/32753

Download Count

549

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Paper Authors

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Mahdi Norouzi Grand Valley State University

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Mahdi Norouzi is currently an Assistant Professor of Mechanical Engineering at the school of engineering at Grand Valley State University in Michigan, USA. His research focuses on stochastic mechanics, reliability-based design & analysis, and wind energy.

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Janice S. Pawloski Grand Valley State University

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Huihui Qi Grand Valley State University

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Dr. Qi is an assistant professor in Mechanical Engineering at Grand Valley State University. She earned her Ph.D degree in Mechanical Engineering from Rutgers University. Dr. Qi’s teaching interests include Engineering Design, Solid Mechanics, Mechanical System Design and Computer Aided Design. Dr. Qi’s areas of interest and expertise include design sustainability, Life Cycle Assessment, decision making for optimal design, and Computer Aided Design.

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Farid Jafari Grand Valley State University

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Farid Jafari got his PhD in Engineering Mechanics from Virginia Tech in 2017 and then joined Grand Valley State University as a visiting professor. His research experience is in Biomechanics, Dynamics, and Control. His teaching experience has spanned several fields of Mechanics including fluid mechanics, solid mechanics, dynamics and control.

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Abstract

Uncertainty is involved in all engineering measurements, and it must be taken into account before making any critical engineering decision. It is essential to draw the attention of engineering students to uncertainty analysis. The law of propagation of uncertainty is conventionally taught in undergraduate engineering programs. However, many students find it cumbersome and intimidating for complex performance functions. In this paper, two alternative methods, Monte Carlo Simulation (MCS) and Sequential Perturbation (SP) are discussed, and their effectiveness in understanding and applying the notion of uncertainty is investigated. The MCS and SP methods are introduced to a group of junior engineering students, who are already familiar with the law of propagation of uncertainty. The students’ perception of uncertainty analysis and their performance in conducting uncertainty analysis through a class activity are compared after the new methods are introduced.

Norouzi, M., & Pawloski, J. S., & Qi, H., & Jafari, F. (2019, June), Enhancing Uncertainty Analysis for Engineering Students Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--32753

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