Experimental Measurement Uncertainty for Engineers and Scientists (1-Day Course) 14 June 2020 0800 - 1700 Reno-Sparks Convention Center, Reno, Nevada
In This Section
This one-day course is devoted to the topic of Experimental Measurement Uncertainty for Scientists and engineers. The course should benefit both those who make measurements as well as consumers of those measurements. Attendees will learn how error leads to uncertainty and the difference between random and systematic (bias) uncertainty. Methods to quantify the uncertainty of individual measurements based upon acquired data and instrument specifications will be discussed as well as how to propagate this information into results that depend on multiple measurements. Both Taylor’s Series Method and Monte Carlo Method of uncertainty propagation, and their pros and cons, will be discussed. Commonly encountered issues such as samples that are correlated in time and error sources that impact multiple measurements will receive special attention. In the latter part of the course, these lessons will be emphasized through computer-based exercises.
- Gain an introduction to Measurement Error and Uncertainty
- Understanding of Uncertainty of a Single Variable
- Random uncertainty of the mean
- Correlated Samples
- Interpreting Instrument Specs
- Property Data
- Understanding of Propagation of Uncertainty Using Taylor Series and Monte Carlo Methods
Who Should Attend
Anyone acquiring or consuming experimental data, including scientists and engineers. Graduate students, faculty, laboratory scientists, or test engineers would benefit greatly from the course.
Please contact Jason Cole if you have any questions about courses and workshops at AIAA forums.
- Uncertainty of a Single Variable
- Random Uncertainties
- Bias Uncertainties
- Propagation of Uncertainty
- Taylor’s Series Method
- Monte Carlo Method
- Computer Exercises
Course notes will be made available about one week prior to the course event. You will receive an email with detailed instructions on how to access your course notes. Since these notes will not be distributed on site, AIAA and your course instructor highly recommend that you bring your computer with the course notes already downloaded.
Course participants are requested to bring a laptop with Excel and a high-level programing language such as MATLAB or Python.
Barton Smith is a professor of Mechanical and Aerospace Engineering at Utah State University. Douglas Neal is a Senior Research Engineer for LaVision, Inc. They have previously collaborated on numerous research projects in measurement uncertainty quantification (MUQ), including pioneering efforts in MUQ for Particle Image Velocimetry (PIV). They are presently working on a graduate level textbook on MUQ.