Offered Every Spring

Credit Hours: 3-0-3
Prerequisites: Graduate standing, a graduate-level understanding of mechanics, and the appropriate mathematical and computational background, or the consent of instructor.
Catalog Description: Experimental methods in mechanics. Includes measurement techniques, instrumentation, data acquisition, signal processing, and linear and digital electronics.
Textbooks: Philip Bevington, D. Keith Robinson, Data Reduction and Error Analysis for the Physical Science, 3rd Edition, McGraw-Hill, 2002.
Instructors: Ari Glezer
References: P. R. Bevington & D. K. Robinson, Data Reduction and Error Analysis for the Physical Sciences, Mc-Graw-Hill, Inc., 1992.
R. J. Goldstein, Fluid Mechanics Measurements, Taylor & Francis, Inc., 1996.
Goals: This course is intended to provide the student with a broad background in the fundamentals of experimental data acquisition and analysis along with an introduction to a few specific measurement techniques. After this course, the student should have a system-level perspective on analyzing, processing and evaluating the data obtained from experimental measurements.
  1. Uncertainty Analysis
  • Error propagation, fractional uncertainty, probability distribution functions, statistics of sample mean, and the central-limit theorem.
  • Time-Series Analysis
    • Zero-crossings statistics, autocorrelations and cross correlations, power spectrum, digitizing of continuous data, and digital filtering.
  • Signal Conditioning
    • Linear circuits, digital circuits, and electronic noise.
  • Data Acquisition
    • Sampling theory, FFT, A/D, and D/A.
  • Special Topics and Projects
    • Velocimetry, anemometry, thermometry, and imaging.