1. ENGR 107: Engineering Fundamentals Lecture 8: Engineering Estimations & Data Acquisition C. Schaefer October 13, 2003

2. October 15, 2002ENGR 1072Administrivia Demo software: – CACI Simprocess http://www.caci.com – The Mathworks Matlab & Simulink http://www.mathworks.com – National Instruments LabView software http://www.natinst.com/labview/lv_dl.htm

3. October 15, 2002ENGR 1073 How do we obtain data? Simulation, e.g.; – Simulation environments, e.g., Matlab. – Custom simulation; C/C++, Fortran, Visual Basic, Java, etc. Experiment/testing, e.g.; – Component or coupon testing. – Sub-scale or full-scale testing. DAQ (‘dak’) – data acquisition.

4. October 15, 2002ENGR 1074 Why and how do we collect data? To understand a physical process or event. Measurements are observations of a physical process or event. Sensors – any device that receives a signal or stimulus and generates measurements as a function of that stimulus. – Thermometer, strain gage, accelerometer, etc. We measure physical quantities such as length, time, temperature, force.

5. October 15, 2002ENGR 1075 ABS Braking Simulation Model

6. October 15, 2002ENGR 1076 Fracture Mechanics FEM Analytical Modeling

7. October 15, 2002ENGR 1077 “Coupon” Fatigue Testing Using an Instron ®

8. October 15, 2002ENGR 1078 Data Acquisition http://secant.ni.com/ramgen/archive/demos/lvdaqdemo.rm

9. October 15, 2002ENGR 1079 Full-Scale Structural Testing BMW Indy Car

10. October 15, 2002ENGR 10710 Testing - ABS

11. Some Data Acquisition Terminology

12. October 15, 2002ENGR 10712 Accuracy and Precision Accuracy – a measure of the nearness of a value to the correct or true value. Precision – refers to repeatability of a measurement, i.e., how close successive measurements are to each other.

13. October 15, 2002ENGR 10713 Accuracy and Precision ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ Inaccurate & Imprecise Precise but inaccurate Accurate and precise Accurate but imprecise

14. October 15, 2002ENGR 10714 Errors in Data Systematic errors – Identifiable and correctable. – E.g. – known instrumentation bias. – E.g., metal tape measure susceptible to temperature. Need coefficient of thermal expansion to correct. Random – Accidental or other nonidentifiable errors. – Noise (e.g., white noise) superimposed on sensor signals.

15. October 15, 2002ENGR 10715 Tabulating Data

16. October 15, 2002ENGR 10716 Plotting Data Test Flight 11 Altitude – 7K ft GW – 23,500 lbs Title Annotation Legend Data x-axis: abscissa y-axis: ordinate Linear-Scale Graph Axis Labels

17. October 15, 2002ENGR 10717 Engineering Software Tools Demonstration Matlab (The Mathworks) Simulink (The Mathworks) Labview (National Instruments) Simprocess (CACI)