Most of the student’s educational exposure is to well behaved, deterministic problems with known results. Most courses expose students to material in compartmentized modules (chapters of a book) with exercises/problems (at the end of the chapter) where the majority of the material is readily found in the compartmentized module. Unfortunately, real world problems never fit this simple mold. Laboratory is the perfect place for students to become exposed to real world problems and solutions to those problems. Laboratory is the perfect place to put all the student’s knowledge of basic STEM material to the test. However, many times the real world measurement is much more complicated than the textbook problems and students often struggle with methods and procedures to solve a given problem (with no answer at the back of the book). This is true for a mechanical measurement of a simple second order mass, spring, dashpot system which is measured with displacement and acceleration instruments in an existing mechanical engineering laboratory exercise. The measurement is plagued with measurement errors, drift, bias, digital data acquisition amplitude/quantization errors, etc. In order to understand the basic underlying measurement and associated “problems” with the measurement, a simple simulation model was developed. The simulation model allows the students to define a basic second order system and then add different types of “problems” (drift, bias, quantization, noise, etc) to the measurement to see their effects. The simulation module further allows the student to “cleanse” the distorted data using common measurement tools such as coupling, filtering, smoothing, etc. to understand the effects of processing the data. The simulation model is built using Simulink/MATLAB and allows a simple GUI to modify the model, the “problems” added to the data and the “cleansing” of the data, to obtain a better understanding of the problem and tools to process the data. The simulation model is presented and discussed in the paper. Several data sets are presented to illustrate the simulation module.
Cross-disciplinary Science and the Structure of Scientific Perspectives