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Experiences in an Undergraduate Laboratory Using Uncertainty Analysis to Validate Engineering Models with Experimental Data

W. G. Steele and J. A. Schneider Mississippi State University

Abstract Traditionally, the goals of engineering laboratory instruction have been to introduce the students to the use of various measurement devices along with the associated methods to interpret the results in the context of experimental uncertainties. There is usually an emphasis on the demonstration of fundamental engineering principles in applications-oriented projects. Often, theoretical engineering models are used to compare predicted outcomes with the experimental results in order to demonstrate the appropriateness and/or limitations of the theoretical model. When making these comparisons, the uncertainty associated with the experiment measurements is usually included; however, there is usually no consideration of the uncertainty associated with the theoretical model calculations. Students in the Mechanical Engineering (ME) program at Mississippi State University (MSU) are applying the concept of engineering model validation using uncertainty analysis into the undergraduate laboratories in addition to graduate research projects. In this paper, experiences are discussed which illustrate how this approach has been implemented into the undergraduate laboratory classes. The methodology is developed for the model validation, and a case study from our senior mechanical engineering laboratory is presented which illustrates how the uncertainty of the model is combined with the experiment results to provide a comparison. Keywords: Uncertainty, Experimentation, Modeling, Validation, Laboratories

Background At MSU, this model validation approach was first implemented into the ME undergraduate laboratory program to provide a bridge between the theoretical aspect of the traditional engineering courses and the practical demonstration of these principles through experimentation. An appreciation of the errors inherent in experimental results is critical, and uncertainty analysis concepts are integrated into the curriculum in an effort to quantify the validity of the test data. This process provides a logical methodology to interpret test results through the application of uncertainty analysis in the planning, design, construction, debugging, execution, data analysis, and reporting phases of experiments [1]. Accuracy of the experiments is investigated along with the appropriateness of a theory or model and its simplifying assumptions. This concept is an extension of the verification and validation research that is currently being done for CFD and other computational design codes [2, 3]. The approach is communicated at the undergraduate level through a three-laboratory sequence consisting of Experimental Orientation (EO), Experimental Techniques I (ET I), and Experimental Techniques II (ET II).

In the undergraduate curriculum, EO gives the students an introduction to the use of instrumentation for basic measurements, to the acquisition and processing of the measurement data, and to the concept of uncertainty associated with the instrumentation selection and the measurement process. The second course in the sequence, ETI, concentrates on identification of the key parameters needed to guide the design of experiments using uncertainty analysis. These concepts merge into ETII, which provides the opportunity for students to combine the knowledge gained in both EO and ETI to compare theoretical predictions with the measured outcome. The complete description of these courses has been presented elsewhere [4].

For the undergraduate ETII class, students select from a range of mechanical systems. Some of the systems are based on potential classroom demonstration units such as a pump test stand, a tensile test machine, or a heat

“Proceedings of the 2005 American Society for Engineering Education Annual Conference & Exposition Copyright © 2005, American Society for Engineering Education”

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Steele, W. G., & Schneider, J. (2005, June), Experiences In An Undergraduate Laboratory Using Uncertainty Analysis To Validate Engineering Models With Experimental Data Paper presented at 2005 Annual Conference, Portland, Oregon. 10.18260/1-2--15046