Teaching and practice in mechanics have been relying largely on analytical and mathematical descriptions, such as equilibrium equations, energy conservation, and constitutive laws, etc.
Teaching and practice in mechanics have been relying largely on analytical and mathematical descriptions, such as equilibrium equations, energy conservation, and constitutive laws, etc. However, more and more engineering modeling practices are relying on raw data analysis whose results are often directly utilized in the design of complex engineering problems. The present talk begins with examples of analytical modeling practices which occupy a bulk of engineering teaching and education of mechanics to date. We then introduce examples of early-day engineering tests/experiments in support of the established analytical models, which gave rise to data analysis and signal processing disciplines. With today’s almost unrestricted computing capabilities, however, we are very much tempted to the direct utilization of processed data and identified system parameters with the economy and convenience of skipping the laborious analytical model construction stages. Clearly something is in the offing in engineering modeling practice, which I would call “Paradigm Changes in Engineering Modeling”, that welcomes as well as alarms us. This talk presents the author’s view (and some experience) of changes in engineering modeling practices, drawn mainly from mechanics discipline.
K.C. Park, Professor EmeritusAnn and H. J. Smead Aerospace Engineering SciencesUniversity of Colorado, Boulder, CO 80309-429, USA