Interactive Simulations of the Performance of Hydrodynamic Bearings in the 'Machine Design'Course

摘要

Bearings are very usual engineering components and are used in almost all types of machinery. They are essential for the correct operation of machines because they provide the separation between loaded surfaces in relative motion, through a hydrodynamic lubricant film. In this work, a pilot experience is set out regarding the teaching and learning process of hydrodynamic bearings in the 'Machine Design' course, through play with a helpful interactive system. Its main aim is to encourage students to deal with real mechanical engineering problems, and improve their ability to model, simulate and predict the influence of each parameter on the results, and as a consequence optimize the solution. The hydrodynamic equations are solved numerically by executing a C program, where input parameters are dimensionless minimum film thickness and the ratio of length to width of the bearing. The results obtained are the following dimensionless parameters: pressure and film thickness distributions, load capacity and bearing characteristic numbers. Finally, a post-processing using Matlab software allows deducing dimensional values of the cited parameters, and also determining other related results such as temperature, friction coefficient, lubricant flow and lubricant lateral losses. In addition, it provides a graphic representation of the results of interest. The system developed is organized in modules which are fully controlled using different applications in Matlab, which interact with the C program when necessary. Teaching and learning modules consist of interactive and parameterized applications, designed for non-expert Matlab users, which deal with practical situations. The modules developed have shown themselves to be useful for learning the mechanisms of hydrodynamic film generation and for analyzing the effects of operating variables on the performance of such films, like variations in load, velocity, eccentricity, design parameters, etc. In addition, they are suitable for deductive teaching and learning purposes.