Graduate Thesis Or Dissertation

 

Computer aided model generation for high performance dynamometers Public Deposited

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/8623j177w

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  • A model has been developed for accurate emulation of the complex behavior of high performance dynamometers used in a broad range of applications including manufacturing systems, biomechanics research, and crash testing. The key objective of this research has been to reach the proper balance between the accuracy of emulation and complexity of the model and user-friendliness of the modeling procedure. As a result, a versatile, step-wise computer aided methodology was developed. The research focused further on the development and validation of efficient procedures and analysis techniques that allow rapid identification of distinctive dynamic relationships in force sensors and encapsulating these relationships in analytical, constitutive models. The assumption of rigid body, lumped parameter nature of the modeled class of dynamometers underlie the methodology used in this research. This assumption is justified since the dominant source of the system's dynamic behavior is the elastic coupling between individual components, rather than deformations of the components themselves. Lagrange's energy formalism and Mathematica's symbolic programming environment, which facilitates arbitrary precision computations, are utilized for model generation. Experimental analysis was implemented to verify the developed model. The actual motion of the dynamometer components caused by external excitation forces was studied and compared with responses predicted by the model. Spatial displacements were obtained by analyzing acceleration signals from several sensors placed in suitable locations on the tested system. To enhance and speed up the validation of modeling results, a visualization module was developed. It allows rapid comparison and rigorous analysis of the spatial motion recorded from the actual system with the model based prediction.
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