Title page for ETD etd-05202009-172358


Type of Document Master's Thesis
Author Allen, James Brandon
URN etd-05202009-172358
Title Estimating Uncertainties in the Joint Reaction Forces of Construction Machinery
Degree Master of Science
Department Mechanical Engineering
Advisory Committee
Advisor Name Title
Sandu, Corina Committee Chair
Ahmadian, Mehdi Committee Member
Wicks, Alfred L. Committee Member
Keywords
  • Caterpillar 980G Series II wheel loader
  • polynomial chaos collocation
  • stochastic rigid body modeling
  • Newtonian projection method
Date of Defense 2009-05-11
Availability unrestricted
Abstract
In this study we investigate the propagation of uncertainties in the input forces through a mechanical system. The system of interest was a wheel loader, but the methodology developed can be applied to any multibody systems. The modeling technique implemented focused on efficiently modeling stochastic systems for which the equations of motion are not available. The analysis targeted the reaction forces in joints of interest.

The modeling approach developed in this thesis builds a foundation for determining the uncertainties in a Caterpillar 980G II wheel loader. The study begins with constructing a simple multibody deterministic system. This simple mechanism is modeled using differential algebraic equations in Matlab. Next, the model is compared with the CAD model constructed in ProMechanica. The stochastic model of the simple mechanism is then developed using a Monte Carlo approach and a Linear/Quadratic transformation method. The Collocation Method was developed for the simple case study for both Matlab and ProMechanica models.

Thus, after the Collocation Method was validated on the simple case study, the method was applied to the full 980G II wheel loader in the CAD model in ProMechanica.

This study developed and implemented an efficient computational method to propagate computational method to propagate uncertainties through “black-box” models of mechanical systems. The method was also proved to be reliable and easier to implement than traditional methods.

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