03/29/2021
By Sokny Long

The College of Engineering, Department of Mechanical Engineering, invites you to attend a doctoral dissertation defense by Deborah Fowler “On the Use of Linear Dynamic Models with Limited Measured Data to Predict Nonlinear Response.”

Ph.D. Candidate: Deborah Fowler
Defense Date: Monday, April 12, 2021
Time: 10 a.m. to 12:30 p.m. EST
Location: This will be a virtual dissertation defense via Zoom. Those interested in attending should contact Deborah_Fowler@student.uml.edu and Peter_Avitabile@uml.edu to request access to the Zoom link.

Committee Chair (Advisor): Peter Avitabile, Professor Emeritus, Department of Mechanical Engineering, University of Massachusetts Lowell

Committee Members:

  • Zhu Mao, Assistant Professor, Department of Mechanical Engineering, University of Massachusetts Lowell
  • Alessandro Sabato, Assistant Professor, Department of Mechanical Engineering, University of Massachusetts Lowell
  • Daniel Roettgen, Senior Member of the Technical Staff, Sandia National Laboratories

Brief Abstract:
Nonlinear dynamic behavior is prevalent in mechanical engineering structures. Nonlinearities can cause significant issues in accurately predicting and modeling behavior, but with more tools to characterize and predict their behavior they can also be used to gain valuable insight into structural response. This insight can be used to develop structural health monitoring techniques, reduce the tolerance needed in design, and allow for safer, more efficient structures. Many techniques used to predict and assess nonlinear behavior require complex and computationally expensive modeling techniques. The aim of this work is to develop a variety of techniques that use linear modal information to predict and diagnose a range of typical nonlinear behavior. This will allow for simpler and more efficient methods to improve physical understanding of nonlinear response behavior, which can then be used inform decisions in the design process and determine whether further nonlinear testing is warranted.

The methodology will rely on a sparse grid of measurement points applied to the structure of interest, as well as a linear finite element model from which full field mode shapes are obtained. To build on previous work expanding piecewise nonlinear response, the first proposed technique will show that globally nonlinear response can be expanded to full field using linear modal information without the need for an iterative or nonlinear model. For the second proposed methodology, a force reconstruction technique utilizing linear modal information will be used to identify nonlinear behavior beyond measured points. The proposed work will use this technique to assess the integrity of bolted joints, as well as identify and diagnose nonlinear component contact. These methods have potential applications in structural health monitoring systems, test diagnostics, or as a tool to characterize nonlinear behavior. Together, the proposed techniques represent a robust set of tools to identify, predict, and diagnose nonlinear behavior without needing to develop nonlinear models.

All interested students and faculty members are invited to attend the online defense via remote access.