Statistical Energy Analysis and Asymptotic Modal Analysis of Nonlinear and Nonconservative Engineering Systems - Research Topic Area 1.3.2 Multi-Dimensional and Dissipative Dynamical Systems

Abstract

The objective of this proposal is to advance Statistical Energy Analysis (SEA) and Asymptotic Modal Analysis (AMA) to consider the effects of nonlinear and/or nonconservative systems. The PI will approach the objectives using a combination of theory and experiment. The PI will divide the study in to nonlinear and nonconservative problems. For nonlinear problems, the PI will begin by studying large deformations of 1-D structures and mimic the results of the Fermi-Pasta-Ulam (FPU) experiment to numerically examine the breakdown of equipartition of energy. The PI will theoretically examine structural boundary layers that are mathematically analogous to fluid boundary layers to build an extensible and general framework. To approach nonconservative problems, the PI will begin with a linear structure undergoing a Hopf bifurcation. Numerical experiments will examine the approach to the SEA limit as the number of modes becomes large. The PI will then combine efforts in nonlinear and nonconservative and extend toward studying coupling in multi-degree-of-freedom systems. Experiments will accompany numerical/theoretical studies.

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Document Details

Document Type
Technical Report
Publication Date
Jun 14, 2018
Accession Number
AD1064578

Entities

People

  • Dean R. Culver
  • Earl H. Dowell

Organizations

  • Duke University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Boundaries
  • Boundary Layer
  • Broadband
  • Couplings
  • Dissipation
  • Elastic Properties
  • Engineering
  • Experimental Data
  • Frequency
  • Frequency Domain
  • Information Operations
  • Layers
  • Military Research
  • Modal Analysis
  • Nonlinear Dynamics
  • Nonlinear Systems
  • Precision
  • Simulations
  • Structural Components
  • Transverse
  • Vibration

Fields of Study

  • Physics

Readers

  • Control Systems Engineering.
  • Neural Network Machine Learning.
  • Theoretical Analysis.