Study of Phase Transition in Homogeneous, Rigid Extended Nematics and Magnetic Suspensions using an Order-Reduction Method

Abstract

We study the phase transition in rigid extended nematics and magnetic suspensions by solving the Smoluchowski equation for magnetically polarized rigid nematic polymers and suspensions in equilibrium, in which the molecular interaction is modeled by a dipolar and excluded volume potential. The equilibrium solution (or the probability distribution of the molecular distribution) is given by a Boltzmann distribution parametrized by the (first-order) polarity vector and the (second-order) nematic order tensor along with material parameters. We show that the polarity vector coincides with one of the principal axes of the nematic order tensor so that the equilibrium distribution can be reduced to a Boltzmann distribution parametrized by three scalar order parameters, i.e., a polar order parameter and two nematic order parameters, governed by three nonlinear algebraic-integral equations. This reduction in the degree of freedom from 8 (3 in the polarity vector and 5 in the nematic order tensor) to 3 significantly simplifies the solution procedure and allows one to conduct a complete analysis on bifurcation diagrams of the order parameters with respect to the material parameters. The stability of the equilibria is inferred from the second variation of the free energy density.

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Document Details

Document Type
Technical Report
Publication Date
Dec 29, 2006
Accession Number
ADA590729

Entities

People

  • Guanghua Ji
  • Hong Zhou
  • Pingwen Zhang
  • Qi Wang

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Coordinate Systems
  • Equations
  • Free Energy
  • Integral Equations
  • Liquid Crystal Polymers
  • Liquid Crystals
  • Materials
  • Orientation (Direction)
  • Phase Diagrams
  • Phase Transformations
  • Polymers
  • Probability
  • Probability Distributions
  • Statistics
  • Steady State
  • Three Dimensional
  • Transitions

Fields of Study

  • Physics

Readers

  • Control Systems Engineering.
  • Materials Science and Engineering.
  • Plasma Physics / Magnetohydrodynamics

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms