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Potential Driven Flows Through Bifurcating Networks

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Title: Potential Driven Flows Through Bifurcating Networks


1
Potential Driven Flows Through Bifurcating
Networks
  • Aerospace and Mechanical Engineering Graduate
    Student Conference 2006
  • 19 October, 2006
  • Jason Mayes
  • Advisor Dr. Mihir Sen

2
Outline
  • Background/Motivation
  • Objectives
  • A self-similar model
  • Simplifying the model
  • Forms of similarity
  • Examples
  • Conclusions / Future work

3
Background / Motivation self-similarity
  • Natural physical examples
  • Broccoli
  • Artificially occurring examples
  • Artificial terrain
  • Non-physical examples
  • Data series
  • Music

"When each piece of a shape is geometrically
similar to the whole, both the shape and the
cascade that generate it are called
self-similar." - Mandelbrot
From Hofstadter's classic, Godel, Escher, Bach
"A fugue is like a canon, in that it is usually
based on one theme which gets played in different
voices and different keys, and occasionally at
different speeds or upside down or backwards."
4
Background / Motivation self-similar systems
  • Self-similarity in engineering
  • many systems can be considered self-similar over
    several scales
  • Large scale problems
  • as systems grow, solutions become more difficult
  • can we simplify?

5
Objectives simplification and reduction
  • Take advantage of similarity to simplify analysis
  • Use known structure / behavior to simplify
  • Using structure
  • Extending behavior from one scale to another
  • Reduce large equation sets

6
A self-similar model a model for analysis
  • The bifurcating tree geometry
  • Geometry seen in a wide variety of applications
  • Potential-driven flow or transfer
  • ex. heat, fluid, energy, ect.
  • Conservation at bifurcation points

7
A self-similar model potential-driven transfer
  • Assumptions
  • Transfer governed by a linear operator
  • i.e., for each branch

8
A self-similar model simplification
  • Goal is to reduce or simplify the system to the
    single equation

Generation two (N2) Network
9
A self-similar model simplification
Apply recursively to eliminate q1,1 and q2,2
10
A self-similar model a simple result
  • Result of simplification for N2 network
  • For the more general N-generation network
  • For integro-differential operators
  • Process repeated in the Laplace domain
  • Inverses become simple algebraic inverses
  • Can be written as a continued fraction

11
Forms of similarity within and between
  • Similarity can be used to further simplify
  • Two forms of similarity
  • Similarity within a generation
  • Symmetric networks the operators within a
    generation are identical
  • Asymmetric networks the operators within a
    generation are not identical
  • Similarity between generations
  • Generation dependent operators depend on the
    generation in which the operator occurs and
    change between successive generations
  • Generation independent operators do not change
    between generations
  • Four possible combinations
  • Symmetric with generation independent operators
  • Asymmetric with generation independent operators
  • Symmetric with generation dependent operators
  • Asymmetric with generation independent operators

12
Examples tree of resistors
  • Symmetric with generation independent operators

13
Examples tree of resistors
  • Tree constructed of identical resistors
  • Current i(t) through each branch is driven by the
    potential difference v(t) across the branch
  • Each branch governed by
  • For an N generational tree of resistors
  • For an infinite tree of resistors

14
Examples fractional order visco-elasticity models
  • Asymmetric with generation independent operators

15
Examples fractional order visco-elasticity models
  • Tree is composed of springs and dashpots
  • Branches governed by linear operators
  • Result is a fractional-order visco-elasticity
    model

16
Examples laminar pipe flows in branching networks
  • Symmetric with generation dependent operators

17
Examples laminar pipe flows in branching networks
  • Each pipe governed by
  • In Laplace domain
  • In the time domain

18
Conclusions / Future Work
  • Known behavior on one scale can be extended to
    better understand behavior on another
  • Similarity or structure can be used to help
    simplify
  • For equation sets patterns or structures can be
    used to simplify
  • Future work
  • Analyze other self-similar geometries
  • Study probabilistically self-similar geometries

19
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