done with inelastic models (gnf).fmorriso/cm4650/lectures/chapter8...dtotal dspring ddashpot...
TRANSCRIPT
Generalized LVE CM4650 4/18/2018
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© Faith A. Morrison, Michigan Tech U.
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Summary: Generalized Newtonian Fluid Constitutive Equations
•A first constitutive equation
•Can match steady shearing data very well
•Simple to calculate with
•Found to predict pressure-drop/flow rate relationships well
•Fails to predict shear normal stresses
•Fails to predict start-up or cessation effects (time-dependence, memory) – only a function of instantaneous velocity gradient
•Derived ad hoc from shear observations; unclear of validity in non-shear flows
PRO:
CON:
Done with Inelastic models (GNF).
Let’s move on to Linear Elastic models
Summary:
Rules for Constitutive Equations
•Must be of tensor order
•Must be a tensor (independent of coordinate system)
•Must be a symmetric tensor
•Must make predictions that are independent of the observer
•Should correctly predict observed flow/deformation behavior
The stress expression:
Innovate and improve constitutive equations
What we know so far…
Chapter 8: Memory Effects: GLVE
© Faith A. Morrison, Michigan Tech U.
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f
Dtotal
initial stateno force
final stateforce, f, resists
displacement
Maxwell’s model combines viscous and elastic responses in series
dashpotspringtotal DDD
Displacements are additive:
Spring (elastic) and dashpot (viscous) in series:
Professor Faith A. Morrison
Department of Chemical EngineeringMichigan Technological University
CM4650 Polymer Rheology
Generalized LVE CM4650 4/18/2018
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Fluids with Memory - Chapter 8
© Faith A. Morrison, Michigan Tech U.
We seek a constitutive equation that includes memory effects.
calculates the stress at a particular time,
2 Constitutive Equations so far:
So far, stress at depends on rate-of-deformation at only
3
Fluids with Memory – Chapter 8
, , , , materialinfo
© Faith A. Morrison, Michigan Tech U.
Newtonian
Generalized Newtonian
Neither can predict:
•Shear normal stresses - this will be wrong so long as we use constitutive equations proportional to
•stress transients in shear (startup, cessation) - this flaw seems to be related to omitting fluid memory
Current Constitutive Equations
We will try to fix this now; we will address the first point when we discuss advanced constitutive equations
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Fluids with Memory – Chapter 8
Generalized LVE CM4650 4/18/2018
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Startup of Steady Shearing
© Faith A. Morrison, Michigan Tech U.
Figures 6.49, 6.50, p. 208 Menezes and Graessley, PB soln 5
Fluids with Memory – Chapter 8
≡ 00
0 00
≡
Ψ ≡
Cessation of Steady Shearing
© Faith A. Morrison, Michigan Tech U.
Figures 6.51, 6.52, p. 209 Menezes and Graessley, PB soln
6
Fluids with Memory – Chapter 8
≡ 00
00 0
≡
Ψ ≡
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How can we incorporate time-dependent effects?
First, we explore a simple memory fluid.
© Faith A. Morrison, Michigan Tech U.
Let’s construct a new constitutive equation that remembers the stress at a time t0 seconds ago
“Newtonian” contribution
contribution from fluid memory
is a parameter of the model (it is constant)
This is the rate-of-deformation tensor 0seconds beforetime
7
Fluids with Memory – Chapter 8
0.8
© Faith A. Morrison, Michigan Tech U.
What does this model predict?
Steady shear
?
?
?
2
1
Shear start-up
?)(
?)(
?)(
2
1
t
t
t
8
Fluids with Memory – Chapter 8
0.8Simple memory fluid
Generalized LVE CM4650 4/18/2018
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© Faith A. Morrison, Michigan Tech U.
What does this model predict?
Steady shear
?
?
?
2
1
Shear start-up
?)(
?)(
?)(
2
1
t
t
t
9
Fluids with Memory – Chapter 8
Let’s try.
0.8Simple memory fluid
Imposed Kinematics:
≡ 00
Steady Shear Flow Material Functions
Material Functions:
Viscosity
© Faith A. Morrison, Michigan Tech U.10
constant
≡
Ψ ≡
Ψ ≡
Material Stress Response:
0
0 0
0,
,
0
First normal-stress coefficient
Second normal-stress coefficient
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≡ 00
Start-up of Steady Shear Flow Material Functions
Shear stress growth
function
© Faith A. Morrison, Michigan Tech U.11
0 00
Ψ , ≡
Ψ , ≡
0 0
0,
0 0
First normal-stress growth coefficient
Second normal-stress growth coefficient
,
,
,
,
,
,
Imposed Kinematics:
Material Functions:
Material Stress Response:
, ≡
Predictions of the simple memory fluid
© Faith A. Morrison, Michigan Tech U.
Steady shear
0
~8.1
21 The steady viscosity reflects
contributions from what is currently happening and contributions from what happened t0 seconds ago.
Shear start-up
0)()(
0
0
~8.1
~0
)(
21
0
0
tt
tt
tt
t
t
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Fluids with Memory – Chapter 8
0.8
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© Faith A. Morrison, Michigan Tech U.
Shear start-up
0)()(
0
0
~8.1
~0
)(
21
0
0
tt
tt
tt
t
t
~ ~8.1
)(t
t
Figures 6.49, 6.50, p. 208 Menezes and Graessley, PB soln
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Fluids with Memory – Chapter 8
Predictions of the simple memory fluid
0.8
© Faith A. Morrison, Michigan Tech U.
Shear start-up
~ ~8.1
)(t
t
0 t
)(21 t
What the data show:
What the GNF models predict:
0 t
)(21 t
Increasing
Increasing
What the simple memory fluid model predict:
t0
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Fluids with Memory – Chapter 8
Predictions of the simple memory fluid
For all rates,
0.8
Generalized LVE CM4650 4/18/2018
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Increasing
Increasing
© Faith A. Morrison, Michigan Tech U.
~ ~8.1
)(t
t
0 t
)(21 t
What the data show:
What the GNF models predict:
0 t
)(21 t
What the simple memory fluid model predict:
t0
Adding that contribution from the past introduces the observed “build-up” effect to the predicted
start-up material functions.
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Fluids with Memory – Chapter 8
Predictions of the simple memory fluid
For all rates,
Shear start-up
0.8
We can make the stress rise smoother by adding more fading memory terms.
© Faith A. Morrison, Michigan Tech U.
Newtonian contribution
contribution from t0
seconds ago
contribution from 2t0
seconds ago
~ ~8.1
)(t
tt0 2t0
~4.2
The memory is fading
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Fluids with Memory – Chapter 8
For all rates,
0.8 0.6 2
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© Faith A. Morrison, Michigan Tech U.
)(t
t
The fit can be made to be perfectly smooth by using a sum of exponentially decaying terms as the weighting functions.
0 1.00
1 0.37
2 0.14
3 0.05
4 0.02
/0pte
0pt
( / scales the decay)17
Fluids with Memory – Chapter 8
start-up prediction:
For all rates,
0.37
0.14 2 0.05 3 ⋯∑ /
© Faith A. Morrison, Michigan Tech U.
This sum can be rewritten as an integral.
)'()(
'
'
0
tptexiaf
tpptxi
tx
tx
ta
tp
N
abxxxiafdxxfI
N
i
b
aN
,)(lim)(1
18
New model:
~ ~8.1
)(t
tt0 2t0
~4.2
(Actually, it takes a bit of renormalizing to make this transformation actually work.)
In the current formulation,
grows as N goes to infinity.
Fluids with Memory – Chapter 8
∑
→ Δ ′
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© Faith A. Morrison, Michigan Tech U.
Maxwell Model (integral version)
Relaxation time - quantifies how fast memory fades
Zero-shear viscosity – gives the value of the steady shear viscosity
Two parameters:
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With proper reformulation, we obtain:
Steps to arrive here:•Add information about past deformations•Make memory fade
Fluids with Memory – Chapter 8
Often we write in terms of a modulus parameter,
′
© Faith A. Morrison, Michigan Tech U.
We’ve seen that including terms that invoke past deformations (fluid memory) can improve the constitutive predictions we make.
This same class of models can be derived in differential form, beginning with the idea of combining viscous and elastic effects.
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Fluids with Memory – Chapter 8
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Hooke’s Law for elastic solids
© Faith A. Morrison, Michigan Tech U.
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Newton’s Law for viscous liquids
The Maxwell Models
The basic Maxwell model is based on the observation that at long times viscoelastic
materials behave like Newtonian liquids, while at short times they behave like elastic solids.
Fluids with Memory – Maxwell Models
f
Dtotal
initial stateno force
final stateforce, f, resists
displacement
© Faith A. Morrison, Michigan Tech U.
Maxwell’s model combines viscous and elastic responses in series
dashpotspringtotal DDD
Displacements are additive:
Spring (elastic) and dashpot (viscous) in series:
22
Fluids with Memory – Maxwell Models
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© Faith A. Morrison, Michigan Tech U.
In the spring:
In the dashpot:
dt
dD
dt
dD
dt
dD
DDD
dashspringtotal
dashspringtotal
dt
dDf
DGf
dash
springsp
dt
dD
dt
df
Gf
fdt
df
G
total
sp
sp
11
23
Fluids with Memory – Maxwell Models
(proportional to displacement)
(proportional to rate)
© Faith A. Morrison, Michigan Tech U.
dt
dD
dt
df
Gf total
sp
By analogy:
shear
all flows
G0 Relaxation time
Two parameter model:
0 Viscosity
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Fluids with Memory – Maxwell Models
Also by analogy:(we’re taking a leap here)
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© Faith A. Morrison, Michigan Tech U.
The Maxwell Model
G0 Relaxation time
Two parameter model:
0 Viscosity
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Fluids with Memory – Maxwell Models
© Faith A. Morrison, Michigan Tech U.
How does the Maxwell model behave at steady state? For short time deformations?
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Fluids with Memory – Maxwell Models
The Maxwell Model
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Example: Solve the Maxwell Model for an expression explicit in the stress tensor
Fluids with Memory – Maxwell Models
To solve first-order, linear differential equations:
Use an integrating function,
xdxaexu )()(
Fluids with Memory – Maxwell Models
Recall:
0
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Example: Solve the Maxwell Model for an expression explicit in the stress tensor
Fluids with Memory – Maxwell Models
Let’s try.
© Faith A. Morrison, Michigan Tech U.
Maxwell Model (integral version)
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We arrived at this equation following two different paths:
•Add up fading contributions of past deformations•Add viscous and elastic effects in series
Fluids with Memory – Maxwell Models
′
This is the same equation we “made up” when starting from the simple memory model!
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© Faith A. Morrison, Michigan Tech U.
What are the predictions of the Maxwell model?
Need to check the predictions to see if what we have done is worth keeping.
Predictions:
•Steady shear
•Steady elongation
•Start-up of steady shear
•Step shear strain
•Small-amplitude oscillatory shear
31
Fluids with Memory – Maxwell Models
What now?
© Faith A. Morrison, Michigan Tech U.
What are the predictions of the Maxwell model?
Need to check the predictions to see if what we have done is worth keeping.
Predictions:
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Fluids with Memory – Maxwell Models
What now?
Let’s get to work.
•Steady shear
•Steady elongation
•Start-up of steady shear
•Step shear strain
•Small-amplitude oscillatory shear
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Imposed Kinematics:
≡ 00
Steady Shear Flow Material Functions
Material Functions:
Viscosity
© Faith A. Morrison, Michigan Tech U.33
constant
≡
Ψ ≡
Ψ ≡
Material Stress Response:
0
0 0
0,
,
0
First normal-stress coefficient
Second normal-stress coefficient
Predictions of the (single-mode) Maxwell Model
© Faith A. Morrison, Michigan Tech U.
Steady shear
021
0
Fails to predict shear normal
stresses.
Fails to predict shear-thinning.
Steady elongation
Trouton’s rule
34
3
Fluids with Memory – Chapter 8
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Steady shear viscosity and first normal stress coefficient
0.1
1
10
100
0.1 1 10 100
stress, Pa
1, sFigure 6.5, p. 173 Binnington and Boger; PIB soln
sPa ,
21, sPa
© Faith A. Morrison, Michigan Tech U.
There are some systems with a constant viscosity but still start-up effects.
BOGER FLUIDS
35
Fluids with Memory – Chapter 8
,
0.1
1
10
100
1000
0.01 0.1 1 10 100
stea
dy
she
ar m
ater
ial f
un
ctio
ns
1 (Pa s2)
(Pa)
2 (Pa s2)
PS/TCP squaresPS/DOP circles
1, s
Steady shear viscosity and first and secondnormal stress coefficient
Figure 6.6, p. 174 Magda et al.; PS solns
© Faith A. Morrison, Michigan Tech U.
BOGER FLUIDS
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Fluids with Memory – Chapter 8
,
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Step Strain Shear Flow Material Functions
Relaxation modulus
© Faith A. Morrison, Michigan Tech U.
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≡ 00
lim→
0 0/ 00
, ≡ , ,
, ≡
, ≡
0
0,
0 0
First normal-stress relaxation modulus
Second normal-stress relaxation modulus
,
,
,
,,,
.
.
.
0
Imposed Kinematics:
Material Functions:
Material Stress Response:
Predictions of the (single-mode) Maxwell Model
© Faith A. Morrison, Michigan Tech U.
Shear start-up
0)()(
1)(
21
/0
tt
et t Does predict a gradual build-up of stresses on start-up.
Step shear strain
0
)(
21
/0
GG
etG t
Does predict a reasonable
relaxation function in step strain (but no normal stresses again).
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Fluids with Memory – Chapter 8
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0.0
0.2
0.4
0.6
0.8
1.0
1.2
0 2 4 6 8 10
o
tG
t
0.001
0.01
0.1
1
0.01 0.1 1 10
o
tG
t
© Faith A. Morrison, Michigan Tech U.
Step-Shear-Strain Material Function G(t) for Maxwell Model
39
Fluids with Memory – Chapter 8
10
100
1,000
10,000
1 10 100 1,000 10,000time, s
G(t), Pa
© Faith A. Morrison, Michigan Tech U.
Comparison to experimental data
Figure 8.4, p. 274 data from Einaga et al., PS 20% soln in chlorinated diphenyl
s
Pg
150
25000
Pa
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Fluids with Memory – Chapter 8
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© Faith A. Morrison, Michigan Tech U.
We can improve this fit by adjusting the Maxwell model to allow multiple relaxation modes
Generalized Maxwell
Model
2 parameters (can fit anything)
41
Fluids with Memory – Chapter 8
2 model parameters: , (constants)
/ ′ ′
© Faith A. Morrison, Michigan Tech U.
Generalized Maxwell
Model
42
Fluids with Memory – Chapter 8
•Steady shear
•Steady elongation
•Start-up of steady shear
•Step shear strain
•Small-amplitude oscillatory shear
Let’s try.
What are the predictions of the Generalized Maxwell model?
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© Faith A. Morrison, Michigan Tech U.
Generalized Maxwell
Model
43
Fluids with Memory – Chapter 8
What are the predictions of the Generalized Maxwell model?
•Steady shear
•Steady elongation
•Start-up of steady shear
•Step shear strain
•Small-amplitude oscillatory shear
Let’s try.
Imposed Kinematics:
≡ 00
Steady Shear Flow Material Functions
Material Functions:
Viscosity
© Faith A. Morrison, Michigan Tech U.44
constant
≡
Ψ ≡
Ψ ≡
Material Stress Response:
0
0 0
0,
,
0
First normal-stress coefficient
Second normal-stress coefficient
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Step Strain Shear Flow Material Functions
Relaxation modulus
© Faith A. Morrison, Michigan Tech U.
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≡ 00
lim→
0 0/ 00
, ≡ , ,
, ≡
, ≡
0
0,
0 0
First normal-stress relaxation modulus
Second normal-stress relaxation modulus
,
,
,
,,,
.
.
.
0
Imposed Kinematics:
Material Functions:
Material Stress Response:
Predictions of the Generalized Maxwell Model
© Faith A. Morrison, Michigan Tech U.
Steady shear
021
1
N
kk Fails to predict shear
normal stresses
Fails to predict shear-thinning
Step shear strain
0
)(
21
1
/
GG
etGN
k
t
k
k k
This function can fit anyobserved data (we show how)
Note that the GMM does not predict shear normal stresses.
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Fluids with Memory – Chapter 8
2 model parameters: , (constants)
Generalized LVE CM4650 4/18/2018
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10
100
1,000
10,000
1 10 100 1000 10000
time, t
G(t), Pa
k k gk1 450 5002 100 9003 30 13004 5.0 1600
k=4 k=3 k=2
k=1
4
1k
tk
keg
© Faith A. Morrison, Michigan Tech U.
Fitting to Generalized Maxwell Model
Figure 8.4, p. 274 data from Einaga et al., PS 20% soln in chlorinated diphenyl
(s) (Pa)
47
Fluids with Memory – Chapter 8
2 model parameters: , (constants)
© Faith A. Morrison, Michigan Tech U.
The Linear-Viscoelastic Models
Differential Maxwell (one mode):
Integral Maxwell (one mode):
Generalized Maxwell model (N modes):
48
Fluids with Memory – Chapter 8
2 model parameters: , (constants)
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Predictions of the Generalized Maxwell Model
© Faith A. Morrison, Michigan Tech U.
Step shear strain
0
)(
21
1
/
GG
etGN
k
t
k
k k
49
Fluids with Memory – Chapter 8
Note: The time-dependent function in the integral of the GMM is the same form as the step strain result:
© Faith A. Morrison, Michigan Tech U.
The Linear-Viscoelastic Models
Differential Maxwell (one mode):
Integral Maxwell (one mode):
Generalized Maxwell model (N modes):
50
Fluids with Memory – Chapter 8
Since the term in brackets is just the predicted relaxation modulus G(t), we can write an even more general linear viscoelastic modelby leaving this function unspecified.
Generalized LVE CM4650 4/18/2018
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© Faith A. Morrison, Michigan Tech U.
The Linear-Viscoelastic Models
Differential Maxwell (one mode):
Integral Maxwell (one mode):
Generalized Maxwell model (N modes):
Generalized Linear-Viscoelastic Model:
51
Fluids with Memory – Chapter 8
′
© Faith A. Morrison, Michigan Tech U.
Generalized Maxwell Model
52
Fluids with Memory – Chapter 8
What are the predictions of these models?
•Steady shear
•Steady elongation
•Start-up of steady shear
•Step shear strain
•Small-amplitude oscillatory shear
Let’s try.
Generalized Linear-Viscoelastic Model ′
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Small-Amplitude Oscillatory Shear Material Functions
SAOS stress
© Faith A. Morrison, Michigan Tech U.
53
≡ 00
cos
, , sin sin cos
′ ≡
cos ′′ ≡
sin
0
0,
0
0(linear viscoelastic regime)
Storage modulus
Loss modulus
0
cos sin
sin
phase difference between stress and strain waves
Imposed Kinematics:
Material Functions:
Material Stress Response:
Predictions of the Generalized Maxwell Model (GMM) and Generalized Linear-Viscoelastic Model (GLVE)
© Faith A. Morrison, Michigan Tech U.
Small-amplitude oscillatory shear
GMM
N
k k
k
N
k k
kk
G
G
12
12
2
)(1)(
)(1)(
GLVE
0
0
sin)()(
cos)()(
dsssGG
dsssGG
54
Fluids with Memory – Chapter 8
2 model parameters: , (constants)
′
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0.00001
0.0001
0.001
0.01
0.1
1
10
0.001 0.01 0.1 1 10 100 1000
1
1'
G
1
1"
G
10-5
10-4
© Faith A. Morrison, Michigan Tech U.
Predictions of (single-mode) Maxwell Model in SAOS
21
12
1
11
21
211
21
2211
)(1)(1)(
)(1)(1)(
gG
gG
55
Fluids with Memory – Chapter 8
Two model parameters: , (constants)
SA
OS
mat
eria
l fun
ctio
ns
© Faith A. Morrison, Michigan Tech U.
Predictions of (multi-mode) Maxwell Model in SAOS
Figure 8.8, p. 284 data from Vinogradov, PS melt
1.E+01
1.E+02
1.E+03
1.E+04
1.E+05
1.E+06
1.E+07
1.E+00 1.E+01 1.E+02 1.E+03 1.E+04 1.E+05 1.E+06 1.E+07
aT, rad/s
G' (Pa)
G'' (Pa)
k k (s) gk(kPa) 1 2.3E-3 16 2 3.0E-4 140 3 3.0E-5 90 4 3.0E-6 400 5 3.0E-7 4000
N
k k
k
N
k k
kk
G
G
12
12
2
)(1)(
)(1)(
k (s) gk(kPa)
56
Fluids with Memory – Chapter 8
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© Faith A. Morrison, Michigan Tech U.
Predictions of (multi-mode) Maxwell Model in SAOS
Figure 8.10, p. 286 data from Laun, PE melt
1.E-01
1.E+00
1.E+01
1.E+02
1.E+03
1.E+04
1.E+05
1.E+06
1.E-04 1.E-02 1.E+00 1.E+02 1.E+04 1.E+06
aTrad/s
mo
duli
G' (Pa)G" Pa
k gk
50 4.42E+025 3.62E+031 4.47E+030.5 3.28E+030.2 9.36E+030.1 1.03E+040.05 1.71E+030.02 1.18E+020.02 2.55E+040.01 3.33E+040.0007 1.83E+05
N
k k
k
N
k k
kk
G
G
12
12
2
)(1)(
)(1)(
57
Fluids with Memory – Chapter 8
© Faith A. Morrison, Michigan Tech U.
58
Fluids with Memory – Chapter 8
http://pages.mtu.edu/~fmorriso/cm4650/PredictionsGLVE.pdf
Predictions of the Generalized Linear Viscoelastic Model
(the “report card”)
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0.1
1
10
100
1000
0.01 0.1 1 10 100
stea
dy
she
ar m
ater
ial f
un
ctio
ns
1 (Pa s2)
(Pa)
2 (Pa s2)
PS/TCP squaresPS/DOP circles
1, s
Steady shear viscosity and first and secondnormal stress coefficient
Figure 6.6, p. 174 Magda et al.; PS solns
© Faith A. Morrison, Michigan Tech U.
BOGER FLUIDS
59
Fluids with Memory – Chapter 8
,
x1
x2
x3
H
W V
v1(x2)
EXAMPLE: Drag flow of a Generalized Linear-Viscoelastic fluid between infinite parallel plates
•steady state•incompressible fluid•infinitely wide, long
60
Fluids with Memory – Chapter 8
′
Generalized LVE CM4650 4/18/2018
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© Faith A. Morrison, Michigan Tech U.
Limitations of the GLVE Models
•Predicts constant shear viscosity
•Only valid in regime where strain is additive (small-strain, low rates)
•All stresses are proportional to the deformation-rate tensor; thus shear normal stresses cannot be predicted
•Cannot describe flows with a superposed rigid rotation (as we will now discuss; see Morrison p296)
61
Fluids with Memory – Chapter 8
′
What else can we try?
© Faith A. Morrison, Michigan Tech U.
Jeffreys Model - Mechanical Analog
Maxwell Model - Mechanical Analog
62
Advanced Constitutive Modeling – Chapter 9
Generalized LVE CM4650 4/18/2018
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© Faith A. Morrison, Michigan Tech U.
Jeffreys Model
Now, solving for explicitly we obtain,
Other modifications of the Maxwell model motivated by springs and dashpots in series and
parallel modify ’ but do not otherwise introduce new behavior.
(Might as well use the Generalized Maxwell model)
63
Advanced Constitutive Modeling – Chapter 9
12
Unfortunately, this change only modifies ;the Jeffreys Model is a GLVE model
© Faith A. Morrison, Michigan Tech U.
64
Advanced Constitutive Modeling – Chapter 9
Limitations of the GLVE Models
•Predicts constant shear viscosity
•Only valid in regime where strain is additive (small-strain, low rates)
•All stresses are proportional to the deformation-rate tensor; thus shear normal stresses cannot be predicted
•Cannot describe flows with a superposed rigid rotation (as we will now discuss; see Morrison p296)
Fluids with Memory – Chapter 8
′
What else can we try?
Where do we go from here?
Generalized LVE CM4650 4/18/2018
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© Faith A. Morrison, Michigan Tech U.
65
Advanced Constitutive Modeling – Chapter 9
Limitations of the GLVE Models
•Predicts constant shear viscosity
•Only valid in regime where strain is additive (small-strain, low rates)
•All stresses are proportional to the deformation-rate tensor; thus shear normal stresses cannot be predicted
•Cannot describe flows with a superposed rigid rotation (as we will now discuss; see Morrison p296)
Fluids with Memory – Chapter 8
′
What else can we try?
Where do we go from here?
Let’s talk about this
issue.
© Faith A. Morrison, Michigan Tech U.
fluid x
y
zyx ,,
zyx ,,
Shear flow in a rotating frame of reference
66
Fluids with Memory – Chapter 8
′
GLVE:
Generalized LVE CM4650 4/18/2018
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© Faith A. Morrison, Michigan Tech U.
67
fluid x
y
zyx ,,
zyx ,,
Shear flow in a rotating frame of reference
Fluids with Memory – Chapter 8
GLVE:
, , coordinate system stationary , , coordinate system rotating at Ω
The choice of coordinate system should make no difference. Let’s check.
Predict:
© Faith A. Morrison, Michigan Tech U.
68
fluid x
y
zyx ,,
zyx ,,
Shear flow in a rotating frame of reference
Fluids with Memory – Chapter 8
GLVE:
, , coordinate system stationary , , coordinate system rotating at Ω
We write the fluid velocity two ways:
Both ways we should get the same answer for
′
GLVE:
Generalized LVE CM4650 4/18/2018
35
© Faith A. Morrison, Michigan Tech U.
Shear flow in a rotating frame of reference
tyybd
tyyc
txxb
txxa
o
o
o
o
cos
sin
sin
cos
oyy
t
P
x
y
x
y
oxx
a
b
d
c
69
Fluids with Memory – Chapter 8
.P
© Faith A. Morrison, Michigan Tech U.
Shear flow in a rotating frame of reference
tyybd
tyyc
txxb
txxa
o
o
o
o
cos
sin
sin
cos
oyy
t
P
x
y
x
y
oxx
a
b
d
c
70
Fluids with Memory – Chapter 8
Location of P: ,
.
Generalized LVE CM4650 4/18/2018
36
© Faith A. Morrison, Michigan Tech U.
Shear flow in a rotating frame of reference
tyybd
tyyc
txxb
txxa
o
o
o
o
cos
sin
sin
cos
oyy
t
P
x
y
x
y
oxx
a
b
d
c
71
Fluids with Memory – Chapter 8
Location of P: ,
.
© Faith A. Morrison, Michigan Tech U.
Summary: Generalized Linear-Viscoelastic (GLVE) Constitutive Equations
•A first constitutive equation with memory
•Can match SAOS, step-strain data very well
•Captures start-up/cessation effects
•Simple to calculate with
•Can be used to calculate the LVE spectrum
•Fails to predict shear normal stresses
•Fails to predict shear-thinning/thickening
•Only valid at small strains, small rates
•Not frame-invariant
PRO:
CON:
72
Fluids with Memory – Chapter 8
′
GLVE:
Generalized LVE CM4650 4/18/2018
37
73
Find the recipe card (has kinematics,
definitions of material functions; (t) is known)
Predict a material function for a constitutive equation
For the given kinematics, calc. needed
stresses from the constitutive equation
From stresses, calculate the material function(s) from the
definition on the recipe card
Sketch the situation and choose an appropriate
coordinate system
Calculate a flow field or stress
field for a fluid in a situation
Solve the differential equations (Solve EOM, continuity, and constitutive
equation for and and apply the boundary conditions)
Model the flowby asking yourself questions about the velocity field (are some components and derivatives zero? The constitutive
equationmust be known)
x1
x2
x3
H
W V
v1(x2)
EXAMPLE: Drag flow between infinite parallel plates
•Newtonian•steady state•incompressible fluid•very wide, long•uniform pressure
2
Step Shear Strain Material Functions
00
00
constant
0
0
0
0
lim)(
t
t
t
t
Kinematics:
123
2
0
0
)(
xt
v
Material Functions:
0
0210
),(),(
t
tG
20
22111
G
20
33222
GRelaxation
modulus
First normal-stress relaxation modulus
Second normal-stress relaxation
modulus
© Faith A. Morrison, Michigan Tech U.
4
What kind of rheology calculation am I considering?
constitutive equation
© Faith A. Morrison, Michigan Tech U.74
Done with GLVE.Let’s move on to Advanced Constitutive Equations
′
Generalized LVE CM4650 4/18/2018
38
Chapter 9: Advanced Constitutive Models
© Faith A. Morrison, Michigan Tech U.75
CM4650 Polymer Rheology
Professor Faith A. Morrison
Department of Chemical EngineeringMichigan Technological University