the millennium bridge required many degrees of freedom to …ocw.snu.ac.kr › sites › default ›...
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Chapter 4 Multiple Degree of Freedom Systems
The Millennium bridge required
many degrees of freedom to model
and design with.
Extending the first 3 chapters to more
then one degree of freedom
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The first step in analyzing multiple degrees of freedom (DOF)
is to look at 2 DOF
• DOF: Minimum number of coordinates to specify the position of a system
• Many systems have more than 1 DOF
• Examples of 2 DOF systems
– Car with sprung and unsprung mass (both heave)
– Elastic pendulum (radial and angular)
– Motions of a ship (roll and pitch)
– Airplane roll, pitch and yaw
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4.1 Two-Degree-of-Freedom Model (Undamped)
A 2 degree of freedom system used to base much of the
analysis and conceptual development of MDOF systems on.
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Free-Body Diagram of each mass
Figure 4.2
k2(x
2 -x
1)
k1 x
1
m1
m2
x1
x
2
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Summing forces yields the equations of motion:
1 1 1 1 2 2 1
2 2 2 2 1
1 1 1 2 1 2 2
2 2 2 1 2 2
( ) ( ) ( ) ( ) (4.1)
( ) ( ) ( )
Rearranging terms:
( ) ( ) ( ) ( ) 0 (4.2)
( ) ( ) ( ) 0
m x t k x t k x t x t
m x t k x t x t
m x t k k x t k x t
m x t k x t k x t
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Note that it is always the case that
• A 2 Degree-of-Freedom system has
– Two equations of motion!
– Two natural frequencies (as we shall see)!
• Thus some new phenomena arise in going from one to two
degrees of freedom
– Look for these as you proceed through the material
• Two instead of one natural frequency
– Leading to two possible resonance conditions
• The concept of a mode shape arises
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The dynamics of a 2 DOF system consists of 2
homogeneous and coupled equations
• Free vibrations, so homogeneous eqs.
• Equations are coupled:
– Both have x1 and x2.
– If only one mass moves, the other follows
– Example: pitch and heave of a car model
• In this case the coupling is due to k2.
– Mathematically and Physically
– If k2 = 0, no coupling occurs and can be solved as two
independent SDOF systems
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Initial Conditions
• Two coupled, second -order, ordinary differential equations
with constant coefficients
• Needs 4 constants of integration to solve
• Thus 4 initial conditions on positions and velocities
1 10 1 10 2 20 2 20(0) , (0) , (0) , (0)x x x x x x x x
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0)()()(
0)()()()(
221222
2212111
txktxktxm
txktxkktxm
Solution by Matrix Methods
The two equations can be written in the form of a
single matrix equation (see pages 272-275 if matrices and vectors are a
struggle for you) :
1 1 1
2 2 2
1 1 2 2
2 2 2
( ) ( ) ( )( ) ( ) ( )
( ) ( ) ( )
0
0
x t x t x tt , t , t
x t x t x t
m k k kM , K
m k k
M K
x x x
x x 0
(4.4), (4.5)
(4.6), (4.9)
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Initial Conditions (two sets needed one for each equation of
motion)
IC’s can also be written in vector form
10 10
20 20
(0) , and (0)x x
x x
x x
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The approach to a Solution:
For 1DOF we assumed the scalar solution aeλt
Similarly, now
we assume the vector form:
2
2
Let ( )
1, , , unknown
-
-
j t
j t
t e
j
M K e
M K
x u
u 0 u
u 0
u 0
(4.15)
(4.16)
(4.17)
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This changes the differential equation of motion into
algebraic vector equation:
2
1
2
- (4.17)
This is two algebraic equation in 3 uknowns
( 1 vector of two elements and 1 scalar):
= , and
M K
u
u
u 0
u
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The condition for solution of this matrix equation
requires that the the matrix inverse does not exist:
2
12
2
If the inv - exists : which is the
static equilibrium position. For motion to occur
- does not exist
or det - (4.19)
M K
M K
M K
u 0
u 0
0
The determinant results in 1 equation in one unknown ω
(called the characteristic equation)
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Back to our specific system: the characteristic equation is
defined as
2
2
1 1 2 2
2
2 2 2
4 2
1 2 1 2 2 1 2 2 1 2
det - 0
det 0
( ) 0
M K
m k k k
k m k
m m m k m k m k k k
Eq. (4.21) is quadratic in ω2 so four solutions result:
2 2
1 2 1 2 and and
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Once ω is known, use equation (4.17) again to calculate
the corresponding vectors u1 and u2
This yields vector equation for each squared frequency:
2
1 1
2
2 2
( ) (4.22)
and
( ) (4.23)
M K
M K
u 0
u 0
Each of these matrix equations represents 2 equations in the 2
unknowns components of the vector, but the coefficient matrix is
singular so each matrix equation results in only 1 independent
equation. The following examples clarify this.
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Examples 4.1.5 & 4.1.6:calculating u and ω
• m1=9 kg,m2=1kg, k1=24 N/m and k2=3 N/m
• The characteristic equation becomes
ω4-6ω2+8=(ω2-2)(ω2-4)=0
ω2 = 2 and ω2 =4 or
1,3 2,42 rad/s, 2 rad/s
Each value of ω2 yields an expression for u:
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Computing the vectors u
112
1 1
12
2
1 1
11
12
11 12 11 12
For =2, denote then we have
(- )
27 9(2) 3 0
3 3 (2) 0
9 3 0 and 3 0
u
u
M K
u
u
u u u u
u
u 0
2 equations, 2 unknowns but DEPENDENT!
(the 2nd equation is -3 times the first)
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Only the direction of vectors u can be determined, not
the magnitude as it remains arbitrary
1111 12
12
2
1
1 1 results from both equations:
3 3
only the direction, not the magnitude can be determined!
This is because: det( ) 0.
The magnitude of the vector is arbitrary. To see this suppose
t
uu u
u
M K
1
2
1 1 1
2 2
1 1 1 1
hat satisfies
( ) , so does , arbitrary. So
( ) ( )
M K a a
M K a M K
u
u 0 u
u 0 u 0
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Likewise for the second value of ω2
212
2 2
22
2
1
21
22
21 22 21 22
For = 4, let then we have
(- )
27 9(4) 3 0
3 3 (4) 0
19 3 0 or
3
u
u
M K
u
u
u u u u
u
u 0
Note that the other equation is the same
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What to do about the magnitude!
Several possibilities, here we just fix one element:
13
12 1
13
22 2
1 1
1 1
u
u
u
u
Choose:
Choose:
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Thus the solution to the algebraic matrix equation is:
13
1,3 1
13
2,4 2
2, has mode shape 1
2, has mode shape 1
u
u
Here we have introduce the name
mode shape to describe the vectors
u1 and u2. The origin of this name comes later
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1 1 2 2
1 1 2 2
1 1 2 2
1 1 2 2
1 1 2 2
1 2
1 1 1 1 2 2 2 2
1 2 1 2
( ) , , ,
( )
( )
sin( ) sin( )
where , , , and are const
j t j t j t j t
j t j t j t j t
j t j t j t j t
t e e e e
t a e b e c e d e
t ae be ce de
A t A t
A A
x u u u u
x u u u u
x u u
u u
ants of integration
Return now to the time response:
(4.24)
(4.26)
We have computed four solutions:
Since linear, we can combine as:
determined by initial conditions.
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Physical interpretation of all that math!
• Each of the TWO masses is oscillating at TWO natural
frequencies ω1and ω2
• The relative magnitude of each sine term, and hence of the
magnitude of oscillation of m1 and m2 is determined by the
value of A1u1 and A2u2
• The vectors u1 and u2 are called mode shapes because the
describe the relative magnitude of oscillation between the two
masses
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1 11
1 1 1 1 1
2 12
( )( ) sin sin
( )
x t ut A t A t
x t u
x u
What is a mode shape?
• First note that A1, A2, Φ1 and Φ2 are determined by
the initial conditions
• Choose them so that A2 = Φ1 = Φ2 =0
• Then:
• Thus each mass oscillates at (one) frequency w1 with
magnitudes proportional to u1 the 1st mode shape
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13
21
u
13
11
u
A graphic look at mode shapes:
If IC’s correspond to mode 1 or 2, then the response is purely in
mode 1 or mode 2.
Mode 1:
Mode 2:
m1
m2
x1 x
2
x1=A/3
x2=A
m1
x1=-A/3 x
2=A
m2
x1 x
2
k1
k1
k2
k2
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Example 4.1.7 given the initial conditions compute
the time response
1 21 2
1
21 1 2 2
1 21 2
1
21 1 2 2
1 0consider (0)= mm, (0)
0 0
sin 2 sin 2( )3 3
( )sin 2 sin 2
2 cos 2 2cos 2( )3 3
( )2 cos 2 2cos 2
A At tx t
x tA t A t
A At tx t
x tA t A t
x x
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At t = 0 we have
2211
22
11
2211
22
11
cos2cos2
cos3
2cos23
0
0
sinsin
sin3
sin3
0
mm 1
AA
AA
AA
AA
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4 equations in 4 unknowns:
1 1 2 2
1 1 2 2
1 1 2 2
1 1 2 2
1 2 1 2
3 sin sin
0 sin sin
0 2 cos 2cos
0 2 cos 2cos
1.5 mm, 1.5 mm, rad2
A A
A A
A A
A A
A A
Yields:
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The final solution is:
1
2
( ) 0.5cos 2 0.5cos 2 mm
( ) 1.5cos 2 1.5cos 2 mm
x t t t
x t t t
These initial conditions gives a response that is a combination of modes.
Both harmonic, but their summation is not.
(4.34)
Figure 4.3a x1(t) Figure 4.3b x
2(t)
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tatat 222111 coscos)( uux
Solution as a sum of modes
Determines how the first
frequency contributes to the
response
Determines how the second
frequency contributes to the
response
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Things to note
• Two degrees of freedom implies two natural frequencies
• Each mass oscillates at with these two frequencies present
in the response and beats could result
• Frequencies are not those of two component systems
1 21 2
1 2
2 1.63, 2 1.732k k
m m
• The above is not the most efficient way to calculate
frequencies as the following describes
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Some matrix and vector reminders
1
2 2
1 2
1 2 2
1 1 2 2
2
1
0
0
0 0 for every value of except 0
T
T
T
a b d bA A
c c c aad cb
x x
mM M m x m x
m
M M
x x
x x
x x x
Then M is said to be positive definite
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4.2 Eigenvalues and Natural Frequencies
• Can connect the vibration problem with the algebraic
eigenvalue problem developed in math
• This will give us some powerful computational skills
• And some powerful theory
• All the codes have eigen-solvers so these painful calculations
can be automated
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Some matrix results to help us use available
computational tools:
TM M
0 for all nonzero vectors T M x x x
A symmetric positive definite matrix M can be factored
TM LL
Here L is upper triangular, called a Cholesky matrix
A matrix M is defined to be symmetric if
A symmetric matrix M is positive definite if
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If the matrix L is diagonal, it defines the matrix square
root
1/2
1/2 1/2
The matrix square root is the matrix such that
If is diagonal, then the matrix square root is just the root
of the diagonal elements:
M
M M M
M
11/2
2
0 (4.35)
0
mL M
m
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A change of coordinates is introduced to capitalize on
existing mathematics
11
2 2
111 1 1/2
1 12
1/2 1/2
1/2 1/2 1/2 1/2
identity symmetric
000, ,
00 0
Let ( ) ( ) and multiply by :
( ) ( ) (4.38)
mm
m m
I K
mM M M
m
t M t M
M MM t M KM t
x q
q q 0
1/2 1/2or ( ) ( ) where
is called the mass normalized stiffness and is similar to the scalar
used extensively in single degree of freedom analysis. The key here is that
i
t K t K M KM
kK
m
K
q q 0
s a SYMMETRIC matrix allowing the use of many nice properties and
computational tools
For a diagonal, positive definite matrix M:
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How the vibration problem relates to the real symmetric
eigenvalue problem
2
2
vibration problem real symmetric eigenvalue problem
(4.40) (4.41)
Assume ( ) in ( ) ( )
, or
j t
j t j t
t e t K t
e K e
K K
q v q q 0
v v 0 v 0
v v v v v 0
2
Note that the martrix contains the same type of information
as does in the single degree of freedom case.n
K
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Properties of the n x n Real Symmetric Matrix
• There are n eigenvalues and they are all real valued
• There are n eigenvectors and they are all real valued
• The eigenvalues are all positive if and only if the matrix is
positive definite
• The set of eigenvectors can be chosen to be orthogonal
• The set of eigenvectors are linearly independent
• The matrix is similar to a diagonal matrix
• Numerical schemes to compute the eigenvalues and
eigenvectors of symmetric matrix are faster and more efficient
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Square n x n Matrix Review
• Let aik be the ikth
element of A then A is symmetric if aik =
aki denoted AT=A
• A is positive definite if xTAx > 0 for all nonzero x (also
implies each λi > 0)
• The stiffness matrix is usually symmetric and positive
semi definite (could have a zero eigenvalue)
• The mass matrix is positive definite and symmetric (and
so far, its diagonal)
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Normal and orthogonal vectors
11 1
, , inner product is
orthogonal to if 0
is normal if 1
if a the set of vectores is is both orthogonal and normal it
i
nT
i i
i
n n
T
T
x yx y
x y
x y x y
x y x y
x x x
2
1
s called an set
The norm of is (4.43)n
T
i
i
orthonormal
x
x x x x
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Normalizing any vector can be done by dividing it by its
norm:
has norm of 1T
x
x x
1T T
TT T T
x x x x x
x xx x x x x x
(4.44)
To see this compute
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Examples 4.2.2 through 4.2.4
1 13 31/2 1/2
2
2 2
1 1 2 2
0 27 3 0
0 1 3 3 0 1
3 1 so which is symmetric.
1 3
3- -1det( ) det 6 8 0
-1 3-
which has roots: 2 and 4
K M KM
K
K I
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1 1
11
12
11 12 1
2 11 2
1
( )
3 2 1 0
1 3 2 0
10
1
(1 1) 1
11
12
K I
v
v
v v
v 0
v
v
v
The first normalized eigenvector
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Likewise the second normalized eigenvector is computed
and shown to be orthogonal to the first, so that the set is
orthonormal
2 1 2
1 1
2 2
11 1, (1 1) 0
1 22
1(1 1) 1
2
1(1 ( 1)( 1)) 1
2
are orthonormal
T
T
T
i
v v v
v v
v v
v
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Modes u and Eigenvectors v are different but related:
1 1 2 2
1/2 1/2
1/2
1 1
and
Note
13 0 13
0 1 11
M M
M
u v u v
x q u v
u v
(4.37)
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This orthonormal set of vectors is used to form an
Orthogonal Matrix
1 2
1 1 1 2
2 1 2 2
1 2 1 1 2 2
1 2 21 1 1 2 1 2
1 2
21 2 1 2 2 2
1 0
0 1
0diag( , )
0
T T
T
T T
T T T
T T
T T
P
P P I
P KP P K K P
v v
v v v v
v v v v
v v v v
v v v v
v v v v
called a matrix of eigenvectors (normalized)
P is called an orthogonal matrix
P is also called a modal matrix
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Example 4.2.4 compute P and show that it is an
orthogonal matrix
1 1
1 11
1 12
1 1 1 11 1
1 1 1 12 2
1 1 1 1 2 01 1
1 1 1 1 0 22 2
T
P
P P
I
v v
From the previous example:
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Example 4.2.5 Compute the square of the frequencies by
matrix manipulation
2
1
2
2
1 1 3 1 1 11 1
1 1 1 3 1 12 2
1 1 2 41
1 1 2 42
4 0 2 0 01
0 8 0 42 0
TP KP
1 22 rad/s and 2 rad/s
2diag diag( ) (4.48)T
i iP KP
In general:
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Example 4.2.6
1 1 1 2 1 2 2
2 2 2 1 2 3 2
( ) 0 (4.49)
( ) 0
m x k k x k x
m x k x k k x
1 2 21
2 2 32
00 (4.50)
0
k k km
k k km
x x
Figure 4.4 The equations of motion:
In matrix form these become:
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Next substitute numerical values and compute P and Λ
1 2 1 3 21 kg, 4 kg, 10 N/m and =2 N/mm m k k k
1/2 1/2
2
1 2
1 2
1 0 12 2,
0 4 2 12
12 1
1 12
12 1det det 15 35 0
1 12
2.8902 and 12.1098
1.7 rad/s and 12.1098 ra
M K
K M KM
K I
d/s
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Next compute the eigenvectors
1
11
21
11 21
1
2 2 2 2 2
1 11 21 11 11
11
For equation (4.41 ) becomes:
12 - 2.8902 1 0
1 3- 2.8902
9.1089
Normalizing yields
1 (9.1089)
0.
v
v
v v
v v v v
v
v
v
21
1 2
1091, and 0.9940
0.1091 0.9940, likewise
0.9940 0.1091
v
v v
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Next check the value of P to see if it behaves as its
suppose to:
1 2
0.1091 0.9940
0.9940 0.1091
0.1091 0.9940 12 1 0.1091 0.9940 2.8402 0
0.9940 0.1091 1 3 0.9940 0.1091 0 12.1098
0.1091 0.9940 0.1091 0.9940
0.9940 0.1091 0.9940 0.109
T
T
P
P KP
P P
v v
1 0
1 0 1
Yes!
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A note on eigenvectors
2
2 2
In the previous section, we could have chosed to be
0.9940 -0.9940 instead of
0.1091 0.1091
because one can always multiple an eigenvector by a constant
and if the constant is -1 th
v
v v
e result is still a normalized vector.
Does this make any difference?
No! Try it in the previous example
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All of the previous examples can and should be solved by
“hand” to learn the methods
However, they can also be solved on calculators with matrix
functions and with the codes listed in the last section
In fact, for more then two DOF one must use a code to solve
for the natural frequencies and mode shapes.
Next we examine 3 other formulations for solving for modal
data
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Matlab commands
• To compute the inverse of the square matrix A: inv(A) or
use A\eye(n) where n is the size of the matrix
• [P,D]=eig(A) computes the eigenvalues and normalized
eigenvectors (watch the order). Stores them in the
eigenvector matrix P and the diagonal matrix D (D=L)
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More commands
• To compute the matrix square root use sqrtm(A)
• To compute the Cholesky factor: L= chol(M)
• To compute the norm: norm(x)
• To compute the determinant det(A)
• To enter a matrix:
K=[27 -3;-3 3]; M=[9 0;0 1];
• To multiply: K*inv(chol(M))
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2 0, 0M K u u
An alternate approach to normalizing mode shapes
Now scale the mode shapes by computing such that
1 1
T
i i i i iT
i i
M
u uu u
2 2
is called and it satisfies:
0 , 1,2
i i i
T
i i i i i i
mass normalized
M K K i
w u
w w w w
From equation (4.17)
(4.53)
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1 12 22 2 1 2(i) (ii) (iii) M K M K M KM
u u u u v v
There are 3 approaches to computing mode shapes and
frequencies
(i) Is the Generalized Symmetric Eigenvalue Problem
easy for hand computations, inefficient for computers
(ii) Is the Asymmetric Eigenvalue Problem
very expensive computationally
(iii) Is the Symmetric Eigenvalue Problem
the cheapest computationally
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Some Review: Window 4.3
Orthonormal Vectors
similar to the unit vectors of statics and dynamics
. and of values all for
if be to set are vectors of set A
if 1
if 0
by dabbreviate is This
0xx if are and
1 and 1 if both are and
2T1
221121
ji
lorthonorman
ji
ji
orthogonal
normal
ijjTi
i
ijjTi
TT
xx
x
xx
xxxxxx
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4.3 - Modal Analysis
• Physical coordinates are not always the easiest to work in
• Eigenvectors provide a convenient transformation to
modal coordinates
– Modal coordinates are linear combination of physical
coordinates
– Say we have physical coordinates x and want to
transform to some other coordinates u
212
211
3
3
xxu
xxu
1 1
2 2
1 3
1 3
u x
u x
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Review of the Eigenvalue Problem
#1) trans. (coord.
let and
as Rewrite . and conditions initial
Assume matrices. are and and vector
a is where )( with Start
21
21
21
21
00
qxqx
0xx
xx
x0xx
q
MM
KMM
KM
KtM
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Eigenproblem (cont)
0qqqq
K
KK
MKM
I
MM
M
T
~
~~
get to by yPremultipl
21
21
21
21
21
• Now we have a symmetric, real matrix
• Guarantees real eigenvalues and distinct, mutually
orthogonal eigenvectors
(4.55)
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Eigenvectors = Mode Shapes?
.synonymous not are they but
tion,transforma simple a by related
are two The matrices. of sticscharacteri
are sEigenvetor s.coordinate physical in
= to solutions are shapes Mode 2 uu KM
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Eigenvectors vs. Mode Shapes
P
K
KMMPKUU
IMUUUMMUPP
UMPPMU
M
IPP
K
TT
TTT
T
~
Similarly,
matrix. mass the w.r.t. only orthogonal are shapes
mode the Thus, .
Now, . shapes modes the
, tiontransforma the Using l?orthonorma
shapes mode the Are . i.e., l,orthonorma are
~ matrix PD symmetric the of rseigenvecto The
21
21
21
21
21
21
21
qx
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The Matrix of eigenvectors can be used to decouple the
equations of motion
1If orthonormal (unitary), T TP P P I P P
Thus, diagonal matrix of eigenvalues.
Back to 0. Make the additional coordinate
transformation and premultiply by
Pr Pr 0 (4.59)
T
T
r
T T
P KP
q Kq
q P P
P P K Ir r
• Now we have decoupled the EOM i.e., we have n
independent 2nd-order systems in modal coordinates r(t)
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Writing out equation (4.59) yields
21 11
22 22
2
1 1 1
2
2 2 2
( ) ( )1 0 00 (4.60)
( ) ( )0 1 00
( ) ( ) 0 (4.62)
( ) ( ) 0 (4.63)
r t r t
r t r t
r t r t
r t r t
We must also transform the initial conditions
101 1/2
0
202
101 1/2
0
202
(0)(0) (0) (4.64)
(0)
(0)(0) (0) (4.65)
(0)
T T
T T
rrP P M
rr
rrP P M
rr
r q x
r q x
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rx PM 21
This transformation takes the problem from couple
equations in the physical coordinate system in to
decoupled equations in the modal coordinates
k1 k
2
1)2
1
r1
2)2
1
r2
x1
x2
m1 m2
Physical Coordinates. Coupled equations Modal Coordinates.
Uncoupled equations
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21
MPT
Modal Transforms to SDOF
• The modal transformation
transforms our 2 DOF into 2 SDOF systems
• This allows us to solve the two decoupled SDOF systems
independently using the methods of chapter 1
• Then we can recombine using the inverse transformation
to obtain the solution in terms of the physical coordinates.
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The free response is calculated for each mode
independently using the formulas from chapter 1:
00
22 10 00 2
0
( ) sin cos , 1,2
or (see Window 4.3 for a reminder)
( ) sin( tan ), 1,2
ii i i i
i
i i ii i i
i i
rr t t r t i
r rr t r t i
r
Note, the above assumes neither frequency is zero
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Once the solution in modal coordinates is determined (ri)
then the response in Physical Coordinates is computed:
• With n DOFs these transformations are:
12
1 1
where nxn
(t) S (t)
n nn n
S M P
nxnn n
x r
(where n = 2 in the previous slides)
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Steps in solving via modal analysis (Window 4.5)
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Example 4.3.1 via MATLAB (see text for hand
calculations)
9 0 27 3 0 0, , (0) , (0)
0 1 3 3 1 0M K
x x
• Follow steps in Window 4.5 (page 337)
21
Calculate 1)
M 21
21~
Calculate 2)
MKMK
» Minv2 = inv(sqrt(M))
Minv2 =
0.3333 0
0 1.0000
»Kt =Minv2*K*Minv2
Kt =
3 -1
-1 3
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Example 4.3.1 solved using MATLAB as a calcuator
% 3) Calculate the symmetric eigenvalue problem for K tilde [P,D] = eig(Kt);
[lambda,I]=sort(diag(D)); % just sorts smallest to largest
P=P(:,I); % reorder eigenvectors to match eigenvalues
»lambda =
2
4
P =
-0.7071 -0.7071
-0.7071 0.7071
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Example 4.3.1 (cont)
% 4) Calculate S = M^(-1/2) * P and Sinv = P^T * M^(1/2)
S = Minv2 * P;
Sinv = inv(S);
% 5) Calculate the modal initial conditions
r0 = Sinv * x0;
rdot0 = Sinv * v0;
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Example 4.3.1 (cont)
% 6) Find the free response in modal coordinates
tmax = 10;
numt = 1000;
t = linspace(0,tmax,numt);
[T,W]=meshgrid(t,lambda.^(1/2));
% Use Tony's trick
R0 = r0(:,ones(numt,1));
RDOT0 = rdot0(:,ones(numt,1));
r = RDOT0./W.*sin(W.*T) + R0.*cos(W.*T);
% 7) Transform back to physical space
x = S*r;
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Example 4.3.1 (cont) % Plot results
figure
subplot(2,1,1)
plot(t,r(1,:),'-',t,r(2,:),'--')
title('free response in modal coordinates')
xlabel('time (sec)')
legend('r_1','r_2')
subplot(2,1,2)
plot(t,x(1,:),'-',t,x(2,:),'--')
title('free response in physical coordinates')
xlabel('time (sec)')
legend('x_1','x_2')
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1 1
1
1
2 1
2
2 2
2
2 4.44sec,
4 2
sec
T
T
Modal and Physical Responses
Modal Coordinates:
Independent
oscillators
Physical
Coordinates:
Coupled oscillators
Note IC
Free response in modal coordinates
Free response in physical coordinates
0 1 2 3 4 5 6 7 8 9 10 -4
-2
0
2
4
Time (s)
x 1
x 2
0 1 2 3 4 5 6 7 8 9 10 -4
-2
0
2
4
r 1
r 2
sec sec
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Section 4.4 More then 2 Degrees of Freedom
Fig 4.8
Extending previous section to
any number of degrees of
freedom
Fig 4.7
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A FBD of the system of figure 4.8 yields the n equations
of motion o the form:
1 1 1 0, 1,2,3 (4.83)i i i i i i i im x k x x k x x i n
Writing all n of these equations and casting them in matrix
form yields:
( ) ( ) , (4.80)M t K t x x 0
where:
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the relevant matrices and vectors are:
1 2 2
1
2 2 3 3
2
3
1
0 00 0
00 0
, 0 (4.83)
0 00 0
n n n
n
n n
k k km
k k k km
M K k
k k km
k k
1 1
2 2
( ) ( )
( ) ( )( ) , ( )
( ) ( )n n
x t x t
x t x tt t
x t x t
x x
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For such systems as figure 4.7 and 4.8 the process stays
the same…just more modal equations result:
0)()(
0)()(
0)()(
0)()(
4.3 section as same the stays Process
2
3233
2222
1211
trtr
trtr
trtr
trtr
nnn
Just get more modal
equations, one for each
degree of freedom (n is
the number of dof)
See example 4.4.2 for details
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The Mode Summation Approach
• Based on the idea that any possible time response is just a
linear combination of the eigenvectors
1 1
1
Starting with ( ) ( ) (4.88)
let ( )= ( )
two linearly independent solutions for each term.
can also write this as ( ) sin (4.92)
i i
n nj t j t
i i i i
i i
n
i i i i
i
t K t
t t a e b e
t d t
q q 0
q q v
q v
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Mode Summation Approach (cont)
jjjTj
jj
n
ii
Tjii
n
iiii
Tj
Tj
ijiTj
n
iiiii
n
iiii
ii
d
ddd
dd
d
cos=)0( ,velocities initial the for Similarly
sinsin=sin=)0(
that such normalized rseigenvecto Assuming
cos)0( and sin)0(
I.C. the from and constants the Find
11
11
qv
vvvvqv
vv
vqvq
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Mode Summation Approach (cont)
1
Solve for and from the two IC equations
(0) (0) and tan
sin (0)
about (0)=
if you just crank it through the above expressions
you might conclude that 0,
i i
T T
i i ii i T
i i
i
d
d
d
v q v q
v q
IMPORTANT NOTE q 0
i.e., the trivial soln.
Be careful with (0) = 0 as well. q
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Mode Summation Approach for zero initial displacement
1
If (0) = 0, the return to
(0) sin
and realize that 0 instead of 0.
The compute from the velocity expression
(0)= cos
n
i i i
i
i i
i
T
i i i i
d
d
d
d
q
q v
v q
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Mode Summation Approach with rigid body modes (ω1 =
0)
1
0 0
1 1 1 1 1
if 0,
( ) ( )v ( )v
does not give two linearly independent solutions.
Now we must use the expansion
j t j t
i iq t a e b e a b
1 1 1
2
( ) ( )v ( )v
and adjust calculation of the constants from the
initial conditions accordingly.
i i
nj t j t
i i i
i
q t a b t a e b e
Note that the underline term is a translational motion
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Example 4.3.1 solved by the mode summation method
1/2
1/2 1/2
0 0 0 0
11 1
2
3 0 1 11From before, we have and =
0 1 1 12
3 0Appropriate IC are = = , = =
0 0
(0) (0) 2tan tan
(0) 0
2
(0)
T T
i i i ii T
i
T
ii
M V
M M
d
q x q v
v q v q
v q
v q 1
2
3 22
sin 3 22
i
d
d
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Example 4.3.1 constructing the summation of modes
1
2
the first mode the second mode
( ) 1 13 2 1 3 2 1sin 2 sin 2
( ) 1 12 2 2 22 2
q tt t
q t
Transforming back to the physical coordinates yields:
1/21 10 01 13 2 1 3 2 13 3( ) sin 2 sin 2
1 12 2 2 22 20 1 0 1
1 13 2 1 3 2 1 sin 2 sin 2
1 12 2 2 23 2 3 2
t M t t
t t
x q
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Example 4.3.1 a comparison of the two solution
methods shows they yield identical results
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21
21~
KMMK
Steps for Computing the Response By Mode Summation
1. Write the equations of motion in matrix form, identify M
and K
2. Calculate M -1/2
(or L)
3. Calculate
4. Compute the eigenvalue problem for the matrix
iiK v and get and ~ 2
5. Transform the initial conditions to q(t)
1 12 2(0) (0) and (0) (0)M M q x q x
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Summary of Mode Summation Continued
6. Calculate the modal expansion coefficients and phase
constants
i
Ti
iTi
Tii
i d
sin
)0( ,
)0(
)0(tan 1 qv
qv
qv
7. Assemble the time response for q
n
iiiii tdt
1
sin)( vq
8. Transform the solution to physical coordinates
1
2
1
( ) ( ) sinn
i i i i
i
t M t d t
x q u
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Nodes of a Mode Shape
• Examination of the mode shapes in Example 4.4.3 shows
that the third entry of the second mode shape is zero!
• Zero elements in a mode shape are called nodes.
• A node of a mode means there is no motion of the mass or
(coordinate) corresponding to that entry at the frequency
associated with that mode.
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2
0.2887
0.2887
0
0.2887
u
The second mode shape of Example 4.4.3 has a node
• Note that for more then 2 DOF, a mode shape may have a
zero valued entry
• This is called a node of a mode.
node
They make great mounting points in machines
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A rigid body mode is the mode associated with a zero
frequency
• Note that the system in Fig 4.12 is not constrained and can
move as a rigid body
• Physically if this system is displaced we would expect it to move
off the page whilst the two masses oscillate back and forth
Fig 4.11
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Example 4.4.4 Rigid body motion
1 1 2 1 2 2 2 1
1 1 1
2 2 2
( ) and ( )
0 1 1 0
0 1 1 0
m x k x x m x k x x
m x xk
m x x
1 2
0 0
1 kg, 4 kg, 400 N subject to
0.01 m and 0
0
m m k
x v
The free body diagram of figure 4.11 yields
Solve for the free response given:
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Following the steps of Window 4.5
1/2
1/2 1/2
2
1 2 1 2
1 0
1. 10
2
1 0 1 01 1 400 200
2. 400 1 11 1 200 1000 0
2 2
4 23. det 100det 100 5 0
2 1
0 and 5 0, 2.236 rad/s
M
K M KM
K I
Indicates a rigid body motion
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Now calculate the eigenvectors and note in particular that
they cannot be zero even if the eigenvalue is zero
11
11 21
21
1 1
2
4 0 2 00 100 4 2 0
2 1 0 0
1 0.4472 or after normalizing
2 0.8944
0.8944 0.4472 0.8944Likewise:
0.4472 0.8944 0.4472
vv v
v
P
v v
v
and diag 0 5T TP P I P KP
As a check note that
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5. Calculate the matrix of mode shapes
1/2
1
1
0
1 0 0.4472 0.8944 0.4472 0.8944
0 1/ 2 0.8944 0.4472 0.4472 0.2236
0.4472 1.7889
0.8944 0.8944
7. Calculate the modal initial conditions:
0.4472 1.7889(0)
0.8944 0.894
S M P
S
S
r x
1
0
0.01 0.004472
4 0 0.008944
(0) 0S
r x
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1 1
1
(0) 0
( )
r r
r t a bt
2 2
2 2
( ) 5 ( ) 0
( ) cos 5
r t r t
r t a t
7. Now compute the solution in modal coordinates and
note what happens to the first mode.
Since ω1 = 0 the first modal equation is
Rigid body translation
And the second modal equation is
Oscillation
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Applying the modal initial conditions to these two
solution forms yields:
1
1
1
2
2
(0) 0.004472
(0) 0.0
( ) 0.0042
as in the past problems the initial conditions for yield
( ) 0.0089cos 5
0.0042( )
0.0089cos 5
r a
r b
r t
r
r t t
tt
r
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8. Transform the modal solution to the physical
coordinate system
1 3
2
0.00450.4472 0.8944( ) ( )
0.4472 0.2236 0.0089cos 5
( ) 2.012 7.60cos 5 ( ) 10 m
( ) 2.012 1.990cos 5
t S tt
x t tt
x t t
x r
x
Each mass is moved a constant distance and then oscillates at a single
frequency.
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Order the frequencies
• It is convention to call the lowest frequency ω1 so that ω1 < ω2 <
ω3 < …
• Order the modes (or eigenvectors) accordingly
• It really does not make a difference in computing the time
response
• However:
– When we measuring frequencies, they appear lowest to highest
– Physically the frequencies respond with the highest energy in
the lowest mode (important in flutter calculations, run up in
rotating machines, etc.)
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The system of Example 4.1.5 solved by Mode Summation
1 1 2 2
1 13 32, , 2,
1 1
u u
1 03(0) , (0)
01
x x
From Example 4.1.6 we have:
Use the following initial conditions and note that only one mode
should be excited (why?)
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Transform coordinates
1 12 2
9 0 3 0 1/ 3 0 and
0 1 0 1 0 1M M M
12
12
13 0 13(0) (0)
0 1 11
3 0 0 0(0) (0)
0 1 0 0
M
M
q x
q x
Thus the initial conditions become
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1 2
1 11 1 and
1 12 2
v v
Transform Mode Shapes to Eigenvectors
12
1 1
12
2 2
13 0 13
0 1 11
13 0 13
0 1 11
M
M
v u
v u
1 2 1 2
11 21 3 Note that 1 1 0, but 1 031 31
T T
v v u u
eigenvectors
Note that unlike the mode shapes, the eigenvectors are orthogonal:
Normalizing yields:
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From Equation (4.92):
iiiii
iiiii
i tdttdt vqvq )cos()()sin()(2
1
2
1
1
1
2
1cos2
1
1
2
1cos2
0
0
cos)0(
1221111
2
1
TTT
iiii
i
dd
d
vvv
vq
1 1 10 cos / 2d
Set t=0 and multiply by v1:
Or directly from Eq. (4.97)
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2
1
( ) sin( )
1 11 2 cos( 2 ) cos( 2 )
1 12
i i i i
i
t d t
t t
q v
From the initial displacement:
11
22
1(0) 1 21 1
1sin( / 2) 2 2
1(0) 11 1 0
1sin( / 2) 2
T
T
d
d
v q
v q
(4.98)
Eigenvector 2
Eigenvector 1
thus
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Transforming Back to Physical Coordinates:
12
1 0 13( ) ( ) cos( 2 )
10 1
1cos 2
3
cos 2
t M t t
t
t
x q
1 2
1( ) cos 2 and ( ) cos 2
3x t t x t t
So, the initial conditions generated motion only in the
first mode (as expected)
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Alternate Path to Symmetric Single-Matrix Eigenproblem
• Square root of matrix conceptually easy, but
computationally expensive
0qqqq
KMKMMM~2
121
21
21
• More efficient to decompose M into product of upper and
lower triangular matrices (Cholesky decomposition)
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Cholesky Decomposition
1
1
1
1 1
Let where is upper triangular
Introduce the coordinate transformation
0
premultiply by to get
note that:
T
T
T
T
T T TT T T
M U U U
U U U U K
U
U
I U K U K
U K U U K U U
x q x q x x
q q q q 0
1T K U
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Cholesky (cont)
1 12 2 M M M
TM U U
• sqrtm requires a singular value decomposition (SVD),
whereas Cholesky requires only simple operations
12 Note that = for diagonal M U M
• Is this really faster? Let’s ask MATLAB
• sqrtm requires a singular value decomposition
(SVD), whereas Cholesky requires only simple
operations
»M = [9 0 ; 0 1];
»flops(0); sqrtm(M); flops
ans = 65
»M = [9 0 ; 0 1];
»flops(0); chol(M); flops
ans = 5
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Section 4.5 Systems with Viscous Damping
Extending the first 4 sections to included
the effects of viscous damping (dashpots)
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Viscous Damping in MDOF Systems
• Two basic choices for including damping
– Modal Damping
• Attribute some amount to each mode based on
experience, i.e., an artful guess or
• Estimate damping due to viscoelasticity using some
approximation method
– Model the damping mechanism directly (hard and still
an area of research-good for physicists but engineers
need models that are correct enough).
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Modal Damping Method
Solve the undamped vibration problem following Window 4.5
0rr0xx )()()()( ttItKtM
Here the mode shapes and eigenvectors are real valued and
form orthonormal sets, even for repeated natural frequencies
(known because 21
21~
KMMK is symmetric)
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)cossin()( tBtAetr diidiit
iii
21di i i
Modal Damping (cont)
• Decouple system based on M and K, i.e., use the
“undamped” modes
• Attribute some zi (zeta) to each mode of the decoupled
system (a guess. Not known beforehand. Can be tested
with gross data like x):
idit
ii
iiiiii
teAtr
rrr
ii
sin)(
02 2
Alternately:
here
(4.106)
(4.107)
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Transform Back to Get Physical Solution
• Use modal transform to obtain modal initial conditions and
compute Ai and Fi:
02
12
11
02
12
11
)0()0()0(
)0()0()0(
xxxr
xxxr
MPMPS
MPMPS
TT
TT
• With r(t) known, use the inverse transform to recover the
physical solution:
)()()()( 21
21
tStPMtMt rrqx
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Modal Damping by Mode Summation
• Can also use mode summation approach
• Again, modes are from undamped system
• The higher the frequency, the smaller the effect (because of the
exponential term). So just few first modes are enough.
)0(+)0(
)0(tan and
sin
)0(
1 and ,
where sin)(
1
2221
21
1
qvqv
qvqv
vv
vq
Tiii
Ti
Tidi
i
i
Ti
i
iidiiii
n
iiidi
ti
d
KMM
tedt ii
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Compute q(t), Transform back
• To get the proper initial conditions use:
)0()0( and ),0()0( 21
21
xqxq MM
• Use the above to compute q(t) and then:
)()( 21
tMt qx
the response in physical coordinates.
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9 0 6 2
0 4 2 2
x x 0
0 0
1 0
0 0
x x
Example
1 20.01 and 0.1
Consider:
Subject to initial conditions:
Experiments do not give C. They provide zeta (in modal
coordinates) by the half power method.
Compute the solution assuming modal damping of:
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Compute the modal decomposition L =sqrt(M)
615.0
788.0
788.0
615.0
615.0
788.0,947.0 and ,
788.0
615.0,240.0
~
500.0333.0
333.0667.0~ ,
20
03
2211
11
P
K
KLLKL
vvvv
0
365.2
846.1
308.0394.0
263.0205.0
0
01
01
r
xr
SPLS
Compute the modal initial conditions:
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Compute the modal solutions:
958.0,963.0,49.0,49.0
,1.0,01.0
2211
21
dd
0.004896
1
0.096
2
( ) 4.208 sin(0.49 1.561)
( ) 3.346 sin(0.958 1.471)
t
t
r t e t
r t e t
Using eq (4.108) and (4.109) yields
Then use x(t)=Sr(t)
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1
2
( )0.205 0.263( ) ( )
( )0.394 0.308
r tx t Sr t
r t
0.004896 0.096
1
0.004896 0.096
2
( ) 0.863 sin(0.49 1.561) 0.88 sin(0.958 1.471)
( ) 1.658 sin(0.49 1.561) 1.029 sin(0.958 1.471)
t t
t t
x t e t e t
x t e t e t
So, first separate solutions in the modal coordinates were
found and then the modes were assembled by the use of S.
The response in the physical coordinates is therefore a
combination of the modal responses just as in the undamped
case. See page 357 for an additional example.
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Lumped Damping models
• In some cases (FEM, machine modeling), the damping
matrix is determined directly from the equations of motion.
• Then our analysis must start with:
0 0
( ) ( ) ( ) ,
subject to and
M t C t K t x x x 0
x x
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1 1 1 1 2 2 1
1 1 2 2 1
2 2 2 2 1
2 2 1
( )
( )
( )
( )
m x c x c x x
k x k x x
m x c x x
k x x
Generic Example:
Fig 4.15
• If the damping
mechanisms are
known then
• Sum forces to find
the equations of
motion
Free Body Diagram:
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Matrix form of Equations of Motion:
0
0
)(
)(
)(
)(
)(
)(
0
0
2
1
22
221
2
1
22
221
2
1
2
1
tx
tx
kk
kkk
tx
tx
cc
ccc
tx
tx
m
m
The C and K matrices have the same form.
It follows from the system itself that consisted damping and stiffness
elements in a similar manner.
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A Question of matrix decoupling
• Can we decouple the system with the same coordinate
transformations as before?
0
? diagonal
0
21
21
rrr
xxx
PCMMPI
KCMT
• In general, these can not be decoupled since K and C can
not be diagonalized simultaneously
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A Little Matrix Theory
• Two symmetric matrices have the same eigenvectors
if and only if the matrices commute
• Define 1 / 2 1 / 2C M C M
• Transform the damped equations of motion into:
0qqq )(ˆ)(~
)( tKtCtI
• Let P be the matrix of eigenvectors vi of and TK P KP
Then PCPT ~ will be diagonal if and only if transformed
K and C have same eigenvectors, i.e.
for all , so i i iC i CK KC v v
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More Matrix Stuff and Normal Mode Systems
CKMKCM
CMKMMKMCMM
CMMKMMKMMCMM
CKKC
11
21
121
21
121
21
21
21
21
21
21
21
21
~~~~
• This does not require a matrix square root to check
• This informs us explicitly whether or not the equations of motion
can be decoupled
• If true, such systems are called “normal mode” systems or said
to possess “classical normal modes”
Happens if and only if CM-1
K is symmetric
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Proportional Damping
• It turns out that CM-1
K = symmetric is a necessary and
sufficient condition for C to be diagonalizable by the
eigenvectors of the “undamped” system, i.e., those based on
M,K
• Best known example is “proportional”damping.
• The coefficients are obtained through experiments or just
by guess.
1 1 1
linear combination of and .
both symmetric
C M K M K
CM K M K M K K KM K
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Proportional Damping (cont)
12
2
Write the system as
diagonal!
Thus, the damping ratios in the decoupled system are
22 2
ii i i i
i
M M K K
M I I K K
P I I
x x x 0
q x q q q 0
q r r r r 0
(4.124)
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Generalized Proportional Damping
For any value of n up to the number of degrees of freedom:
1
1
1
0
0 1
ni
i
i
C K
C K K I K
For example for n = 2 we get the previous proportional
damping formulation:
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Section 4.6 Modal Analysis of the Forced
Response
Extending the chapters 2 and 3 to more then one degree of
freedom
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Forced Response: the response of an mdof system to a
forcing term
1 1 12 2 2
4
00 0 0 0
00 0 0 0( ) (4.126)
00 0 0 0
( )0 0 0 1
Assume diagonalizable for now, i.e.,
where
M C K B t
F t
C
C K M C M CM
x x x F
q q q F
x2
k2
x1
13
11
u
c2
m1
m2
k1
c1
F1 F2
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If the system of equations decouple then the methods of
Chapters 2 and 3 can be applied
12
th 2
Decouple the system with the eigenvalues of
2
so the equation would be 2 ( )
T
i i i
i i i i i i i
K
I P M B
i r r r f t
r r r F
• Responses to harmonic, periodic, or general forces as in
Chapters 2 and 3
• Note that the modal forcing function is a linear combination of
many physical forces
(4.129)
(4.130)
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With the modal equation in hand the general solution is
given
2
0
( ) 2 ( ) ( ) ( ) (4.130)
( ) sin
1 ( ) sin (4.131)
i i
i i i i
i i i i i i i
t
i i di i
t
t
i di
di
r t r t r t f t
r t d e t
e f t e t d
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The applied force is distributed across the all of the
modes except in a special case.
12( ) ( ) for the decoupled EOM.Tt P M B t
f F
• An excitation on a single physical DOF may “spread” to all modal DOFs
(one F generates many f’s)
• It is actually possible to drive a MDOF system at one of its natural
frequencies and not experience resonant response (an unusual
circumstance)
th
Let ( ) ( ), where is some spatial vector
and ( ) is any fuction of time. What if happens
to be related to the mode shape by u ?i
t f t
f t
i M
F b b
b
b
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Example 4.6.1
A 2-dof system
1
131/2 1/2
1 13 31/2 1/2
09 0 2.7 0.3 27 3
( )0 1 0.3 0.3 3 3
3 0 0,
0 1 0 1
0 2.7 0.3 0 0.3 0.1
0 1 0.3 0.3 0 1 0.1 0.3
F t
M M
C M CM
x x x
Figure 4.16
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Compute the mass normalized stiffness matrix and its
eigen solution
1 13 31/2 1/2
1
2
0 27 3 0 3 1
0 1 3 3 0 1 1 3
2 1 1, 0.707
4 1 1
K M KM
K P
v v
From before:
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Transform the damping matrix, the forcing function and
write down the modal equations
1/2
2
1 1 0.3 0.1 1 1 0.2 00.707 0.707
1 1 0.1 0.3 1 1 0 0.4
2 0
0 4
00.2357 0.7071( ) ( )
( )0.2357 0.7071
T
T
T
P CP
P KP
t P M B tF t
f F
1 1 1
2 2 2
( ) 0.2 ( ) 2 ( ) (0.7071)(3)cos 2 2.1213cos 2
( ) 0.4 ( ) 4 ( ) (0.7071)(3)cos 2 2.1213cos 2
r t r t r t t t
r t r t r t t t
From the above coefficients the modal equations
become (note that the force is distributed to each mode)
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1
2
2
1 1 1
2
2 2 2
0.20.0707
2 2
0.40.1000
2(2)
1 1.41
1 1.99
d
d
Compute the modal values using the single degree of
freedom formulas
• The modal damping
ratios and damped
natural frequencies are
computed using the usual
formulas and the
coefficients from the
terms in the modal
equations:
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Use SDOF formula for the particular solution given in
equation (2.36)
1
2
12
1
2
( ) 1.040cos(2 0.1974)
( ) 2.6516sin(2 )
1.040cos(2 0.1974)( )
2.6516sin(2 )
( ) 0.2451cos(2 0.1974) 0.6249sin 2
( ) 0.7354cos(2 0.1974) 1.8749sin 2
p
p
ss
r t t
r t t
tt M P
t
x t t t
x t t t
x
Now transform back to physical coordinates
Note that the force effects both degrees of freedom even though it is applied to one.
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The Frequency Response of each mode is plotted:
• This graph shows the amplitude of each mode due to an input modal force f
1 and f
2.
• A force applied to mass # 2 F
2 will
contribute to both modal forces!
0 1 2 3 4 5 -30
-20
-10
0
10
20
Frequency ()
Amplitude (dB)
R 1 ( )/f
1 ( ))
R 2 ( )/f
2 ( ))
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The frequency response of each degree of freedom is
plotted
• This graph shows
the amplitude of
each mass due to
an input force on
mass #2.
• Each mass is
excited by the
force on mass #2
• Both masses are
effected by both
modes
0 1 2 3 4 5 -50
-40
-30
-20
-10
0
10
Frequency ω
Amplitude (dB)
X 1 ( )/F
2 ( ))
X 2 ( )/F
2 ( ))
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Resonance for multiple degree of freedom systems can
occur at each of the systems natural frequencies
• Note that the frequency response of the previous example
shows two peaks
• If in the odd case that b is orthogonal to one of the mode
shapes then resonance in that mode may not occur (see
example 4.6.2)
• If the modes are strongly coupled the resonant peaks may
combine (see X1/F2 in the previous slide) and be hard to
notice
Special cases:
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9 0 27 3 3cos 2
0 1 3 3 1t
x x
Example: Illustrating the effect of the input force
allocation
1/2 1/21/ 3 0
0 1
1 0 3 1 1/ 3 0 3 1cos 2 cos 2
0 1 1 3 0 1 1 1
M M
t t
x q
q q
Consider:
Compute the modal equations and discuss resonance.
Solution:
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1 11
1 12P
Calculating the natural frequencies and mode shapes yields:
1 22 and 2 rad/s
1 13 3
1 2, 1 1
u u
1 2
1 11 1,
1 12 2
v v
The mass normalized eigenvectors are:
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Transform and compute the modal equations:
1 1
2 2
1 1
2 2
yields
1 0 2 0 1 1 11cos 2
0 1 0 4 1 1 12
2 2 cos 2
4 0
P
r rt
r r
r r t
r r
q r
2 2 , the driving frequency
No resonance even though
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An example with three masses
1 1 2 2
2 2 2 3 3
3 3 3 4
0 0 0
0 0
0 0 0
m k k k
M m K k k k k
m k k k
k1
k3
k4
k2
x1 x
2 x
3
F1 F
2 F
3
c1 c
2 c
3 c
4 m
1 m
2 m
3
m1=m
2=m
3=2Kg k
1=k
2=k
3= k
1=3N/m C=0.02K
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Solving a system with 3 masses is best done using a code.
Using Matlab we can calculate the eigenvectors and eigenvalues and
hence the mode shapes and natural frequencies.
1/2
0.707 0 0 0.5 0.707 0.5
0 0.707 0 0.707 0 -0.707
0 0 0.707 0.5 -0.707 0.5
M P
35405.0354.0
5.005.0
354.05.00.354
.
U 1 2 3
1 2 3
0.94 1.73 2.26
0.0094 0.017 0.0226
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The frequency response of each mode computed
separately:
0 1 2 3 4 5
-30
-20
-10
0
10
20
30
40
50
Frequency ω
Amplitude (dB)
r 1 ( )/f 1
( ))
r 2 ( )/f 2
( ))
r 3 ( )/f 3
( ))
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A comparison of the Frequency response between driving
mass #1 and driving mass #2
0 1 2 3 4 5
-80
-60
-40
-20
0
20
40
Frequency ω Frequency ω
0 1 2 3 4 5 -60
-40
-20
0
20
40
Amplitude (dB)
X 1 ( )/F
2 ( ))
X 2 ( )/F
2 ( ))
X 3 ( )/F
2 ( ))
X1 w( ) / F1 w( )
X2 w( ) / F1 w( )
X3 w( ) / F1 w( )
X1 w( ) / F2 w( )
X2 w( ) / F2 w( )
X3 w( ) / F2 w( )
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( ) ( ) ( )M t K t t x x F
12ˆ( ) ( ) ( )t K t M t
q q F
Computing the forced response via the mode summation
technique
1
( ) sin (4.134)n
H i i i i
i
t d t
q v
Consider
Transform:
From eq. (4.92) the homogeneous solution in mode
summation form is
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The total solution in mode summation form is:
1particular
homogenous
( ) [ sin cos ] ( ) (4.135)n
i i i i i p
i
t b t c t t
q v q
12( ) ( )p pt M tq x
1
2
1
( ) sin cos ( ) (4.136)n
i i i i i p
i
t b t c t M t
q v x
But
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Next use the initial conditions and orthogonality to
evaluate the constants
12
12
12
12
(0) (4.138)
(0) (4.139)
(0)
1 (0)
T T
i i i p
T T
i i i i p
T T
i i i p
T T
i i i p
i
c M
b M
c M
b M
0
0
0
0
v q v x
v q v x
v q v x
v q v x
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Substitution of the constants into Equation (4.136) and
multiplying by M-1/2 yields
1
( ) sin cos ( )n
i i i i i p
i
t d t c t t
x u x
(4.141)
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1/2( ) ( )t M P tx r
Decoupled Forced EOM
Physical Co-ordinates. Coupled equations
Modal Co-ordinates. Uncoupled equations
k1
m1
x1
m2
x2
k2
F1
1
1
r2
f1
f2
w1
2
w 2
2
r1
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4.7 Lagrange’s Equations
Defining work, energy and
virtual displacements and
work we will learn an
alternate method of deriving
equations of motion
Generalized coordinates: 2 not 4!
Recall equations (1.63) and (1.64)
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Definitions (from Dynamics)
2
1
0
1
1 2
0
1 1Kinetic Energy:
2 2
Work Done by a force:
a reference position then the potential energy is
( )
TT m m
W d
V r d
r
r
r
r
r r r r
F r
r
F r
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Strain Energy in a Spring
curve vs )( the under area the is which
2
1)()(
:spring a for
energy) potential (elastic energy Strain
200
xxF
kxdkdFxV
kxF
xx
F(x)
x
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Strain energy in an elastic material
The variation of , denoted ( ) is given by
( , )( )= ( , )
( , )The axial stress is ( , ) ( , )
( )
dx dx
u x tdx dx x t dx
x
P x tx t E x t
A x
P(x,t) P(x,t) +Px(x,t) dx
dx +ux(x,t)dx
Example of a bar of cross section
A(x) elongated by force P(x,t) Stress σ
Strain ε
Slope E
so P=EAε
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Strain energy continued
2
2
0
1 1( , ) ( ) ( , ) ( , )
2 2
1 [ ( ) ( , )] ( , )
2
1 ( ) ( , )
2
Integrating yields the strain energy for axial tension
in a bar element:
1 ( ) ( , )
2
dV P x t dx P x t x t dx
EA x x t x t dx
EA x x t dx
V E A x x t dx
2
0
1 ( , ) ( )
2
u x tE A x dx
x
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r
Virtual Reality (actually: virtual displacement)
A virtual displacement
Based on variational math
Small or infinitesimal
changes compatible with
constraints
No time associated with
change
Variation or
Change in:
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Consequence of satisfying the constraint:
1
Constraint: ( ) , a constant
( )
Taylor expansion:
( )
0
n
i
i i
f
f c
f c
ff x c
x
f
rr
r
r r
r
rr
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Virtual work
th
1
Suppose the mass is acted on by with system in
static equilibrium
0,
the principle of virtual work:
0
which states that if a system is in equi
i
i i i
n
i i
i
i
W
f
f r
F r
librium, the
work done by externally applied forces through a
virtual displacement is zero: 0
has an critical value
V
V
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Dynamic Equilibrium
D'Alembert's Principle move inertia force to left side and
treat dynamics as statics. From Newton's law in terms of
change in momentum:
0
This allows us to use virtual work i
i i
F p F p
n the dynamic case:
0
0
i
i m
F p r
F r r
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Hamilton’s Principle
( )
1 ( )
2
1( ) ( ), multiply by
2
( )
( )
d
dt
dm
dt
dW m T
dt
dT W m
dt
r r r r r r
r r r r
r r r r r r
r r
r r
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Integrate this last expression
22
1
1
2 2
11
2
1
2
1
path indepence
( )
0
0, for conservative forces
0, Hamilton's principle
tt
tt
t t
tt
t
t
t
t
dT W dt m dt
dt
T W dt m
T W dt W V
T V dt
r r
r r
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Lagrange’s Equation
1 2 3Let ( , , ... , ), called generalized coordinates
Let a generalized force (or moment)
The Lagrange formulation, derived from Hamilton's principle
for determining the equations of moti
n i
i
i
q q q q t q
WQ
q
r r
on are
i
i i i
d T T VQ
dt q q q
(4.143)
(4.144)
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The Lagrangian, L
0, 1,2, (1.146)i i
d L Li n
dt q q
Let L = (T - U), called the Lagrangian
Then (4.145) becomes:
For the (common) case that the potential
energy does not depend on the velocity:
0i
U
q
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Advantages
• Equations contain only scalar quantities
• One equation for each degree of freedom
• Independent of the choice of coordinate system since the
energy does not depend on coordinates
• See examples in Section 4.7 pages 369-377
• Useful in situations where F = ma is not obvious
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22
212
212
21
21
21 )()( yyxxyx
Example of Generalize Coordinates
2211 and qq
How many dof?
What are they?
Are there constraints?
m1
(x1,y
1)
m2
(x2,y
2)
x
y
1
2
q1
q2
There are only 2 DOF and one choice is:
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Example 4.7.3 (also illustrates linear approximation method)
)( ),( 21 tqtxq
Here G is mass center and e is
the distance to the elastic axis.
Let m denote the mass of the
wing and J the rotational inertia
about G.
Take the generalized coordinates to be:
Called the pitch and plunge model
.
e
x(t)
G
k1
k2 q t( )
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2 21 1
2 2GT mx J
Computing the Energies
( ) ( ) sin ( )
( ) ( ) cos ( ) ( ) cos ( )
G
G
x t x t e t
dx t x t e t x t e t
dt
2 21 1[ cos ]
2 2T m x e J
The Kinetic Energy is
The relationship between xG and x is
So the kinetic energy is
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22
21
2
1
2
1kxkU
Potential Energy and the Lagrangian
22 2 2
1 2
1 1 1 1 cos
2 2 2 2
L T U
m x e J k x k
The potential energy is:
The Lagrangian is:
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2
1cos sin 0mx me em k x
2 2 2 2
2cos cos sin cos 0J me x me me k
Computing Derivatives for Equation (1.146)
1
2
1
1
[ os ]
sin
L Lm x e c
q x
d Lmx me me
dt x
L Lk x
q x
Now use the Lagrange equation to get:
Likewise differentiation with respect to q2 = θ yields:
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Next Linearize and write in matrix form
1cos sin
1
2
2
0( ) ( ) 0
0( ) ( ) 0
km me x t x t
kme me J t t
Using the small angle approximations:
In matrix form this becomes:
Note that this is a dynamically coupled system
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Next consider the Single Spring-Mass System and
compute the equation of motion using the Largranian
approach
2 2
2 2
1 1,
2 2
1 1
2 2
,
0 0
T mx U kx
L T U mx kx
L Lmx kx
x x
d L Lmx kx
dt x x