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Random Matrix Models for Structural Dynamics S Adhikari Department - - PowerPoint PPT Presentation

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Random Matrix Models for Structural Dynamics S Adhikari Department of Aerospace Engineering, University of Bristol, Bristol, U.K. Email: S.Adhikari@bristol.ac.uk URL: http://www.aer.bris.ac.uk/contact/academic/adhikari/home.html Random Matrices

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Bristol, April 20, 2006

Random Matrix Models for Structural Dynamics

S Adhikari

Department of Aerospace Engineering, University of Bristol, Bristol, U.K. Email: S.Adhikari@bristol.ac.uk URL: http://www.aer.bris.ac.uk/contact/academic/adhikari/home.html

Random Matrices in Structural Dynamics – p.1/46

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Stochastic structural dynamics

The equation of motion: M¨ x(t) + C ˙ x(t) + Kx(t) = p(t) Due to the presence of uncertainty M, C and K become random matrices. The main objectives are: to quantify uncertainties in the system matrices to predict the variability in the response vector x

Random Matrices in Structural Dynamics – p.2/46

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Current Methods

Three different approaches are currently available Low frequency : Stochastic Finite Element Method (SFEM) - considers parametric uncertainties in details High frequency : Statistical Energy Analysis (SEA) - do not consider parametric uncertainties in details Mid-frequency : Hybrid method - ‘combination’

  • f the above two

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Random Matrix Method (RMM)

The objective : To have an unified method which will work across the frequency range. The methodology : Derive the matrix variate probability density functions of M, C and K Propagate the uncertainty (using Monte Carlo simulation or analytical methods) to

  • btain the response statistics (or pdf)

Random Matrices in Structural Dynamics – p.4/46

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Outline of the presentation

In what follows next, I will discuss: Introduction to Matrix variate distributions Maximum entropy distribution Optimal Wishart distribution Numerical examples Open problems & discussions

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Matrix variate distributions

The probability density function of a random matrix can be defined in a manner similar to that of a random variable. If A is an n × m real random matrix, the matrix variate probability density function of A ∈ Rn,m, denoted as pA(A), is a mapping from the space of n × m real matrices to the real line, i.e., pA(A) : Rn,m → R.

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Gaussian random matrix

The random matrix X ∈ Rn,p is said to have a matrix variate Gaussian distribution with mean matrix M ∈ Rn,p and covariance matrix Σ ⊗ Ψ, where Σ ∈ R+

n and Ψ ∈ R+ p provided

the pdf of X is given by pX (X) = (2π)−np/2 |Σ|−p/2 |Ψ|−n/2 etr

  • −1

2Σ−1(X − M)Ψ−1(X − M)T

  • (1)

This distribution is usually denoted as X ∼ Nn,p (M, Σ ⊗ Ψ).

Random Matrices in Structural Dynamics – p.7/46

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Gaussian orthogonal ensembles

A random matrix H ∈ Rn,n belongs to the Gaussian

  • rthogonal ensemble (GOE) provided its pdf of is

given by pH(H) = exp

  • −θ2Trace
  • H2

+ θ1Trace (H) + θ0

  • where θ2 is real and positive and θ1 and θ0 are real.

This is a good model for high-frequency vibration problems.

Random Matrices in Structural Dynamics – p.8/46

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Wishart matrix

An n × n random symmetric positive definite matrix S is said to have a Wishart distribution with parameters p ≥ n and Σ ∈ R+

n , if its pdf is given by

pS (S) =

  • 2

1 2np Γn

1 2p

  • |Σ|

1 2p

−1 |S|

1 2(p−n−1)etr

  • −1

2Σ−1S

  • (2)

This distribution is usually denoted as S ∼ Wn(p, Σ). Note: If p = n + 1, then the matrix is non-negative definite.

Random Matrices in Structural Dynamics – p.9/46

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Matrix variate Gamma distribution

An n × n random symmetric positive definite matrix W is said to have a matrix variate gamma distribution with parameters a and Ψ ∈ R+

n , if its pdf is given by

pW (W) =

  • Γn (a) |Ψ|−a−1

|W|a− 1

2(n+1) etr {−ΨW} ;

ℜ(a) > (n − 1)/2 (3) This distribution is usually denoted as W ∼ Gn(a, Ψ). Here the multivariate gamma function: Γn (a) = π

1 4n(n−1)

n

  • k=1

Γ

  • a − 1

2(k − 1)

  • ; for ℜ(a) > (n−1)/2 (4)

Random Matrices in Structural Dynamics – p.10/46

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Distribution of the system matrices

The distribution of the random system matrices M, C and K should be such that they are symmetric positive-definite, and the moments (at least first two) of the inverse of the dynamic stiffness matrix D(ω) = −ω2M + iωC + K should exist ∀ ω

Random Matrices in Structural Dynamics – p.11/46

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Distribution of the system matrices

The exact application of the last constraint requires the derivation of the joint probability density function of M, C and K, which is quite difficult to obtain. We consider a simpler problem where it is required that the inverse moments of each of the system matrices M, C and K must exist. Provided the system is damped, this will guarantee the existence of the moments of the frequency response function matrix.

Random Matrices in Structural Dynamics – p.12/46

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Maximum Entropy Distribution

Suppose that the mean values of M, C and K are given by M, C and K respectively. Using the notation G (which stands for any one the system matrices) the matrix variate density function of G ∈ R+

n is given by pG (G) : R+ n → R. We have the

following constrains to obtain pG (G):

  • G>0

pG (G) dG = 1 (normalization) (5) and

  • G>0

G pG (G) dG = G (the mean matrix) (6)

Random Matrices in Structural Dynamics – p.13/46

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Further constraints

Suppose the inverse moments (say up to order ν) of the system matrix exist. This implies that E

  • G−1
  • F

ν

should be finite. Here the Frobenius norm of matrix A is given by AF =

  • Trace
  • AAT1/2.

Taking the logarithm for convenience, the condition for the existence of the inverse moments can be expresses by E

  • ln |G|−ν

< ∞

Random Matrices in Structural Dynamics – p.14/46

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MEnt Distribution - 1

The Lagrangian becomes: L

  • pG
  • = −
  • G>0

pG (G) ln

  • pG (G)
  • dG+

(λ0 − 1)

  • G>0

pG (G) dG − 1

  • −ν
  • G>0

ln |G| pG dG + Trace

  • Λ1
  • G>0

G pG (G) dG − G

  • (7)

Note: ν cannot be obtained uniquely!

Random Matrices in Structural Dynamics – p.15/46

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MEnt Distribution - 2

Using the calculus of variation ∂L

  • pG
  • ∂pG

= 0

  • r − ln
  • pG (G)
  • = λ0 + Trace (Λ1G) − ln |G|ν
  • r pG (G) = exp {−λ0} |G|ν etr {−Λ1G}

Random Matrices in Structural Dynamics – p.16/46

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MEnt Distribution - 3

Using the matrix variate Laplace transform (T ∈ Rn,n, S ∈ Cn,n, a > (n + 1)/2)

  • T>0

etr {−ST} |T|a−(n+1)/2 dT = Γn(a) |S|−a and substituting pG (G) into the constraint equations it can be shown that pG (G) = r−nr {Γn(r)}−1 G

  • −r |G|ν etr
  • −rG

−1G

  • (8)

where r = ν + (n + 1)/2.

Random Matrices in Structural Dynamics – p.17/46

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MEnt Distribution - 4

Comparing it with the Wishart distribution we have: Theorem 1. If ν-th order inverse-moment of a system matrix G ≡ {M, C, K} exists and only the mean of G is available, say G, then the maximum-entropy pdf of G follows the Wishart distribution with parameters p = (2ν + n + 1) and Σ = G/(2ν + n + 1), that is G ∼ Wn

  • 2ν + n + 1, G/(2ν + n + 1)
  • .

Random Matrices in Structural Dynamics – p.18/46

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Properties of the Distribution

Covariance tensor of G: cov (Gij, Gkl) = 1 2ν + n + 1

  • GikGjl + GilGjk
  • Normalized standard deviation matrix

δ2 G = E

  • G − E [G] 2

F

  • E [G] 2

F

= 1 2ν + n + 1   1 + {Trace

  • G
  • }2

Trace

  • G

2

   δ2 G ≤ 1 + n 2ν + n + 1 and ν ↑ ⇒ δ2 G ↓.

Random Matrices in Structural Dynamics – p.19/46

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Distribution of the inverse - 1

If G is Wn(p, Σ) then V = G−1 has the inverted Wishart distribution: PV(V) = 2m−n−1n/2 |Ψ|m−n−1 /2 Γn[(m − n − 1)/2] |V|m/2etr

  • −1

2V−1Ψ

  • where m = n + p + 1 and Ψ = Σ−1 (recall that

p = 2ν + n + 1 and Σ = G/p)

Random Matrices in Structural Dynamics – p.20/46

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Distribution of the inverse - 2

Mean: E

  • G−1

= pG

−1

p − n − 1 cov

  • G−1

ij , G−1 kl

  • =
  • 2ν + n + 1)(ν−1G

−1 ij G −1 kl + G −1 ik G −1 jl + G −1ilG −1 kj

  • 2ν(2ν + 1)(2ν − 2)

Random Matrices in Structural Dynamics – p.21/46

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Distribution of the inverse - 3

Suppose n = 101 & ν = 2. So p = 2ν + n + 1 = 106 and p − n − 1 = 4. Therefore, E [G] = G and E

  • G−1

= 106 4 G

−1 = 26.5G −1 !!!!!!!!!!

From a practical point of view we do not expect them to be so far apart! One way to reduce the gap is to increase p. But this implies the reduction of variance.

Random Matrices in Structural Dynamics – p.22/46

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Optimal Wishart Distribution - 1

My argument: The distribution of G must be such that E [G] and E

  • G−1

should be closest to G and G

−1 respectively.

Suppose G ∼ Wn

  • n + 1 + θ, G/α
  • . We need to

find α such that the above condition is satisfied. Therefore, define (and subsequently minimize) ‘normalized errors’: ε1 =

  • G − E [G]
  • F /
  • G
  • F

ε2 =

  • G

−1 − E

  • G−1
  • F /
  • G

−1

  • F

Random Matrices in Structural Dynamics – p.23/46

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Optimal Wishart Distribution - 2

Because G ∼ Wn

  • n + 1 + θ, G/α
  • we have

E [G] = n + 1 + θ α G and E

  • G−1

= α θ G

−1

We define the objective function to be minimized as χ2 = ε12 + ε22 =

  • 1 − n+1+θ

α

2 +

  • 1 − α

θ

2

Random Matrices in Structural Dynamics – p.24/46

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Optimal Wishart Distribution - 3

The optimal value of α can be obtained as by setting ∂χ2

∂α = 0 or

α4 − α3θ − θ4 + (−2 n + α − 2) θ3 +

  • (n + 1) α − n2 − 2 n − 1
  • θ2 = 0.

The only feasible value of α is α =

  • θ(n + 1 + θ)

Random Matrices in Structural Dynamics – p.25/46

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Optimal Wishart Distribution - 4

From this discussion we have the following: Theorem 2. If ν-th order inverse-moment of a system matrix G ≡ {M, C, K} exists and only the mean of G is available, say G, then the unbiased distribution of G follows the Wishart distribution with parameters p = (2ν + n + 1) and Σ = G/

  • 2ν(2ν + n + 1), that is

G ∼ Wn

  • 2ν + n + 1, G/
  • 2ν(2ν + n + 1)
  • .

Random Matrices in Structural Dynamics – p.26/46

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Optimal Wishart Distribution - 5

Again consider n = 100 and ν = 2, so that θ = 2ν = 4. In the previous approach α = 2ν + n + 1 = 105. For the

  • ptimal distribution, α =
  • θ(θ + n + 1) = 2

√ 105 = 20.49. We have E [G] =

105 2 √ 105G = 5.12G and

E

  • G−1

= 2

√ 105 4

G

−1 = 5.12G −1.

The overall normalized difference for the previous case is χ2 = 0 + (1 − 105/4)2 = 637.56. The same for the optimal distribution is χ2 = 2(1 − √ 105/2)2 = 34.01, which is considerable smaller compared to the non-optimal distribution.

Random Matrices in Structural Dynamics – p.27/46

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Simulation Algorithm

Obtain θ = 1 δ2 G   1 + {Trace

  • G
  • }2

Trace

  • G

2

   − (n + 1) If θ < 4, then select θ = 4. Calculate α =

  • θ(n + 1 + θ)

Generate samples of G ∼ Wn

  • n + 1 + θ, G/α
  • (Matlab command wishrnd can be used to generate

the samples) Repeat the above steps for all system matrices and solve for every samples

Random Matrices in Structural Dynamics – p.28/46

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Example: A cantilever Plate

0.5 1 1.5 2 2.5 0.5 1 1.5 −0.5 0.5 1 X direction (length) Output Input Y direction (width) Fixed edge

A Cantilever plate with a slot: µ = 0.3, ρ = 8000 kg/m3, t = 5mm, Lx = 2.27m, Ly = 1.47m

Random Matrices in Structural Dynamics – p.29/46

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Plate Mode 4

0.5 1 1.5 2 2.5 0.5 1 1.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 X direction (length)

Mode 4, freq. = 9.2119 Hz

Y direction (width)

Fourth Mode shape

Random Matrices in Structural Dynamics – p.30/46

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Plate Mode 5

0.5 1 1.5 2 2.5 0.5 1 1.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 X direction (length)

Mode 5, freq. = 11.6696 Hz

Y direction (width)

Fifth Mode shape

Random Matrices in Structural Dynamics – p.31/46

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Deterministic FRF

200 400 600 800 1000 1200 −200 −180 −160 −140 −120 −100 −80 −60 −40 Frequency ω (Hz) Log amplitude (dB) log |H(448,79) (ω)| log |H(79,79) (ω)|

FRF of the deterministic plate

Random Matrices in Structural Dynamics – p.32/46

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Frequency Spacing

50 100 150 200 250 0.005 0.01 0.015 0.02 0.025 0.03 0.035 Natural frequency spacing (s), rad/s Spacing density p(s) Number of modes: 486 Simulation Rayleigh (Wigner surmise) exponential

Natural frequency spacing distribution (without slot)

Random Matrices in Structural Dynamics – p.33/46

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Frequency Spacing

20 40 60 80 100 120 140 160 180 0.005 0.01 0.015 0.02 0.025 0.03 0.035 Natural frequency spacing (s), rad/s Spacing density p(s) Number of modes: 486 Simulation Rayleigh (Wigner surmise) exponential

Natural frequency spacing distribution (with slot)

Random Matrices in Structural Dynamics – p.34/46

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Random FRF - 1

Direct finite-element MCS of the amplitude of the cross-FRF of the plate with randomly placed masses; 30 masses, each weighting 0.5% of the total mass of the plate are simulated.

Random Matrices in Structural Dynamics – p.35/46

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Random FRF - 2

Direct finite-element MCS of the amplitude of the driving-point FRF of the plate with randomly placed masses; 30 masses, each weighting 0.5% of the total mass of the plate are simulated.

Random Matrices in Structural Dynamics – p.36/46

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Wishart FRF - 1

MCS of the amplitude of the cross-FRF of the plate using optimal Wishart mass matrix, n = 429, δM = 2.0449.

Random Matrices in Structural Dynamics – p.37/46

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Wishart FRF - 2

MCS of the amplitude of the driving-point-FRF of the plate using optimal Wishart mass matrix, n = 429, δM = 2.0449.

Random Matrices in Structural Dynamics – p.38/46

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Comparison of Mean - 1

200 400 600 800 1000 1200 −200 −180 −160 −140 −120 −100 −80 −60 −40 Frequency ω (Hz) Log amplitude (dB) of H(448,79) (ω) Deterministic Ensamble average: Simulation Ensamble average: RMT

Comparison of the mean values of the amplitude of the cross-FRF.

Random Matrices in Structural Dynamics – p.39/46

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Comparison of Mean - 2

200 400 600 800 1000 1200 −160 −140 −120 −100 −80 −60 −40 Frequency ω (Hz) Log amplitude (dB) of H(79,79) (ω) Deterministic Ensamble average: Simulation Ensamble average: RMT

Comparison of the mean values of the amplitude of the driving-point-FRF.

Random Matrices in Structural Dynamics – p.40/46

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Comparison of Variation - 1

200 400 600 800 1000 1200 −180 −160 −140 −120 −100 −80 −60 −40 −20 Frequency ω (Hz) Log amplitude (dB) of H(448,79) (ω) 5% points: Simulation 5% points: RMT 95% points: Simulation 95% points: RMT

Comparison of the 5% and 95% probability points of the amplitude of the cross-FRF.

Random Matrices in Structural Dynamics – p.41/46

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Comparison of Variation - 2

200 400 600 800 1000 1200 −160 −140 −120 −100 −80 −60 −40 −20 Frequency ω (Hz) Log amplitude (dB) of H(79,79) (ω) 5% points: Simulation 5% points: RMT 95% points: Simulation 95% points: RMT

Comparison of the 5% and 95% probability points of the amplitude of the driving-point-FRF.

Random Matrices in Structural Dynamics – p.42/46

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Summary & conclusions

Wishart matrices can used as the distribution for the system matrices in structural dynamics. The parameters of the distribution can be

  • btained by solving an optimisation problem

Random Matrices in Structural Dynamics – p.43/46

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Next steps

Numerical works (validation against??) Eigenvalues, eigenvector statistics and calculation of dynamic response. Distribution of the dynamic stiffness matrix (complex Wishart matrix?) Inversion of the dynamic stiffness matrix (FRFs) Distribution of Y(ω) =

  • RD(ω)−1P
  • where

P ∈ Cn,r and R ∈ Rp,n Cumulative distribution function of the response (reliability problem)

Random Matrices in Structural Dynamics – p.44/46

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Open problems & discussions

Is MEnT appropriate here? G is just one ‘observation’ - not an ensemble mean. What happens if we know the covariance tensor

  • f G (e.g., using Stochastic Finite element

Method)? What if the zeros in G are not preserved?

Random Matrices in Structural Dynamics – p.45/46

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Structure of the Matrices

20 40 60 10 20 30 40 50 60

Column indices

Mass matrix

Row indices

20 40 60 10 20 30 40 50 60

Column indices

Stiffess matrix

Row indices

Nonzero elements of the system matrices

Random Matrices in Structural Dynamics – p.46/46