probabilistic analysis using fea
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Probabilistic Analysis using FEA. A. Petrella. What is Probabilistic Analysis. All input parameters have some uncertainty What is the uncertainty in outcome metrics? How sensitive are outcomes to different inputs - PowerPoint PPT PresentationTRANSCRIPT
Probabilistic Analysis using FEA
A. Petrella
What is Probabilistic Analysis
‣All input parameters have some uncertainty
‣What is the uncertainty in outcome metrics?
‣How sensitive are outcomes to different inputs
‣Which inputs are most important and how can we design for a specific probability of performance?
What is Probabilistic Analysis
Model Input Uncertainties
Validated Deterministic Model
Tissue Properties
External Loads
Device Placement
Outcome Probabilities &
Sensitivities
Response and Failure Prediction
Pro
bab
ility
Performance Metric
Location Radius Load S-N Scatter0
0.2
0.4
0.6
0.8
1
Pro
ba
bili
stic
Se
ns
itiv
ity F
ac
tor
Sensitivity Factors
Probabilistic Methods
‣Monte Carlo (MC) is the simplest prob method… input distributions randomly sampled to form trials
‣MC is robust and will always converge, but this usually requires many thousands of trials
‣ It may be impractical to perform 1000’s of trials with an FE model that requires hours for one solution
‣There are more advanced methods that require fewer trials and many modern programs implement these methods… e.g., ANSYS uses DOE + Response Surface
Prob… an example with Excel
P
b
hL = 2400 mm
Random variables,normally distributed
h = 400 ± 20 mmb = 100 ± 5 mmP = 1000 ± 50 NE = 200 ± 10 GPa
Standard Normal Distribution
PDF CDF
0
0.1
0.2
0.3
0.4
0.5
-6.0 -4.0 -2.0 0.0 2.0 4.0 6.0x
f(x)
0
0.2
0.4
0.6
0.8
1
-6.0 -4.0 -2.0 0.0 2.0 4.0 6.0x
F(x
)
m = 0s = 1
Standard Normal Distribution
‣Normal (m=0, s=1)
‣Standard normal variate – (Note: Halder uses S)
‣All normal distributions can be simply transformed to the standard normal distribution
x
xxz
))a(z())b(z(dss21
exp)bxa(P 2)b(z
)a(z
Generating Random Trials
Back to the Beam Example… 500 MC
To get the 10% lower and 90% upper bounds…Use Excel functions: “large()” and “small()”
Beam Example in ANSYS
‣ ANSYS uses the term…“Sig Sigma Analysis”…this is most likely marketing since 6s is popular in industry
‣ Prob trials are taken from a response surface (quadratic polynomial regression) built on a results from a DOE
‣ This is how ANSYS avoids 1000’s of trials required for a brute force MC
Beam Example in ANSYS - Deflection
Beam Example in ANSYS - Stress
Beam Example in ANSYS - SensitivitySensitivity factors are the components of a unit vector in the direction of the function gradient…(i.e., stress = f(h,b,P,E)) …then sqrt(sum(si
2)) = 1
sh sb sP sE
sh sb sP sE
How does Prob Compare?
‣Provides information on sensitivities similar to DOE and Response Surface methods
‣Prob provides insight into how uncertainty in your input parameters will affect outcome metrics
‣Allows you to design for probability of specific outcomes… e.g., 90% upper bound