Download NESSUS Overview and General Capabilities

NESSUS Overview and General
Capabilities
Uncertainty Quantification
Seminar Series
Arlo F. Fossum (9117)
What is NESSUS?
Engineering Sciences Center
Numerical Evaluation of Stochastic
Structures Under Stress
 A Modular Computer Software System
 Probabilistic Analysis of Components and
Systems
SOA Probabilistic Algorithms Work
with SOA Structural Analysis Methods
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Probabilistic Response and Reliability of
Engineering Structures
 Propagation of Uncertainties in
– Loading
– Material Properties
– Geometry
– Boundary Conditions
– Initial Conditions
Many Deterministic Modeling Tools
Can Be Interfaced with NESSUS
Engineering Sciences Center
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Analytical (User-Defined Fortran Subroutines)
 Numerical
– Finite Element Programs (ABAQUS, PRONTO,
NASTRAN, ANSYS, DYNA, NESSUS/Fem)
– Finite Difference Programs
– Boundary Element Programs
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Combinations
NESSUS Is Well-Documented, Verified
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Documented in User’s, Theoretical, and
Installation Manuals
 Documented in the Open Literature
 Over 200 Verification Test Problems
A Wide Range of Probabilistic Analysis
Methods Is Available
Engineering Sciences Center
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First-Order Reliability Method (FORM)
Second-Order Reliability Method (SORM)
Fast Probability Integration (FPI)
Mean Value (MV)
Advanced Mean Value (AMV, AMV+)
Response Surface Method (RSM)
– Koshal
– Box-Behnken
– Central Composite
A Wide Range of Probabilistic Analysis
Methods Is Available (Con’t)
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Monte Carlo Simulation (MCS)
 Latin Hypercube Simulaton (LHS)
 Sphere-Based Importance Sampling
– FORM-Generated with Reduction Factor
– User-Defined Radius
A Wide Range of Probabilistic Analysis
Methods Is Available (Con’t)
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Adaptive Importance Sampling
– Hyperplane
– Parabolic
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System Risk Assessment (SRA)
 Probabilistic Fault-Tree Analysis (PFTA)
NESSUS Provides Flexible Output
Options
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Full Cumulative Distribution Function
 Probability of Failure for Given Performance
 Performance for Given Reliability
 Probability Contours
NESSUS Provides Flexible Output
Options (Con’t)
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Probabilistic Sensitivities (3 types)
– Relative Importance Factors
– Probability Change wrt Change in Mean
– Probability change wrt Change in Standard
Deviation
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Confidence bounds (approximate)
 User-Subroutine for Printing/Processing
NESSUS Offers a Wide Range of
Random Variable Models
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Normal
Weibull
Type I Extreme Value
Lognormal
Chi-Square (1 dof)
NESSUS Offers a Wide Range of
Random Variable Models (Con’t)
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Maximum Entropy
Curve-Fit
Frechet
Truncated Weibull
Truncated Normal
What if the Random Variables Are Not
Mutually Independent?
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The User Has Two Options:
 Use a Rosenblatt Transformation outside of FPI.
 Transform each correlated non-normal random
variable into a normal variable and generate a
new set of correlation coefficients. Use
correlation coefficients to generate independent
normal random variables.