Transcript NESSUS Overview and General Capabilities

NESSUS Overview and General
Capabilities
Uncertainty Quantification
Seminar Series
Arlo F. Fossum (9117)
What is NESSUS?
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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
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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
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A Wide Range of Probabilistic Analysis
Methods Is Available
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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.