NESSUS Overview and General Capabilities

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Presentation transcript:

NESSUS Overview and General Capabilities Uncertainty Quantification Seminar Series Arlo F. Fossum (9117)

What is NESSUS? 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 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 Analytical (User-Defined Fortran Subroutines) Numerical Finite Element Programs (ABAQUS, PRONTO, NASTRAN, ANSYS, DYNA, NESSUS/Fem) Finite Difference Programs Boundary Element Programs Combinations

NESSUS Is Well-Documented, Verified 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 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) 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) Adaptive Importance Sampling Hyperplane Parabolic System Risk Assessment (SRA) Probabilistic Fault-Tree Analysis (PFTA)

NESSUS Provides Flexible Output Options Full Cumulative Distribution Function Probability of Failure for Given Performance Performance for Given Reliability Probability Contours

NESSUS Provides Flexible Output Options (Con’t) Probabilistic Sensitivities (3 types) Relative Importance Factors Probability Change wrt Change in Mean Probability change wrt Change in Standard Deviation Confidence bounds (approximate) User-Subroutine for Printing/Processing

NESSUS Offers a Wide Range of Random Variable Models Normal Weibull Type I Extreme Value Lognormal Chi-Square (1 dof)

NESSUS Offers a Wide Range of Random Variable Models (Con’t) Maximum Entropy Curve-Fit Frechet Truncated Weibull Truncated Normal

What if the Random Variables Are Not Mutually Independent? 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.