NESSUS 8.2.0 is supported by a graphical user interface that assists the user in defining probabilistic problems. This new capability greatly reduces the learning curve when using NESSUS. Over the years, NESSUS development has been supported by a number of organizations including NASA Glenn Research Center, Los Alamos National Laboratory, and SwRI internal funding.

Problem statements are entered using free-form mathematical expressions. A very powerful feature of NESSUS is the ability to divide the problem into "sequentially linked" models. The mathematical expressions also support functions that can be defined by external analysis packages or pre-programmed subroutines.

NESSUS includes a graphical fault tree definition screen to support modeling multiple failure modes or multiple components in a system. Each failure event can be modeled using analytical functions an/or numerical models. Efficient probabilistic methods provide for practical system reliability analysis.

NESSUS supports a wide range of probability distribution functions.

The user has the option to define functions as either analytical, numerical, regression or predefined models. The numerical model allows the use of finite element solvers such as ABAQUS, ANSYS, NASTRAN, or user-defined.

Random variables are mapped to deterministic input files via sophisticated visual mapping tool.

Response selection is customized based on selected external analysis tool.

Random variable correlations are defined by filling in values in the lower left triangle of the correlation matrix.

Each random variable in the problem statement may have some amount of statistical uncertainty assigned to it. The cumulative effect of these statistical uncertainties gives rise to confidence bounds on the computed cumulative distribution function. The mean coefficient of variation (COV) and standard deviation COV may be set, with a mean distribution type of either uniform or normal.

Traditional and advanced probabilistic analysis methods are easily selected and default parameters are provided.

Results visualization is integrated into the toolset.

NESSUS also supports parameter variation analysis to graphically evaluate how the response varies with changes in the random variable values.