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Dakota Reference Manual
Version 6.2
Large-Scale Engineering Optimization and Uncertainty Analysis
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Dakota Reference Manual
Version 6.2
Large-Scale Engineering Optimization and Uncertainty Analysis
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Local reliability method
This keyword is related to the topics:
Alias: nond_local_reliability
Argument(s): none
| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description | |
|---|---|---|---|---|
| Optional | mpp_search | Specify which MPP search option to use | ||
| Optional | response_levels | Values at which to estimate desired statistics for each response | ||
| Optional | reliability_levels | Specify reliability levels at which the response values will be estimated | ||
| Optional | distribution | Selection of cumulative or complementary cumulative functions | ||
| Optional | probability_levels | Specify probability levels at which to estimate the corresponding response value | ||
| Optional | gen_reliability_levels | Specify generalized relability levels at which to estimate the corresponding response value | ||
| Optional | model_pointer | Identifier for model block to be used by a method | ||
Local reliability methods compute approximate response function distribution statistics based on specified uncertain variable probability distributions. Each of the local reliability methods can compute forward and inverse mappings involving response, probability, reliability, and generalized reliability levels.
The forward reliability analysis algorithm of computing reliabilities/probabilities for specified response levels is called the Reliability Index Approach (RIA), and the inverse reliability analysis algorithm of computing response levels for specified probability levels is called the Performance Measure Approach (PMA).
The different RIA/PMA algorithm options are specified using the mpp_search specification which selects among different limit state approximations that can be used to reduce computational expense during the MPP searches.
The Mean Value method (MV, also known as MVFOSM in [41]) is the simplest, least-expensive method in that it estimates the response means, response standard deviations, and all CDF/CCDF forward/inverse mappings from a single evaluation of response functions and gradients at the uncertain variable means. This approximation can have acceptable accuracy when the response functions are nearly linear and their distributions are approximately Gaussian, but can have poor accuracy in other situations.
All other reliability methods perform an internal nonlinear optimization to compute a most probable point (MPP) of failure. A sign convention and the distance of the MPP from the origin in the transformed standard normal space ("u-space") define the reliability index, as explained in the section on Reliability Methods in the Uncertainty Quantification chapter of the Users Manual [5]. Also refer to variable_support for additional information on supported variable types for transformations to standard normal space. The reliability can then be converted to a probability using either first- or second-order integration, may then be refined using importance sampling, and finally may be converted to a generalized reliability index.
These keywords may also be of interest:
1.8.4 
