Application of the Generalized Conditional Expectation Method for Enhancing a Probabilistic Design Fatigue Code

Abstract

Traditionally, probabilistic design codes are developed for a particular field of application such as aerospace, nuclear and offshore structures. These codes contain specific mechanics models and random variables. Also, the probabilistic methods implemented are specific and highly optimized for the particular problem. Over a period of time these codes may need to be enhanced and certain variables that were once considered deterministic need to be made random. In addition, the source code may not be available. In such a scenario, the generalized conditional expectation method is implemented to enhance the probabilistic design code and add new random variables without changing the source code. This methodology can also be used to assist the developer in evaluating the enhancement of code, before changing the source code. In this paper, the generalized conditional expectation methodology is used to enhance a probabilistic fatigue code by adding more random variables to the existing code without changing the source code. Also, new features such as estimating the sensitivities of the probability of failure to the parameters of random variables are demonstrated. A numerical example is solved to demonstrate the methodology.

Probabilistic prediction of minimum fatigue life behaviour in α + β titanium alloys

“…DARWIN is then run yielding a conditional probability of failure, P fi , given the current values of c and n . This was repeated a sufficient number of times, N , typically 10 000, and the results were combined to calculate probability of failure, P f , using the generalized conditional expectation method as described in Momin et al in Equation . …”