Filtern
Dokumenttyp
- Vortrag (2) (entfernen)
Sprache
- Englisch (2)
Referierte Publikation
- nein (2) (entfernen)
Schlagworte
- Probability of failure (2) (entfernen)
Organisationseinheit der BAM
Eingeladener Vortrag
- nein (2)
The presentation explains the term “safety” in a broader sense and leads to the need for a more risk-based understanding of safety. Based on this, the Monte Carlo simulation is used to show where the weak points of the ECE R 134 are and in which direction the GTR 13 could be optimized during the currently running phase 2. For this purpose, it is helpful to improve the currently operated tool for Monte Carlo simulation, for which BAM introduced the research idea “MoCaSiB”.
The key point of structural reliability analysis is the estimation of the failure probability. This probability is defined as the integral over the failure domain which is given by a limit state function. Usually, this function is only implicit given by an underlying finite element simulation of the structure. It is generally not possible to solve the integral analytically. For that reason, numerical methods based on sampling and surrogates have been developed. Nevertheless, these sampling methods still require a few thousand calculations of the underlying finite element model, making reliability analysis computationally expensive for relevant applications.
Coupling a reduced order model (proper generalized decomposition) with an efficient variance reducing sampling algorithm can reduce the computational cost of reliability analysis drastically. In the proposed method, an importance sampling technique is coupled with a reduced structural model by means of PGD to estimate the failure probability. Instead of calculating the design point e.g. with optimization algorithms, the design point is adaptively estimated by using the idea of subset simulation. The failure probability is estimated in an iterative scheme based on adaptively computing the design point and refining the PGD model.