Probabilistic simulation for the certification of railway vehicles

The present dynamic certification process that is based on experiments has been essentially built on the basis of experience. The introduction of simulation techniques into this process would be of great interest. However, an accurate simulation of complex, nonlinear systems is a difficult task, in particular when rare events (for example, unstable behaviour) are considered. After analysing the system and the currently utilized procedure, this paper proposes a method to achieve, in some particular cases, a simulation-based certification. It focuses on the need for precise and representative excitations (running conditions) and on their variable nature. A probabilistic approach is therefore proposed and illustrated using an example. First, this paper presents a short description of the vehicle / track system and of the experimental procedure. The proposed simulation process is then described. The requirement to analyse a set of running conditions that is at least as large as the one tested experimentally is explained. In the third section, a sensitivity analysis to determine the most influential parameters of the system is reported. Finally, the proposed method is summarized and an application is presented

OPTIMAL INPUT POTENTIAL FUNCTIONS IN THE INTERACTING PARTICLE SYSTEM METHOD

The assessment of the probability of a rare event with a naive Monte-Carlo method is computationally intensive, so faster estimation methods, such as variance reduction methods, are needed. We focus on one of these methods which is the interacting particle (IPS) system method. The method requires to specify a set of potential functions. The choice of these functions is crucial, because it determines the magnitude of the variance reduction. So far, little information was available on how to choose the potential functions. To remedy this, we provide the expression of the optimal potential functions minimizing the asymptotic variance of the estimator of the IPS method