Simulation for Safety and Reliability Analysis

For safety and reliability analyses, the response of complex (safety-critical) systems (e.g., nuclear, aerospace, civil, chemical plants and infrastructures) under different perturbated conditions is typically studied by means of mathematical and computational models to identify unsafe states of operation. Robustly framing the operational envelope is of paramount importance for designing and operating safety-critical systems, through the proper definition of accident prevention and mitigation barriers and actions.

The feasibility of the analysis is challenged by the fact that the mathematical models and simulation codes are:

  1. computationally demanding (i.e., require significant computational resources to simulate a single scenario);
  2. high-dimensional (i.e., involve large number of inputs and/or outputs);
  3. somewhat black-box (i.e., the mathematical function underlying the input-output relation is eventually not fully explicit and nonlinear);
  4. dynamic (i.e., evolve in time); and
  5. affected by severe uncertainties (due to the scarcity of data available for parameters calibration) and the unavoidable simplifications and approximations necessary to build the models.

Thus, there is a need for advanced simulation methods and framework for computationally affordable and accurate safety and reliability assessments of complex, safety-critical systems. Examples of interest to the present Committee include (but are not limited to):

  • ultra-powerful supercomputing capability that exploits distribute/cloud computing which utilizing an infrastructure of CPUs and GPUs to process and analyze data at a new incredible speed;
  • adaptive sampling and designs of experiments, which makes use of active learning algorithms algorithms to extract information from the available simulations and drive the exploration of the system state-space towards the (critical, unsafe) conditions of interest to the analysis (thus limiting the number of computationally expensive calls to the simulation model);
  • meta-modeling supported by machine learning and artificial intelligence to mimic the behavior of the original, long-running simulation codes at a reduced computational cost.
  • Verified methods and automatic uncertainty to perform the analysis with a guaranteed level of confidence.

The purpose of this Technical Committee is to provide a forum for discussion and experience-sharing with regards to the above-mentioned issues and approaches, both from the theoretical/methodological and practical points of view, in various industrial fields so as to consider the differences in practical needs and challenges, limitations and difficulties when applying the different methods to the specific situations.

Nicola Pedroni, Edoardo Patelli


Michael Beer - Institute for Risk and Reliability, Leibniz University Hannover, Germany
Jean-Yves Choley - ISAE-Supméca, France
Alicja Dąbrowska - Wroclaw University of Science and Technology, Faculty of Mechanical Engineering, Department of Technical Systems Operation and Maintenance, Poland
Ewa Dąbrowska - Gdynia Maritime University, Poland
Robert Giel - Wrocław University of Science and Technology - Faculty of Mechanical Engineering, Department of Technical Systems Maintenance and Operation, Poland
Sambor Guze - Gdynia Maritime Univeristy, Poland
Jean-François Petin - Université de Lorraine - Centre de Recherche en Automatique de Nancy, France
Monika Reif - ZHAW, Zurich University of Applied Sciences, Institute of Applied Mathematics and Physics, Safety Critical Systems Research Lab, Switzerland
Gina Torres - TNO-Netherlands Organization for Applied Scientific Research, Netherlands
Shizhen Yu - Université libre de Bruxelles, Belgium
Guoxiang Zhao - Danish Institute of Fire and Security Technology, Denmark

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