COSSAN: A multidisciplinary software suite for uncertainty quantification and risk management

Abstract

Computer-aided modeling and simulation is now widely recognized as the third "leg" of scientific method, alongside theory and experimentation. Many phenomena can be studied only by using computational processes such as complex simulations or analysis of experimental data. In addition, in many engineering fields computational approaches and virtual prototypes are used to support and drive the design of new components, structures, and systems. One of the greatest challenges of virtual prototyping is to improve the fidelity of the computational analysis. This can only be achieved by explicitly including variability and uncertainties from different sources. Variability is inherent in many natural systems and therefore cannot be reduced. Uncertainty is also always present since it is not possible to perfectly model or predict future events for which no real-world data is available. Although stochastic methods offer a much more realistic approach for analysis and design, their utilization in practical applications remains quite limited. One of the reasons is that the developments of software for stochastic analysis have received considerably less attention than their deterministic counterparts. Another common limitation is that the computational cost of stochastic analysis is often by orders of magnitude higher than the deterministic analysis. Hence, robust, efficient, and scalable computational tools are necessary, i.e., by making use of the computational power of a cluster and grid computing. This chapter presents the COSSAN project: A developed multidisciplinary general-purpose software suite for uncertainty quantification and risk analysis. The computational tools satisfy the industry requirements regarding usability, numerical efficiency, flexibility, and scalability. The software can be used to solve a wide range of engineering and scientific problems. The availability of such software is particularly important for the analysis and design of resilient structures and systems. In fact, despite the different levels of uncertainty, decision makers still need to take clear choices based on the available information. They need to trust the methodology adopted to propagate the uncertainties through multidisciplinary analysis, in order to quantify the risk with the current level of information and to avoid wrong decisions due to artificial restrictions introduced by the modeling.