A certain level of accuracy always characterizes the engineering parameters and the mathematical models involved in system design processes. Due to the scarce knowledge available on the system configuration, especially during the conceptual analyses, the system experts are asked to deal with uncertain and often not available data. Consequently, the designer is often forced to make approximations and to impose indicative values of significant quantities. This leads to the possibility of making relevant mistakes, especially referring to sensitive quantities. Moreover, the state identification and mathematical modeling of physical phenomena are always subject to errors and approximation. The previous considerations claim for the development of effective tools for uncertainty analysis and management in engineering.
Dinamica Srl shows a strong expertise in Stochastic methods (e.g., Monte Carlo simulations) widely used in the past for uncertainty analysis. However, due to the strong dependence of the accuracy on the number of processed samples, accurate Monte Carlo simulations are usually related to high computational cost. Additionally, even if sufficiently accurate, statistical methods only deal with uncertainty analysis and can not be directly used to manage uncertainties, leading to the necessity of separated tools.
Dinamica Srl proposes new and alternative tools for uncertainty management. Relevant results have been gained on the deterministic management of uncertainties in space system and trajectory design, especially referring to: assessment of the uncertainty level on space system performances during the preliminary phases of the mission design; inclusion and use of the resulting information in efficient optimization algorithms for the seek of robust optimal design solutions; development of robust optimal control tools for the simultaneous analysis and management of uncertainties in space trajectory design.