Engineering Risk Assessment of a dynamic space propulsion system benchmark problem

• Space propulsion benchmark problem challenges classical analysis methods.
• Monte Carlo simulation model captured dynamic mission risks and failure drivers.
• Hybrid deterministic models captured varying levels of risk dynamics.
• Model comparisons illustrate the relative impact of capturing dynamic risk factors.
• Comparison of both approaches highlights their strengths and weaknesses.

The Engineering Risk Assessment (ERA) team at NASA Ames Research Center develops dynamic models with linked physics-of-failure analyses to produce quantitative risk assessments of space exploration missions. This paper applies the ERA approach to the 2014 Probabilistic Safety Assessment and Management conference Space Propulsion System Benchmark Problem, which investigates dynamic system risk for a deep space ion propulsion system over three missions with time-varying thruster requirements and operations schedules. The dynamic missions are simulated using commercial software to generate integrated loss-of-mission (LOM) probability results via Monte Carlo sampling. The simulation model successfully captured all dynamics aspects of the benchmark missions, and convergence studies are presented to illustrate the sensitivity of integrated LOM results to the number of Monte Carlo trials. In addition, to evaluate the relative importance of dynamic modeling, the Ames Reliability Tool (ART) was used to build a series of quasi-dynamic, deterministic models that incorporated varying levels of the problem׳s dynamics. The ART model did a reasonable job of matching the simulation results for the simpler mission case, while auxiliary dynamic models were required to adequately capture risk-driver rankings for the more dynamic cases. This study highlights how state-of-the-art techniques can adapt to a range of dynamic problems.