Model Based Systems Engineering approach to support safety assessment of suborbital vehicles

The emergence of suborbital vehicles for space tourism and scientific missions has introduced complex safety challenges that require robust engineering solutions. Traditional safety assessment methods, such as Fault Tree Analysis (FTA) and Failure Mode and Effects Analysis (FMEA), have been essential but insufficient in addressing the high complexity and integration requirements of modern aerospace systems. This thesis proposes the use of Model-Based Systems Engineering (MBSE) to enhance the safety assessment of suborbital vehicles, particularly focusing on Virgin Galactic’s SpaceShipTwo as a case study. Leveraging the ARCADIA methodology and Capella tool, an MBSE approach is applied to model the entire system architecture, including its subsystems: - Propulsion; - Hydraulic; - Structure; - Control systems. This thesis was conducted in collaboration with ENAC (Ente Nazionale per l’Aviazione Civile). Where it was not possible to find useful information for the analysis, we proceeded in a qualitative manner, also following recommendations found in the literature. Safety-critical elements are analyzed using both Functional Hazard Analysis (FHA) and Fault tree analysis (FTA), ensuring early identification of potential failure modes and their propagation paths. The methodology demonstrates how MBSE enhances traceability and supports the integration of safety requirements within the design process, enabling efficient management of risk across different mission phases. Results from the safety analysis reveal the most critical failure points in suborbital missions, such as hydraulic actuator failures during reentry and structural fatigue during high-speed flight. Furthermore, this thesis shows how human error, highlighted in the SpaceShipTwo incident, can be mitigated through a model-based framework that improves system transparency and verification processes. This study concludes that MBSE, when integrated with traditional safety methods, significantly improves the reliability and safety of suborbital vehicles, offering a scalable solution for future space missions.