Uncertainty Quantification in Rocket Engine Development and its Implementation Using MBSE

Uncertainty quantification is important in introducing Additive Manufacturing (AM) for rocket engines due to its inherent challenges. Notably, variability in geometric accuracy and material properties that potentially impact rocket engine performance. This is paramount for highly reliable and versatile rocket engines such as the RS-25, the core stage engine for the SLS. An immense potential exists to model reliability by accounting for AM uncertainties in rocket engines with the advent of advanced computational tools and analytical frameworks. AM product quality is heavily reliant on understanding and controlling uncertainty relative to material properties, processing parameters, and post product treatment. Current applications of uncertainty quantification for AM focus on characterizing the microstructure that influences mechanical properties but lacks focus and extension to a specific rocket engine development application. Model Based Systems Engineering (MBSE) has garnered recent attention and increased utilization to support NASA's Design for Reliability (DFR) and Model-Based Mission Assurance Activities (MBMA). MBSE can help increase multi-disciplinary knowledge capture, create requirement traceability, and reduce risks. This helps achieve efficient communication and manage complex systems whose paradigm are well-grounded in the Systems Engineering (SE) processes. MBSE utilization in tandem with reliability modeling can enable tracking of system level sensitivities to uncertainties. Thus, using uncertainty quantification and its implementation using MBSE paradigms helps to view whether a specific change in behavior will impact performance, highlighting the importance of implementing MBSE alongside uncertainty quantification. Consequently, this will help in establishing clear relationships between requirements, design elements, and potential uncertainties for a holistic approach in capturing system behavior. By comprehensively understanding system behavior, AM, MBSE, and uncertainty quantification, rocket engine testing effectiveness can be improved. This paper discusses the significance of uncertainty quantification in the application of AM for rocket engines, particularly focusing on its application to highly reliable and versatile engines like the RS-25s used in the SLS core stage. Furthermore, the research entailed in this paper explores the potential of advances in computational methods in uncertainty quantification to improve affordability and dependability by accounting for AM design challenges. It also delves into the utilization of MBSE alongside reliability modeling to track system-level sensitivities to uncertainties, aiming for optimal outcomes in AM product efficiency and functionality for rocket engine applications.