Susie Go

Simulation assisted risk assessment applied to launch vehicle conceptual design

A simulation-based risk assessment approach is presented and is applied to the analysis of abort during the ascent phase of a space exploration mission. The approach utilizes groupings of launch vehicle failures, referred to as failure bins, which are mapped to corresponding failure environments. Physical models are used to characterize the failure environments in terms of the risk due to blast overpressure, resulting debris field, and the thermal radiation due to a fireball. The resulting risk to the crew is dynamically modeled by combining the likelihood of each failure, the severity of the failure environments as a function of initiator and time of the failure, the robustness of the crew module, and the warning time available due to early detection. The approach is shown to support the launch vehicle design process by characterizing the risk drivers and identifying regions where failure detection would significantly reduce the risk to the crew.

A historical survey with success and maturity estimates of launch systems with RL10 upper stage engines

Pratt & Whitneys RL10 engine line has a long and rich history, beginning in 1958 and continuing today. This paper provides a historical summary of launch vehicles using RL10 engine derivatives dating from 1962 - 2005. The historical launch data is used to derive baseline launch success rates and growth curves for vehicles configured with RL10 engines in the upper stage.Because it was the first liquid hydrogen fueled rocket engine, the RL10 engine launch history provides a unique opportunity to investigate the maturity trends for revolutionary new complex systems. All of the data used in this survey was acquired through publicly-available sources. In all, 190 vehicles configured with RL10 upper stage engines were launched between 1962 and 2005. There were 12 upper stage failures that either failed to reach orbit, or reached a lower, unintended orbit. The early failures were dominated by knowledge gaps in system interactions and operational flight conditions. There is a clear trend of early development growth with an eventual plateau as system knowledge improved as a result of flight experience and more thorough test programs. Failures due to process-based issues (fabrication techniques, quality control, etc.), however, do not appear to exhibit maturity growth. Eventually, as the knowledge-based failures are removed, these process-based failures become the dominant risk driver. Vehicles that use mature, highly-reliable components are still vulnerable to process or functional changes, and failures of this type occur fairly uniformly with flight experience. In order to improve future reliability estimates for such systems, it is important to understand the trends and relationship between the knowledge-based and process-based issues, and determine which class of issues currently dominates. It should be noted that of the 12 upper stage failures, only one was caused by a defective part.

Comparative analysis of static and dynamic probabilistic risk assessment

This study examines three different methodologies for producing loss-of-mission (LOM) and loss-of-crew (LOC) risks estimates for probabilistic risk assessments (PRA) of crewed spacecraft. The three bottom-up, component-based PRA approaches examined are a traditional static fault tree, a dynamic Monte Carlo simulation, and a fault tree hybrid that incorporates some dynamic elements. These approaches were used to model the reaction control system thruster pod of a generic crewed spacecraft and mission, and a comparative analysis of the methods is presented. The methodologies are assessed in terms of the process of modeling a system, the actionable information produced for the design team, and the overall fidelity of the quantitative risk evaluation generated. The system modeling process is compared in terms of the effort required to generate the initial model, update the model in response to design changes, and support mass-versus-risk trade studies. The results are compared by examining the top-level LOM/LOC estimates and the relative risk driver rankings at the failure mode level. The fidelity of each modeling methodology is discussed in terms of its capability to handle real-world system dynamics such as cold-sparing, changes in mission operations due to loss of redundancy, and common cause failure modes. The paper also discusses the applicability of each methodology to different phases of system development and shows that a single methodology may not be suitable for all of the many purposes of a spacecraft PRA. The fault tree hybrid approach is shown to be best suited to the needs of early assessments during conceptual design phases. As the design begins to mature, the level of detail represented in the risk model must go beyond redundancy and nominal mission operations to include dynamic, time- and state-dependent system responses as well as diverse system capabilities. This is best accomplished using the dynamic simulation approach, since these phenomena are not easily captured by static methods. Ultimately, once the design has been finalized and the goal of the PRA is to provide design validation and requirement verification, more traditional, static fault tree approaches may become as appropriate as the simulation method.

Integrated risk sensitivity study for Lunar Surface Systems

This paper illustrates an innovative approach to assessing the reliability of conceptual Lunar Surface Systems architectures using an integrated analysis model. The integrated model represents systems, dependencies, and interactions to develop risk-based reliability requirements that balance functional characteristics, needs, demands, and constraints to achieve availability goals. The model utilizes “availability” metrics based on first-order descriptions of the architecture to begin providing reliability impacts even before much design detail exists. Sensitivity analyses are performed to identify key risk parameters and find “knees” in the curve for establishment of system architecture- and element-level requirements.

Engineering Risk Assessment of a dynamic space propulsion system benchmark problem

Abstract 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.

Human space mission architecture risk analysis

A human space flight mission is extremely dynamic in nature. A spacecraft faces multiple physical environments and is exposed to dramatically different hazards over the typical phases of a mission: from a minutes-long ascent phase, to a months-long orbital phase, and then an hours-long entry, descent and landing phase. The space transportation vehicles configuration also changes as each of the stages of the launch vehicles engines are ignited, burned, turned off, and jettisoned, with new stages and engines taking over the thrusting of the vehicle until the spacecraft is separated from the launch vehicle. Once in orbit, the spacecraft performs its orbital tasks and then returns to earth for safe landing of the astronauts. All the changes in the physical environments encountered during the mission and the response of the system to failures or changes in the configuration of the vehicle call for a modular, dynamic probabilistic risk model that integrates the modeling pieces and faithfully tracks the entire mission timeline in order to understand the risks to the crew across all mission phases. Using a flexible modeling framework that is capable of incorporating various levels of data fidelity, modeling inputs, and timescales allows for a risk analysis methodology that grows with the maturity of the systems design definition while capturing the risk drivers at the right levels throughout the mission.