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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2014

MARGInS: Model-Based Analysis of Realizable Goals in Systems

Under NASAs Constellation effort, the Exploration Technology Development Program funded research toward a system validation capability that applied machine learning and test-case generation techniques to the analysis of black-box system behavior. The behavior analysis capability scaled to spaces of hundreds of input parameters and tens of thousands of test cases. Aerospace systems at the vehicle level, especially those systems which contain some level of autonomy, are best described by hybrid and non-linear mathematics. Even simplified models of such systems need parameter dimensionalities in the hundreds or thousands of parameters in order to capture sufficient fidelity. The System Safety Assessments (such as those described in the SAE ARP 4761A Safety Assessment Process guidelines) for these systems are prone to errorinteractions between the vehicles subsystems are complex, and can display emergent behaviors. NASA captured this new analysis in the Model-based Analysis of Realizable Goals in Systems (MARGInS) tool and applied it to the Pad Abort 1 (PA-1) simulation as part of the independent validation and verification cycle before the PA-1 flight test in May of 2010. MARGInS evaluated the adherence of the high-fidelity simulation to its requirements, and deter- mined the margins to failure from the expected nominal input conditions. Following the PA-1 test, the capabilities within the MARGInS framework have been extended with sophisticated statistical and white-box test case generation techniques and applied to other NASA missions. The frame- work now includes a critical factors analysis that was applied to NASAs Orion simulation and design. NASAs Aeronautics Research Mission Directorate (ARMD) leveraged the existing MARGInS framework for work on aviation safety for civil transport vehicles and for research on autonomy issues. The NASA ARMD effort created a time series output prediction capability that has been used to characterize trajectories for a plane with an adaptive control system, and a safety boundary detection capability that has been applied to an air traffic control concept of operation for the Federal Aviation Administration. The statistical and machine- learning based techniques within MARGInS have been successfully combined with concolic execution to improve the coverage of a critical unit by driving system-level inputs. The use case driving the concolic execution and MARGInS integration was inspired by the Air France 447 disaster in which the loss of a critical functionality (the airspeed calculation from the pitot tubes) led to loss of the entire plane with the people aboard. To illustrate capabilities and limitations, we will highlight the analyses for the applications listed above. We will then discuss the future plans for MARGInS and its interfaces with other tools.

Validation↗
NASA NTRS1989

Safety policy and requirements for payloads using the space transportation system

The safety policy and requirements are established applicable to the Space Transportation System (STS) payloads and their ground support equipment (GSE). The requirements are intended to protect flight and ground personnel, the STS, other payloads, GSE, the general public, public-private property, and the environment from payload-related hazards. The technical and system safety requirements applicable to STS payloads (including payload-provided ground and flight supports systems) during ground and flight operations are contained.

Source record↗
NASA NTRS2016

Safety Characteristics in System Application Software for Human Rated Exploration

NASA and its industry and international partners are embarking on a bold and inspiring development effort to design and build an exploration class space system. The space system is made up of the Orion system, the Space Launch System (SLS) and the Ground Systems Development and Operations (GSDO) system. All are highly coupled together and dependent on each other for the combined safety of the space system. A key area of system safety focus needs to be in the ground and flight application software system (GFAS). In the development, certification and operations of GFAS, there are a series of safety characteristics that define the approach to ensure mission success. This paper will explore and examine the safety characteristics of the GFAS development.

exploration↗
NASA NTRS2022

SWS TechTalk Systems Modelling for SWS Safety Demonstrator 1 (SD-1): Wildfire Response Operations

Build out a System Safety Model for System-Wide Safety’s first Technical Challenge 5 (TC5) Safety Demonstrator (SD-1), which will be a demonstration of an In-Time Aviation Safety Management System in emerging wildfire response operations. Supporting the Systems Engineer and using Model-Based Systems Engineering (MBSE) practices, languages, and tools. Summer 2022 SD1 milestones: TC5-03-01 Initial Stakeholder Engagement Concept of Operations Development milestone and the upcoming TC5-03-02 Identification of key gaps and prioritization of NASA, SWS milestone.

model-based systems engineering↗