Curious About How DOORS Supported the Curiosity Rover?
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This paper presents a scalable method to efficiently search for the most likely state trajectory leading to an event given only a simulator of a system. Our approach uses a reinforcement learning formulation and solves it using Monte Carlo Tree Search (MCTS). The approach places very few requirements on the underlying system, requiring only that the simulator provide some basic controls, the ability to evaluate certain conditions, and a mechanism to control the stochasticity in the system. Access to the system state is not required, allowing the method to support systems with hidden state. The method is applied to stress test a prototype aircraft collision avoidance system to identify trajectories that are likely to lead to near mid-air collisions. We present results for both single and multi-threat encounters and discuss their relevance. Compared with direct Monte Carlo search, this MCTS method performs significantly better both in finding events and in maximizing their likelihood.
In numerical simulations involving boundaries that evolve in time, the primary challenge is updating the computational mesh to reflect the physical changes in the domain. In particular, the fundamental objective for any such \mesh motion" scheme is to maintain mesh quality and suppress unphysical geometric anamolies and artifacts. External to a physical process of interest, mesh motion is an added component that determines the specifics of how to move the mesh given certain limited information from the main system. This paper develops a set of boundary conditions designed to eliminate tangling and internal collision within the context of PDE-based mesh motion (linear elasticity). These boundary conditions are developed for two- and three-dimensional meshes. The paper presents detailed algorithms for commonly occuring topological scenarios and explains how to apply them appropriately. Notably, the techniques discussed herein make use of none of the specifics of any particular formulation of mesh motion and thus are more broadly applicable. The two-dimensional algorithms are validated by an extensive verification procedure. Finally, many examples of diverse geometries in both two- and three-dimensions are shown to showcase the capabilities of the tangle-free boundary conditions.
The International Space Station (ISS) Simplified Aid for Extra Vehicular Activity (EVA) Rescue (SAFER) is the spacewalking astronaut's final safety measure against separating from the ISS and being unable to return safely. Since the late 1990s, the SAFER has been a standard element of the spacewalking astronaut's equipment. The ISS SAFER project was chartered to develop a new block of SAFER units using a highly similar design to the legacy SAFER (known as the USA SAFER). An on-orbit test module was also included in the project to enable periodic maintenance/propulsion system checkout on the ISS SAFER. On the ISS SAFER project, model-based systems engineering (MBSE) was not the initial systems engineering (SE) approach, given the volume of heritage systems engineering and integration (SE&I) products. The initial emphasis was ensuring traceability to ISS program standards as well as to legacy USA SAFER requirements. The requirements management capabilities of the Cradle systems engineering tool were to be utilized to that end. During development, however, MBSE approaches were applied selectively to address specific challenges in requirements validation and test and verification (T&V) planning, which provided measurable efficiencies to the project. From an MBSE perspective, ISS SAFER development presented a challenge and an opportunity. Addressing the challenge first, the project was tasked to use the original USA SAFER operational and design requirements baseline, with a number of additional ISS program requirements to address evolving certification expectations for systems operating on the ISS. Additionally, a need to redesign the ISS SAFER avionics architecture resulted in a set of changes to the design requirements baseline. Finally, the project added an entirely new functionality for on-orbit maintenance. After initial requirements integration, the system requirements count was approaching 1000, which represented a growth of 4x over the original USA SAFER system. This presented the challenge - How to confirm that this new set of requirements set would result in the creation of the desired capability.
On September 8, 2016, the third NASA New Frontiers mission launched on an Atlas V 411. The Origins, Spectral Interpretation, Resource Identification, Security-Regolith Explorer (OSIRIS-REx) will rendezvous with asteroid Bennu in 2018, collect a sample in 2020, and return that sample to Earth in September 2023. The development team has overcome a number of challenges in order to design and build a system that will make contact with an unexplored, airless, low-gravity body. This paper will provide an overview of the mission, then focus in on the system-level challenges and some of the key system-level processes. Some of the lessons here are unique to the type of mission, like discussion of operating at a largely-unknown, low-gravity object. Other lessons, particularly from the build phase, have broad implications. The OSIRIS-REx risk management process was particularly effective in achieving an on-time and under-budget development effort. The systematic requirements management and verification and the system validation also helped identify numerous potential problems. The final assessment of the OSIRIS-REx performance will need to wait until the sample is returned in 2023, but this post-launch assessment will capture some of the key systems-engineering lessons from the development team.
An orthotropic three-dimensional material model suitable for use in modeling impact tests has been developed that has three major components elastic and inelastic deformations, damage and failure. The material model has been implemented as MAT213 into a special version of LS-DYNA and uses tabulated data obtained from experiments. The prominent features of the constitutive model are illustrated using a widely-used aerospace composite the T800S3900-2B[P2352W-19] BMS8-276 Rev-H-Unitape fiber resin unidirectional composite. The input for the deformation model consists of experimental data from 12 distinct experiments at a known temperature and strain rate: tension and compression along all three principal directions, shear in all three principal planes, and off axis tension or compression tests in all three principal planes, along with other material constants. There are additional input associated with the damage and failure models. The steps in using this model are illustrated composite characterization tests, verification tests and a validation test. The results show that the developed and implemented model is stable and yields acceptably accurate results.
The Adverse Condition Awareness Tool (ACAT) promotes search capability and discovery of software-related adverse conditions across NASA missions.
We present a detailed overview of the structure and activities associated with the NASA-led ground validation component of the NASA-JAXA Global Precipitation Measurement (GPM) mission. The overarching philosophy and approaches for NASA’s GV program are presented with primary focus placed on aspects of direct validation and a summary of physical validation campaigns and results. We describe a spectrum of key instruments, methods, field campaigns and data products developed and used by NASA’s GV team to verify GPM level-2 precipitation products in rain and snow. We describe the tools and analysis framework used to confirm that NASA’s Level-1 science requirements for GPM are met by the GPM Core Observatory. Examples of routine validation activities related to verification of Integrated Multi-satellitE Retrievals for GPM (IMERG) products for two different regions of the globe (Korea and the U.S.) are provided, and a brief analysis related to IMERG performance in the extreme rainfall event associated with Hurricane Florence is discussed.
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There is a trend towards increased use of automation in V&V. Automation can yield savings in time and effort. For critical systems, where thorough V&V is required, these savings can be substantial.
Cost, performance and functionality concerns are driving a trend towards use of self-sufficient autonomous systems in place of human-controlled mechanisms.
This abstract describes the problem we faced, the solution we employed, and the applicability of our approach in a general V&V setting.
The exceptional progress in the field of machine learning (ML) in recent years has attracted a lot of interest in using this technology in aviation. Possible airborne applications of ML include safety-critical functions, which must be developed in compliance with rigorous certification standards of the aviation industry. Current certification standards for the aviation industry were developed prior to the ML renaissance without taking specifics of ML technology into account. There are some fundamental incompatibilities between traditional design assurance approaches and certain aspects of ML-based systems. In this paper, we analyze the current airborne certification standards and show that all objectives of the standards can be achieved for a low-criticality ML-based system if certain assumptions about ML development workflow are applied.
A research program has been underway for five years to study vortex interaction aerodynamics that are relevant to military air vehicle performance. The program has been conducted under the auspices of the NATO Science and Technology Organization (STO), Applied Vehicle Technology (AVT) panel by a Task Group with the identification of AVT-316. Seven special sessions have been established to highlight accomplishments from the AVT-316 research. An overview of the AVT-316 program is presented in this paper.
In recent years, Artificial Intelligence (AI) systems, enabled by Machine Learning (ML)technology, have demonstrated impressive progress and provides historic opportunities for the aviation industry. However, several key aspects of ML technology are not compatible with existing design assurance standards and make certification problematic. In this paper, we present a case study of a visual system with a Deep Neural Network (DNN) intended to detect and identify airport runway signs. Different use cases and variants of this system exhibit different levels of criticality ranging from design assurance level (DAL) D to B. We use the case study to illustrate the challenges of certification according to the current standards, such asDO-178C. We present the system design, data generation, training, and verification in detail and describe how the design assurance objectives can be met for a DAL D variant of the system. We also discuss gaps and potential approaches for the higher design assurance levels.