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At least 523 records · Page 29

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NASA NTRSDate not supplied

NASA Earth Observations and the NASA Earth Science Disasters Program: Providing Data, Services and Science Expertise to Inform End User Decision-Making Throughout the Disaster Management Cycle

Program Goals: - Harness NASA Capabilities for Disaster Risk Reduction (DRR) and resilience - Engage stakeholders in the use of Earth Observations (EO) throughout the disaster lifecycle - Demonstrate the value and impact of EO to support decision making and actions - Grow as a trusted source for delivering useful results

Ronan Lucey↗

NASA NTRSDate not supplied

A PPE Use Case on Configuration Management Approach for MBSE

Systems engineers worldwide have been working to implement Model-based Systems Engineering (MBSE) environments, tools, and methodologies. MBSE is a formalized application of modeling to support systems engineering, including requirements, design, analysis, verification, and validation activities over the project’s lifecycle[1]; MBSE captures the system data into a digital environment. Significant benefits of MBSE includes a reduction in the time in performing systems engineering activities and an improvement higher fidelity data production. As more Systems Engineers are using MBSE, the models it produces are becoming the source of truth for Systems Engineering artifacts. As we move towards using these models as the source of truth, a more rigorous Configuration Management (CM) infrastructure is needed. Many of the MBSE tools provide CM options but utilizing them efficiently and effectively can be challenging. More rigorous methods and tools are needed to assist with keeping track of changes in the model, making sure inadvertent changes to baseline data did not occur, visibility of changes in the different model versions, and the impacts of changes to the models. System engineers and configuration management personnel from the Power and Propulsion Element (PPE) project at NASA Glenn Research Center have been working to develop a modeling construct that allows models to be the source of truth and maintain a configuration managed baseline. This paper presents a process that leverages the existing CM tools and describes how PPE used this process to manage changes more rigorously. It will describe the process behind building the model architecture that utilizes the MBSE tool capabilities and the configuration management process. It will contain some of the advantages and disadvantages of the architecture that the PPE project had settled upon utilizing, as well as some enhanced capabilities that the PPE MBSE team has developed.

MBSE↗

NASA NTRSDate not supplied

Development of Space Mission Integrated Operations Scenarios

Integrated Operations Scenarios (IOS) describe nominal operations planned for a space mission. Operational scenarios are key to an operations concept, as they help identify issues and drivers on the operations system. This paper covers the tools, processes, and inputs that feed into an IOS product, and how IOS iterations during a mission lifecycle can buy down operational risks. A case used for this paper is nominal operations for an initial crewed Artemis mission that includes cross-program sequence of tasks required to accomplish end to end functions from launch through transit, on-orbit operations, to safely return to Earth. An IOS product serves as a reference for discussions during requirements development, task and risk analyses, concept of operations development, and as foundation to prepare space mission flight plans.

integrated operations scenarios↗

NASA NTRSDate not supplied

Architecting Safer Autonomous Aviation Systems

The aviation literature gives relatively little guidance to practitioners about the specifics of architecting systems for safety, particularly the impact of architecture on allocating safety requirements, or the relative ease of system assurance resulting from system or subsystem level architectural choices. As an exemplar, this paper considers common architectural patterns used within traditional aviation systems and explores their safety and safety assurance implications when applied in the context of integrating artificial intelligence (AI) and machine learning (ML) based functionality. Considering safety as an architectural property, we discuss both the allocation of safety requirements and the architectural trade-offs involved early in the design lifecycle. This approach could be extended to other assured properties, similar to safety, such as security. We conclude with a discussion of the safety considerations that emerge in the context of candidate architectural patterns that have been proposed in the recent literature for enabling autonomy capabilities by integrating AI and ML. A recommendation is made for the generation of a property-driven architectural pattern catalogue.

Architecture patterns↗

NASA NTRS2022

Satellite Data Applications for Sustainable Energy Transitions

Transitioning to a sustainable energy system poses a massive challenge to communities, nations, and the global economy in the next decade and beyond. A growing portfolio of satellite data products is available to support this transition. Satellite data complement other information sources to provide a more complete picture of the global energy system, often with continuous spatial coverage over targeted areas or even the entire Earth. We find that satellite data are already being applied to a wide range of energy issues with varying information needs, from planning and operation of renewable energy projects, to tracking changing patterns in energy access and use, to monitoring environmental impacts and verifying the effectiveness of emissions reduction efforts. While satellite data could play a larger role throughout the policy and planning lifecycle, there are technical, social, and structural barriers to their increased use. We conclude with a discussion of opportunities for satellite data applications to energy and recommendations for research to maximize the value of satellite data for sustainable energy transitions.

energy↗

NASA NTRSDate not supplied

Metal Additive Manufacturing for Rocket Engines: Successes and Failures

NASA has been developing additive manufacturing (AM) for various technical and programmatic advantages for complex rocket engine and aerospace components. This maturation has focused on various AM processes, new alloys, characterizing material properties, developing standards, producing demonstrator parts, and integrating AM hardware in liquid rocket engines through test-fail-fix cycles, as well as application and dissemination of lessons learned of the AM lifecycle. The importance of proper AM processing was made evident in the failure of a Laser Powder Bed Fusion (L-PBF) copper-alloy combustion chamber during a hot-fire test due to a degraded material quality. The hot-fire test aimed to demonstrate high duty cycle under a risk-tolerant development project, where consequences of component failure would be minimal. However, the unintentional component failure emphasized the necessity of robust material characterization and rigorous process control procedures for the safe use of AM components in critical applications. This presentation provides an overview of the development failure, a discussion on the evaluation of the failed chamber and supplemental chambers produced at the same time, a representative material samples that included intentional build witness lines, and a summary of the key results and recommendations from the evaluations. NASA continues to approach AM processes and designs with a level of risk and acceptance of failures that is appropriate for the project objectives, with the overall goal of safe implementation of AM technology and transferring AM technology into commercial space applications. This presentation with provide critical awareness to the community lessons learned on proper implementation of AM.

L-PBF↗

NASA NTRSDate not supplied

Development of a Weather Capability for the Urban Air Mobility Airspace Research Roadmap

Traditionally, the transportation system’s resiliency to the impacts of weather is an area where neglected or incorrect assumptions can lead to difficulties later in the research and development lifecycle. To mitigate this, NASA has ongoing efforts to develop a set of research roadmaps for organizing, integrating, and communicating research into new aviation infrastructure and transportation modalities, within which weather is being addressed early on. An effort has been undertaken to add weather assumptions and requirements to an already-existing roadmap for the Urban Air Mobility (UAM) airspace, seeking to integrate weather requirements early in the system design. This effort addresses the way in which state-of-the art and evolving weather science and technology can enable safe and efficient travel with increasing tempo of UAM operations over time. This paper describes the addition of weather as one of 10 capabilities into the UAM Airspace research roadmap, laying out the anticipated weather technology and information requirements needed to facilitate operations at various UAM Maturity Levels. The process developed and exercised by MIT Lincoln Laboratory researchers produced 41 unique requirements to be satisfied by a Weather capability for the UAM ecosystem, with more than 300 dependencies identified across the system. These requirements cover measurement, analysis, modeling, forecasting, decision support, dissemination, and overarching policy, and are provided with an overview of weather challenges for UAM. The requirements were mainly defined based on subject matter expert review of existing UAM Airspace system requirements, and refined based on iterative feedback with various stakeholders including regulators, academia, and industry. Going forward, this roadmap will help researchers and developers align to a common vision in ensuring that weather is appropriately considered in the UAM ecosystem.

Timothy Bonin↗

NASA NTRSDate not supplied

Development of a Weather Capability for the Urban Air Mobility Airspace Research Roadmap

research roadmap↗

NASA NTRS2022

Research Report: Progress on Building a File Observatory for Secure Parser Development

Parsing untrusted data is notoriously challenging.Failure to handle maliciously crafted data correctly can (anddoes) lead to a wide range of vulnerabilities. The Languagetheoretic security (LangSec) philosophy seeks to obviate the needfor developers to apply ad hoc solutions by, instead, offeringformally correct and verifiable input handling throughout thesoftware development lifecycle. One of the key components indeveloping secure parsers is a broad coverage corpus that enablesdevelopers to understand the problem space for a given formatand to use, potentially, as seeds for fuzzing and other automatedtesting. In this paper, we offer an update on work reportedat the LangSec 2021 conference on the development of a fileobservatory to gather and enable analysis on a diverse collectionof files at scale. The initial focus of the observatory is on PortableDocument Format (PDF) files and file formats typically embeddedin PDFs. In this paper, we report on refactoring the ingestprocess, applying new analytic methods, and improving the User Interface.

Stonebraker, Ryan↗

NASA NTRS2022

Understanding Sampling Hardware Cleanliness from Perseverance Lessons Learned, and Forward Approach to Biosignature Missions

The search for biosignatures on other solar system bodies drives the scientific objectives of many ongoing and proposed exploration missions, including the Europa Lander mission concept.[1] The detection of trace and unfamiliar biosignatures in extreme environments necessitates state-of-the-art scientific instrumentation with extraordinary sensitivity – and often, extraordinary susceptibility to terrestrial and spacecraft self-induced contamination vectors.[2] While instruments can be carefully designed to operate remotely at high performance, in situ scientific measurements can only analyze the samples they’re given: samples or sample handling hardware that have been inadvertently tainted by outgassed organic molecules, thruster plume effluents, or other common sources of spacecraft contamination may yield ambiguous or false results. Maintaining and verifying the purity of collected samples and the cleanliness of sample handling hardware throughout the lifecycle of biosignature detection missions like Europa Lander is a primary responsibility of the Contamination Control group at JPL.

Alred, John↗

NASA NTRS2022

Feedback-Directed Random Sequence Generation for Verifying Spacecraft Flight Rule Violations

The Psyche: Journey to a Metal World mission will be launched in 2022 to study the largest metal asteroid in the main asteroid belt, (16) Psyche. The spacecraft will perform a Mars Flyby in 2023 and enter (16) Psyche’s orbit in 2026. Throughout the mission, safely operating the spacecraft will require abiding by a set of flight rules that can be defined at any point in the mission lifecycle. These flight rules ensure that the spacecraft is operating within allowed regimes and a discrete event simulation tool, SEQGEN, will be used to model all command sequences prior to uplink. One of SEQGEN’s responsibilities is to determine if a command sequence violates any flight rules. For each flight rule, the necessary logic to determine if a violation has occurred is implemented in the SEQGEN adaptation, which is maintained by the mission. This adaptation must be tested thoroughly to ensure that the flight rule logic was interpreted and implemented correctly. This work describes a tool, RandSEQ, that autogenerates a suite of flight rule violating test sequences for each flight rule implemented in SEQGEN, and can be used by any mission using SEQGEN. The Psyche SEQGEN adaptation is still being developed, but so far, RandSEQ has been used to generate 532 test cases, that can be reviewed by various stakeholders, for 40 flight rules. The ability to autogenerate these test cases has significantly reduced the amount of time required to implement and test each flight rule.

Su, Laura↗

NASA NTRSDate not supplied

Lean Model-Based Systems Engineering on the NASA High-Density Vertiplex Subproject

The High Density Vertiplex (HDV) subproject of NASA’s Advanced Air Mobility (AAM) project adopted Model-Based Systems Engineering (MBSE) in July of 2020, prior to subproject formulation. A small and lean team of HDV Systems Engineers (SE) are utilizing MagicDraw to execute NASA SE processes via MBSE. The SEs learned how to use MagicDraw from scratch and HDV is the first project for which the SEs have utilized MagicDraw. This presentation will demonstrate project technical execution via MBSE, utilizing the digital elements built into the SysML (Systems Modeling Language). SysML provides a model-centric means of carrying out the NASA SE common technical processes by providing tools for complete system modeling, including requirements and interface management and design capture. The authors also leverage and extend SysML to perform other SE tasks, such as Verification and Validation (V&V) tracking. MBSE has two main purposes for HDV: 1) documenting the subproject’s logical architecture for distribution outside of the subproject, 2) capturing the subproject’s physical architecture in a single-source-of-truth for use by the subproject’s members. This presentation details the challenges, lessons learned, and solutions that were encountered in implementing MBSE in the first iteration on a multi-iteration, full-lifecycle design, build, fly project.

systems engineering↗

NASA NTRS2022

Assurance Equations: A Cost and Criticality Model for Optimizing Quality Assurance Surveillance

The cost of quality vs cost of failure correction has been a long-running topic of discussion within the Aerospace community. It leads directly to concepts of “risk tolerance”, and risk-based decision-making. It would be valuable if there was a way to compute the optimal investment in customer-executed quality assurance activities using defect significance with respect to performance objectives, the activities’ defect detection effectiveness, and the cost-penalty for late discovery of impactful defects. This optimization is particularly of interest to projects whose budget constraints significantly limit their risk management options.The cost to fix defects (i.e., failure correction) escalates as the project matures. There have been studies attempting to determine the relative cost of fixing defects discovered during various phases of a project life cycle with important implications, all of which suggest growth factors are large. The commonly referred to 1:10:100 rule represents a cost multiplier for repair/rework across the Design to Fab to Test hardware development phases. Cost premiums for QA activities also accumulate when they are treated as mandatory (due to schedule drag) or are performed later than their assigned phase.This paper describes the modeling of development phase -dependencies in the conduct of typical customer-executed quality assurance activities. Our initial modeling encompasses:• Distinct phases of the production lifecycle• Multiple kinds of Defects, each with some a-priori likelihood of being present• Each defect’s impact on performance Objectives for a type of hardware• The cost and efficacy of assurance techniques at detecting such Defects• The costs of fixing those Defects detected in a given phase of the production lifecycleThe model captures assurance activities’ abilities to Detect defects. Upon detection it is assumed that the Defect is immediately fixed. Defects that “escape” detection by some activity may thereafter be detected by a later activity, but by then the cost of fixing the Defect may have escalated. Defects are related to the performance Objectives they would detract from, were those Defects to remain present in the operating system.We have constructed and are exploring, a model that relates the importance of hardware system elements to mission objectives, the impact of types of Defects on those hardware types, the cost of customer-executed assurance activities (i.e., supplier controls) and their effectiveness towards reducing an impactful quality escape, and the cost of Defect correction across production phase. We describe the approach taken to select the key model aspects, why they are relevant to our NASA mission, and our efforts to populate it with relevant and contemporary data. We use a notional example to illustrate model design and function.

Plante, Jeannette↗

NASA NTRS2022

Reliability-Aware Requirements Development for Autonomy Software

Autonomy is the ability of a system to make decisions and take actions in the presence of uncertainty. Validating autonomy, therefore, is a matter of validating that the system makes intelligent decisions even when there might be discrepancies between the observed and the expected state of the world as well as when the possible outcome of each of the possible courses of action is uncertain. During the requirements engineering phase, the functions, including those that are required to be autonomous and their expected behavior are specified. However, in the case of autonomous software system, it is challenging to specify all possible scenarios that the software may encounter. Additionally, since autonomous behaviors cannot always be fully predetermined, it is difficult to reason about their completeness and correctness. While these challenges are also faced with non-autonomous system, they are more prevalent for autonomous system due to their complexity and emergent behavior. Our efforts have been three-fold. On one hand, we have developed a goal-based method for requirements decomposition. We have embedded this approach into a higher-level systems engineering framework and then developed methods for decision and reliability analysis within this framework. And finally, we use simulation analysis to validate the more theoretical methods and provide a feedback mechanism. The decision analysis approach (DA) helps in identifying and mitigating the run-time risks, by bringing to the forefront the uncertainties, decisions, interactions, and other factors that may cause autonomous software to make erroneous decisions. The DA framework is used to quantify, for each decision that the software can make, failure risk due to uncertainties. The information about the key decisions and circumstances that can cause autonomous system to make incorrect decisions are used to generate test scenarios to be run in a simulator to ensure that the system can handle error-prone circumstances. The results or outcomes from decisions, based on the simulated scenarios, can then be leveraged to further gain insight of the assured system. We combine a goal-based approach with simulation analysis to facilitate requirements development for autonomous systems and further provide a method for run-time systems level reliability considerations during the autonomous software development process. The DA and the reliability analysis methods provide a formalism for the consideration of uncertainty in the run time decision-making process for autonomous software. This formalism considers the uncertainty involved in the outcome of different courses of action, in terms of performance and cost, as well as a method to consider the system level reliability implications. System level reliability considerations for autonomous software decision making contribute to the satisfaction of the system level goals over the lifecycle of a given mission. We demonstrate our approach using a hypothetical rover path planning example. The requirements for our rover simulator are based on existing literature about Mars rovers and lessons learned from real world observations at JPL. The specifics of the autonomy design for the Mars rovers, that have not been previously cleared for external release, are not used for our demonstrations.

Lindvall, Mikael↗

NASA NTRS2024

X-Plane Knowledge Capture Workshop Summary Report

The history of experimental aircraft research and development at the National Aeronautics and Space Administration (NASA) provides a large body of work for current and future team members to learn from and apply. NASA values the knowledge it gains from all the research it performs and desires to have that knowledge grow with each new project, incrementally adding to the collective knowledge base. To support this knowledge growth and sharing, in May 2019, NASA convened over 60 engineers; researchers; and other personnel (internal and external to NASA) for the X-plane1 Knowledge Capture Workshop to discuss their over 50-years’ worth of experiences in developing, operating, and managing experimental aircraft. Over the course of a two-day workshop, the group not only focused, primarily, on current X-plane development but also discussed a wide range of topics about experimental aircraft and flight research. In addition to the discussions, senior NASA aeronautics leaders shared their personal lessons learned with the workshop audience. Broadly, these discussions centered around the topics of organizational and cultural practices, the project lifecycle, flight, and risk and safety. Many of the observations offered by participants are applicable to other NASA team-based projects, demonstrating the value in sharing lessons learned and best practices across projects regardless of their research focus. To share the most valuable lessons learned as broadly as possible, a summary of these discussions has been compiled into the following report. These discussions are mostly included in their entirety, with some light editing for clarity and privacy. NASA is sharing this knowledge internally and making it available to the public in hopes that others can learn from the experimental aircraft challenges and successes at NASA. Note: this document represents the perspectives, experiences, and opinions of others but does not necessarily represent the official view of NASA.

Steven R Hirshorn↗

NASA NTRSDate not supplied

Trustworthy Autonomy for Gateway Vehicle System Manager

The Vehicle System Manager (VSM) is the highest-level software control system in the Gateway hierarchical Autonomous System Management Architecture. The VSM provides four function categories: Mission Management and Timeline Execution, Resource Management, Fault Management, Vehicle Control and Operation. VSM provides various levels of automation ranging from fully autonomous operations with no flight crew and minimal ground monitoring to advisory automation when Gateway is crewed and has full ground monitoring. Trustworthiness is achieved via verified specification, comprehensive development verification, and real-time verification using assume-guarantee contracts. Development verification includes semantic verification of the data model via peer review and testing and assume-guarantee contracts implemented using the PlusCal/TLA+ environment. VSM also uses runtime assume-guarantee contracts, implemented in R2U2 via a runtime monitor that feeds the necessary telemetry data to R2U2 and which receives and responds to the R2U2 verdict stream. The full lifecycle verification approach and use of assume-guarantee contracts provides increased trustworthiness to VSM. Preliminary results provide encouragement that VSM can be both autonomous and trustworthy.

Assume-guarantee contracts↗

NASA NTRS2023

A Surface Radiation Balance Data Set from Siple Dome in West Antarctica for Atmospheric and Climate Model Evaluation

A field campaign at Siple Dome in West Antarctica during the austral summer 2019-2020 offers an opportunity to evaluate climate model performance, particularly cloud microphysical simulation. Over Antarctic ice sheets and ice shelves, clouds are a major regulator of the surface energy balance, and in the warm season their presence occasionally induces surface melt that can gradually weaken an ice shelf structure. This dataset from Siple Dome, obtained using transportable and solar-poweredequipment, includes surface energy balance measurements, meteorology and cloud remote sensing. To demonstrate how these data can be used to evaluate model performance, comparisons are made with meteorological reanalysis known to give generally good performance over Antarctica (ERA5). Surface albedo measurements show expected variability with observed cloud amount, and can be used to evaluate a model's snowpack parameterization. One case study discussed involves a squall with northerly winds, during which ERA5 fails to produce cloud cover throughout one of the days. A second case study illustrates how shortwave spectroradiometer measurements that encompass the 1.6-micron atmospheric window reveal cloud phase transitions associated with cloud lifecycle. Here, continuously precipitating mixed-phase clouds become mainly liquid water clouds from local morning through the afternoon, not reproduced by ERA5. We challenge researchers to run their various regional or global models in a manner that has the large-scale meteorology follow the conditions of this field campaign, compare cloud and radiation simulations with this Siple Dome dataset, and potentially investigate why cloud microphysical simulations or other model components might produce discrepancies with these observations.

cloud remote sensing↗

NASA NTRS2023

Information Systems Technology for NASA Earth Systems Digital Twins (ESDT)

The term “Digital Twin” was first used in 2002 for product lifecycle management. Since then, Digital Twin concepts have been proposed in various domains until very recently for Earth Science. For NASA’s Advanced Information Systems Technology (AIST) Program, an Earth System Digital Twin (ESDT) is defined as composed of three components: 1. A Digital Replica, i.e., an integrated picture of the past and current states of Earth systems 2. Forecasting capabilities, providing an integrated picture of how Earth systems will evolve in the future from the current state 3. Impact Assessment capabilities, providing an integrated picture of how Earth systems could evolve under different hypothetical what-if scenarios. Developing such a vision will require technologies related to: integrating continuous observations from various disparate sources; developing frameworks that builds on inter-connected models; improving the speed and accuracy of integrated prediction, analysis and visualization capabilities (e.g., by using machine learning); and utilizing causality and uncertainty quantification to improve our understanding of the evolution of Earth Science systems as a function of their interactions with other Earth and human systems. In addition, AIST is also investigating interoperability standards to federate multiple Digital Twins, as well as computational resources required by those systems.

Earth Science Remote Sensing; Information Systems↗