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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

What FM can offer DFCS design

The results of aircrafts and spacecrafts flight tests are reported. It is shown that the problems of Digital Flight Control Systems (DFCS) are the problems of systems whose complexity has exceeded the reach of the intellectual tools employed. It is also shown that intuition, experience, and techniques derived from mechanical and analog systems are insufficient for complex, integrated, digital systems. Formal Methods (FM) of computer science can offer DFCS systematic techniques for the construction of trustworthy software, including: techniques for the precise specification of requirements and the development of designs; systematic approaches to the design and structuring of distributed and concurrent systems; fault tolerance algorithms; and systematic methods of testing and analytic methods of verification.

Rushby, John↗
NASA NTRS1993

The Trustworthy Digital Camera: Restoring Credibility to the Photographic Image

The increasing sophistication of computers has made digital manipulation of photographic images incredibly easy to perform and, as time goes on, increasingly difficult to detect. The proposed device is a new type of digital camera whose output can be certified as being unretouched, and can restore the credibility that the photographic image once enjoyed. The Trustworthy Digital Camera employs public key encryption techniques at the system level, and produces an encrypted digital signature and a standard-format digital image file each time a picture is taken. Although digitally retouching or altering these image files would still be possible, doing so will cause a mismatch with the verifying signature, proving that the image is not an untouched original...

Friedman, Gary L.↗
NASA NTRSDate not supplied

TRUST, Trustworthiness and EOSDIS

In recent years there has been considerable attention by the international scientific research and applications community to ensure high quality of data and information management. The terms FAIR (Findable, Accessible, Interoperable, Reusable) data, TRUST (Transparency, Responsibility, User Community, Sustainability, and Technology) principles, and CARE (Collective Benefit, Authority to Control, Responsibility, and Ethics) principles have come into vogue during the last decade. NASA has been managing data and information for over 60 years. NASA’s Earth Observing System Data and Information System (EOSDIS) has been in operation for over 25 years, managing most of NASA’s Earth science data. Trustworthiness is a goal that NASA has always strived to achieve or exceed, because it: enables the success of any NASA science mission; inspires general science research and applications; justifies the cost of operations; contributes to the value of NASA’s Open Data Policy; and influences the long term, historical view for the data collection. Given the recent growth of interest in TRUST principles, it is useful to assess and show how NASA’s attention to trustworthiness maps into those principles. This presentation addresses shows how the various steps that have been taken by the Earth Science Data and Information System (ESDIS) Project in the implementation and evolution of EOSDIS map into the TRUST principles.

Remote Sensing↗
NASA NTRSDate not supplied

Uncertainty Quantification using Deep Ensembles for Decision Making in Cyber-Physical-Human Systems

In this paper and its companion, Differential Equation Approximation Using Gradient-Boosted Quantile Regression, Robison et al., we examine an approach to quantifying model uncertainty with the aim of increasing the trustworthiness of computational models in human-machine interactions. In Differential Equation Approximation Using Gradient-Boosted Quantile Regression, we focus on gradient-boosted decision trees, while in this one, we give more details about deep ensembles. Uncertainty quantification is crucial for building trustworthy autonomous decision-making agents in human-machine teams. There are two types of uncertainties: aleatoric and epistemic. The former is related to the inherent stochasticity (noise) of the process, whereas the latter is associated with the lack of knowledge or representation capability of models, such as neural networks. By lack of knowledge, we mean the model’s inability to accurately predict outputs for all possible inputs. The aleatory uncertainty can be estimated fairly easily with, for example, filters, whereas epistemic uncertainty is challenging to compute. This paper uses deep ensembles to quantify both aleatory and epistemic uncertainty. It can act as an uncertainty-aware surrogate transition model for decision-making frameworks. "Uncertainty-aware" means that the surrogate transition model should make predictions along with confidence in those predictions. In the context of decision-making, the transition models are ordinary differential equations (ODEs). Since ODEs can be simulated to make one-step or multi-step predictions, a good surrogate model for them should perform reasonably well in both modes. In a multi-step approach, the trajectory sampling method TS∞ was used to propagate uncertainty over multiple steps. The cartpole dynamical system was selected to demonstrate the ability of deep ensembles as good surrogate transition models for decision-making frameworks. The deep ensembles modeled the dynamics of cartpole ODEs and made uncertainty-aware predictions in single-step and multi-step transition modes.

CPH systems↗
NASA NTRS1977

Numerical aerodynamic simulation facility preliminary study, volume 2 and appendices

Data to support results obtained in technology assessment studies are presented. Objectives, starting points, and future study tasks are outlined. Key design issues discussed in appendices include: data allocation, transposition network design, fault tolerance and trustworthiness, logic design, processing element of existing components, number of processors, the host system, alternate data base memory designs, number representation, fast div 521 instruction, architectures, and lockstep array versus synchronizable array machine comparison.

Source record↗
NASA NTRS2020

Human-in-the-loop Robotic Control for Human-Robot Collaboration

The prospect of a collaborative work environment between humans and robotic automation in a manufacturing setting [1] provides the motivation for finding innovative solutions to human-in-the-loop control for safe, efficient, and trustworthy human-robot collaboration (HRC), or HR interaction (HRI), in cyberphysical human systems (CPHSs) [2], [3]. Studies in [4] show that collaborative automation can be beneficial to 90% of approximately 300,000 small-to-medium-scale enterprises in the United States. In the paradigm of human-centered automation [5], human safety, ergonomics, and the collaborative efficiency of the work are given the utmost importance. Traditional methods to ensure the safety of humans around factory robots involve the use of cages. Recent work looked beyond cage-based safety to provide robot control and sensing-driven solutions for human safety around robots [6]-[8].

Ashwin P. Dani↗
NASA NTRS1995

Summary of a Crew-Centered Flight Deck Design Philosophy for High-Speed Civil Transport (HSCT) Aircraft

Past flight deck design practices used within the U.S. commercial transport aircraft industry have been highly successful in producing safe and efficient aircraft. However, recent advances in automation have changed the way pilots operate aircraft, and these changes make it necessary to reconsider overall flight deck design. Automated systems have become more complex and numerous, and often their inner functioning is partially or fully opaque to the flight crew. Recent accidents and incidents involving autoflight system mode awareness Dornheim, 1995) are an example. This increase in complexity raises pilot concerns about the trustworthiness of automation, and makes it difficult for the crew to be aware of all the intricacies of operation that may impact safe flight. While pilots remain ultimately responsible for mission success, performance of flight deck tasks has been more widely distributed across human and automated resources. Advances in sensor and data integration technologies now make far more information available than may be prudent to present to the flight crew.

Palmer, Michael T.↗
NASA NTRS2018

A Standard Reference Model for Data Archives

An implementable Data Archive Architecture is being developed for trusted digital repositories based on the Reference Model for an Open Archival Information System (OAIS) – ISO 14721. A set of interoperable protocols and interface specifications are planned that will offer capabilities for accessing, merging, and re-using data, both within and across the operational boundaries of trustworthy digital repositories. The model will also provide support for the fundamental scientific need to verify the reproducibility of results. This standards development task is being performed by the Data Archive Interoperability (DAI) working group within the Consultative Committee for Space Data Systems (CCSDS). The architecture integrates concepts from the OAIS Reference Model, the ISO/IEC 11179 Metadata Registry (MDR) standard, the CCSDS Reference Architecture for Space Information Management (RASIM), the proposed draft recommended practice document, Information Preparation to Enable Long Term Use (IPELTU), and three decades of digital repository development for science research.

Ambacher, Bruce↗
NASA NTRS1995

Metrics and Benchmarks for Visualization

What is a "good" visualization? How can the quality of a visualization be measured? How can one tell whether one visualization is "better" than another? I claim that the true quality of a visualization can only be measured in the context of a particular purpose. The same image generated from the same data may be excellent for one purpose and abysmal for another. A good measure of visualization quality will correspond to the performance of users in accomplishing the intended purpose, so the "gold standard" is user testing. As a user of visualization software (or at least a consultant to such users) I don't expect visualization software to have been tested in this way for every possible use. In fact, scientific visualization (as distinct from more "production oriented" uses of visualization) will continually encounter new data, new questions and new purposes; user testing can never keep up. User need software they can trust, and advice on appropriate visualizations of particular purposes. Considering the following four processes, and their impact on visualization trustworthiness, reveals important work needed to create worthwhile metrics and benchmarks for visualization. These four processes are (1) complete system testing (user-in-loop), (2) software testing, (3) software design and (4) information dissemination. Additional information is contained in the original extended abstract.

Uselton, Samuel P.↗
NASA NTRS2017

NASA's EOSDIS, Trust and Certification

NASA's Earth Observing System Data and Information System (EOSDIS) has been in operation since August 1994, managing most of NASA's Earth science data from satellites, airborne sensors, filed campaigns and other activities. Having been designated by the Federal Government as a project responsible for production, archiving and distribution of these data through its Distributed Active Archive Centers (DAACs), the Earth Science Data and Information System Project (ESDIS) is responsible for EOSDIS, and is legally bound by the Office of Management and Budgets circular A-130, the Federal Records Act. It must follow the regulations of the National Institute of Standards and Technologies (NIST) and National Archive and Records Administration (NARA). It must also follow the NASA Procedural Requirement 7120.5 (NASA Space Flight Program and Project Management). All these ensure that the data centers managed by ESDIS are trustworthy from the point of view of efficient and effective operations as well as preservation of valuable data from NASA's missions. Additional factors contributing to this trust are an extensive set of internal and external reviews throughout the history of EOSDIS starting in the early 1990s. Many of these reviews have involved external groups of scientific and technological experts. Also, independent annual surveys of user satisfaction that measure and publish the American Customer Satisfaction Index (ACSI), where EOSDIS has scored consistently high marks since 2004, provide an additional measure of trustworthiness. In addition, through an effort initiated in 2012 at the request of NASA HQ, the ESDIS Project and 10 of 12 DAACs have been certified by the International Council for Science (ICSU) World Data System (WDS) and are members of the ICSUWDS. This presentation addresses questions such as pros and cons of the certification process, key outcomes and next steps regarding certification. Recently, the ICSUWDS and Data Seal of Approval (DSA) organizations merged their Core Trustworthy Data Repositories Requirements and require that members be recertified every three years. Given the rigor with which NASA manages the ESDIS Project and the DAACs, the recertification through WDSDSA, while involving some additional work, is a relatively simple process.

Certification↗
NASA NTRS2024

Discrete Event Simulation-Based Timeline Validation Using R2U2

The Gateway Vehicle Systems Manager (VSM), the top-level software control system in a distributed, hierarchical Autonomous System Management Architecture is, like most modern spacecraft software control systems, heavily data-driven. For example, schedules (timelines) will be developed on the ground and, due to the high degree of autonomy, contain complex procedures involving conditional branching, variable timing, and resource contention resolution. In order to verify that an uploaded timeline will function correctly, it is necessary to explore the feasible set of possible executions. While it is possible to test a timeline using a mission simulation, the complexity of the system and duration of a timeline limits the number of trials and therefore the test coverage. To address this problem, the VSM team is using a discrete event system model that can rapidly generate from a timeline sets of event sequences using Monte Carlo techniques. To achieve rapid and trustworthy checking of the event sequences, we use an offline version of the runtime model checking tool R2U2. This presentation describes the approach the VSM team is using to implement the discrete event simulation and evaluate event sequences using R2U2. The presentation will discuss: 1. Description of the timelines by VSM in the context of VSM operations 2. Expansion of a timeline into a sequence of atomic events 3. Adjustment, in the Monte Carlo environment, of an event sequence to account for uncertainty, external events, and failures 4. Definition of R2U2 input and mission-time linear temporal logic files 5. Generation and use of R2U2 verdict sequences 6. Lessons learned and future work

Verification↗
NASA NTRS2017

ExEP Yield Modeling Tool and Validation Test Results

EXOSIMS is an open-source simulation tool for parametric modeling of the detection yield and characterization of exoplanets. EXOSIMS has been adopted by the Exoplanet Exploration Programs Standards De nition and Evaluation Team (ExSDET) as a common mechanism for comparison of exoplanet mission concept studies. To ensure trustworthiness of the tool, we developed a validation test plan that leverages the Python-language unit-test framework, utilizes integration tests for selected module interactions, and performs end-to-end cross- validation with other yield tools. This paper presents the test methods and results, with the physics-based tests such as photometry and integration time calculation treated in detail and the functional tests treated summarily. The test case utilized a 4m unobscured telescope with an idealized coronagraph and an exoplanet population from the IPAC radial velocity (RV) exoplanet catalog. The known RV planets were set at quadrature to allow deterministic validation of the calculation of physical parameters, such as working angle, photon counts and integration time. The observing keepout region was tested by generating plots and movies of the targets and the keepout zone over a year. Although the keepout integration test required the interpretation of a user, the test revealed problems in the L2 halo orbit and the parameterization of keepout applied to some solar system bodies, which the development team was able to address. The validation testing of EXOSIMS was performed iteratively with the developers of EXOSIMS and resulted in a more robust, stable, and trustworthy tool that the exoplanet community can use to simulate exoplanet direct-detection missions from probe class, to WFIRST, up to large mission concepts such as HabEx and LUVOIR.

Nunez, Paul↗
NASA NTRS2003

Personalizing Knowledge Delivery Services for Emerging Knowledge Processes (EKPs): A Conceptual Framework

The contents include: 1) What do most KMS in use today assume?; 2) Assumptions are violated when KMS is used by EKP workers - Why?; 3) Current State of KMS for EKPs are inadequate; 4) What would an "adequate" KMS for EKPs look like?; 5) "User-as-Consumer" Analogue: Ecommerce/Eem ployee Services; 6) Why is an ideal KMS for EKPs hard to achieve?; 7) So, what type of KMS design would work?; 8) Human-Based KMS for EKP - Proposal Call Managers at R&DLAB; 9) Proposal Call Managers (PCMs); 10) Specific PCM tasks; 11) Why is a R&DLAB PCM a human metaphor for a KMS for EKP?; 12) Data Collection; 13) Finding #1; 14) Finding #2; 15) Finding #3; 16) Factors affecting How/when; 17) Finding #4; 18) Finding #5; 19) Implication#l for a KMS for EKP: From System to Service; 20) Implication #2: From technology or human-centric to Mixed Mode; 21) Implication #3: From Simple User Profiles to Dynamic Delivery Profiles; 22) Implication #4: Maintaining a trustworthy environment; 23) Implication #5: Constructing a dynamic delivery profile; 24) Implications for Research: Model; and 25) Example Research Qs on KMS Support for EKPs.

knowledge management↗
NASA NTRS2016

Traveler: Trustworthy Autonomy

This presentation discusses the FAA belief that this research may hold the key to enabling safe autonomous operation of vehicles.

complex systems↗
NASA NTRS1998

Analytic Verification of Flight Software

In the realm of space exploration, the biggest obstacle to widespread application of autonomy in flight software is not technical feasibility; it is doubt about its trustworthiness as a replacement for human-in-the-loop decision-making.

model↗