Results for “software software requirements quality” — Page 23

NASA NTRSDate not supplied

Custom Integration of Multiple Medical Functionalities

INTRODUCTION: Previous spaceflight experience and results from probabilistic risk assessment of spaceflight medical risk have highlighted the need for vital sign measurements, medical scopes, and clinical imaging tools for managing medical conditions during spaceflight. The Human Research Program’s Exploration Medical Capability (ExMC) Element and the Mars Campaign Office’s Exploration Medical Integrated Product Team (XMIPT) have performed ground-based evaluations of two Commercial-off-the-Shelf (COTS) Multi-functional Integrated Medical (MIM) devices, which integrate various medical capabilities together in one device. The key findings from these evaluations are presented in a complementary presentation, leaving this presentation to focus on forward recommendations for customized integration of multiple medical functionalities. KEY COMPONENTS: The key features of a custom integration of multiple medical functionalities includes devices and capabilities that optimally reduce medical risk. The COTS MIM devices incorporated functionality for best supporting Earth-based, emergency, pre-hospital care. Our custom integration will use probabilistic risk assessment tools, such as the Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT), to determine the optimal functionality to include based on medical risk minimization. An additional feature of custom integration includes the ability to adapt to different requirements within different vehicles and/or missions. The COTS MIM devices store data in patient specific records, however, the format of the records is not modifiable, and data are not easily transferred from the MIM device to a central data architecture outside of the manufacturer’s established system. Our concept for custom integration will use devices that have an open application programming interface, which can easily connect to independent data architectures and third-party visualization software. The ultrasound capabilities included within the COTS MIM devices did not satisfy many of the Artemis Research and Operations Working Group’s ultrasound functional needs, and therefore, incorporation of higher quality ultrasound capabilities within a customized integration will be beneficial. The COTS MIM devices included minimal procedural guidance and clinical decision support tools. Supplemental tools of this type would need to be supplied along with the COTS MIM devices if they were to be used operationally, so another advantage of customization is the ability to integrate these support tools along with the medical functionality, for a more streamlined user experience. CONCLUSION: Investigation of a customized integration of medical functionality provides a method for further understanding the needs of a long-term exploration spaceflight medical system. The crew members of these exploration missions will need to operate more and more independently from Earth-based ground support. Therefore, having an optimized, streamlined medical system, which contains the functionality and supporting information needed, while remaining within mission and vehicle constraints, will help to maintain crew health and performance, which is necessary for achieving high levels of mission success.

B E Lewandowski↗

NASA NTRS2016

The Core Flight System (cFS) Community: Providing Low Cost Solutions for Small Spacecraft

In February 2015 the NASA Goddard Space Flight Center (GSFC) completed the open source release of the entire Core Flight Software (cFS) suite. After the open source release a multi-NASA center Configuration Control Board (CCB) was established that has managed multiple cFS product releases. The cFS was developed and is being maintained in compliance with the NASA Class B software development process requirements and the open source release includes all Class B artifacts. The cFS is currently running on three operational science spacecraft and is being used on multiple spacecraft and instrument development efforts. While the cFS itself is a viable flight software (FSW) solution, we have discovered that the cFS community is a continuous source of innovation and growth that provides products and tools that serve the entire FSW lifecycle and future mission needs. This paper summarizes the current state of the cFS community, the key FSW technologies being pursued, the development/verification tools and opportunities for the small satellite community to become engaged. The cFS is a proven high quality and cost-effective solution for small satellites with constrained budgets.

System↗

NASA NTRSDate not supplied

Assessing the Needs of NASA's Near Real-Time Earth Observation Products

"The 2017-2027 Decadal Survey for Earth Science and Applications from Space stated that NASA's Earth Science with planned implementation of applications provides sustained earth observations for societal benefits [1]. The Decadal Survey indicated that data latency is invaluable for time-sensitive applications including disaster risk reduction, wildland fire carbon emissions quantification, real-time measurements of the state of the hydrologic systems and many more. Data latency refers to the time between earth observation and data products available to users. During the past 13 years, NASA's Land, Atmosphere Near Real-Time Capability for Earth Observing Systems (LANCE) continues to provide free access to earth observation products that are made available much quicker than routine processing allows. The latency of most LANCE data products is Near Real-time (NRT) which is defined as less than three hours from satellite observations [2]. LANCE is managed by the Earth Science Data and Information System (ESDIS) Project at NASA Goddard Space Flight Center [3], and a User Working Group (UWG) is responsible for providing guidance to LANCE. LANCE data are used by direct users and brokers who add value to the data [4]. NASA Earth Applied Sciences Program (ASP) is one of the primary users of LANCE, which collaborates with partner organizations and provides support to scientists to solve problems in applications of earth observations. ASP promotes the use of LANCE NRT data products to demonstrate applications in decision making, facilitates end-user feedback to the science team to improve data products, and provides information on future demands for research. LANCE supports applications that need a rapid response including detecting wildland fires and volcanic eruptions, tracking smoke, ash and dust plumes, monitoring air quality and tracking extreme weather events such as hurricanes, landslides, and floods. To gather feedback regarding the availability, accessibility and actionability of NASA's NRT data products for societal benefit, three surveys and a few discussions with experts involved in the topic within ASP were conducted from the perspective of users. Feedback has been collected from users who are interested in using low latency NASA data within application communities of agriculture, disasters, water resources, health and air quality, ecological conservation, wildland fires and capacity building. Analysis-ready NRT data products in a variety of formats have been mentioned many times in the collected feedback, especially for applied users with little to no experience using research-grade earth observation products. Users prefer to have products that can be easily integrated into their existing workflows and take their analysis to the data. HDF5 is a commonly used data format for research, but typically requires some conversion to a more friendly format for applications and regular use in decision-making. Users prefer the GeoTIFF data format that can be directly ingested into a GIS mapping software and platform for data analysis and visualization. For example, LANCE’s fire, flood, SO2 and Black Marble Nighttime Blue/Yellow Composite data products have been integrated into NASA Disasters Mapping Portal, which is an GIS-based open data portal, for users in the disaster management community. There are 291 LANCE NRT layers available through GIBS and Worldview, where users can download a snapshot in GeoTIFF format. Operational users expect data to be processed as close to the user as possible. The collected feedback indicates that LANCE fire products within 3 hours latency would meet the needs of the wildland fire community. The ideal latency for volcanic application is 10-15 minutes. Users in Volcanic Ash Advisory Centers (VAAC) reported that the first forecast volcanic product should be issued within 75 minutes from the volcano eruption [5]. Overall, for disaster applications, data latency within 3 hours is useful while latency greater than 12 hours is not timely enough for operational use. Capacity building and training are critical for users to be able to access, interpret and use data products and tools for their decision making, especially for applied users with limited experience using earth observation products. LANCE data products have been used in a number of capacity building projects domestically and internationally [6]. As LANCE continues to bring new products into the system, users request training to utilize LANCE new and upcoming data products and capabilities in their applications. Due to the limitation of bandwidth and downstream flow paths, users in some developing countries need tools to select and download data for a specific area of interest instead of bulk downloads. The collected feedback also shows the lack of available SAR satellite low latency data products. The advantages of SAR to monitor conditions and changes on the ground through darkness, clouds, volcanic ash, and other atmospheric conditions, are appealing to low latency users. For example, terabytes of low latency but cloudy optical images are not helpful in rapidly identifying the extent of flood or fire impacts. LANCE could be complemented with low latency measurements via the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) mission [7]. Requests for higher spatial resolution products are expressed. A user from the wildland fire management community reported that products with 30-m spatial resolution could be used to detect small fires. The 30-m Landsat OLI fire data is now part of NASA’s Fire Information for Resource Management System (FIRMS) US/Canada [8]. Within the open and free NASA resources, LANCE disseminates NRT data products in a manner that allows them to be accessible and understandable to both scientific and applied users. In many application areas, latency plays an important or even decisive role where low latency earth observations help people to observe areas of interest, detect and track changes in the environment and make timely decisions. NASA’s Earth Applied Sciences Program promotes the use of LANCE NRT products and builds a bridge between application users and research teams. The collected feedback indicates data latency within 3 hours is useful for most of the applications, and shows the needs of user-friendly, analysis-ready products, and requests training on LANCE’s new and upcoming data products. User feedback has been provided to LANCE UWG for guidance and recommendations, and for translating findings into something actionable.

Tian Yao↗

NASA NTRSDate not supplied

NASA Earth Systems Digital Twins (ESDT)

"Similarly to artificial intelligence, which is now revolutionizing many aspects of our daily lives, Earth system digital twin technologies have the potential to revolutionize the way Earth Science research will be conducted in the future, and how results and knowledge from this research will provide information to support decision making and yield impactful societal benefits. An Earth System Digital Twin or ESDT is a dynamic and interactive information system that first provides a digital replica of the past and current states of the Earth or Earth system as accurately and timely as possible; second, allows for computing forecasts of future states under nominal assumptions and based on the current replica; and third, offers the capability to investigate many hypothetical scenarios under varying impact assumptions. In other words, an ESDT provides the integrated What-Now, What-Next, and What-If pictures of the Earth or Earth system, by continuously ingesting newly observed data and by leveraging multiple interconnected models, machine learning as well advanced computing and visualization capabilities. Digital twins have been developed in engineering since 2002, but the interest in digital twins for the Earth domain is more recent and stems from the convergence of several developments: - The huge amount of diverse data that has now been collected continuously for more than 50 years, and that is becoming more and more difficult to access, understand, and utilize. - At the same time, because of climate change and its impacts the information produced by all of this data is becoming of interest to many new non-traditional users for analyzing and predicting various phenomena. - Because of advances in computational and visualization capabilities and the parallel unprecedented development of machine learning (ML), extracting relevant information from these large amounts of data and running complex models faster has become possible. As a result, it is becoming necessary and possible to build intuitive and interactive frameworks that will enable users with various skill levels and/or organizational hierarchy levels to easily access large amounts of targeted information along with the relevant tools and models (Earth system and human activity models), to support them in analyzing and visualizing this information, to help them understand interactions among models, to visualize the potential outcomes of various impacts, and to support decision or policy making. The full power of digital twins is that, through an integrated representation and standardized tools and software technologies, the same digital replica can address the needs of multiple users at various resolutions (spatial and temporal) and for various applications (science, economic, policy, etc.) – “from farmer to scientist”. With all these interests at stake, the challenges of building optimal digital twins are many and complex. The first challenge is to determine if a Digital Twin should be global or local, and multi-domain or thematic. For example, some domains such as Climate or Weather will require a global Digital Twin or Digital Twin capabilities while science areas such as Biodiversity might be more local. We can also envision that multiple thematic ESDTs, e.g., Air Quality, Wildfires, Hydrology could be federated or provide input to other ESDTs, either on a regional level or to a more global ESDT. Overall, we can imagine a future “web” of Digital Twins co-existing in a hierarchy or in a network, and capable of being connected or federated depending on the needs. This last point brings up the very important challenge of interoperability, including standards and protocols that will need to be built into these systems from the beginning. Each individual digital twin would have full flexibility in internal construction but would need standards-based interfaces (input and output) or hooks to make it compatible with others. Another challenge when building digital twins will be to decide how to organize each digital replica. Based on the applications targeted by the DT under implementation, various amounts and types of raw data, Analysis Ready Data (ARD) and information will need to be incorporated. Depending on the required latencies and needs of the users, various solutions can be considered, including Data Cubes, Data Lakes, pointers, or computing information on demand. We envision that each ESDT will choose a solution adapted to its specific objectives. Another important challenge is the type(s) of visualization that will be used, as well as the level of interactivity and refresh rate that will be required. Again, this will depend on the objectives of the ESDT, but also on the various users’ needs. In most cases, several types of visualizations and human interfaces will need to be offered depending on the projected users of that system. In parallel to the challenges highlighted above, there are also many tools and technologies that will need to be developed or improved for all types of digital twins. Among those are improved machine learning technologies, for example providing explainability, but also ML techniques for causality and providing a better integration of physics models. Additionally, reliable uncertainty quantification methods will be needed for all ESDT components, from validating data fusion and assimilation to assessing the accuracy of ML models and weighing the values of decisions supported by those systems. This presentation introduces the ESDT concept, presents several ESDT use cases, and a proposed ESDT architecture framework, as well as various technologies being developed by the Advanced Information Systems Technology (AIST) Program."

Earth Science Remote Sensing; Information Systems↗

NASA NTRS1981

NALNET book system: Cost benefit study

The goals of the NASA's library network system, NALNET, the functions of the current book system, the products and services of a book system required by NASA Center libraries, and the characteristics of a system that would best supply those products and services were assessed. Emphasis was placed on determining the most cost effective means of meeting NASA's requirements for an automated book system. Various operating modes were examined including the current STIMS file, the PUBFILE, developing software improvements for products as appropriate to the Center needs, and obtaining cataloging and products from the bibliographic utilities including at least OCLC, RLIN, BNA, and STIF. It is recommended that NALNET operate under the STIMS file mode and obtain cataloging and products from the bibliographic utilities. The recommendations are based on the premise that given the current state of the art in library automation it is not cost effective for NASA to maintain a full range of cataloging services on its own system. The bibliographic utilities can support higher quality systems with a greater range of services at a lower total cost.

Dewath, N. V.↗

NASA NTRS2011

Software Development Standard Processes (SDSP)

A JPL-created set of standard processes is to be used throughout the lifecycle of software development. These SDSPs cover a range of activities, from management and engineering activities, to assurance and support activities. These processes must be applied to software tasks per a prescribed set of procedures. JPL s Software Quality Improvement Project is currently working at the behest of the JPL Software Process Owner to ensure that all applicable software tasks follow these procedures. The SDSPs are captured as a set of 22 standards in JPL s software process domain. They were developed in-house at JPL by a number of Subject Matter Experts (SMEs) residing primarily within the Engineering and Science Directorate, but also from the Business Operations Directorate and Safety and Mission Success Directorate. These practices include not only currently performed best practices, but also JPL-desired future practices in key thrust areas like software architecting and software reuse analysis. Additionally, these SDSPs conform to many standards and requirements to which JPL projects are beholden.

Lavin, Milton L.↗

NASA NTRSDate not supplied

Production of Flight Instruments for Multi-Satellite Constellations

Recent National Academy of Science Decadal Surveys in space and Earth-science have called for simultaneous, distributed multi-point measurements in and from space, requiring constellations of small spacecraft. Small satellites have demonstrated their utility for enabling high-quality science measurements and observations. NASA missions have leveraged advances in sensor miniaturization, technology innovations, and new small satellite mission architectures to enable meaningful measurement-based scientific investigations that operate on small satellites and that are responsive to science objectives described in National Academy of Science Decadal Surveys. The advent of high capability small spacecraft enables consideration of science missions involving multiple small spacecraft, constellations of a few or many for simultaneous distributed in situ observations or remote observations from a variety of viewpoints. The central challenge for fielding instrumented space-flight constellations is to provide the required multiple sets of fully verified and calibrated instrument hardware, software, and operational processes from within a one-off project-based scientific space flight culture. Although there are numerous commercial entities providing “off-the shelf” spacecraft and avionics, the challenge lies in the multi-unit production of the uniquely targeted instrumentation necessary to perform the specific measurements required for a particular science investigation. Traditionally, the cost of such instrumentation has represented a significant portion of the hardware cost for a mission and posed the highest risk area for implementation. A shift in paradigm from large science platforms to constellations of smaller satellites drives the challenge to build instruments in a quasi-production environment. This paper describes key aspects of, and challenges encountered in the development program for the successful production of instruments for the Fast-Plasma Investigation (FPI) instrument suite of 64 flight plasma spectrometers and supporting electronics on the NASA Magnetospheric Multiscale (MMS) 4 satellite constellation mission. Although the MMS mission was not composed of small satellites, there are key aspects of the instrument production that apply directly to constellations of small satellites.

Arthur D Jacques↗

NASA NTRSDate not supplied

Problem Complexity and LLM: H-M Team Reliability in Challenging Environments

In traditional human-machine operations, the functional decomposition of actions and responsibilities among various agents is assigned a priori. For instance, in current air traffic operations, although assisted by software, human pilots have the ultimate control of aircraft. Multiagent human-machine and machine-machine systems will face problems of varying and potentially unpredicted complexity in future challenging scenarios of planetary, en route, and orbital activities. Hence, it is important to enable dynamic transfer of decision-making to appropriate team members, human or machine, depending on which agent is best equipped to solve that specific problem on a time budget. In this paper, we consider aspects of problem-solving and its modeling that affect the outcomes of decision-making as a function of solution quality and the likelihood of solving the problem on a required time budget. We focus on Large Language Models (LLM) as potential machine teammates and conclude that practical, predictive modeling of their performance, at the current stage of their development, is infeasible. Simple examples help us illustrate that current LLM will require fundamental advancements to provide reliable support in team decision-making, especially in safety-critical and time-critical domains. The study is not meant to diminish the value of the remarkable capabilities of LLM, but rather to gain a better understanding of the technology’s appropriate use and the needed additions.

function allocation↗

NASA NTRSDate not supplied

Production of Flight Instruments for Multi-Satellite Constellations

Recent National Academy of Science Decadal Surveys in space and Earth-science have called for simultaneous, distributed multi-point measurements in and from space, requiring constellations of small spacecraft. Small satellites have demonstrated their utility for enabling high-quality science measurements and observations. NASA missions have leveraged advances in sensor miniaturization, technology innovations, and new small satellite mission architectures to enable meaningful measurement-based scientific investigations that operate on small satellites and that are responsive to science objectives described in National Academy of Science Decadal Surveys. The advent of high capability small spacecraft enables consideration of science missions involving multiple small spacecraft, constellations of a few or many for simultaneous distributed in situ observations or remote observations from a variety of viewpoints. The central challenge for fielding instrumented space-flight constellations is to provide the required multiple sets of fully verified and calibrated instrument hardware, software, and operational processes from within a one-off project-based scientific space flight culture. Although there are numerous commercial entities providing “off-the shelf” spacecraft and avionics, the challenge lies in the multi-unit production of the uniquely targeted instrumentation necessary to perform the specific measurements required for a particular science investigation. Traditionally, the cost of such instrumentation has represented a significant portion of the hardware cost for a mission and posed the highest risk area for implementation. A shift in paradigm from large science platforms to constellations of smaller satellites drives the challenge to build instruments in a quasi-production environment. This paper describes key aspects of, and challenges encountered in the development program for the successful production of instruments for the Fast-Plasma Investigation (FPI) instrument suite of 64 flight plasma spectrometers and supporting electronics on the NASA Magnetospheric Multiscale (MMS) 4 satellite constellation mission. Although the MMS mission was not composed of small satellites, there are key aspects of the instrument production that apply directly to constellations of small satellites.

Arthur D Jacques↗

NASA NTRS1993

TransFit: Finite element analysis data fitting software

The Advanced X-Ray Astrophysics Facility (AXAF) mission support team has made extensive use of geometric ray tracing to analyze the performance of AXAF developmental and flight optics. One important aspect of this performance modeling is the incorporation of finite element analysis (FEA) data into the surface deformations of the optical elements. TransFit is software designed for the fitting of FEA data of Wolter I optical surface distortions with a continuous surface description which can then be used by SAO's analytic ray tracing software, currently OSAC (Optical Surface Analysis Code). The improved capabilities of Transfit over previous methods include bicubic spline fitting of FEA data to accommodate higher spatial frequency distortions, fitted data visualization for assessing the quality of fit, the ability to accommodate input data from three FEA codes plus other standard formats, and options for alignment of the model coordinate system with the ray trace coordinate system. TransFit uses the AnswerGarden graphical user interface (GUI) to edit input parameters and then access routines written in PV-WAVE, C, and FORTRAN to allow the user to interactively create, evaluate, and modify the fit. The topics covered include an introduction to TransFit: requirements, designs philosophy, and implementation; design specifics: modules, parameters, fitting algorithms, and data displays; a procedural example; verification of performance; future work; and appendices on online help and ray trace results of the verification section.

Freeman, Mark↗

NASA NTRS2013

Web Application Software for Ground Operations Planning Database (GOPDb) Management

A Web application facilitates collaborative development of the ground operations planning document. This will reduce costs and development time for new programs by incorporating the data governance, access control, and revision tracking of the ground operations planning data. Ground Operations Planning requires the creation and maintenance of detailed timelines and documentation. The GOPDb Web application was created using state-of-the-art Web 2.0 technologies, and was deployed as SaaS (Software as a Service), with an emphasis on data governance and security needs. Application access is managed using two-factor authentication, with data write permissions tied to user roles and responsibilities. Multiple instances of the application can be deployed on a Web server to meet the robust needs for multiple, future programs with minimal additional cost. This innovation features high availability and scalability, with no additional software that needs to be bought or installed. For data governance and security (data quality, management, business process management, and risk management for data handling), the software uses NAMS. No local copy/cloning of data is permitted. Data change log/tracking is addressed, as well as collaboration, work flow, and process standardization. The software provides on-line documentation and detailed Web-based help. There are multiple ways that this software can be deployed on a Web server to meet ground operations planning needs for future programs. The software could be used to support commercial crew ground operations planning, as well as commercial payload/satellite ground operations planning. The application source code and database schema are owned by NASA.

Lanham, Clifton↗

DOE OSTI2021

Development of the IES Plug-and-Play Framework

This report discusses the status of the flexible plug-and-play framework development currently ongoing that aims to integrate Modelica/Dymola with the Risk Analysis and Virtual ENvironment (RAVEN) software in terms of both Functional Mock-Up Interface (FMI)/Functional Mock-Up Unit (FMU) construction and repository structures that aim to ease the sharing and simulation of complex dynamic models. This report aims to provide an overview of all the performed activities resolving around the deployment of methods, software infrastructures, guidelines and workflow for the construction and usage of models, encapsulated using the FMI/FMU protocols and standards. In particular, the report is organized in three main macro-subjects, which are connected to each other: - FMI/FMU adaptors for modelica models - HYBRID repository new structure and open-source deployment - RAVEN FMI/FMU exporting capabilities and Artificial Intelligence (AI)-based analysis acceleration. The first part of the report discusses the FMI/FMU adaptors that have been created within the HYBRID repository to allow users to quickly export models, such as FMUs. Several examples are shown that highlight the step-by-step process of converting an existing Modelica model into an FMU for use within the Dymola platform. Simulation results demonstrate that, while minor differences may occur, the overall control, trends, and solution integrity are maintained between standard Modelica simulation and FMU simulation results. However, it is worth noting that, for small systems, the FMU results have a slower simulation time than the Modelica only simulation. Using this process, a company can provide models that contain proprietary information to entities without disclosing any of the information about the model that could be considered business sensitive. Such an ability would allow institutions to bypass the necessity of “whitewashing” data. In the second part of the report, the new structure of the HYBRID repository is discussed with a major focus on the series of updates that has been completed. These updates include the addition of Modelica system-level regression tests and software quality assurance documentation that ensure that modifications to the Modelica models do not alter system-level model results. The third and final part of the report aims to report the work that has been performed for the deployment of methods and workflows for the construction of RAVEN AI-based models compliant with the FMI/FMU standard. Such development represents the key for the deployment of the concept of “Flexible ecosystem” since it allows for the replacement of high-fidelity modelica models (or any other FMI/FMU compliant model) with RAVEN generated AI surrogate models. Overall, extensive work has been completed on developing FMUs and FMIs from existing models, understanding the requirements and limitations of FMUs, and open-sourcing the HYBRID repository with an integrated regression system.

42 ENGINEERING↗

DOE OSTI2021

Development of the IES Plug-and-Play Framework

This report discusses the status of the flexible plug-and-play framework development currently ongoing that aims to integrate Modelica/Dymola with the Risk Analysis and Virtual ENvironment (RAVEN) software in terms of both Functional Mock-Up Interface (FMI)/Functional Mock-Up Unit (FMU) construction and repository structures that aim to ease the sharing and simulation of complex dynamic models. This report aims to provide an overview of all the performed activities resolving around the deployment of methods, software infrastructures, guidelines and workflow for the construction and usage of models, encapsulated using the FMI/FMU protocols and standards. In particular, the report is organized in three main macro-subjects, which are connected to each other: - FMI/FMU adaptors for modelica models - HYBRID repository new structure and open-source deployment - RAVEN FMI/FMU exporting capabilities and Artificial Intelligence (AI)-based analysis acceleration. The first part of the report discusses the FMI/FMU adaptors that have been created within the HYBRID repository to allow users to quickly export models, such as FMUs. Several examples are shown that highlight the step-by-step process of converting an existing Modelica model into an FMU for use within the Dymola platform. Simulation results demonstrate that, while minor differences may occur, the overall control, trends, and solution integrity are maintained between standard Modelica simulation and FMU simulation results. However, it is worth noting that, for small systems, the FMU results have a slower simulation time than the Modelica only simulation. Using this process, a company can provide models that contain proprietary information to entities without disclosing any of the information about the model that could be considered business sensitive. Such an ability would allow institutions to bypass the necessity of “whitewashing” data. In the second part of the report, the new structure of the HYBRID repository is discussed with a major focus on the series of updates that has been completed. These updates include the addition of Modelica system-level regression tests and software quality assurance documentation that ensure that modifications to the Modelica models do not alter system-level model results. The third and final part of the report aims to report the work that has been performed for the deployment of methods and workflows for the construction of RAVEN AI-based models compliant with the FMI/FMU standard. Such development represents the key for the deployment of the concept of “Flexible ecosystem” since it allows for the replacement of high-fidelity modelica models (or any other FMI/FMU compliant model) with RAVEN generated AI surrogate models. Overall, extensive work has been completed on developing FMUs and FMIs from existing models, understanding the requirements and limitations of FMUs, and open-sourcing the HYBRID repository with an integrated regression system.

14 SOLAR ENERGY↗

NASA NTRS2016

ISS Double-Gimbaled CMG Subsystem Simulation Using the Agile Development Method

This paper presents an evolutionary approach in simulating a cluster of 4 Control Moment Gyros (CMG) on the International Space Station (ISS) using a common sense approach (the agile development method) for concurrent mathematical modeling and simulation of the CMG subsystem. This simulation is part of Training systems for the 21st Century simulator which will provide training for crew members, instructors, and flight controllers. The basic idea of how the CMGs on the space station are used for its non-propulsive attitude control is briefly explained to set up the context for simulating a CMG subsystem. Next different reference frames and the detailed equations of motion (EOM) for multiple double-gimbal variable-speed control moment gyroscopes (DGVs) are presented. Fixing some of the terms in the EOM becomes the special case EOM for ISS's double-gimbaled fixed speed CMGs. CMG simulation development using the agile development method is presented in which customer's requirements and solutions evolve through iterative analysis, design, coding, unit testing and acceptance testing. At the end of the iteration a set of features implemented in that iteration are demonstrated to the flight controllers thus creating a short feedback loop and helping in creating adaptive development cycles. The unified modeling language (UML) tool is used in illustrating the user stories, class designs and sequence diagrams. This incremental development approach of mathematical modeling and simulating the CMG subsystem involved the development team and the customer early on, thus improving the quality of the working CMG system in each iteration and helping the team to accurately predict the cost, schedule and delivery of the software.

Inampudi, Ravi↗

NASA NTRS2011

Systems, methods and apparatus for modeling, specifying and deploying policies in autonomous and autonomic systems using agent-oriented software engineering

Systems, methods and apparatus are provided through which in some embodiments, an agent-oriented specification modeled with MaCMAS, is analyzed, flaws in the agent-oriented specification modeled with MaCMAS are corrected, and an implementation is derived from the corrected agent-oriented specification. Described herein are systems, method and apparatus that produce fully (mathematically) tractable development of agent-oriented specification(s) modeled with methodology fragment for analyzing complex multiagent systems (MaCMAS) and policies for autonomic systems from requirements through to code generation. The systems, method and apparatus described herein are illustrated through an example showing how user formulated policies can be translated into a formal mode which can then be converted to code. The requirements-based programming systems, method and apparatus described herein may provide faster, higher quality development and maintenance of autonomic systems based on user formulation of policies.

Hinchey, Michael G.↗

DOE OSTI2020

Software-defined network for end-to-end networked science at the exascale

Domain science applications and workflow processes are currently forced to view the network as an opaque infrastructure into which they inject data and hope that it emerges at the destination with an acceptable Quality of Experience. There is little ability for applications to interact with the network to exchange information, negotiate performance parameters, discover expected performance metrics, or receive status/troubleshooting information in real time. The work presented here is motivated by a vision for a new smart network and smart application ecosystem that will provide a more deterministic and interactive environment for domain science workflows. The Software-Defined Network for End-to-end Networked Science at Exascale (SENSE) system includes a model-based architecture, implementation, and deployment which enables automated end- to-end network service instantiation across administrative domains. An intent based interface allows applications to express their high-level service requirements, an intelligent orchestrator and resource control systems allow for custom tailoring of scalability and real-time responsiveness based on individual application and infrastructure operator requirements. This allows the science applications to manage the network as a first-class schedulable resource as is the current practice for instruments, compute, and storage systems. Deployment and experiments on production networks and testbeds have validated SENSE functions and performance. Emulation based testing verified the scalability needed to support research and education infrastructures. Key contributions of this work include an architecture definition, reference implementation, and deployment. This provides the basis for further innovation of smart network services to accelerate scientific discovery in the era of big data, cloud computing, machine learning and artificial intelligence.

47 OTHER INSTRUMENTATION↗

NASA NTRS2006

Control of Technology Transfer at JPL

Controlled Technology: 1) Design: preliminary or critical design data, schematics, technical flow charts, SNV code/diagnostics, logic flow diagrams, wirelist, ICDs, detailed specifications or requirements. 2) Development: constraints, computations, configurations, technical analyses, acceptance criteria, anomaly resolution, detailed test plans, detailed technical proposals. 3) Production: process or how-to: assemble, operated, repair, maintain, modify. 4) Manufacturing: technical instructions, specific parts, specific materials, specific qualities, specific processes, specific flow. 5) Operations: how-to operate, contingency or standard operating plans, Ops handbooks. 6) Repair: repair instructions, troubleshooting schemes, detailed schematics. 7) Test: specific procedures, data, analysis, detailed test plan and retest plans, detailed anomaly resolutions, detailed failure causes and corrective actions, troubleshooting, trended test data, flight readiness data. 8) Maintenance: maintenance schedules and plans, methods for regular upkeep, overhaul instructions. 9) Modification: modification instructions, upgrades kit parts, including software

Jet Propulsion Laboratory (JPL)↗

NASA NTRS2014

Investigation into the Use of the Concept Laser QM System as an In-Situ Research and Evaluation Tool

The NASA Marshall Space Flight Center (MSFC) is using a Concept Laser Fusing (Cusing) M2 powder bed additive manufacturing system for the build of space flight prototypes and hardware. NASA MSFC is collecting and analyzing data from the M2 QM Meltpool and QM Coating systems for builds. This data is intended to aide in understanding of the powder-bed additive manufacturing process, and in the development of a thermal model for the process. The QM systems are marketed by Concept Laser GmbH as in-situ quality management modules. The QM Meltpool system uses both a high-speed near-IR camera and a photodiode to monitor the melt pool generated by the laser. The software determines from the camera images the size of the melt pool. The camera also measures the integrated intensity of the IR radiation, and the photodiode gives an intensity value based on the brightness of the melt pool. The QM coating system uses a high resolution optical camera to image the surface after each layer has been formed. The objective of this investigation was to determine the adequacy of the QM Meltpool system as a research instrument for in-situ measurement of melt pool size and temperature and its applicability to NASA's objectives in (1) Developing a process thermal model and (2) Quantifying feedback measurements with the intent of meeting quality requirements or specifications. Note that Concept Laser markets the system only as capable of giving an indication of changes between builds, not as an in-situ research and evaluation tool. A secondary objective of the investigation is to determine the adequacy of the QM Coating system as an in-situ layer-wise geometry and layer quality evaluation tool.

Bagg, Stacey↗