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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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At least 91 records · Page 5

NASA Integrated Model Centric Architecture (NIMA) Model Use and Re-Use

This whitepaper accepts the goals, needs and objectives of NASA's Integrated Model-centric Architecture (NIMA); adds experience and expertise from the Constellation program as well as NASA's architecture development efforts; and provides suggested concepts, practices and norms that nurture and enable model use and re-use across programs, projects and other complex endeavors. Key components include the ability to effectively move relevant information through a large community, process patterns that support model reuse and the identification of the necessary meta-information (ex. history, credibility, and provenance) to safely use and re-use that information. In order to successfully Use and Re-Use Models and Simulations we must define and meet key organizational and structural needs: 1. We must understand and acknowledge all the roles and players involved from the initial need identification through to the final product, as well as how they change across the lifecycle. 2. We must create the necessary structural elements to store and share NIMA-enabled information throughout the Program or Project lifecycle. 3. We must create the necessary organizational processes to stand up and execute a NIMA-enabled Program or Project throughout its lifecycle. NASA must meet all three of these needs to successfully use and re-use models. The ability to Reuse Models a key component of NIMA and the capabilities inherent in NIMA are key to accomplishing NASA's space exploration goals. 11

Conroy, Mike↗

Characterizing and Assessing a Large-Scale Software Maintenance Organization

One important component of a software process is the organizational context in which the process is enacted. This component is often missing or incomplete in current process modeling approaches. One technique for modeling this perspective is the Actor-Dependency (AD) Model. This paper reports on a case study which used this approach to analyze and assess a large software maintenance organization. Our goal was to identify the approach's strengths and weaknesses while providing practical recommendations for improvement and research directions. The AD model was found to be very useful in capturing the important properties of the organizational context of the maintenance process, and aided in the understanding of the flaws found in this process. However, a number of opportunities for extending and improving the AD model were identified. Among others, there is a need to incorporate quantitative information to complement the qualitative model.

Briand, Lionel↗

CSTI Earth-to-orbit propulsion research and technology program overview

NASA supports a vigorous Earth-to-orbit (ETO) research and technology program as part of its Civil Space Technology Initiative. The purpose of this program is to provide an up-to-date technology base to support future space transportation needs for a new generation of lower cost, operationally efficient, long-lived and highly reliable ETO propulsion systems by enhancing the knowledge, understanding and design methodology applicable to advanced oxygen/hydrogen and oxygen/hydrocarbon ETO propulsion systems. Program areas of interest include analytical models, advanced component technology, instrumentation, and validation/verification testing. Organizationally, the program is divided between technology acquisition and technology verification as follows: (1) technology acquisition; and (2) technology verification.

Gentz, Steven J.↗

Model-based software process improvement

The activities of a field test site for the Software Engineering Institute's software process definition project are discussed. Products tested included the improvement model itself, descriptive modeling techniques, the CMM level 2 framework document, and the use of process definition guidelines and templates. The software process improvement model represents a five stage cyclic approach for organizational process improvement. The cycles consist of the initiating, diagnosing, establishing, acting, and leveraging phases.

Zettervall, Brenda T.↗

O/S analysis of conceptual space vehicles

The application of recently developed computer models in determining operational capabilities and support requirements during the conceptual design of proposed space systems is discussed. The models used are the reliability and maintainability (R&M) model, the maintenance simulation model, and the operations and support (O&S) cost model. In the process of applying these models, the R&M and O&S cost models were updated. The more significant enhancements include (1) improved R&M equations for the tank subsystems, (2) the ability to allocate schedule maintenance by subsystem, (3) redefined spares calculations, (4) computing a weighted average of the working days and mission days per month, (5) the use of a position manning factor, and (6) the incorporation into the O&S model of new formulas for computing depot and organizational recurring and nonrecurring training costs and documentation costs, and depot support equipment costs. The case study used is based upon a winged, single-stage, vertical-takeoff vehicle (SSV) designed to deliver to the Space Station Freedom (SSF) a 25,000 lb payload including passengers without a crew.

Ebeling, Charles E.↗

Interagency Collaborators Develop and Implement ForWarn, a National, Near Real Time Forest Monitoring Tool

ForWarn is a satellite-based forest monitoring tool that is being used to detect and monitor disturbances to forest conditions and forest health. It has been developed through the synergistic efforts, capabilities and contributions of four federal agencies, including the US Forest Service Eastern Forest and Western Wildland Environmental Threat Assessment Centers, NASA Stennis Space Center (SSC), Department of Energy's (DOE) Oak Ridge National Laboratory (ORNL) and US Geological Survey Earth (USGS) Earth Research Observation System (EROS), as well as university partners, including the University of North Carolina Asheville's National Environmental Modeling and Analysis Center (NEMAC). This multi-organizational partnership is key in producing a unique, path finding near real-time forest monitoring system that is now used by many federal, state and local government end-users. Such a system could not have been produced so effectively by any of these groups on their own. The forests of the United States provide many societal values and benefits, ranging from ecological, economic, cultural, to recreational. Therefore, providing a reliable and dependable forest and other wildland monitoring system is important to ensure the continued health, productivity, sustainability and prudent use of our Nation's forests and forest resources. ForWarn does this by producing current health indicator maps of our nation's forests based on satellite data from NASA's MODIS (Moderate Resolution Imaging Spectroradiometer) sensors. Such a capability can provide noteworthy value, cost savings and significant impact at state and local government levels because at those levels of government, once disturbances are evident and cause negative impacts, a response must be carried out. The observations that a monitoring system like ForWarn provide, can also contribute to a much broader-scale understanding of vegetation disturbances.

Underwood, Lauren↗

Event Report for The Ethical Artificial Intelligence Quantification Workshop

Artificial Intelligence (AI) is a powerful emerging technology area which requires special attention to using it ethically. AI ethics is still an emerging field, and the partners for this workshop and report seek to move AI ethics discussion ahead by experimenting with ways to measure AI ethics criteria. The following document describes the outcomes and learnings from The Ethical Artificial Intelligence Quantification Workshop held at the National Institute for Aerospace (NIA), Hampton, Virginia on May 12th, 2022. The purpose of the workshop was for participants to evaluate and experiment-with the methodology and process presented by AIEthics.World in cooperation with Intel Corporation. The meeting participants learned about the Ethical AI Certification and Maturity Model™ and applied the methodology to selected notional AI systems. The workshop facilitated the evaluation of the maturity of the AI system according to ethical considerations relevant to NASA, NIA and other participants. The workshop consisted of three main phases. The first phase focused on understanding and summarizing NASA’s ethical approaches, mission and values based on published documentation, discussions and individual insights & opinions of participants. This information was prioritized, weighted, ordered, and quantified in phase two, to formulate an alignment between human values (ethics) and their applicability to AI systems during all lifecycle phases. The first two phases were summarized as a form of ethical genealogy for artificial intelligence, specific to NASA’s ethical approaches. In the third and last phase of the workshop the participants evaluated notional examples of artificial intelligence to qualify and quantify its ability to adhere to the organizational ethics approaches, using the Ethical AI Certification and Maturity Model™. The workshop uses the concept of genealogy, in the traditional sense: the study and traceability of lines of ancestors in the process of evolutionary development from earlier forms. However, as it is applied to an Ethical AI definition, it is providing the insights to the necessary and mandatory traceability of content, data, metrics, telemetry, elements, and structures which are used in the AI’s lifecycle to foster and measure AI ethics in all steps of its lifecycle. The Ethical Artificial Intelligence Quantification Workshop provided NASA with the opportunity to apply the Ethical AI Certification and Maturity Model™, in combination with existing and well-known decision-making and quality control methods to identify the metrics and measurements for an Ethical AI and assess its ethical condition and quality aligned with NASA ethics approaches. The result of the workshop is the capacity for NASA to apply the maturity model assessment to its AI Systems as desired and if necessary, publish the ability of these AI Systems to adhere to the organizational ethical goals. AI ethics frameworks need to be customized for each application domain, for example, individual NASA Mission Directorates. General principles that work in one area such as AI/Machine Learning-based text analysis (the ethics of information-extraction) may need to be adapted for another such as sense-and-avoid decision-making in a flight environment. The workshop was conducted among approximately twenty NASA subject matter experts, so the elements noted above should be considered examples, not definitive NASA ethical AI principles, genealogy, etc. Generating a definitive AI ethics framework for an organization as diverse as NASA would require far more discussion, debate, review, etc. However, the workshop provided valuable insight into mechanisms and processes for quantifying AI ethical qualities.

Artificial Intelligence↗

Effort to Accelerate MBSE Adoption and Usage at JSC

This paper describes the authors' experience in adopting Model Based System Engineering (MBSE) at the NASA/Johnson Space Center (JSC). Since 2009, NASA/JSC has been applying MBSE using the Systems Modeling Language (SysML) to a number of advanced projects. Models integrate views of the system from multiple perspectives, capturing the system design information for multiple stakeholders. This method has allowed engineers to better control changes, improve traceability from requirements to design and manage the numerous interactions between components. As the project progresses, the models become the official source of information and used by multiple stakeholders. Three major types of challenges that hamper the adoption of the MBSE technology are described. These challenges are addressed by a multipronged approach that includes educating the main stakeholders, implementing an organizational infrastructure that supports the adoption effort, defining a set of modeling guidelines to help engineers in their modeling effort, providing a toolset that support the generation of valuable products, and providing a library of reusable models. JSC project case studies are presented to illustrate how the proposed approach has been successfully applied.

Wang, Lui↗

Computationally Guided Development of Components for High Energy Density Solid-State Lithium-Sulfur Batteries

All electric vertical take-off and landing vehicles (eVTOL) for urban air mobility (UAM) concepts face numerous challenging technical barriers before their introduction into the consumer marketplace. The most challenging of these technical barriers to overcome is developing an energy storage system capable of meeting the rigorous aerospace safety and performance criteria1. The performance metrics for eVTOL craft, such as specific energy, specific power, and safety, exceed those of electric automobiles by a factor of two to four. Current state-of-the-art (SOA) lithium-ion batteries are incapable of meeting the key performance criteria of energy and safety for eVTOL. Therefore, next generation advanced chemistries and designs must be developed to meet required performance metrics for electric aviation2. Beyond lithium-ion chemistries, such as lithium-sulfur, show promise in their high energy, while limitations exist in their power and cyclability due to low electrical conductivity and high intermediate solubility in organic liquid electrolytes. Several strategies to overcome the low electrical conductivity involve the use of selenium as a dopant in the active sulfur material, along with the incorporation of 2-dimensional electron-conducting holey-graphene to improve the composite cathodes electronic conductivity. Furthermore, combining this chemistry with a solid-electrolyte avoids the components’ dissolution issues3. However, the development of composite solid-state cathodes is non-trivial as several components must be intimately mixed so that the active component has sufficient access to both electrons and lithium ions to undergo full electrochemical conversion. Mathematical modeling of battery components can assist experimental design through a robust and rigorous combination of computational modeling techniques covering multiple length scales. The objective is to leverage modern computational materials methods combined with battery multiphysics tools to develop radically advanced compatible cathode and electrolyte materials, build and test solid state lithium-sulfur cells and packs. A NASA-based cross-organizational team of high-powered experts combined integrated computational predictive modeling, fundamental chemistry analysis, advanced material science, and battery cell development to tackle this very challenging, multidisciplinary problem. This presentation will show a multiscale computational modeling approach that has produced a novel particle dynamics method called Solid Electrolyte Sphere Approximation Model (SESAM). SESAM modeling targets the 1-10 µm scale structures and provides electromechanical and grain interactions for predictive design guidelines for the manufacturing of solid-state components. Parameters such as particle size and volume fraction of the constituent materials were modeled and experimentally fabricated to optimize electrochemical performance through improved microstructure design. Experimental feedback was provided through ionic and electronic conductivity assessment and structural analysis of developed materials and cell components.

battery↗

Forum on Workforce Development

APPEL Mission: To support NASA's mission by promoting individual, team, and organizational excellence in program/project management and engineering through the application of learning strategies, methods, models, and tools. Goals: a) Provide a common frame of reference for NASA s technical workforce. b) Provide and enhance critical job skills. c) Support engineering, program and project teams. d) Promote organizational learning across the agency. e) Supplement formal educational programs.

Hoffman, Edward↗

Progress Towards Integrating the Finite-Volume Cubed-Sphere (FV3) Dynamical Core Tangent Linear and Adjoint Models into JEDI

The Joint Effort for Data assimilation Integration (JEDI) -- led by the Joint Center for Satellite Data Assimilation (JCSDA) -- is an inter-organizational endeavor to develop a common framework for performing data assimilation. This extensive framework will ultimately provide solvers, observation operators, interpolation and model interfaces using object oriented modeling. Two partners involved in JEDI use or plan to use the Finite Volume Cubed-Sphere (FV3) dynamical core to produce weather forecasts; these are NASA's Global Modeling and Assimilation Office and NOAA's National Center for Environment Prediction. In this work we present an update on ongoing efforts to integrate the FV3 tangent linear and adjoint models into the prototype JEDI framework. We setup and run a simple cycled data assimilation experiment using 4DVAR on the cubed sphere grid and with the FV3 tangent linear and adjoint models. Development of the observation operators for JEDI is separately underway. Instead of using real observations a simplified set of simulated observations will be used. We discuss the steps required to bring the FV3 linearized model into the object oriented framework and consider what would be the computational requirements of running this configuration for an operational system. FV3 uses a small time-step to ensure that small scales are well resolved, however this presents design challenges when running 4DVAR with the adjoint. An approach to storing the FV3 model trajectory has been developed that maintains the flexibility of using automatic differentiation. We discuss how this approach is incorporated into the framework. Other important uses of adjoint models include computing observation impacts and singular vectors, we consider how these tools can be included in JEDI.

Holdaway, Daniel↗

Organizational change: Incentives and resistance

Topics concerning Space Exploration Initiative technical interchange are presented in viewgraph form and include the following: models of change, elements of the current period, the signs of change, leaders' contribution, paradigms - our worldview, paradigm change, the effects of revealing paradigms, a checklist for change, and organizational control.

Bishop, Peter C.↗

Optimization of Energy Flow through Synthetic Metabolic Modules and Regulatory Networks in a Model Photosynthetic Eukaryotic Microbe

Photosynthetic organisms have recently gained considerable attention for a role in development of renewable energy sources. Genome-enabled systems biology methods, coupled with functional and synthetic genomics, present opportunities to develop sustainable and economical applications such as fuel production within the next 10 to 15 years. However, optimization of light-driven metabolism for biomass or biofuel production will require a detailed systems biology understanding of photosynthetic processes and cellular metabolism. Genome-scale metabolic models (GEMs) are at the core of systems analysis of cellular processes and form a common organizational framework for analyses of data resulting from functional genomics experimental work and computational studies. Therefore, there is a clear demand for high quality photosynthetic model organisms and the appropriate computational tools that enable systems analysis of light-driven metabolism. Through research conducted we expanded the currently available repertoire of photosynthetic GEMs to include the commercially valuable model diatom Phaeoctylum tricornutum. Diatoms have a peculiar and distinct evolutionary footprint and represent a major eukaryotic lineage that is taxonomically and functionally distinct from green and red algae and vascular plants. Therefore, the true potential for light-driven metabolism aimed at biofuel production remains poorly understood at a systems level for a large subset of the global diversity of photosynthetic organisms. The metabolic capabilities of P. tricornutum were comparatively modeled with those from other photosynthetic groups in order to elucidate the occurrence of metabolic traits within and between phototrophs. Additionally, this research resulted in significant extension of the COnstraints Based Reconstruction and Analysis (COBRA) Toolbox to accommodate the crucial need for infrastructure required for ‘omics data integration and analysis in the context of genome-scale models. Therefore, the proposed research achieved two important goals. First, within the broad scope of photosynthetic organisms, we functionally compared and, as a result, identified cellular processes that require optimization in order to enable deployment as biofuel feedstock. Second, the proposed research resulted in development of key computational infrastructure, which can be further extended to other biological systems, that is currently lacking but necessary for multiple ‘omics data integration.

59 BASIC BIOLOGICAL SCIENCES↗

A Model-Based Systems Engineering Journey to Developing a Concept of Operations

Starting in 2017, NASA’s Human Research Program (HRP) Exploration Medical Capability (ExMC) element began a systems engineering transition from traditional, document-centric development to model-centric development when defining its foundation medical systems. These foundation medical systems define a Concept of Operations (ConOps) and identify the generic requirements for a medical system based on assumptions about a generic crew and mission environments and guidance from NASA standards (e.g., Medical “Levels of Care”). By making the transition, ExMC intends to improve communication among stakeholders about foundation medical system requirements and content. In addition, this transition will enable ExMC to lower both development and crew treatment risks for future, mission-specific medical systems. ExMC followed a Model Based Systems Engineering (MBSE) paradigm when developing the foundation medical systems. A model-based approach provides several advantages over a traditional, document-centric approach. First, when Systems Engineers (SE) develop diagrams in a model using a standard modeling language, they produce information dense pictures that facilitate understanding much more efficiently with less room for misinterpretation than text. Second, due to the evolving nature of projects, documentation becomes out of date the minute it is published. This can result in people making decisions based on information that is no longer current, especially if they are referencing a locally-stored copy of a document. A model, on the other hand, is always up to date with the latest approved changes and information. It serves as a single point of truth. Third, a model-centric approach centralizes all important information in one place. Rather than having to flip through separate ConOps documents, design specifications, requirements specifications, and the like to coordinate information, a model captures the content in one, integrated spot. This integration makes tracing information from end-to-end easier with greater reliability. The ExMC Systems Engineering Lifecycle follows a well-defined process. ExMC Systems Engineers perform all major steps of the process, regardless of the development methodology. One of the first steps in the process is developing the ConOps that describes the operation of the system from the point of view of the users. It includes a list of the users and their needs, the goals of the medical system, key assumptions about the system, and definitions of the medical system’s operational environments. For this development effort, ExMC chose to replace the traditional text-based ConOps document with a model. While the decision to change the development workflow was not difficult, implementing the structural and organizational workflows were. It required showing ExMC’s users, most of whom are not Systems Engineers, how the information they require would be presented in the model and to gain their acceptance of this approach. This paper documents key lessons learned during the ConOps transformation by focusing on how the model represents information, the agile workflow used by SEs when developing the model and how it integrates into a project plan, how leadership influenced key users to accept the transformation, and how the users interact with the model information.

Jeffrey Robert Cohen↗

Integrated System Planning: Emerging Software Requirements in the Power Industry

Power system planning software remains fragmented across organizational boundaries, with specialized tools for capacity expansion, production cost modeling, power flow, and dynamic analysis operating on incompatible data models and assumptions. This article argues that the fragmentation is not merely a technical problem but a predictable consequence of Conway's law: software architectures mirror the departmental structures within which they are developed. Regulatory milestones like Federal Energy Regulatory Commission (FERC) Order 888 formalized these divisions, but the roots trace back to the distinct engineering disciplines-mechanical, chemical, and electrical-that staffed generation and transmission planning departments in vertically integrated utilities. As the industry moves toward integrated system planning (ISP) that coordinates generation, transmission, and distribution investment decisions, the software ecosystem must evolve accordingly. We identify five categories of software requirements to enable this transition: coherent data inputs decoupled from individual applications, unified and extensible data schemas, modular component representations that support multiple abstraction levels, lifecycle management of planning datasets, and well-defined application programming interface (API) contracts that separate data exchange from algorithmic control. We examine how these requirements interact with three common workflow patterns-serial gate clearing, sequential multiapplication, and convergence oriented-and discuss the interface design principles each demands. We then outline a vision for platform-based planning architectures where specialized analytical services compose through standardized interfaces and where artificial intelligence (AI)/machine learning (ML) tools augment decision support within a disciplined software infrastructure. The practices proposed here offer a path from today's siloed tool collections toward collaborative planning ecosystems capable of handling the complexity of modern power system transformation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Access Innovation (Final)

Over the past two years, with the support of the Department of Energy, Powerhouse developed and launched Access Innovation, a partnership program to better connect clean energy startups with industry and investors for partnership. The program has successfully exceeded the objectives of the grant to pilot and validate successful methods of facilitating partnerships between early stage clean energy companies and industry and investors, secure follow on partnerships for early stage clean energy startups in the form of investment, pilot projects, new advisors, customer agreements, and joint development agreements and to track metrics, analyze, draft findings and distribute lessons on what models are most effective in forming partnerships to inform other organizational best practices.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

A Decision Tool that Combines Discrete Event Software Process Models with System Dynamics Pieces for Software Development Cost Estimation and Analysis

The development process for a large software development project is very complex and dependent on many variables that are dynamic and interrelated. Factors such as size, productivity and defect injection rates will have substantial impact on the project in terms of cost and schedule. These factors can be affected by the intricacies of the process itself as well as human behavior because the process is very labor intensive. The complex nature of the development process can be investigated with software development process models that utilize discrete event simulation to analyze the effects of process changes. The organizational environment and its effects on the workforce can be analyzed with system dynamics that utilizes continuous simulation. Each has unique strengths and the benefits of both types can be exploited by combining a system dynamics model and a discrete event process model. This paper will demonstrate how the two types of models can be combined to investigate the impacts of human resource interactions on productivity and ultimately on cost and schedule.

Mizell, Carolyn Barrett↗

Cost-estimating relationships for space programs

Cost-estimating relationships (CERs) are defined and discussed as they relate to the estimation of theoretical costs for space programs. The paper primarily addresses CERs based on analogous relationships between physical and performance parameters to estimate future costs. Analytical estimation principles are reviewed examining the sources of errors in cost models, and the use of CERs is shown to be affected by organizational culture. Two paradigms for cost estimation are set forth: (1) the Rand paradigm for single-culture single-system methods; and (2) the Price paradigms that incorporate a set of cultural variables. For space programs that are potentially subject to even small cultural changes, the Price paradigms are argued to be more effective. The derivation and use of accurate CERs is important for developing effective cost models to analyze the potential of a given space program.

Mandell, Humboldt C., Jr.↗