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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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The proposed research addresses all three focus areas, but primarily (2): Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI driven model /component/ parameterization selection)
Autonomic Computing (AC), self-management based on high level guidance from humans, is increasingly gaining momentum as the way forward in designing reliable systems that hide complexity and conquer IT management costs. Effectively, AC may be viewed as Policy-Based Self-Management. The Model Driven Architecture (MDA) approach focuses on building models that can be transformed into code in an automatic manner. In this paper, we look at ways to implement Policy-Based Self-Management by means of models that can be converted to code using transformations that follow the MDA philosophy. We propose a set of UML-based models to specify autonomic and autonomous features along with the necessary procedures, based on modification and composition of models, to deploy a policy as an executing system.
Over the last few decades, advances in high-performance computing, new materials characterization methods, and, more recently, an emphasis on integrated computational materials engineering (ICME) and additive manufacturing have been a catalyst for multiscale modeling and simulation-based design of materials and structures in the aerospace industry. While these advances have driven significant progress in the development of aerospace components and systems, that progress has been limited by persistent technology and infrastructure challenges that must be overcome to realize the full potential of integrated materials and systems design and simulation modeling throughout the supply chain. As a result, NASA's Transformational Tools and Technology (TTT) Project sponsored a study (performed by a diverse team led by Pratt & Whitney) to define the potential 25-year future state required for integrated multiscale modeling of materials and systems (e.g., load-bearing structures) to accelerate the pace and reduce the expense of innovation in future aerospace and aeronautical systems. This report describes the findings of this 2040 Vision study (e.g., the 2040 vision state; the required interdependent core technical work areas, Key Element (KE); identified gaps and actions to close those gaps; and major recommendations) which constitutes a community consensus document as it is a result of over 450 professionals input obtain via: 1) four society workshops (AIAA, NAFEMS, and two TMS), 2) community-wide survey, and 3) the establishment of 9 expert panels (one per KE) consisting on average of 10 non-team members from academia, government and industry to review, update content, and prioritize gaps and actions. The study envisions the development of a cyber-physical-social ecosystem comprised of experimentally verified and validated computational models, tools, and techniques, along with the associated digital tapestry, that impacts the entire supply chain to enable cost-effective, rapid, and revolutionary design of fit-for-purpose materials, components, and systems. Although the vision focused on aeronautics and space applications, it is believed that other engineering communities (e.g., automotive, biomedical, etc.) can benefit as well from the proposed framework with only minor modifications. Finally, it is TTT's hope and desire that this vision provides the strategic guidance to both public and private research and development decision makers to make the proposed 2040 vision state a reality and thereby provide a significant advancement in the United States global competitiveness.
Over the last few decades, advances in high-performance computing, new materials characterization methods, and, more recently, an emphasis on integrated computational materials engineering (ICME) and additive manufacturing have been a catalyst for multiscale modeling and simulation-based design of materials and structures in the aerospace industry. While these advances have driven significant progress in the development of aerospace components and systems, that progress has been limited by persistent technology and infrastructure challenges that must be overcome to realize the full potential of integrated materials and systems design and simulation modeling throughout the supply chain. As a result, NASA's Transformational Tools and Technology (TTT) Project sponsored a study (performed by a team led by Pratt & Whitney) to define the potential 25-year future state required for integrated multiscale modeling of materials and systems (e.g., load-bearing structures) to accelerate the pace and reduce the expense of innovation in future aerospace and aeronautical systems. This talk will describe the findings of this 2040 Vision study (e.g., the 2040 vision state; the required interdependent core technical work areas, Key Element (KE); associated critical gaps and actions to close those gaps; and major recommendations). This roadmap is a community consensus document and is a result of over 450 professionals input obtain via: i) four society workshops (AIAA, NAFEMS, and two TMS), ii) community-wide survey, and iii) the establishment of 9 expert panels (one per key element) consisting on average 10 non-team members from academia, government and industry to review, update content, and prioritize gaps and actions. It is NASA's hope and desire that this roadmap provides the strategic guidance to both public and private research and development decision makers to make the proposed 2040 vision state a reality and thereby provide a significant advancement in the United States global competitiveness.
Low lunar frozen orbits are of continued interest in the astrodynamics community for trajectory design and space domain awareness. This paper presents a data-driven approach to analyzing a wide variety of numerically-generated lunar trajectories in a 100 × 100 lunar gravity model with the point mass gravity of the Earth and Sun. First, clustering is used to extract a summary of these trajectories: within each cluster, trajectories possess a geometrically similar evolution of perilune but varying drift and lifetimes. Within some clusters, trajectories with a bounded perilune evolution are also identified to produce candidates for lunar frozen orbits of distinct geometries.
Pulsed Plasma Thrusters (PPTs) are advanced electric space propulsion devices that are characterized by simplicity and robustness. They suffer, however, from low thrust efficiencies. This summer, two approaches to improve the thrust efficiency of PPTs will be investigated through both numerical modeling and experimental testing. The first approach, an inductively-driven PPT, uses a double-ignition circuit to fire two PPTs in succession. This effectively changes the PPTs configuration from an LRC circuit to an LR circuit. The LR circuit is expected to provide better impedance matching and improving the efficiency of the energy transfer to the plasma. An added benefit of the LR circuit is an exponential decay of the current, whereas a traditional PPT s under damped LRC circuit experiences the characteristic "ringing" of its current. The exponential decay may provide improved lifetime and sustained electromagnetic acceleration. The second approach, a high-energy PPT, is a traditional PPT with a variable size capacitor bank. This PPT will be simulated and tested at energy levels between 100 and 450 joules in order to investigate the relationship between efficiency and energy level. Arbitrary Coordinate Hydromagnetic (MACH2) code is used. The MACH2 code, designed by the Center for Plasma Theory and Computation at the Air Force Research Laboratory, has been used to gain insight into a variety of plasma problems, including electric plasma thrusters. The goals for this summer include numerical predictions of performance for both the inductively-driven PPT and high-energy PFT, experimental validation of the numerical models, and numerical optimization of the designs. These goals will be met through numerical and experimental investigation of the PPTs current waveforms, mass loss (or ablation), and impulse bit characteristics.
One of the goals of NASA's Revolutionary Vertical Lift Technology Project (RVLT) is to provide validated tools for multidisciplinary design, analysis and optimization (MDAO) of vertical lift vehicles. As part of this effort, the software package, RotorCraft Optimization Tools (RCOTOOLS), is being developed to facilitate incorporating key rotorcraft conceptual design codes into optimizations using the OpenMDAO multi-disciplinary optimization framework written in Python. RCOTOOLS, also written in Python, currently supports the incorporation of the NASA Design and Analysis of RotorCraft (NDARC) vehicle sizing tool and the Comprehensive Analytical Model of Rotorcraft Aerodynamics and Dynamics II (CAMRAD II) analysis tool into OpenMDAO-driven optimizations. Both of these tools use detailed, file-based inputs and outputs, so RCOTOOLS provides software wrappers to update input files with new design variable values, execute these codes and then extract specific response variable values from the file outputs. These wrappers are designed to be flexible and easy to use. RCOTOOLS also provides several utilities to aid in optimization model development, including Graphical User Interface (GUI) tools for browsing input and output files in order to identify text strings that are used to identify specific variables as optimization input and response variables. This paper provides an overview of RCOTOOLS and its use
The purpose of this Special Issue of Agricultural Systems is to lay the foundation for the next generation of agricultural systems data, models and knowledge products. In the Introduction to this Special Issue, we described a vision for accelerating the rate of agricultural innovation and meeting the growing global need for food and fiber. In this concluding article of the NextGen Special Issue we synthesize insights and formulate a strategy to advance data, models, and knowledge products that are consistent with this vision. This strategy is designed to facilitate a transition from the current, primarily supply-driven approach toward a more demand-driven approach that would address key Use Cases where better data, models and knowledge products are seen by end-users as essential to meet their needs.
The BOREAS TE-19 team developed a model called the Spruce and Moss Model (SPAM) designed to simulate the daily carbon balance of a black spruce/moss boreal forest ecosystem. It is driven by daily weather conditions, and consists of four components: (1) soil climate, (2) tree photosynthesis and respiration, (3) moss photosynthesis and respiration, and (4) litter decomposition and associated heterotrophic respiration. The model simulates tree gross and net photosynthesis, wood respiration, live root respiration, moss gross and net photosynthesis, and heterotrophic respiration (decomposition of root litter, young needle and moss litter, and humus). These values can be combined to generate predictions of total site net ecosystem exchange of carbon (NEE), total soil dark respiration (live roots + heterotrophs + live moss), spruce and moss net productivity, and net carbon accumulation in the soil. To date, simulations have been of the BOREAS NSA-OBS and SSA-OBS tower sites, from 1968-95 (except 1990-93). The files include source code and sample input and output files in ASCII format. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Activity Archive Center (DAAC).
With an increased demand on reducing the time, cost, and effort to develop new materials, Integrated Computational Materials Engineering (ICME) has received widespread attention in various engineering disciplines as a catalyst for significantly reducing experimental testing during the material design process. An ICME approach to design can enable ‘fit-for-purpose’ materials to be realized in engineering applications by incorporating well-understood process-property-performance relationships between the various length and time scales in a material’s structure, enabling material optimization. However, such an approach requires validated multiscale models at the various length scales for a material, which in turn requires a large amount of data, a robust means of storing the data, and the ability to link data to developed material models. The NASA Vision 2040 [1] has identified nine key elements to enabling ICME approaches in system level design, with one being “Data, Information, and Visualization”, thus outlining the importance of a robust information management system for ICME. As the relationship between microstructure, properties, and material performance become better understood and incorporated into multiscale models that can be leveraged in application design, the emergence of new materials with application-driven properties can be realized. One such new material class that has seen growing attention are shape memory materials (SMM), in which a material can transition between a deformed and undeformed state via a reversible phase transformation when subject to a thermal, mechanical, or magnetic load [2]. SMMs have been used widely in aerospace and biomedical industries, including applications such as actuators, low-shock mechanisms, medical staples, braces, and stents [3, 4]. These materials exhibit unique behavior due to their ability to transition between phases, and thus the mechanisms that enable this transition must be captured in a data information management system and incorporated into SMM material models. At NASA Glenn Research Center, the Shape Memory Materials Database (SMMD) Tool has been developed to capture the necessary information that governs SMM material behavior and provide users the ability to select and visualize various SMMs for a specific application [5]. The database contains point-wise data for published SMM materials, along with the pedigree metadata for traceability necessary for a robust information management system. The database is also capable of storing in-house test data performed at NASA GRC by interacting with the developed Shape Memory Alloy (SMA) Analytics tool to extract the necessary point-wise values and populate the database. Although the SMMD Tool offers its users a single, authoritative source for SMM material data that is critical for model development and material design, the full material pedigree of the in-house test data for SMMs is not currently captured and is out of the scope for the SMMD tool. In this work, the schema for capturing SMM test data within the larger NASA GRC ICME Schema [6, 7, 8, 9] will be developed and implemented for thermomechanical tests conducted at NASA GRC. The developed schema will not only store the relevant data needed for the SMMD tool, but also the material pedigree (i.e., production of the bulk material, bulk material analysis, sample cut-out diagrams, sample fabrication procedure, etc.), test pedigree (i.e., test equipment used, measurement systems used, raw test data), and analysis pedigree (i.e., how the data in the SMMD tool is calculated). Furthermore, a Python-based framework will be developed to seamlessly interact between the SMA Analytics and SMMD tools, which will write the full dataset and associated metadata to the GRC Information Management System before passing the required point-wise data to the SMMD tool. Data informatics is a key element of the NASA Vision 2040, which requires not only that data is stored and maintained throughout the material lifecycle, but that the data is also accessible and reusable such that material development efforts can be minimized. Therefore, for an ICME design approach to be realized, a centralized information management system that drives the ICME process must be able to communicate with other databases. The work that will be presented in this presentation will therefore not only demonstrate the ability of NASA GRC’s information management system to capture SMM data, but also its ability to interact with pre-existing tools specialized for such materials.
Design, fabrication, and test of research model roller-gear drive consisting of transmission driven by dc motor
The NASA Ames Mission Design Center (MDC) has undergone a significant transformation process in recent years, culminating in becoming a true Concurrent Engineering Center. The underlying goal of this transformation is to better serve the Ames PI community in a cost-effective and rapidly-responsive way, providing quality products pivotal to the decision-making proposal strategy of our center. The four pillars of the change focus on four areas: personnel, physical places, tools, and training. Personnel: the MDC switched from using a dedicated pool of engineers—mostly entry-level career—for a matrixed approach with more experienced Subject Matter Experts, usually involved in spaceflight projects. These SMEs get detailed to the MDC for the duration of the study on a part-time or full-time basis. Physical places: The physical aspect pertains mostly to the layout of the new Engineering building and the dedicated concurrent engineering layout to foster collaboration. The current plan includes a dedicated space for concurrent engineering sessions as well as dedicated project rooms for concept studies. Tool: the MDC team is developing a new in-house concurrent engineering tool to facilitate concurrent engineering sessions. The team leveraged the many lessons learned and insight gained from using the current concurrent engineering tool, in use since 2007. SMEs have been involved throughout its design and development, ensuring accuracy and providing validity to the implementation approach. This new tool is database-driven and allows a team to work concurrently on the same model of a mission concept. “Workspaces” are provided for each of the traditional spacecraft mission design disciplines, including cost accounting and systems engineering. There are multiple tiers of fidelity available within each workspace, which can be completed parametrically or independently from the other disciplines as needed or synchronously with the other disciplines. A “commit” step synchronizes a workspace with the mission database and identifies any potential conflicts, along with the user who entered the conflicting data. A Mission Summary workspace enables a Study Lead to run an effective CE session, displaying key graphics, workspace status information, and technical and monetary budget information. This new CE tool will be undergoing Beta Testing with its primary user base and a limited Parts database in the summer of 2020. The team plans to release the first fully operational version in December 2020. Training: working in early concept development and maturation for a space mission requires a unique mindset and being comfortable with uncertainties in an environment where requirements are not yet fully developed, and where changes and trade-offs happen at a high tempo. The MDC started a weekly training program to ensure a common knowledge base on how do develop and mature early concept studies, as well as how to operate in a concurrent engineering environment. In conclusion, the MDC transformation will provide the overall PI community focused on cost-effective small satellite scientific missions with a powerful tool to address and mature early concepts more effectively and efficiently.
The Diurnal Land-Atmosphere Coupling Experiment (DICE) aims to explore the complex interactions between the land surface and atmospheric boundary layer, which are generally not well understood and difficult to isolate in models. The project involves over 10 different models, combining expertise from both land surface and atmospheric boundary-layer modelling groups. A simple three-stage methodology is designed to assess land-atmosphere feedbacks. Stage 1: the individual components are assessed in isolation, driven and evaluated against observational data; stage 2: the impact of coupling is investigated; stage 3: the sensitivity of the stand-alone models to variations in driving data is explored. For this initial study, a three-day clear-sky period in the mid-west USA over, an assumed simple, predominantly grass surface was simulated using data from the CASES-99 field campaign. Key conclusions from the study include: (1) the memory of vegetation state within Land Surface Models (LSMs) needs attention; (2) the height of atmospheric forcing for LSMs is important, particularly for the nocturnal boundary layer, and this has implications for both observations and vertical resolution for atmospheric models; (3) land-atmosphere feedbacks reduce errors in simulated surface fluxes at the expense of the accuracy of the variables that the models are designed to simulate (e.g., temperature, humidity and wind speed); (4) problems remain in representing the stable boundary layer in atmospheric models; (5) the mixing of temperature and humidity within the boundary layer may need to be represented separately; (6) differences in daytime profiles of heat, moisture and momentum between models are mainly due to the way the models erode the inversion at the top of the boundary layer, rather than differences in the surface fluxes. Resultant variations in modelled boundary layer heights have a substantial impact on relative humidity and could partially explain variations in coupling strength between models in the GLACE experiment.
Today's NASA is facing significant challenges and changes. The Exploration initiative indicates a large increase in projects with limited increase in budget. The Columbia report has criticized NASA for its lack of insight and technical integration impacting its ability to provide safety. The Aldridge report is advocating NASA find new ways of doing business. Very early in the Space Launch Initiative (SLI) program a small team of engineers at MSFC were asked to propose a process for performing a system level assessment of a launch vehicle. The request was aimed primarily at providing insight and making NASA a "smart buyer." Out of this effort the VIPA team was created. The difference between the VIPA effort and many integration attempts is that VIPA focuses on using experienced people from various disciplines and a process which focuses them on a technically integrated assessment. Most previous attempts have focused on developing an all encompassing software tool. In addition, VIPA anchored its process formulation in the experience of its members and in early developmental Space Shuttle experience. The primary reference for this is NASA-TP-2001-210092, "Launch Vehicle Design Process: Characterization, Technical Integration, and Lessons Learned," and discussions with its authors. The foundations of VIPA's process are described. The VIPA team also recognized the need to drive detailed analysis earlier in the design process. Analyses and techniques typically done in later design phases, are brought forward using improved computing technology. The intent is to allow the identification of significant sensitivities, trades, and design issues much earlier in the program. This process is driven by the T-model for Technical Integration described in the aforementioned reference. VIPA's approach to performing system level technical integration is discussed in detail. Proposed definitions are offered to clarify this discussion and the general systems integration dialog. VIPA's capabilities and process can now be used to significantly enhance the development and monitoring of realizable project requirements. This is done through the use of VIPA's V-model. Starting with a given concept, VIPA's assessment validates the concept's stated performance, identifies significant issues either with the concept or the requirements, and then re-integrates these issues to determine impacts. This process is discussed along with a description of how it may be integrated into a program's insight and review process. The VIPA process has been employed successfully on several projects including SLI, Orbital Space Plane (OSP), and several heavy lift concepts for Exploration. It has also been proposed for use on the Jupiter Icy Moon (JIMO) spacecraft. The VIPA process has gained favor with both engineering and project organizations for being responsive and insightful. Selected results from these assessments will be presented.
In the frame of ESA's Solar Orbiter scientific mission, Almatech has been selected to design, develop and test the Slit Change Mechanism of the SPICE (SPectral Imaging of the Coronal Environment) instrument. In order to guaranty optical cleanliness level while fulfilling stringent positioning accuracies and repeatability requirements for slit positioning in the optical path of the instrument, a linear guiding system based on a double flexible blade arrangement has been selected. The four different slits to be used for the SPICE instrument resulted in a total stroke of 16.5 mm in this linear slit changer arrangement. The combination of long stroke and high precision positioning requirements has been identified as the main design challenge to be validated through breadboard models testing. This paper presents the development of SPICE's Slit Change Mechanism (SCM) and the two-step validation tests successfully performed on breadboard models of its flexible blade support system. The validation test results have demonstrated the full adequacy of the flexible blade guiding system implemented in SPICE's Slit Change Mechanism in a stand-alone configuration. Further breadboard test results, studying the influence of the compliant connection to the SCM linear actuator on an enhanced flexible guiding system design have shown significant enhancements in the positioning accuracy and repeatability of the selected flexible guiding system. Preliminary evaluation of the linear actuator design, including a detailed tolerance analyses, has shown the suitability of this satellite roller screw based mechanism for the actuation of the tested flexible guiding system and compliant connection. The presented development and preliminary testing of the high-precision long-stroke Slit Change Mechanism for the SPICE Instrument are considered fully successful such that future tests considering the full Slit Change Mechanism can be performed, with the gained confidence, directly on a Qualification Model. The selected linear Slit Change Mechanism design concept, consisting of a flexible guiding system driven by a hermetically sealed linear drive mechanism, is considered validated for the specific application of the SPICE instrument, with great potential for other special applications where contamination and high precision positioning are dominant design drivers.
The detailed outer mold line shaping of a Mach 1.6, demonstrator-sized low-boom concept is presented. Cruise trim is incorporated a priori as part of the shaping objective, using an equivalent-area-based approach. Design work is performed using a gradient-driven optimization framework that incorporates a three-dimensional, nonlinear flow solver, a parametric geometry modeler, and sensitivities derived using the adjoint method. The shaping effort is focused on reducing the under-track sonic boom level using an inverse design approach, while simultaneously satisfying the trim requirement. Conceptual-level geometric constraints are incorporated in the optimization process, including the internal layout of fuel tanks, landing gear, engine, and crew station. Details of the model parameterization and design process are documented for both flow-through and powered states, and the performance of these optimized vehicles presented in terms of inviscid L/D, trim state, pressures in the near-field and at the ground, and predicted sonic boom loudness.
Flight testing and modeling techniques were developed to accurately identify global nonlinear aerodynamic models for aircraft in real time. The techniques were developed and demonstrated during flight testing of a remotely-piloted subscale propeller-driven fixed-wing aircraft using flight test maneuvers designed to simulate a Learn-To-Fly scenario. Prediction testing was used to evaluate the quality of the global models identified in real time. The real-time global nonlinear aerodynamic modeling algorithm will be integrated and further tested with learning adaptive control and guidance for NASA Learn-To-Fly concept flight demonstrations.
The purpose of Safety and Mission Assurance (SMA) at NASA’s Jet Propulsion Laboratory is to ensure mission safety and success. As the complexity of technical systems increases, the tools, processes, and infrastructure used to facilitate SMA activities must evolve. A transition from a document-centric approach to a data-driven, model-based architecture is critical in allowing SMA efforts to grapple with complex system designs. This work describes how a sample subset of existing SMA processes can be executed in a model-based environment, while also describing ways in which a model-based architecture can further augment SMA efforts. One key aspect is supporting the complex interplay between the different SMA processes and the systems engineering effort to provide additional insight into the system from an SMA perspective. The proposed architecture for model-based mission assurance provides an avenue for assuring mission safety and success in increasingly complex systems.