SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
The Ohio Fuel Cell Coalition (OFCC) with an award from the Department of Energy Fuel Cell Fuel Cell Technologies Office (FCTO), Award No. DE-EE0006931, conducted a project to facilitate the development of a robust supply chain for fuel cell and hydrogen systems that would accelerate mass production, reduced cost, and improve performance and durability of Proton Exchange Membrane (PEM) fuel cell systems.
This research work has been supported by the DOE-FIU Science & Technology Workforce Development Initiative, an innovative program developed by the U.S. Department of Energy’s Office of Environmental Management (DOE-EM) and Florida International University’s Applied Research Center (FIU-ARC). During the summer of 2021, a DOE Fellow intern, Phuong Pham, spent 10 weeks doing a summer internship at Savannah River National Laboratory under the supervision and guidance of Hansell Gonzalez-Raymat. The intern’s project was initiated on May 24, 2021, and continued through July 30, 2021, with the objective of gaining a better understanding of the dominant attenuation mechanisms for 129 I in the wetlands, the strength of the attenuation, and what conditions would influence it.
This research work has been supported by the DOE-FIU Science & Technology Workforce Development Initiative, an innovative program developed by the U.S. Department of Energy’s Office of Environmental Management (DOE-EM) and Florida International University’s Applied Research Center (FIU-ARC). During the spring of 2022, a DOE Fellow intern, Philip Moore, spent 16 weeks doing a spring internship at Savannah River National Lab under the supervision and guidance of Corey Hopper. The intern’s project was initiated on January 24, 2022, and continued through April 2, 2022, with the objective of contributing to a robotic system for automation of CCO inspection.
The electric industry sector is facing an “explosion” of data from a variety of sources. Electric sector stakeholders need to define how to capitalize on large datasets, both those they create and those from other sources (like data on weather, buildings, electric vehicles, etc.), to improve reliability and resilience and meet the changing system dynamics from renewable integration. For the electricity sector to fully utilize these vast new datasets, it must undergo a transformation in how it manages data quality, storage, and processing. The U.S. Department of Energy (DOE) Office of Electricity (OE) is committed to accelerating research, development, and demonstration of new technologies and tools within the electricity sector to advance reliability, resilience, and affordable operation of the power system. Through the prize mechanism, OE identified two widespread data-related challenges for utilities—load modeling and data analysis automation—and offered an opportunity for utilities and teams of software engineers to identify additional challenges faced by utilities. After completing one round of the American-Made Digitizing Utilities Prize, OE, the National Renewable Energy Laboratory (NREL) as the prize administrator, and Pacific Northwest National Laboratory (PNNL) as the domain experts have compiled the results and lessons learned to feed into the second round of the prize.
Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.
The U.S. Department of Energy (DOE), Office of Nuclear Energy (NE), Advanced Materials and Manufacturing Technologies (AMMT) program aims to develop extreme-environment materials solutions for use in the deployment of advanced nuclear reactors and the sustainment of the current fleet. To achieve this objective, a combination of experiment, a computational tool, and machine learning (ML) for the design of materials is adopted for the maturation of materials for nuclear technology. Through advanced manufacturing techniques such as laser powder bed fusion (LPBF) and laser powder direct energy deposition (LP-DED), components with complex geometries can be fabricated with reduced time and effort. Such advanced manufacturing methods can also provide the opportunity to improve materials performance through optimized microstructures and mechanical properties. However, existing engineering alloys are not always well suited for fabrication with additive manufacturing (AM), as their compositions have been tuned to optimize fabrication via conventional methods. Thus, similar alloys with modified compositions that are better suited for AM can be studied for improved performance. Over the past three years, the AMMT teams from Argonne National Laboratory (ANL) and Pacific Northwest National Laboratory (PNNL) studied various known Fe-based alloys by evaluating their initial printability using LPBF, and an AMMT-developed down-selection and decision matrix reduced the number of alloys to be studied from six to three in fiscal year (FY) 2024. Additionally, in FY 2024, for parallel evaluation, these three alloys were studied using LPDED. While LPBF is better for small- to medium-sized components with high detail and internal features, LP-DED combines a material feed system to place the powder onto the exact spot where the laser will melt the material. This AM method can be easily scaled to extremely large components and provides high build rate speeds compared to those of conventional LPBF systems. Additionally, DED is a better choice for complex geometries and compositional gradients.
The Hydropower Flexibility Framework (HFF) tool focuses on providing the hydropower community with an effective means of assessing optimized hydropower plant outcomes. This tool combines both site specific characteristics, which act to constrain plant operation, and the hydrologic and grid characteristics which drive hydropower plant operation. The hydropower community faces a confluence of factors which drive the importance of developing such a capability, including an aging hydropower fleet subject to a range of modernization opportunities, a large number of hydropower plant relicensing activities which may affect operational requirements, an electrical grid with increasing levels of variable resources which must be balanced to maintain grid stability, and climate change influencing riverine hydrologic patterns outside of design characteristics. With support from the hydropower community, the project team developed the HFF tool and demonstrated the tool through a series of Use Cases. This guidance was developed as a part of the larger HFF tool User’s Manual (see Appendix B), a resource designed to inform other users and to empower community uptake of the tool. The HFF tool, hosted at https://hfftool.com/, was developed with the support of the U.S. Department of Energy (DOE) Water Power Technologies Office (WPTO). EPRI is currently exploring alternatives to support the continued maintenance and functionally of the online tool.
The Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy aims to develop and qualify additively manufactured materials for nuclear applications. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture microstructural variability is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.
These proceedings report the results of a workshop on space telerobotics, which was held at the Jet Propulsion Laboratory, January 20-22, 1987. Sponsored by the NASA Office of Aeronautics and Space Technology (OAST), the Workshop reflected NASA's interest in developing new telerobotics technology for automating the space systems planned for the 1990s and beyond. The workshop provided a window into NASA telerobotics research, allowing leading researchers in telerobotics to exchange ideas on manipulation, control, system architectures, artificial intelligence, and machine sensing. One of the objectives was to identify important unsolved problems of current interest. The workshop consisted of surveys, tutorials, and contributed papers of both theoretical and practical interest. Several sessions were held on the themes of sensing and perception, control execution, operator interface, planning and reasoning, and system architecture.
This paper reports the results of a project supported by the National Aeronautics and Space Administration, Office of Aeronautics and Space Technology (NASA-OAST) under the Advanced Life Support Development Program. It is an initial attempt to integrate artificial intelligence techniques (via expert systems) with conventional quantitative modeling tools for advanced physical-chemical life support systems. The addition of artificial intelligence techniques will assist the designer in the definition and simulation of loosely/well-defined life support processes/problems as well as assist in the capture of design knowledge, both quantitative and qualitative. Expert system and conventional modeling tools are integrated to provide a design workstation that assists the engineer/scientist in creating, evaluating, documenting and optimizing physical-chemical life support systems for short-term and extended duration missions.
This report provides a technology survey on current commercially available microprocessors. This report is part of an effort funded by the Office of Aeronautics, Exploration, and Technology (OAET) and the Space Station Freedom (SSF) Advanced Development program to determine computational needs and capability for advanced automation and robotics. It will be used in conjunction with a user requirements survey to determine a plan to meet computational requirements for future NASA missions.
The Marshall Space Flight Center's Space Transportation Programs Office is responsible for the development and demonstration of advanced launch vehicle and propulsion technologies. One of these advanced propulsion concepts being pursued is solar thermal propulsion. This concept employs a concentrated beam of sunlight to heat a working fluid within a solar thermal engine. Expansion of the fluid would provide thrust at an increased specific impulse. One way to concentrate the sun's light to a specific point in the engine cavity is to use a fresnel lens that is supported by an inflatable structure. Such propulsion systems could provide an inexpensive way of transferring and maintaining satellites to upper stage orbit trajectories. This paper describes the test hardware, procedures, and non-contacting measurement methodologies used to determine the modal characteristics of an inflatable concentrator under the thermal-vacuum conditions of space. These characteristics were necessary to identify for validation of a finite element model to be used for developing the spacecraft's pointing and control system.
Orbital Sciences Corporation has been awarded a contract by NASA's Marshall Space Flight Center, in cooperation with the U.S. Air Force Research Laboratory's Military Space Plane Technology Program Office, for the Upper Stage Flight Experiment (USFE) program. Orbital is designing, developing, and will flight test a new low-cost, 10,000 lbf hydrogen peroxide/ JP-8 pressure fed liquid rocket. During combustion chamber tests at NASA Stennis Space Center (SSC) of the USFE engine, the catalyst bed showed a low frequency instability occurring as the H202 flow reached about 1/3 its design rate. This paper reviews the USFE catalyst bed and combustion chamber and its operation, then discusses the dynamics of the instability. Next the paper describes the dynamic computer model used to recreate the instability. The model was correlated to the SSC test data, and used to investigate possible solutions to the problem. The combustion chamber configuration which solved the instability is shown, and the subsequent stable operation presented.
The use of Nonuniform Rational B-Splines (NURBS) to represent geometry and data offers a standard way to facilitate the multidisciplinary analysis and design of aeropropulsion products. Using standard geometry defined by NURBS throughout design, analysis, part definition, manufacture, and test processes saves money and time. The Portable Extensible Viewer (PEV) offers engineers of different disciplines a means to view and manipulate NURBS geometry and associated data. Under the guidance of a team of Lewis, Boeing Company, and Navy personnel, PEV was developed by NASA Lewis Research Center's Computer Services Division for Lewis' Interdisciplinary Technology Office. The aeropropulsion industry provided input to the design requirements.
The Office of Chief Technologist (OCT), NASA has identified the need for research and technology development in part from NASAs Strategic Goal 3.3 of the NASA Strategic Plan to develop and demonstrate the critical technologies that will make NASAs exploration, science, and discovery missions more affordable and more capable. Furthermore, the Game Changing Development Program (GCDP) is a primary avenue to achieve the Agencys 2011 strategic goal to Create the innovative new space technologies for our exploration, science, and economic future. In addition, recently released NASA Space Technology Roadmaps and Priorities, by the National Research Council (NRC) of the National Academy of Sciences stresses the need for NASA to invest in the very near term in specific EDL technologies. The report points out the following challenges (Page 2-38 of the pre-publication copy released on February 1, 2012): Mass to Surface: Develop the ability to deliver more payload to the destination. NASA's future missions will require ever-greater mass delivery capability in order to place scientifically significant instrument packages on distant bodies of interest, to facilitate sample returns from bodies of interest, and to enable human exploration of planets such as Mars. As the maximum mass that can be delivered to an entry interface is fixed for a given launch system and trajectory design, the mass delivered to the surface will require reductions in spacecraft structural mass more efficient, lighter thermal protection systems more efficient lighter propulsion systems and lighter, more efficient deceleration systems. Surface Access: Increase the ability to land at a variety of planetary locales and at a variety of times. Access to specific sites can be achieved via landing at a specific location(s) or transit from a single designated landing location, but it is currently infeasible to transit long distances and through extremely rugged terrain, requiring landing close to the site of interest. The entry environment is not always guaranteed with a direct entry, and improving the entry systems robustness to a variety of environmental conditions could aid in reaching more varied landing sites. The National Research Council (NRC) Space Technology Roadmaps and Priorities report highlights six challenges and they are: 1) Mass to Surface, 2) Surface Access, 3) Precision Landing, 4) Surface Hazard Detection and Avoidance, 5) Safety and Mission Assurance, and 6) Affordability. In order for NASA to meet these challenges, the report recommends immediate focus on Rigid and Flexible Thermal Protection Systems. Rigid TPS systems such as Avcoat or SLA are honeycomb based and PICA is in the form of tiles. The honeycomb systems is manufactured using techniques that require filling of each (3/8 cell) by hand and within a limited amount of time once the ablative compound is mixed, all of the cells have to be filled and the entire heat-shield has to be cured. The tile systems such as PICA pose a different challenge as the mechanical strength characteristic and the manufacturing limitations require large number of small tiles with gap-fillers between the tiles. Recent investments in flexible ablative systems have given rise to the potential for conformal ablative TPS> A conformal TPS over a rigid aeroshell has the potential to solve a number of challenges faced by traditional rigid TPS materials.
Standards Development, IEC TC 114, IEA-OES Annual Contribution presentation from Water Power Technologies Office FY14-FY16 Peer Review.
Office of Nuclear Smuggling Detection and Deterrence (NSDD) is charged to identify and develop technologies to detect, disrupt, and investigate smuggling of radiological and nuclear materials •Border protection involves primary inspections using Radiation Portal Monitors (RPM), complimented with secondary and area survey inspections, usually performed manually using portable radiation detectors (PRDs) -RPM rely on fast technologies which provide quick scans of passing cargo/vehicles -Secondary inspections rely on trained, field deployed staff responsible for additional interdiction of screened cargo/vehicle putting them in potentially hazardous environment -Area survey inspections rely on trained, field deployed staff responsible for scanning and identifying presence of radiation while patrolling through a facility or public space •This project explores the use of technology which could reduce risks to field inspectors and add efficiency •The work presented here is a proof of principle of detector-robot integration to perform area survey inspections
Poster for the 2024 NETL Resource Sustainability Meeting. The poster presents a new project award by the Office of Technology Transitions to accelerate application and commercial utilization of an NETL-developed technology to rapidly characterize critical mineral occurrences within secondary and/or unconventional feedstocks, such as coal refuse or waste impoundments.