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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 181 records · Page 10

Trends in Human Spaceflight: Analysis of Observed Propulsion Failure Modes Following NASA’s Artemis I Mission

In 2022, NASA’s Artemis program returned the agency to lunar space, propelled by the Orion European Service Module (ESM). Through rigorous ground test campaigns for Artemis I and II and the collection and transmission of operational data during the Artemis I flight, the Safety and Mission Assurance (S&MA) team created and maintained a robust database of component nonconformances. This study analyzes in-flight and ground processing findings on the Artemis ESM propulsion subsystem to track reliability and safety metrics, as well as comparing the data to that of previous human spaceflight programs, Apollo and Space Shuttle. In drawing a comparison to the Artemis I flight, recommendations can be made for the Artemis II crewed mission to ensure best practices for flight safety, including redundancy and failure tolerance in the system. The results reinforce a need for robust safety standards and caution against the complacency of a single successful test flight.

human spaceflight safety↗

Some computational techniques for estimating human operator describing functions

Computational procedures for improving the reliability of human operator describing functions are described. Special attention is given to the estimation of standard errors associated with mean operator gain and phase shift as computed from an ensemble of experimental trials. This analysis pertains to experiments using sum-of-sines forcing functions. Both open-loop and closed-loop measurement environments are considered.

Levison, W. H.↗

Thermal Design of a Collapsible Cryogenic Vessel

Strategic planning for human exploration missions to Mars has conclusively identified in-situ resource utilization (ISRU) as an enabling technology. Most mission scenarios include an ISRU plant to produce propellants for ascent from Mars as well as the production of backup reserves of water, oxygen, and process gases. Current mission scenarios call for an ISRU plant to be deployed and then produce and store the required propellants and life support reserves before the arrival of the first human mission. Reliable cryogenic propellant liquefaction and storage technologies for extended period missions are especially critical. This report examines the cryogenic storage problem for liquid oxygen produced by an ISRU plant for a human mission scenario. The analysis examines various hardware configurations including insulation types, packaging techniques, and required cryocoolers to minimize the initial launch mass to low Earth orbit. Results of the analyses indicate that high vacuum insulation systems requiring vacuum pressures below one millitorr will be required to minimize the 'initial launch mass into low Earth orbit even though the temperature on the surface of Mars is much lower than Earth.

Hegab, Hisham E.↗

Acceptance Testing of the Vapor Phase Catalytic Ammonia Removal Engineering Development Unit

This paper describes the results of acceptance testing of the Vapor Phase Catalytic Ammonia Removal (VPCAR) technology. The VPCAR technology is currently being developed by NASA as a Mars transit vehicle water recycling system. NASA has recently completed a grant to develop a next generation VPCAR system. This grant was peer reviewed and funded through the Advanced Life Support (ALS) National Research Announcement (NRA). The grant funded a contract with Water Reuse Technology Inc. to construct an engineering development unit. This contract concluded with the shipment of the final deliverable to NASA on 8/31/03. The objective of the acceptance testing was to characterize the performance of this new system. This paper presents the results of mass power, and volume measurements for the delivered system. In addition, product water purity analysis for a Mars transit mission and a planetary base wastewater ersatz are provided. Acoustic noise levels, interface specifications and system reliability results are also discussed. An assessment of the readiness of the technology for human testing and recommendations for future improvements are provided.

Flynn, Michael↗

In-Situ Resource Utilization for Space Exploration: Resource Processing, Mission-Enabling Technologies, and Lessons for Sustainability on Earth and Beyond

As humanity begins to reach out into the solar system, it has become apparent that supporting a human or robotic presence in transit and/or on station requires significant expendable resources including consumables (to support people), fuel, and convenient reliable power. Transporting all necessary expendables is inefficient, inconvenient, costly, and, in the final analysis, a complicating factor for mission planners and a significant source of potential failure modes. Over the past twenty-five years, beginning with the Space Exploration Initiative, researchers at the NASA Glenn Research Center (GRC), academic collaborators, and industrial partners have analyzed, researched, and developed successful solutions for the challenges posed by surviving and even thriving in the resource limited environment(s) presented by near-Earth space and non-terrestrial surface operations. In this retrospective paper, we highlight the efforts of the co-authors in resource simulation and utilization, materials processing and consumable(s) production, power systems and analysis, fuel storage and handling, propulsion systems, and mission operations. As we move forward in our quest to explore space using a resource-optimized approach, it is worthwhile to consider lessons learned relative to efficient utilization of the (comparatively) abundant natural resources and improving the sustainability (and environment) for life on Earth. We reconsider Lunar (and briefly Martian) resource utilization for potential colonization, and discuss next steps moving away from Earth.

Hepp, A. F.↗

In-Situ Resource Utilization for Space Exploration: Resource Processing, Mission-Enabling Technologies, and Lessons for Sustainability on Earth and Beyond

As humanity begins to reach out into the solar system, it has become apparent that supporting a human or robotic presence in transit andor on station requires significant expendable resources including consumables (to support people), fuel, and convenient reliable power. Transporting all necessary expendables is inefficient, inconvenient, costly, and, in the final analysis, a complicating factor for mission planners and a significant source of potential failure modes. Over the past twenty-five years, beginning with the Space Exploration Initiative, researchers at the NASA Glenn Research Center (GRC), academic collaborators, and industrial partners have analyzed, researched, and developed successful solutions for the challenges posed by surviving and even thriving in the resource limited environment(s) presented by near-Earth space and non-terrestrial surface operations. In this retrospective paper, we highlight the efforts of the co-authors in resource simulation and utilization, materials processing and consumable(s) production, power systems and analysis, fuel storage and handling, propulsion systems, and mission operations. As we move forward in our quest to explore space using a resource-optimized approach, it is worthwhile to consider lessons learned relative to efficient utilization of the (comparatively) abundant natural resources and improving the sustainability (and environment) for life on Earth. We reconsider Lunar (and briefly Martian) resource utilization for potential colonization, and discuss next steps moving away from Earth.

Specific impulse↗

Generative AI for Power Grid Operations

Generative artificial intelligence (AI) has captured into the mainstream, demonstrating capabilities that once belonged solely to the realm of human cognition. From defeating world champions in complex games to generating human-quality text and images, Generative AI has proven its potential to revolutionize countless industries. The electric power grid is no exception. Generative AI's ability to process vast amounts of data rapidly, assist decision support and identify patterns could significantly enhance power grid operations. For example, Generative AI could improve state estimation where measurements are not available or integrate renewable energy sources more efficiently with probabilistic forecasting. The key contributions of this whitepaper are outlined below: (1) Comprehensive overview of Generative AI's applications in power grid operations: It highlights the opportunities in areas such as forecasting, state estimation, and demonstrating the potential for enhancing efficiency, reliability, and resilience. (2) Expanding Generative AI's impact through synergies with emerging technologies: The paper introduce NREL developed eGridGPT and explores how AI orchestration, multi-agent systems, and Digital Twins can collaborate to optimize grid operations, addressing the complexities of a decarbonized and electrified future. (3) In-depth analysis of challenges in implementing Generative AI: This includes considerations like data availability and quality, model validation, certification, and ethical concerns, ensuring responsible AI deployment. (4) Emphasizing human-AI collaboration: The whitepaper underscores the importance of trustworthy, transparency, and explainability in AI systems to promote seamless interaction between human operators and AI, ultimately improving decision-making. (5) Exploring future research and development: It identifies critical areas for further advancement to fully realize Generative AI's potential in power grid operations. This whitepaper serves as a valuable resource for researchers, practitioners, and policymakers looking to harness Generative AI for a more reliable, stable, and cost-effective power grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Demonstration and Evaluation of the Human-Technology Integration Function Allocation Methodology

There is an imminent need for the existing nuclear power plants to reduce their operating and maintenance (O&M) costs to remain economically viable. Digital technology, including automation, provides a significant opportunity for the existing nuclear power plant fleet to transform the way in which work is accomplished, reducing O&M costs, and allowing the fleet to remain economically competitive. One notable opportunity to significantly reduce O&M costs pertains to modifications to the plant equipment and main control room (MCR). Existing instrumentation and control (I&C) technologies in the MCR are highly analog, costly to operate and maintain, and demand a high cognitive and physical workload from plant staff (i.e., operators). Digitalizing the MCR has a range of broad economic benefits, including improved plant performance and reduced manual work. Further, digital I&C systems can fundamentally change the way in which plant staff operate the plant; this is the concept of operation. Human-technology integration is important to ensure that impacts to the concept of operation are done in a way that account for capabilities of people and technology. Human-technology integration employs human factors engineering (HFE) methods and principles to maximize the benefits of digital technology, reducing human error, improving overall decision-making and usability. The U.S. Department of Energy Light Water Reactor Sustainability Program is applying human-technology integration research to ensure digital technologies are safe, reliable, and efficient. This paper documents the demonstration of the human-technology guidance developed by the Light Water Reactor Sustainability Program from a first-of-a-kind digital I&C upgrade, specifically addressing function analysis and allocation for a new digital I&C system that included changes in automation levels. The program’s specific approach is included in this work, following lessons learned. This document serves as a resource for industry to follow in applying human-technology integration and HFE to digital modifications, specific to function analysis and allocation. The lessons learned should be considered in the planning and execution of HFE activities that support such digital modifications.

99 GENERAL AND MISCELLANEOUS↗

Certifying Auto-Generated Flight Code

Model-based design and automated code generation are being used increasingly at NASA. Many NASA projects now use MathWorks Simulink and Real-Time Workshop for at least some of their modeling and code development. However, there are substantial obstacles to more widespread adoption of code generators in safety-critical domains. Since code generators are typically not qualified, there is no guarantee that their output is correct, and consequently the generated code still needs to be fully tested and certified. Moreover, the regeneration of code can require complete recertification, which offsets many of the advantages of using a generator. Indeed, manual review of autocode can be more challenging than for hand-written code. Since the direct V&V of code generators is too laborious and complicated due to their complex (and often proprietary) nature, we have developed a generator plug-in to support the certification of the auto-generated code. Specifically, the AutoCert tool supports certification by formally verifying that the generated code is free of different safety violations, by constructing an independently verifiable certificate, and by explaining its analysis in a textual form suitable for code reviews. The generated documentation also contains substantial tracing information, allowing users to trace between model, code, documentation, and V&V artifacts. This enables missions to obtain assurance about the safety and reliability of the code without excessive manual V&V effort and, as a consequence, eases the acceptance of code generators in safety-critical contexts. The generation of explicit certificates and textual reports is particularly well-suited to supporting independent V&V. The primary contribution of this approach is the combination of human-friendly documentation with formal analysis. The key technical idea is to exploit the idiomatic nature of auto-generated code in order to automatically infer logical annotations. The annotation inference algorithm itself is generic, and parametrized with respect to a library of coding patterns that depend on the safety policies and the code generator. The patterns characterize the notions of definitions and uses that are specific to the given safety property. For example, for initialization safety, definitions correspond to variable initializations while uses are statements which read a variable, whereas for array bounds safety, definitions are the array declarations, while uses are statements which access an array variable. The inferred annotations are thus highly dependent on the actual program and the properties being proven. The annotations, themselves, need not be trusted, but are crucial to obtain the automatic formal verification of the safety properties without requiring access to the internals of the code generator. The approach has been applied to both in-house and commercial code generators, but is independent of the particular generator used. It is currently being adapted to flight code generated using MathWorks Real-Time Workshop, an automatic code generator that translates from Simulink/Stateflow models into embedded C code.

Denney, Ewen↗

Ensembles of radial basis function networks for spectroscopic detection of cervical precancer

The mortality related to cervical cancer can be substantially reduced through early detection and treatment. However, current detection techniques, such as Pap smear and colposcopy, fail to achieve a concurrently high sensitivity and specificity. In vivo fluorescence spectroscopy is a technique which quickly, noninvasively and quantitatively probes the biochemical and morphological changes that occur in precancerous tissue. A multivariate statistical algorithm was used to extract clinically useful information from tissue spectra acquired from 361 cervical sites from 95 patients at 337-, 380-, and 460-nm excitation wavelengths. The multivariate statistical analysis was also employed to reduce the number of fluorescence excitation-emission wavelength pairs required to discriminate healthy tissue samples from precancerous tissue samples. The use of connectionist methods such as multilayered perceptrons, radial basis function (RBF) networks, and ensembles of such networks was investigated. RBF ensemble algorithms based on fluorescence spectra potentially provide automated and near real-time implementation of precancer detection in the hands of nonexperts. The results are more reliable, direct, and accurate than those achieved by either human experts or multivariate statistical algorithms.

Cervix Neoplasms/pathology↗

The Lithuania 100% Renewable Energy Study - Interim Results: Electricity System Scenarios for 2030 [Slides]

Lithuania's Energy Vision aims to achieve self-sufficiency in electricity generation by 2035 and transition to 100% renewable energy as soon as possible while maintaining affordability, reliability, and energy security. The Lithuania Energy Agency (LEA) is partnering with the National Renewable Energy Laboratory (NREL) to conduct the Lithuania 100% Renewable Energy Study (Lithuania 100) to provide evidence-based analysis for development of Lithuania's National Energy Independence Strategy. The Lithuania 100 Study leverages unique tools and capabilities of NREL to provide rigorous technical analysis of clean energy policies to achieve 100% renewable energy, and assess impacts on electricity grid operations, hydrogen system development, electricity distribution networks, air quality, and human health outcomes. The study is supported by a stakeholder committee chaired by the Ministry of Energy of Lithuania and implemented by four technical working groups. This report provides highlights of key interim results from modeling of Lithuania's near-term electricity grid through the year 2030. Results show that Lithuania has sufficient renewable energy potential, flexible generation capacity, and interconnection with neighboring European Union countries to reliably meet projected 2030 electricity demand with 100% renewable energy. A range of scenarios were modeled, each of which achieves at least 100% renewable energy in electricity, on average over the year, by 2030. Potential demands for hydrogen across industrial and transportation sectors were also evaluated, as well as the cost of hydrogen produced in Lithuania by 2030.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

A comparative bear model for immobility-induced osteopenia

The National Institutes of Health (NIH) and the National Aeronautics and Space Administration (NASA) are seeking solutions to the human problem of osteopenia, or immobility-induced bone loss. Bears, during winter dormancy, appear uniquely exempted from the debilitating effects of immobility osteopenia. NIH and ESA, Inc. are creating a large database of metabolic information on human ambulatory and bedrest plasma samples for comparison with metabolic data obtained from bear plasma samples collected in different seasons. The database generated from NASA's HR113 human bedrest study showed a clear difference between plasma samples of ambulatory and immobile subjects through cluster analysis using compounds determined by high performance liquid chromatography with coulometric electrochemical array detection (HPLC-EC). We collected plasma samples from black bears (Ursus americanus) across 4 seasons and from 3 areas and subjected them to similar analysis, with particular attention to compounds that changed significantly in the NASA human study. We found seasonal differences in 28 known compounds and 33 unknown compounds. A final database contained 40 known and 120 unknown peaks that were reliably assayed in all bear and human samples; these were the primary data set for interspecies comparison. Six unidentified compounds changed significantly but differentially in wintering bears and immobile humans. The data are discussed in light of current theories regarding dormancy, starvation, and anabolic metabolism. Work is in progress by ESA Laboratories on a larger database to confirm these findings prior to a chemical isolation and identification effort. This research could lead to new pharmaceuticals or dietary interventions for the treatment of immobility osteopenia.

NASA Discipline Musculoskeletal↗

The use of computer graphic techniques for the determination of ventricular function.

Description of computer techniques employed to increase the speed, accuracy, reliability, and scope of angiocardiographic analyses determining human heart dimensions. Chamber margins are traced with a Calma 303 digitizer from projections of the angiographic films. The digitized margins of the ventricular images are filed in a computer for subsequent analysis. The margins can be displayed on the television screen of a graphics unit for individual study or they can be viewed in real time (or at any selected speed) to study dynamic changes in the chamber outline. The construction of three dimensional images of the ventricle is described.

Sandler, H.↗

Trust in Artificial Intelligence: Meta-Analytic Findings

Objective The present meta-analysis sought to determine significant factors that predict trust in artificial intelligence (AI). Such factors were divided into those relating to (a) the human trustor, (b) the AI trustee, and (c) the shared context of their interaction. Background There are many factors influencing trust in robots, automation, and technology in general, and there have been several meta-analytic attempts to understand the antecedents of trust in these areas. However, no targeted meta-analysis has been performed examining the antecedents of trust in AI. Method Data from 65 articles examined the three predicted categories, as well as the subcategories of human characteristics and abilities, AI performance and attributes, and contextual tasking. Lastly, four common uses for AI (i.e., chatbots, robots, automated vehicles, and nonembodied, plain algorithms) were examined as further potential moderating factors. Results Results showed that all of the examined categories were significant predictors of trust in AI as well as many individual antecedents such as AI reliability and anthropomorphism, among many others. Conclusion Overall, the results of this meta-analysis determined several factors that influence trust, including some that have no bearing on AI performance. Additionally, we highlight the areas where there is currently no empirical research. Application Findings from this analysis will allow designers to build systems that elicit higher or lower levels of trust, as they require.

Behavioral Sciences↗

A multimodal and integrated approach to interrogate human kidney biopsies with rigor and reproducibility: guidelines from the Kidney Precision Medicine Project

Comprehensive and spatially mapped molecular atlases of organs at a cellular level are a critical resource to gain insights into pathogenic mechanisms and personalized therapies for diseases. The Kidney Precision Medicine Project (KPMP) is an endeavor to generate three-dimensional (3-D) molecular atlases of healthy and diseased kidney biopsies by using multiple state-of-the-art omics and imaging technologies across several institutions. Obtaining rigorous and reproducible results from disparate methods and at different sites to interrogate biomolecules at a single-cell level or in 3-D space is a significant challenge that can be a futile exercise if not well controlled. Here we describe a “follow the tissue” pipeline for generating a reliable and authentic single-cell/region 3-D molecular atlas of human adult kidney. Our approach emphasizes quality assurance, quality control, validation, and harmonization across different omics and imaging technologies from sample procurement, processing, storage, shipping to data generation, analysis, and sharing. We established benchmarks for quality control, rigor, reproducibility, and feasibility across multiple technologies through a pilot experiment using common source tissue that was processed and analyzed at different institutions and different technologies. A peer review system was established to critically review quality control measures and the reproducibility of data generated by each technology before their being approved to interrogate clinical biopsy specimens. The process established economizes the use of valuable biopsy tissue for multiomics and imaging analysis with stringent quality control to ensure rigor and reproducibility of results and serves as a model for precision medicine projects across laboratories, institutions and consortia.

59 BASIC BIOLOGICAL SCIENCES↗

Enhanced Geothermal Shot Analysis for the Geothermal Technologies Office

In 2021, the U.S. Department of Energy (DOE) began the Energy Earthshots initiatives to accelerate breakthroughs of reliable clean energy solutions within the next 10 years. In 2022, the National Renewable Energy Laboratory (NREL) was asked by the DOE Geothermal Technologies Office (GTO) to provide analysis for developing Energy Earthshot targets for Enhanced Geothermal Systems (EGS), human-made underground reservoirs that extract thermal energy from the earth for electricity generation and/or heating applications. The Enhanced Geothermal Shot analysis is based on the technology assumptions in the 2019 GTO report GeoVision: Harnessing the Heat Beneath Our Feet. For Earthshot, we updated some of the technology cost and performance assumptions based on recent technology advances and updated the EGS resource potential to include more detailed analysis. We used the updated EGS supply cost curves to forecast the amount of geothermal electricity generation that could be deployed in the US by 2050 using a capacity expansion model. The results were used to develop a cost target for EGS. On September 8th, 2022, the Enhanced Geothermal Shot was announced. Its target - reduce the cost of EGS by 90%, to $45 per megawatt hour by 2035. This paper summarizes the cost and resource assumptions used in the Enhanced Geothermal Shot. It describes the assumptions used in the Regional Energy Deployment System (ReEDS) capacity expansion model to forecast geothermal deployment and discusses the results.

15 GEOTHERMAL ENERGY↗

Enhanced Geothermal Shot Analysis for the Geothermal Technologies Office: Preprint

In 2021, the U.S. Department of Energy (DOE) began the Energy Earthshots initiatives to accelerate breakthroughs of reliable clean energy solutions within the next 10 years. In 2022, the National Renewable Energy Laboratory (NREL) was asked by the DOE Geothermal Technologies Office (GTO) to provide analysis for developing Energy Earthshot targets for Enhanced Geothermal Systems (EGS), human-made underground reservoirs that extract thermal energy from the earth for electricity generation and/or heating applications. The Enhanced Geothermal Shot analysis is based on the technology assumptions in the 2019 GTO report GeoVision: Harnessing the Heat Beneath Our Feet. For Earthshot, we updated some of the technology cost and performance assumptions based on recent technology advances and updated the EGS resource potential to include more detailed analysis. We used the updated EGS supply cost curves to forecast the amount of geothermal electricity generation that could be deployed in the US by 2050 using a capacity expansion model. The results were used to develop a cost target for EGS. On September 8th, 2022, the Enhanced Geothermal Shot was announced. Its target - reduce the cost of EGS by 90%, to $45 per megawatt hour by 2035. This paper summarizes the cost and resource assumptions used in the Enhanced Geothermal Shot. It describes the assumptions used in the Regional Energy Deployment System (ReEDS) capacity expansion model to forecast geothermal deployment and discusses the results.

analysis↗

Multi defect detection and analysis of electron microscopy images with deep learning

Electron microscopy is widely used to explore defects in crystal structures, but human detecting of defects is often time-consuming, error-prone, and unreliable, and is not scalable to large numbers of images or real-time analysis. In this work, we discuss the application of machine learning approaches to find the location and geometry of different defect clusters in irradiated steels. We show that a deep learning based Faster R-CNN analysis system has a performance comparable to human analysis with relatively small training data sets. Furthermore, this study proves the promising ability to apply deep learning to assist the development of automated microscopy data analysis even when multiple features are present and paves the way for fast, scalable, and reliable analysis systems for massive amounts of modern electron microscopy data.

36 MATERIALS SCIENCE↗