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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Commissioning of the Second JLAB C75 Cryomodule & Performance Evaluation of Installed C75 Cavities

JLAB has long been a hub of SRF technology with the CEBAF accelerator as one of its first large scale adopters. As SRF technology has advanced, the C50 and C100 programs have allowed for the extension of CEBAF's total energy to 6 GeV and nearly 12 GeV respectively. Along with the increase in energy reach, rates of accelerating gradient degradation have been extracted for these cryomodule designs. A plan to mitigate these losses & maintain robust gradient headroom to deliver the 12 GeV program - the CEBAF Performance Plan - established a multi-year effort of cryomodule refurbishments and replacements. Part of this plan included a cost optimization of the C50 program with more modern processing techniques and the replacement of existing cavities with larger grain boundary cavities produced from ingot Niobium (dubbed C75 for 75 MeV gain). Reports have been made on the prototype pair of C75 cavities installed in a C50 cryomodule and the first full C75 cryomodule installed in 2017 and 2021. This paper reports on the results from the qualification of the cavities for the second C75 module in both a vertical cryostat and the commissioning results of the cryomodule in the CEBAF tunnel. This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under contract DE-AC05-06OR23177.

McCaughan, Michael↗
NASA NTRS2001

Phase 1 research program overview

The Phase 1 research program was unprecedented in its scope and ambitious in its objectives. The National Aeronautics and Space Administration committed to conducting a multidisciplinary long-duration research program on a platform whose capabilities were not well known, not to mention belonging to another country. For the United States, it provided the first opportunity to conduct research in a long-duration space flight environment since the Skylab program in the 1970's. Multiple technical as well as cultural challenges were successfully overcome through the dedicated efforts of a relatively small cadre of individuals. The program developed processes to successfully plan, train for and execute research in a long-duration environment, with significant differences identified from short-duration space flight science operations. Between August 1994 and June 1998, thousands of kilograms of research hardware was prepared and launched to Mir, and thousands of kilograms of hardware and data products were returned to Earth. More than 150 Principal Investigators from eight countries were involved in the program in seven major research disciplines: Advanced Technology; Earth Sciences; Fundamental Biology; Human Life Sciences; International Space Station Risk Mitigation; Microgravity; and Space Sciences. Approximately 75 long-duration investigations were completed on Mir, with additional investigations performed on the Shuttle flights that docked with Mir. The flight phase included the participation of seven US astronauts and 20 Russian cosmonauts. The successful completion of the Phase 1 research program not only resulted in high quality science return but also in numerous lessons learned to make the ISS experience more productive. The cooperation developed during the program was instrumental in its success. c2001 AIAA. Published by Elsevier Science Ltd.

short duration↗
NASA NTRS2017

Magnesium Alloys for Space Hardware Design

There have been advances in magnesium alloy development that NASA has not taken into consideration for space hardware because of a lack of test data. Magnesium alloys offer excellent weight reduction, specific strength, and deep space radiation mitigation. Traditionally, magnesium has been perceived as having too poor of a flammability resistance and corrosion resistance to be used for flight. Recent developments in magnesium alloying has led to the formation of two alloys, WE43 and Elektron 21, which are self-extinguishing and significantly less flammable because of their composition. Likewise, an anodizing process called Tagnite was formulated to deter any concern with galvanic and saltwater corrosion. The Materials Science Branch at Kennedy Space Center is currently researching these new alloys and treatments to better understand how they behave in the harsh environment of space. Successful completion of the proposed testing should result in a more thorough understanding of modern aerospace materials and processes, and possibly the permission to use magnesium alloys in future NASA designs.

microstructure↗
DOE OSTI2024

40th Eastern Regional Photosynthesis Conference

Photosynthesis has shaped global biochemistry and geochemistry in its production of almost all atmospheric oxygen, and almost every electron that supports the processes of life on Earth was extracted from water by the light-dependent reactions, many of which were then used to fix carbon in the dark reactions. To humans, that fixed carbon is the originator of all fossil fuels and a critical step in conversion of solar photons to human-usable energy. The Eastern Regional Photosynthesis Conference (ERPC) serves as a forum for advancement of science in this field. As a regional-level conference, it specializes in providing developmental opportunities for early-career researchers from undergraduates to pre-tenure professors, as well as broadening the field in terms of participating scholars and research approaches to produce new interdisciplinary collaborations. Funding from the Department of Energy is used to mitigate registration and attendance costs for junior (non-PI) researchers. The ERPC is one of the three regional conferences in the field of photosynthesis research in the United States, alongside the Western Photosynthesis Conference (currently in California) and the Midwestern Photosynthesis Conference (Turkey Run, Indiana). A longstanding incubator for scientific discourse, discovery, and collaborations, this conference has spawned many collaborations which led to Department of Energy-funded projects and brought two full generations of scientists into the field. In 2023 the landmark 40th such conference was held at the Woods Hole Marine Biological Laboratory in Massachusetts. The conference was held April 14th-16th, 2023. The majority of the total attendees are traditionally undergraduates, graduate students, and postdoctoral researchers, whose scientific development greatly benefits from conferences like these. Almost all talks besides the ones given by invited speakers were given by young scientists who benefited from this award. Many posters presented at this conference were also the work of junior scientists. This conference is many young scientists' first exposure to the photosynthesis research community and its primary goal is to provide a positive atmosphere for those attendees, as exemplified by the three poster sessions and dedicated interactions between senior and junior scientists. Additionally, this was the first year of a successful equitable outreach and speaker recruitment plan to provide fair representation at the conference in line with Department of Energy policies and mission. The central focus of the ERPC is the understanding of energy flow from photons to usable chemical products in both natural and artificial photosynthesis. We support the DOE-BES aim of understanding of the biochemical/biophysical processes of photosynthesis to inform new technology development and field applications. This year’s theme, “Photosynthesis Across Scales,” reflected the scope of work being done from the individual exciton to the crop scale. This research encompasses applied biology, chemistry, physics, and materials engineering. The aim of this conference is to present new discoveries, techniques, and questions which can be sourced from any of these disciplines and provide an audience which is broadly qualified to use or respond to these developments.

08 HYDROGEN↗
NASA NTRS2009

Space Environmental Effects on Materials and Processes

The Materials and Processes (M&P) Branch of the Structural Engineering Division at Johnson Space Center (JSC) seeks to uphold the production of dependable space hardware through materials research, which fits into NASA's purpose of advancing human exploration, use, and development of space. The Space Environmental Effects projects fully support these Agency goals. Two tasks were assigned to support M&P. Both assignments were to further the research of material behavior outside of Earth's atmosphere in order to determine which materials are most durable and safe to use in space for mitigating risks. One project, the Materials on International Space Station Experiments (MISSE) task, was to compile data from International Space Station (ISS) experiments to pinpoint beneficial space hardware. The other project was researching the effects on composite materials of exposure to high doses of radiation for a Lunar habitat project.

Sabbann, Leslie M.↗
DOE OSTI2025

Online thermal profile prediction for large format additive manufacturing: A hybrid CNN-LSTM based approach

Large format additive manufacturing (LFAM) is an advanced 3D printing technique that efficiently fabricates large-scale components through a layer-by-layer extrusion and deposition process. Accurate surface layer temperature monitoring is essential to prevent manufacturing failures and ensure final product quality. Traditional physics-based offline approaches for simulating thermal behavior are often inefficient and complex, posing challenges on real-time, in-situ monitoring. Here, to address this, we propose a data-driven hybrid CNN-LSTM model to predict sequential thermal images of arbitrary length using real-time infrared thermal imaging. In this approach, a Convolutional Neural Networks (CNN) is trained offline to capture spatial features, reduce dimensional complexity, and enhance time efficiency, while a stacked Long Short-Term Memory (LSTM) is applied online to capture temporal information for improved prediction of future thermal behavior in subsequent printing layers. Model performance is evaluated using MSE, SSIM, and PSNR metrics and is benchmarked against stacked LSTM and convolutional LSTM models, demonstrating superior accuracy and applicability. Additionally, to mitigate noise from moving extruders and gantry backgrounds in thermal images, a fine-tuned semantic segmentation model is implemented offline to extract printing geometry, enabling precise temperature tracking along the tool path for further thermal analysis. The frameworks developed in this study significantly advance temperature monitoring, thermal analysis, and in-situ manufacturing control for LFAM, bridging the gap between theoretical modeling and practical application.

Geometry extraction↗
DOE OSTI2021

Utilizing Oxygen Redox in Layered Cathode Materials from Multiscale Perspective

We report in high-capacity layered oxide cathode materials, utilization of lattice oxygen as a redox center is considered to be one of the most promising approaches to overcome the capacity limitation set by conventional transition metal redox centers. However, rapid material degradation is often associated with oxygen oxidation, leading to formidable challenges in utilizing oxygen redox. Further mechanistic understanding of the oxygen activities thus becomes critical to better control oxygen redox reactions. This review summarizes recent advances for investigating oxygen redox reactions in cathode materials from a multiscale perspective, i.e., from the atomistic level to the microstructure regime. First the mechanistic aspects of oxygen redox and the consequences of this reaction on various electrode degradation pathways during battery operation (e.g., oxygen loss, transition metal migration, irreversible phase transition), relating structural changes at the crystallographic scale to those at the macro scale, are discussed. Then recent developments based on atomic and microstructure modifications that are promising for improving the reversibility of oxygen redox reaction or mitigating the harmful processes arising from oxidation of the oxygen centers under high operating voltage are recounted. The analysis is concluded with a commentary on further research directions toward optimizing the oxygen activity for high-capacity charge storage.

25 ENERGY STORAGE↗
DOE OSTI2021

A regional inter-disciplinary partnership focusing on the development of a carinata-centered bioeconomy

Brassica carinata or Ethiopian mustard, a non-edible oilseed brassica, is a low carbon, purpose-grown, and none-to-low indirect land-use change bioenergy feedstock for the production of drop-in sustainable aviation fuel, biodiesel, renewable diesel, and a suite of value-added coproducts. Carinata oil converted to drop-in fuel using an American Society for Testing and Materials approved Catalytic Hydrothermolysis process has been successfully tested in commercial and military aviation. Carinata meal, the residue after oil extraction, is a high-protein feed supplement for livestock, poultry, and swine, and can also yield specialty products. The Southeast Partnership for Advanced Renewables from Carinata (SPARC) is a public–private partnership formed with a twofold mission: (1) Removing physical, environmental, social, and economic constraints that prevent regional intensification of carinata production as a low-carbon feedstock for renewable fuel and coproducts and (2) demonstrating enhanced value across the entire value chain by mitigating risk to farmers and other stakeholders. The partnership's goal is to energize the US bioeconomy through sustainable agriculture and thus contribute to energy security and economic diversification. SPARC relies on a combination of cutting-edge multidisciplinary research and active industry engagement to facilitate adoption of the crop. This involves informing stakeholders along the entire supply chain, from producers to end-users, policymakers, influencers, and the public, about the opportunities and best practices related to carinata. This article provides context and background concerning carinata commercialization as a winter cash crop in the Southeast US for renewable fuels and bioproducts. The advances made to date in the areas of feedstock development, fuel and coproduct development, meal valorization, supply chain logistics, and stakeholder engagement are outlined.

09 BIOMASS FUELS↗
DOE OSTI2024

Revolutionizing Materials Design: The Intersection of Quantum Mechanics and Data Modeling

The field of materials design is currently experiencing a notable evolution, driven by the convergence of sophisticated computational methodologies based on first principles and data-driven modeling approaches. I will review our recent endeavors employing AI/ML to expedite first-principles simulations and mitigate traditional methods' temporal and spatial limitations. Central to our efforts is developing and utilizing ML interatomic potentials (MLPs) across a diverse spectrum of materials. We show that MLPs serve as invaluable tools for navigating the complexities of the simulations, such as understanding the behavior of MgO at extreme environments of ~1 terapascal and temperatures >10,000 Kelvin. Moreover, we show that MLPs can provide precise details of the intricate dynamics governing the oxidation processes of binary alloy systems due to the competition between surface segregation and reconstruction tendencies. In summation, advancements in MLPs open the door to fresh possibilities in material modeling and, ultimately, discovery.

Saidi, Wissam↗
NASA NTRSDate not supplied

Advancing Metasurfaces Towards New Frontiers: Nonvolatile Reconfigurable Optics

The applications of adaptive optics extend across multiple sectors, encompassing areas such as LiDAR, biological and chemical sensing, and free-space communications. In this study, we report on the design, fabrication, testing, and modeling of electrically reconfigurable metasurfaces using a low-loss high contrast phase change material, Ge2Sb2Se4Te integrated with an IR-transparent silicon microheater. Through this work, we introduce a reliable architecture for switching PCM-based metasurfaces within an integrated circuit configuration which is compatible with standard foundry fabrication processes. We demonstrate the capability of controlling the transmission of electromagnetic waves through the precise stimulation of PCM-based pixels, each spanning a few hundred microns, over numerous cycles. By leveraging PCM-based pixels, we unlock the potential to create metasurfaces encompassing a diverse range of functionalities such as dielectric filters, metalens, or beam steering devices, which is governed by the design of the meta-atoms. Further, we perform an in-depth investigation into the failure mechanism utilizing techniques such as Fourier transform IR spectroscopy (FTIR), transmission electron microscopy (TEM), energy dispersive spectroscopy (EDS), and thermal modeling. According to our results, due to a sharp temperature rise at the PCM/heater interface, we observe severe delamination of the PCM from the heater. More uniform PCM deposition, better adhesion at the PCM/heater interface, and a more uniform temperature distribution in the device could potentially mitigate the failure and lead to a longer life-time. By addressing these challenges, we aim to unlock the full potential of PCM-based devices and advance the field of adaptive optics.

Phase change material↗
DOE OSTI2020

SAXS studies of X-ray induced disulfide bond damage: Engineering high-resolution insight from a low-resolution technique

A significant problem in biological X-ray crystallography is the radiation chemistry caused by the incident X-ray beam. This produces both global and site-specific damage. Site specific damage can misdirect the biological interpretation of the structural models produced. Cryo-cooling crystals has been successful in mitigating damage but not eliminating it altogether; however, cryo-cooling can be difficult in some cases and has also been shown to limit functionally relevant protein conformations. The doses used for X-ray crystallography are typically in the kilo-gray to mega-gray range. While disulfide bonds are among the most significantly affected species in proteins in the crystalline state at both cryogenic and higher temperatures, there is limited information on their response to low X-ray doses in solution, the details of which might inform biomedical applications of X-rays. In this work we engineered a protein that dimerizes through a susceptible disulfide bond to relate the radiation damage processes seen in cryo-cooled crystals to those closer to physiologic conditions. This approach enables a low-resolution technique, small angle X-ray scattering (SAXS), to detect and monitor a residue specific process. A dose dependent fragmentation of the engineered protein was seen that can be explained by a dimer to monomer transition through disulfide bond cleavage. This supports the crystallographically derived mechanism and demonstrates that results obtained crystallographically can be usefully extrapolated to physiologic conditions. Fragmentation was influenced by pH and the conformation of the dimer, providing information on mechanism and pointing to future routes for investigation and potential mitigation. The novel engineered protein approach to generate a large-scale change through a site-specific interaction represents a promising tool for advancing radiation damage studies under solution conditions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Optimal carbon storage reservoir management through deep reinforcement learning

Model-based optimization plays a central role in energy system design and management. The complexity and high-dimensionality of many process-level models, especially those used for geosystem energy exploration and utilization, often lead to formidable computational costs when the dimension of decision space is also large. This work adopts elements of recently advanced deep learning techniques to solve a sequential decision-making problem in applied geosystem management. Specifically, a deep reinforcement learning framework was formed for optimal multiperiod planning, in which a deep Q-learning network (DQN) agent was trained to maximize rewards by learning from high-dimensional inputs and from exploitation of its past experiences. To expedite computation, deep multitask learning was used to approximate high-dimensional, multistate transition functions. Both DQN and deep multitask learning are pattern based. As a demonstration, the framework was applied to optimal carbon sequestration reservoir planning using two different types of management strategies: monitoring only and brine extraction. Both strategies are designed to mitigate potential risks due to pressure buildup. Results show that the DQN agent can identify the optimal policies to maximize the reward for given risk and cost constraints. Finally, experiments also show that knowledge the agent gained from interacting with one environment is largely preserved when deploying the same agent in other similar environments.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

A Redundancy-Guided Approach for the Hazard Analysis of Digital Instrumentation and Control Systems in Advanced Nuclear Power Plants

We report digital instrumentation and control (I&C) upgrades are a vital research area for the nuclear industry. Despite their performance benefits, deployment of digital I&C in nuclear power plants (NPPs) has been limited. Digital I&C systems exhibit complex failure modes including common cause failures (CCFs), which can be difficult to identify. This paper describes the development of a redundancy-guided application of the Systems-Theoretic Process Analysis and fault tree analysis for the hazard analysis of digital I&C in advanced NPPs. The resulting Redundancy-Guided Systems-Theoretic Hazard Analysis (RESHA) is applied for the case study of a representative state-of-the-art digital reactor trip system. The analysis qualitatively and systematically identifies the most critical CCFs and other hazards of digital I&C systems. Ultimately, the RESHA can help researchers make informed decisions for how, and to what degree, defensive measures such as redundancy, diversity, and defense in depth can be used to mitigate or eliminate the potential hazards of digital I&C systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Summary: Nuclear Energy Critical Material Waste Reduction and Supply Chain Solutions Enabled by Advanced Manufacturing

In September 2020, the U.S. government issued an executive order to address the threat to the domestic supply chain from its reliance on critical minerals (CMs) from foreign competitors and to support the domestic mining and processing industry. A national strategy on CMs with impact on the U.S. Department of Energy’s (DOE’s) vision for 2021–2031 was developed. This vision embraces science and technology to re-establish U.S. competitiveness in the CM and material supply chains by (a) scientific innovation and technologies to ensure resilient and secure CMs and maintain a domestic material supply chain, (b) building a long-term minerals and materials innovation ecosystem to foster new capabilities to mitigate CM supply chain challenges, (c) increasing private sector adoption for sustaining the domestic CM supply chain, and (d) coordinating with international partners and federal agencies to diversify global supply chains and ensure the adoption of best practices for sustainable mining and processing (DOE 2021).

36 MATERIALS SCIENCE↗
DOE OSTI2022

Reflection on the Challenges, Accomplishments, and New Frontiers of Gene Drives

Ongoing pest and disease outbreaks pose a serious threat to human, crop, and animal lives, emphasizing the need for constant genetic discoveries that could serve as mitigation strategies. Gene drives are genetic engineering approaches discovered decades ago that may allow quick, super-Mendelian dissemination of genetic modifications in wild populations, offering hopes for medicine, agriculture, and ecology in combating diseases. Following its first discovery, several naturally occurring selfish genetic elements were identified and several gene drive mechanisms that could attain relatively high threshold population replacement have been proposed. This review provides a comprehensive overview of the recent advances in gene drive research with a particular emphasis on CRISPR-Cas gene drives, the technology that has revolutionized the process of genome engineering. Herein, we discuss the benefits and caveats of this technology and place it within the context of natural gene drives discovered to date and various synthetic drives engineered. Later, we elaborate on the strategies for designing synthetic drive systems to address resistance issues and prevent them from altering the entire wild populations. Lastly, we highlight the major applications of synthetic CRISPR-based gene drives in different living organisms, including plants, animals, and microorganisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Recent Advances in Assessing Environmental Effects of Marine Renewable Energy Around the World

Marine renewable energy (MRE) is increasingly of interest to coastal nations as a source of renewable energy that can support climate change mitigation goals as well as provide secure locally-produced energy for coastal and island communities. MRE extracts power from tidal streams, waves, ocean currents, run of rivers, and gradients in the ocean, with specialized devices developed and tested for each energy resource. Alongside development of MRE technologies and systems, first in Europe and then in North America, Australia, Asia, and other regions, it has been universally recognized that there is also a need to examine potential effects on marine animals, habitats, ecosystem processes, local communities and other sea users, to ensure that the MRE industry can be developed in a responsible and sustainable manner. This paper looks at the status of assessment and monitoring for potential environmental effects associated with MRE projects around the world. Over 80 projects were identified worldwide as having been tested, demonstrated, or commercially deployed with associated environmental monitoring. Five of the projects that represent tidal stream, wave, and run of river projects are examined in more detail to determine the types of data and information collected for those projects, the outputs of the monitoring campaigns, and the actions taken as a result of the data collection and analysis. Recommendations are provided for standardization of the monitoring approaches, instruments, and analysis methods at MRE project sites worldwide.

marine renewable energy, environmental effects, ri↗
NASA NTRS2016

Architecture for Integrated Medical Model Dynamic Probabilistic Risk Assessment

Probabilistic Risk Assessment (PRA) is a modeling tool used to predict potential outcomes of a complex system based on a statistical understanding of many initiating events. Utilizing a Monte Carlo method, thousands of instances of the model are considered and outcomes are collected. PRA is considered static, utilizing probabilities alone to calculate outcomes. Dynamic Probabilistic Risk Assessment (dPRA) is an advanced concept where modeling predicts the outcomes of a complex system based not only on the probabilities of many initiating events, but also on a progression of dependencies brought about by progressing down a time line. Events are placed in a single time line, adding each event to a queue, as managed by a planner. Progression down the time line is guided by rules, as managed by a scheduler. The recently developed Integrated Medical Model (IMM) summarizes astronaut health as governed by the probabilities of medical events and mitigation strategies. Managing the software architecture process provides a systematic means of creating, documenting, and communicating a software design early in the development process. The software architecture process begins with establishing requirements and the design is then derived from the requirements.

Probability Theory↗
NASA NTRSDate not supplied

Physics-Guided Deep Learning for Complex System Health Management and Decision Making

The landscape of complex engineered systems is rapidly evolving, from smart manufacturing facilities to next-generation transportation vehicles. As these systems become increasingly sophisticated and interconnected, the need for advanced health management systems grows ever more critical. These systems must go beyond simple monitoring, actively predicting potential failures before they occur. This paradigm shift from fixed maintenance schedules to condition-based predictions is key to optimizing system performance, enhancing safety, and paving the way for autonomous decision-making across various industries. Whether in industrial processes, energy systems, or advanced transportation, the ability to anticipate and prevent failures is becoming a cornerstone of operational excellence. To accurately predict the future health of any complex system, knowledge of its current health state and future operational conditions is essential. Recent advancements in data-driven algorithms have generated growing interest in artificial intelligence for industrial applications. However, the limitations of pure data-driven methods, particularly in industries where data acquisition is costly and limited, have become apparent. This has led to a focus on blending physics with data-driven algorithms, mitigating the drawbacks of both approaches while emphasizing their respective advantages. This research proposes a novel framework for integrating physics-based performance models with deep learning algorithms for the prognostics of complex safety-critical systems. In this approach, physics-based models serve as a blueprint, capturing fundamental system behaviors, while deep learning algorithms, leveraging real-world sensor data, fill in gaps and identify subtle patterns indicative of potential problems. This hybrid methodology, utilizing techniques such as Physics-Informed Neural Networks (PINNs), offers a powerful solution for predicting system health. By fusing domain knowledge with data-driven insights, this approach promises more accurate, adaptable, and reliable models for health prediction. The resulting framework is versatile, applicable across various sectors including aerospace, manufacturing, and energy systems, ultimately contributing to safer, more efficient operations in our increasingly complex technological landscape.

Diagnostics↗