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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

E2C Successful Project Implementation Cohort: Overview of Solar, Microgrid and Resilience Project Development [Slides]

The Energy to Communities (E2C) peer-learning cohort program provides technical assistance to groups of 15 community entities around a common energy topic over the course of 6 months. Every month, participants join a virtual meeting where they hear from experts and exchange strategies and best practices with their peers. This cohort, "Successful Energy Project Implementation" will explore common challenges in implementing energy projects and learn strategies to turn project plans into reality. Each participant will focus on a local energy project or priority as a cornerstone for their learning throughout the series. This presentation overviews the basics of developing solar, microgrid and resilience projects with emphasis on understanding the project development process.

14 SOLAR ENERGY↗
DOE OSTI2022

MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks

Abstract We report a workflow and the output of a natural language processing (NLP)-based procedure to mine the extant metal–organic framework (MOF) literature describing structurally characterized MOFs and their solvent removal and thermal stabilities. We obtain over 2,000 solvent removal stability measures from text mining and 3,000 thermal decomposition temperatures from thermogravimetric analysis data. We assess the validity of our NLP methods and the accuracy of our extracted data by comparing to a hand-labeled subset. Machine learning (ML, i.e. artificial neural network) models trained on this data using graph- and pore-geometry-based representations enable prediction of stability on new MOFs with quantified uncertainty. Our web interface, MOFSimplify, provides users access to our curated data and enables them to harness that data for predictions on new MOFs. MOFSimplify also encourages community feedback on existing data and on ML model predictions for community-based active learning for improved MOF stability models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Data for Resilience and Reliability [Slides]

The Energy to Communities (E2C) peer-learning cohort program provides technical assistance to groups of 15 community entities around a common energy topic over the course of 6 months. Every month, participants join a virtual meeting where they hear from experts and exchange strategies and best practices with their peers. This cohort, "Planning for Major Energy Disruptions in the Southeast" focuses on strategies for entities in the Southeast to quickly recover from impacts to energy infrastructure caused by extreme events, such as severe weather and natural disasters. This presentation focuses on the data available related to energy disruptions. The workshop is on April 23, 2026.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

E2C Cohort on Major Energy Disruptions in the Southeast: Trade-Offs and Feasibility of Energy Sector Investment for Community Resilience [Slides]

The Energy to Communities (E2C) peer-learning cohort program provides technical assistance to groups of 15 community entities around a common energy topic over the course of 6 months. Every month, participants join a virtual meeting where they hear from experts and exchange strategies and best practices with their peers. This cohort, "Planning for Major Energy Disruptions in the Southeast" focuses on strategies for entities in the Southeast to quickly recover from impacts to energy infrastructure caused by extreme events, such as severe weather and natural disasters. This presentation focuses on the tradeoffs and relative costs of different strategies to improve outcomes from these extreme events. The workshop is on May 21, 2026.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Utilities' Role in Major Grid Disruptions [Slides]

The Energy to Communities (E2C) peer-learning cohort program provides technical assistance to groups of 15 community entities around a common energy topic over the course of 6 months. Every month, participants join a virtual meeting where they hear from experts and exchange strategies and best practices with their peers. This cohort, "Planning for Major Energy Disruptions in the Southeast" focuses on strategies for entities in the Southeast to quickly recover from impacts to energy infrastructure caused by extreme events, such as severe weather and natural disasters. This presentation focuses on the role of utilities in preparing for and responding to major energy disruptions. The workshop is on April 23, 2026.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRSDate not supplied

Advancements in Lunar Dust Mitigation and Leveraging the Contamination Control Community

Lunar Dust Contamination: As we get closer to putting landers, rovers, payloads, and astronauts on the Moon, dust mitigation is becoming more critical than ever. To address the many challenges of dust mitigation on the surface, engineers and scientists are looking at various technologies that may have relevance for the contamination control community. This includes active, passive, architectural, and operational solutions. There have been advances in coatings for dust mitigation at NASA and through commercial partnerships (see Wiesner, Wohl, et al). There have been advancements to previous solutions as well as novel solutions to address dust mitigation on the surface and on-orbit. There have been several efforts to better understand the effectiveness of various terrestrial and dust mitigation solutions with much of this data published in recent years. In the area of dust mitigation, dust contamination or infiltration refers to the impingement or contact of planetary dust with items not normally dusty and whose operation may therefore be compromised. Recent Advancements in Dust Mitigation: Dust mitigation strategies are becoming more common for hardware heading to the Moon, with most major programs implementing lunar dust contamination requirements for human health and equipment. In the last couple of years, NASA has created more guidance to assist hardware developers with dust mitigation. This includes NASA-STD-1008 (Classifications and Requirements for Testing Systems and Hardware to be Exposed to Dust in Planetary) and NASA TP 20220018746 (Lunar Dust Mitigation Guide and Reference). Several projects are testing hardware in relevant dusty environments to understand their tolerance to lunar dust. There has been a significant increase in the amount of testing with lunar simulants, with the NASA Simulant Advisory Committee serving a resource for NASA and our partners. There are new solutions for detecting and monitoring the dust internally and externally. Learning from the contamination control community: In addition to sharing potentially relevant solutions at the workshop, the dust mitigation community is also interested in learning from the contamination control community on ways to address lunar dust. For instance, a major aspect of managing the dust is monitoring and detection. This is critical for understanding when maintenance is needed and to inform future missions. Another challenge of working with lunar dust is quantifying and characterizing the smallest of particles. There are also challenges in modeling lunar dust and its behavior. The dust mitigation community can also leverage lessons learned from the planetary protection community.

dust mitagation↗
DOE OSTI2021

Seeding picoscale solutions for social macro goals: complex thinking in projects for vulnerable communities

This chapter presents lessons learned from two experiences of implementing sociopoliticalsustainability strategies to achieve environmental and economic sustainability.The projects were carried out in rural communities in Guinea-Bissau andJamaica, which are both vulnerable to climate change. Each experience, developedfrom a systemic perspective and under two frameworks (the water–food–energynexus and ‘Appropriate Technology’), is described in four phases (problemcharacterization, solution design, solution pre-evaluation, and implementation),with the significant findings made by the team in terms of challenges, rewards, andessential conditions for development. Both deal with environment-friendly solutionsthat solve the problems of water and energy provision. Based on complex thinking,the experiences are analyzed from the social inclusion, participation, empowerment,and learning theory viewpoints. Finally, the insights gained by the team arehighlighted: communication requirements, the level of dependency of industrializedsolutions, budget constraints, training, testing, and planning recommendations, thediversity of interaction with the communities, and partners’ skills. As a result, futureactions can exploit these experiences and contribute to the success of subsequentprojects.

Pereira Pinto, Joao↗
DOE OSTI2022

McLennan Community College Requirements Analysis Report

EPOC uses the Deep Dive process to discuss and analyze current and planned science, research, or education activities and the anticipated data output of a particular use case, site, or project to help inform the strategic planning of a campus or regional networking environment. This includes understanding future needs related to network operations, network capacity upgrades, and other technological service investments. A Deep Dive comprehensively surveys major research stakeholders’ plans and processes in order to investigate data management requirements over the next 5–10 years. In March of 2022, staff members from the Engagement and Performance Operations Center (EPOC) met with researchers and staff from LEARN and McLennan Community College (MCC) for the purpose of a Deep Dive into scientific and research drivers. The goal of this activity was to help characterize the requirements for a number of campus use cases, and to enable cyberinfrastructure support staff to better understand the needs of the researchers within the community.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Evaluation of Seismic Artificial Intelligence with Uncertainty

Artificial intelligence has transformed the seismic community with deep learning models (DLMs) that are trained to complete specific tasks within workflows. However, there is still a lack of robust evaluation frameworks for evaluating and comparing DLMs. Here, we address this gap by designing an evaluation framework that jointly incorporates two crucial aspects: performance uncertainty and learning efficiency. To target these aspects, we meticulously construct the training, validation, and test splits using a clustering method tailored to seismic data and enact an expansive training design to segregate performance uncertainty arising from stochastic training processes and random data sampling. The framework’s ability to guard against misleading declarations of model superiority is demonstrated through the evaluation of PhaseNet (Zhu and Beroza, 2018), a popular seismic phase picking DLM, under three training approaches. Our framework helps practitioners choose the best model for their problem and set performance expectations by explicitly analyzing model performance with uncertainty at varying budgets of training data.

58 GEOSCIENCES↗
DOE OSTI2020

Coastal Wetland Carbon Processes in a Warmer Climate (Final Report)

Coastal wetlands are global hotspots of carbon storage. Marshes, mangroves, and seagrass meadows account for about half of the total marine soil carbon budget and bury carbon at rates roughly equivalent to terrestrial forests despite occupying just 2.5% of Earth’s land area. Coastal wetlands have recently been recognized as important for storing carbon, but the future sink strength and carbon stock stability of these systems is uncertain because global change drivers such as temperature and elevated CO 2 disturb the complex ecology that drives high rates of soil carbon sequestration. The dynamics of coastal wetland carbon pools are not presently represented in earth system models. Our goals are to understand the influence of warming and elevated CO 2 on C cycling and to forecast the stability of tidal wetland soil carbon pools at the terrestrial-aquatic interface. We designed and implemented a novel field manipulation of elevated CO 2 and temperature and operated it from 2016 to 2020. We learned that plant communities and microbial communities respond differently to rising temperature, and that the net effects of these responses means that a modest amount of warming will increase tidal marsh carbon sequestration, while warming above that level will diminish the potential for additional carbon sequestration. In mathematical terms the system showed non-linear responses which are important for building forecast models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Increasing Diversity in the Field of Radiological Security

This paper will report on a diversity initiative undertaken by the NNSA Office of Radiological Security (ORS), Brookhaven National Laboratory and the Institute of Nuclear Materials Management. This paper will report on the planning associated with inclusion of radiological and nuclear security in the annual meeting program, the process of identifying and selecting individuals who will receive grants to attend the meeting, the expected impact of the INMM/ORS diversity initiative on the nuclear security community, and lessons learned.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2026

Cully Community Solar

This report summarizes the outcomes, accomplishments, challenges, and lessons learned from Verde’s Cully Community Solar project, funded by the U.S. Department of Energy’s Solar Energy Technologies Office. The project focused on advancing community solar development through engineering, permitting, interconnection, ecological site design, workforce development, and community engagement. During the project period, Verde and its partners successfully developed and commissioned a 1.25 MW ground-mounted community solar system while also advancing planning for additional community solar capacity and developing approaches that can inform future projects in complex urban environments.

14 SOLAR ENERGY↗
NASA NTRSDate not supplied

Increasing accessibility to deep learning-based analytics for space biology: pretrained models, transfer learning, and analytics platform development

Biological systems react in complex ways to the stressors of spaceflight, and the data capturing these relationships is concomitantly high-dimensional and complex. Deep learning and machine learning approaches are increasingly popular as an analytical approach for space biosciences, due to their ability to model complex relationships in complex data. However, such approaches often require large datasets and extensive computational resources. New approaches that minimize data sizes and computational power needed to leverage machine learning, and resources that make these approaches accessible, are needed to increase accessibility and adoption of machine learning in the space biosciences. Transfer learning, in which a pretrained model of broad utility is trained on a large dataset, and subsequently reused on downstream applications for which data is more limited, is one approach to minimizing data and computational intensity of deep learning applications. This transfer learning approach results in more performant models in high-dimensional, low-sample-size settings such as space biology, as compared to training models on limited data from scratch. This presentation will outline efforts to generate pretrained models for the space biology community, and highlight transfer learning applications modeling microbial antibiotic resistance during spaceflight. Finally, in order to increase accessibility of these models and tools, as well as others, for the broader space biology community, we present a modeling and analysis platform facilitating machine learning applications in space biology. This platform streamlines machine learning training and analysis in a notebook format, facilitates download and use of space biology data from the NASA GeneLab database, and can be utilized on NASA-hosted servers or downloaded and hosted locally. This effort, as part of the AI4LS (Artificial Intelligence for Life in Space) working group, will increase accessibility, feasibility, and performance of machine learning approaches for the space biology community.

Adrienne Hoarfrost↗
DOE OSTI2022

Machine Learning Assisted Gap-Filled Discharge Data for the East River Community Watershed, Colorado, for Water Years 2014-2021

This dataset contains a collection of machine learning assisted gap-filled discharge data created for all discharge stations across the East River Watershed, Colorado. This data was generated by using raw discharge data collected by Rosemary Carroll, and conducting a random forest machine learning analysis to gap-fill discharge data across all years at the hourly time level. Discharge data with gaps creates problems for analysis of measured and modeled fluxes of carbon and nitrogen exported out of each sub-watershed. Gap-filled data is also required as an input to surface water models, which helps to address our main research question related to how snowmelt timing impacts the timing and magnitude of nitrogen exports. Data is provided in one csv file.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A database of ultrastable MOFs reassembled from stable fragments with machine learning models

High-throughput screening of hypothetical metal-organic framework (MOF) databases can uncover new materials, but their stability in real-world applications is often unknown. We leverage community knowledge and machine learning (ML) models to identify MOFs that are thermally stable and stable upon activation. We separate these MOFs into their building blocks and recombine them to make a new hypothetical MOF database of over 50,000 structures with orders of magnitude more (1) connectivity nets and (2) inorganic building blocks than were present in prior databases. Further, this database shows a 10-fold enrichment of ultrastable MOF structures that are stable upon activation and more than 1 standard deviation more thermally stable than the average experimentally characterized MOF. For nearly 10,000 ultrastable MOFs, we compute elastic moduli to confirm that these materials have good mechanical stability, and we report methane deliverable capacities. We identify privileged metal nodes in ultrastable MOFs that optimize gas storage and mechanical stability simultaneously.

36 MATERIALS SCIENCE↗
NASA NTRS2009

A History of Space Toxicology Mishaps: Lessons Learned and Risk Management

After several decades of human spaceflight, the community of space-faring nations has accumulated a diverse and sometimes harrowing history of toxicological events that have plagued human space endeavors almost from the very beginning. Lessons have been learned in ground-based test beds and others were discovered the hard way - when human lives were at stake in space. From such lessons one can build a risk-management framework for toxicological events to minimize the probability of a harmful exposure, while recognizing that we cannot foresee all events. Space toxicologists have learned that relatively harmless compounds can be converted by air revitalization systems into compounds that cause serious harm to the crew. Our toxic risk management strategy now includes an assessment of the fate of any compound that might be released into the atmosphere. Propellants are highly toxic compounds, yet we have not always been able to thoroughly isolate the crew from exposure to these toxicants. Leakage of fluids from systems has resulted in hazardous conditions at times, and the behavior of such compounds inside a spacecraft has taught us how to manage potentially harmful escapes should they occur. Potential combustion events are an ever-present threat to the wellbeing of the crew. Such events have been sufficiently common that we have learned that one cannot judge the health threat of a given fire by the magnitude of the event. Management of such risks demands monitoring of combustion products. In the category of unpredictable toxic events, if one assumes that fires are predictable, we can place experience with toxic microbial metabolites, upsets during repair operations, and discharges from filters that have accumulated a substantial load of pollutants in their absorption beds. Management of such events requires a broad-spectrum, real-time analytical capability to discern the identity and concentrations of pollutants if they enter the atmosphere. Adverse events are an integral part of any human activity, and the spacefaring community must learn as much as possible from mistakes and near misses.

James, John T.↗
DOE OSTI2020

A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5

Abstract. Land models are essential tools for understanding and predicting terrestrial processes and climate–carbon feedbacks in the Earth system, but uncertainties in their future projections are poorly understood. Improvements in physical process realism and the representation of human influence arguably make models more comparable to reality but also increase the degrees of freedom in model configuration, leading to increased parametric uncertainty in projections. In this work we design and implement a machine learning approach to globally calibrate a subset of the parameters of the Community Land Model, version 5 (CLM5) to observations of carbon and water fluxes. We focus on parameters controlling biophysical features such as surface energy balance, hydrology, and carbon uptake. We first use parameter sensitivity simulations and a combination of objective metrics including ranked global mean sensitivity to multiple output variables and non-overlapping spatial pattern responses between parameters to narrow the parameter space and determine a subset of important CLM5 biophysical parameters for further analysis. Using a perturbed parameter ensemble, we then train a series of artificial feed-forward neural networks to emulate CLM5 output given parameter values as input. We use annual mean globally aggregated spatial variability in carbon and water fluxes as our emulation and calibration targets. Validation and out-of-sample tests are used to assess the predictive skill of the networks, and we utilize permutation feature importance and partial dependence methods to better interpret the results. The trained networks are then used to estimate global optimal parameter values with greater computational efficiency than achieved by hand tuning efforts and increased spatial scale relative to previous studies optimizing at a single site. By developing this methodology, our framework can help quantify the contribution of parameter uncertainty to overall uncertainty in land model projections.

54 ENVIRONMENTAL SCIENCES↗