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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 1,783 records · Page 99
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Are humans still necessary? Expanding the discussion

The rise of automation, artificial intelligence (AI), and autonomous systems raises important questions about the future role of humans and the field of human factors/ergonomics in workplaces. This paper builds on Dr. Peter Hancock’s 2023 ‘Are Humans Still Necessary?’ article published in the Ergonomics journal. Using a multi-method approach that included a debate, opinion polling, roundtable discussions, and AI queries, the current effort examined the necessity of human involvement in future work environments. Debate team members presented arguments for and against the need for human workers, considering human factors, technology, and socioeconomic factors. Observations indicate that while AI may handle routine tasks, humans will likely remain essential for complex decision making, creativity, and ethical considerations. The paper advocates for viewing workplace dynamics as collaborative human-AI partnerships rather than competition, highlighting the need for a transdisciplinary approach in which human factors/ergonomics professionals play a vital role in enhancing these relationships.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Further investigation on the fusion of 6 Li with 209 Bi target at near-barrier energies

The complete and incomplete fusion cross sections for 6 Li+ 209 Bi were measured using the in-beam γ-ray method around the Coulomb barrier. The cross sections of (deuteron captured) incomplete fusion (ICF) products were re-quantified experimentally for this reaction system. Here, the results reveal that the ICF cross section is equivalent to that of complete fusion (CF) above the Coulomb barrier and dominant near or below the Coulomb barrier. A theoretical calculation based on the continuum discretized coupled channel (CDCC) method was performed for the aforementioned CF and ICF cross sections; the result is consistent with the experiments. The universal fusion function (UFF) was also compared with the measured CF cross section for different barrier parameters, demonstrating that the CF suppression factor is significantly influenced by the choice of potential, which can reflect both dynamic and static effects of breakup on the fusion process.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

A rapid embodied carbon assessment tool for priority materials

Embodied carbon limits within building materials are a driving factor in global trade, generating new research and analysis tools in industry. These product assessments, which require utilizing life-cycle assessment (LCA) across broad supply chains, can be expensive, time and data-intensive, and subject to significant variations. Existing methods and tools, such as specific environmental product declarations, typically do not capture these variations and dynamics in supply and manufacturing. Moreover, models and tools must enable stakeholders to assess customized supply chains and future scenarios. In this study, we present the Rapid Embodied Carbon Assessment and Target-setting for Emissions-intensive Materials (REDuCE) tool for building materials. We developed a tool that allows users to select production technologies, transportation mode and distances, concrete carbonation, fuel sources, and regional electricity mixes to supply customization for cement and concrete produced and consumed in California. We generated and integrated a material demand model using residential building stock projections. We provide the user with a wide range of mitigation alternatives through low-carbon production pathways, material use efficiency, transportation modes, and projected electricity grid mixes. In a case study application through four mitigation scenarios, we find emission savings up to 80% by maximizing user mitigation alternatives, primarily driven by reductions in material use intensities. This work represents a foundation for expanding LCA and embodied carbon tools to better enable stakeholders to rapidly and accurately assess customized supply chains while meeting trade requirements.

building materials↗
DOE OSTI2023

Nuclear three-body short-range correlations in coordinate space

Here, we study the effects of three-nucleon short-range correlations on nuclear coordinate-space densities. For this purpose, novel three-body densities are calculated for ground-state nuclei using the auxiliary-field diffusion Monte Carlo method. The results are analyzed in terms of the generalized contact formalism, extended to include three-body correlations, revealing the universal behavior of nucleon triplets at short distances. We identify the quantum numbers of such correlated triplets and extract scaling factors of triplet abundances that can be compared to upcoming inclusive electron-scattering data. We also show that the dynamics of these triplets is sensitive to three-body forces, and, therefore, the short-range part of three-body force models could be tested against appropriate exclusive electron-scattering data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Cracked Gridline Wear Out Follows a Power Law

Cracks can form in Silicon solar cells in photovoltaic modules due to mechanical stresses arising from various extrinsic factors like handling and weather. While the immediate performance degradation may be minor, continuous loading overtime will degrade module performance. One probable reason is gridline surface wear across the cracked silicon with increased cyclic loading. In this work we propose a method to correlate gridline wear to module electrical degradation. We begin by conducting cyclic four-point bending tests on laminated silicon solar cells with a single crack and 22 intact gridlines for 10,000 cycles. We measure the progressive change in resistance during each loading cycle. We correlate it to a length scale called critical crack opening displacement (CCOD) that signifies failure of individual gridlines. By employing Weibull analysis, we determine the characteristic CCOD for all cycles and fit this data to a modified version of a wear power law. We observe that this i ts the data well. We also propose to study the effect of individual parameters in the power law equation and extend the equation to include material properties.

bending↗
DOE OSTI2022

PowerNet: Multi-agent Deep Reinforcement Learning for Scalable Powergrid Control

This paper develops an efficient multi-agent deep reinforcement learning algorithm for cooperative controls in powergrids. Specifically, we consider the decentralized inverter-based secondary voltage control problem in distributed generators (DGs), which is first formulated as a cooperative multi-agent reinforcement learning (MARL) problem. We then propose a novel on-policy MARL algorithm, PowerNet, in which each agent (DG) learns a control policy based on (sub-)global reward but local states and encoded communication messages from its neighbors. Motivated by the fact that a local control from one agent has limited impact on agents distant from it, we exploit a novel spatial discount factor to reduce the effect from remote agents, to expedite the training process and improve scalability. Furthermore, a differentiable, learning-based communication protocol is employed to foster the collaborations among neighboring agents. In addition, to mitigate the effects of system uncertainty and random noise introduced during on-policy learning, we utilize an action smoothing factor to stabilize the policy execution. To facilitate training and evaluation, we develop PGSim, an efficient, high-fidelity powergrid simulation platform. Here, experimental results in two microgrid setups show that the developed PowerNet outperforms the conventional model-based control method, as well as several state-of-the-art MARL algorithms. The decentralized learning scheme and high sample efficiency also make it viable to large-scale power grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Linking remotely sensed ecosystem resilience with forest mortality across the continental United States

Episodes of forest mortality have been observed worldwide associated with climate change, impacting species composition and ecosystem services such as water resources and carbon sequestration. Yet our ability to predict forest mortality remains limited, especially across large scales. Time series of satellite imagery has been used to document ecosystem resilience globally, but it is not clear how well remotely sensed resilience can inform the prediction of forest mortality across continental, multi-biome scales. Here, we leverage forest inventories across the continental United States to systematically assess the potential of ecosystem resilience derived using different data sets and methods to predict forest mortality. Here we found high resilience was associated with low mortality in eastern forests but was associated with high mortality in western regions. The unexpected resilience–mortality relation in western United States may be due to several factors including plant trait acclimation, insect population dynamics, or resource competition. Overall, our results not only supported the opportunity to use remotely sensed ecosystem resilience to predict forest mortality but also highlighted that ecological factors may have crucial influences because they can reverse the sign of the resilience–mortality relationships.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

COVIDScholar: An automated COVID-19 research aggregation and analysis platform

The ongoing COVID-19 pandemic produced far-reaching effects throughout society, and science is no exception. The scale, speed, and breadth of the scientific community’s COVID-19 response lead to the emergence of new research at the remarkable rate of more than 250 papers published per day. This posed a challenge for the scientific community as traditional methods of engagement with the literature were strained by the volume of new research being produced. Meanwhile, the urgency of response lead to an increasingly prominent role for preprint servers and a diffusion of relevant research through many channels simultaneously. These factors created a need for new tools to change the way scientific literature is organized and found by researchers. With this challenge in mind, we present an overview of COVIDScholar https://covidscholar.org/, an automated knowledge portal which utilizes natural language processing (NLP) that was built to meet these urgent needs. The search interface for this corpus of more than 260,000 research articles, patents, and clinical trials served more than 33,000 users at an average of 2,000 monthly active users and a peak of more than 8,600 weekly active users in the summer of 2020. Additionally, we include an analysis of trends in COVID-19 research over the course of the pandemic with a particular focus on the first 10 months, which represents a unique period of rapid worldwide shift in scientific attention.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Enabling Lean and Stoichiometric Gasoline Direct Injection Engines through Mitigation of Nanoparticle Emissions

This project had the objective to efficiently reduce particulate mass (PM) and particulate number (PN) from lean and stoichiometric gasoline direction injection engines used in light duty vehicle applications. It also sought to use suspended particle instruments to measure the effective density, illustrating a pathway for new methods for accurately measuring soot mass at low concentration. The three-year effort took a systems level approach to evaluate fuel and lubricant impacts on GDI soot formation in lean and stoichiometric operation and to evaluate the impact of these factors on soot filtration aftertreatment in three-way catalyst-coated gasoline particulate filters (GPFs).

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Construction of Women’s All-Around Speed Skating Event Performance Prediction Model and Competition Strategy Analysis Based on Machine Learning Algorithms

Introduction Accurately predicting the competitive performance of elite athletes is an essential prerequisite for formulating competitive strategies. Women’s all-around speed skating event consists of four individual subevents, and the competition system is complex and challenging to make accurate predictions on their performance. Objective The present study aims to explore the feasibility and effectiveness of machine learning algorithms for predicting the performance of women’s all-around speed skating event and provide effective training and competition strategies. Methods The data, consisting of 16 seasons of world-class women’s all-around speed skating competition results, used in the present study came from the International Skating Union (ISU). According to the competition rules, distinct features are filtered using lasso regression, and a 5,000 m race model and a medal model are built using a fivefold cross-validation method. Results The results showed that the support vector machine model was the most stable among the 5,000 m race and the medal models, with the highest AUC (0.86, 0.81, respectively). Furthermore, 3,000 m points are the main characteristic factors that decide whether an athlete can qualify for the final. The 11th lap of the 5,000 m, the second lap of the 500 m, and the fourth lap of the 1,500 m are the main characteristic factors that affect the athlete’s ability to win medals. Conclusion Compared with logistic regression, random forest, K-nearest neighbor, naive Bayes, neural network, support vector machine is a more viable algorithm to establish the performance prediction model of women’s all-around speed skating event; excellent performance in the 3,000 m event can facilitate athletes to advance to the final, and athletes with outstanding performance in the 500 m event are more likely competitive for medals.

Liu, Meng↗
DOE OSTI2021

Forecasting Dynamic Line Rating with Spatial Variation Considerations

Dynamic line rating (DLR) is a technology that allows the ampacity of an electrical conductor to be calculated using real-time or forecasted weather conditions. Historically, the ampacity of a conductor has been determined using a static line rating method which assumes conservative weather assumptions. Therefore, not only can DLR give a more accurate measurement of the true ampacity of a conductor, but it can also increase its ampacity during weather conditions with greater thermal mitigations. The two primary cooling factors in the ampacity calculations are wind speed and direction. In complex terrain, wind speed and direction can have large variations over short distances. Therefore, accurately identifying the limiting span of a transmission line requires high spatial resolution of the wind along its path. One solution is to install dense weather stations along their path, though this can become costly over long distances. Therefore, researchers have investigated the use of Computation Fluid Dynamic (CFD) simulations to accurately compute the wind field along the path of a transmission line and use these results to identify the limiting section of the conductor. This work presents a case study that evaluates the coupling of CFD simulations and forecasted weather simulations using the High-Resolution Rapid Refresh (HRRR) model points over a 2-year span within a region in south eastern Idaho. The primary goal of the work is the evaluation of the number of HRRR model points used, i.e., weather stations, along the path of the line and the accuracy of the resulting DLR ampacity. This was done using 4, 10, 17, 26, and 35 HRRR model points along two transmission line paths. The results indicate that as the number of model points are increased, the DLR ampacity of the lines decrease, yet converge as more points are added and demonstrate little change with additional HRRR points. It is expected that these results can help transmission line operators identify the number of weather stations that must be installed when coupled with CFD simulations and DLR ampacity to ensure accurate ratings and safe operations.

17 WIND ENERGY↗
DOE OSTI2021

Forecasting Dynamic Line Rating with Spatial Variation Considerations

Dynamic line rating (DLR) is a technology that allows the ampacity of an electrical conductor to be calculated using real-time or forecasted weather conditions. Historically, the ampacity of a conductor has been determined using a static line rating method which assumes conservative weather assumptions. Therefore, not only can DLR give a more accurate measurement of the true ampacity of a conductor, but it can also increase its ampacity during weather conditions with greater thermal mitigations. The two primary cooling factors in the ampacity calculations are wind speed and direction. In complex terrain, wind speed and direction can have large variations over short distances. Therefore, accurately identifying the limiting span of a transmission line requires high spatial resolution of the wind along its path. One solution is to install dense weather stations along their path, though this can become costly over long distances. Therefore, researchers have investigated the use of Computation Fluid Dynamic (CFD) simulations to accurately compute the wind field along the path of a transmission line and use these results to identify the limiting section of the conductor. This work presents a case study that evaluates the coupling of CFD simulations and forecasted weather simulations using the High-Resolution Rapid Refresh (HRRR) model points over a 2-year span within a region in south eastern Idaho. The primary goal of the work is the evaluation of the number of HRRR model points used, i.e., weather stations, along the path of the line and the accuracy of the resulting ampacity. This was done using 4, 10, 17, 26, and 35 HRRR model points along two transmission line paths. The results indicate that as the number of model points are increased, the DLR ampacity of the lines decrease, yet converge as more points are added and demonstrate little change with additional HRRR points. It is expected that these results can help transmission line operators identify the number of weather stations that must be installed when coupled with CFD simulations and DLR ampacity to ensure accurate ratings and safe operations.

17 WIND ENERGY↗
DOE OSTI2025

Non-Destructive Plutonium Assay in Pyroprocessing Bulk Materials with a 3D Boron-Coated-Straw Detector Array

Assessment of plutonium content through all the processing steps is needed and is a challenging task. While several destructive assay methods have been developed for nuclear material accountability, a nondestructive assay (NDA) system for the assessment of plutonium in bulk materials is still needed. This system should withstand pyroprocessing harsh environments and have consistent sensitivity and accuracy despite different fuel form factors. We aim to enable the accurate assessment of the plutonium content of nuclear material during pyroprocessing to improve the separation process and enhance its proliferation resistance. We plan to achieve this goal by developing and demonstrating a new 3D boron-coated-straw neutron detector array (3D-BCSDA) with high efficiency and spatial resolution.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Comparison of the Arrhenius parameters between conventional hydrothermal and microwave-assisted synthesis methods for tin oxide nanoparticles

Microwave (MW) irradiation has emerged as a powerful tool for accelerating materials synthesis, yet the origins of its specific influence on reaction kinetics remain elusive. While multiple studies have attributed the observed enhancements in reaction rates under MW heating to reduced activation energies, other accounts have suggested modifications to the Arrhenius pre-exponential factor as the predominant cause. Distinguishing between these parameters in modern applications of MW processing in nanomaterials requires experimental approaches capable of resolving the dynamic and nuanced structural kinetics that govern MW-assisted chemistry. Here, we combine in-situ synchrotron X-ray total scattering with pair distribution function (PDF) analysis to track the structural evolution of SnO 2 nanoparticles synthesized via MW-assisted and conventional hydrothermal conditions. Avrami modeling and Arrhenius analysis suggest that although MW irradiation yields a higher apparent activation energy, the enhanced crystallization is better explained by a pre-exponential factor several orders of magnitude larger than that of conventional heating. These findings suggest that the MW field induces a higher frequency of successful molecular rearrangements rather than lowering the intrinsic activation barrier. The results contribute further insights clarifying the role of the applied MW field for materials design where MW-specific effects can be deliberately harnessed. Furthermore, this work presents a framework promoting the utility of in-situ PDF characterization coupled with kinetic analysis for developing more sophisticated descriptions of nanoscale transformations for MW-driven reaction kinetics.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Effects of Copolymer Structure on Enzyme-Catalyzed Polyester Recycling

Polyesters are an omnipresent material used for a variety of applications (e.g., bottles, packaging, textile, windshield), roughly comprising 8% of plastics produced worldwide. Enzymatic recycling is an emerging solution to deal with the increasingly diverse polyesters that are not suitable for mechanical recycling. However, enzyme activity and efficiency are still the limiting factors impeding enzymatic recycling for different plastic waste forms. The effects of thermal and structural properties (e.g., glass transition temperature, crystallinity, specific surface area), which are determined by chemical composition and preprocessing, directly influence enzyme recycling efficiency. This work investigates two extrusion methods (single screw and twin-screw extrusion) to pretreat a range of copolyesters (RPET, PETG, Ecozen, Tritan and PBT) and modify their properties (i.e., glass transition temperature (T g ), crystallinity (%), and molecular weight (M n )). A PET-specific enzyme, leaf-branch compost cutinase (LCC ICCG ), produced from a fed-batch fermentation of Escherichia coli BL21(DE3), was used for the enzymatic depolymerization of different polyesters. Several copolyesters showed improved depolymerization after pretreatment, as measured by rate and amount of monomers produced. Furthermore, those that did not depolymerize were found to have exceptionally high glass transition temperature or percent crystallinity, highlighting the importance of these physical parameters on conversion efficiency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Wear Behavior of Ti-Al{sub 2}O{sub 3} Biocomposites in 9 g/L NaCl Solution

Ti-based biocomposites reinforced with Al{sub 2}O{sub 3} particles were processed by powder metallurgy. Wear behavior was investigated by reciprocating wear tests in 9 g/L NaCl solution. The effect of Al{sub 2}O{sub 3} volume fraction, normal load, frequency and sliding time was studied using 2{sup 4} full factorial design method. Statistical analysis showed that normal load and frequency, together with the interaction between the reinforcement volume fraction and normal load, was statistically significant on the wear volume loss. The fragmentation of the reinforcement particles during sliding was considered as the key factor in the wear resistance of Ti-Al{sub 2}O{sub 3} composites.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Implementation of Triply Periodic Minimal Surfaces (TPMS) as surface objects in OpenMC

Triply Periodic Minimal Surfaces (TPMS) represent a promising geometry for future fuel designs due to their significant surface-to-volume ratio, which facilitates efficient cooling of nuclear fuel, a crucial factor for safety and efficiency. Demonstrating the remarkable capabilities of TPMS fuel requires initial modeling and simulation. This paper presents an implementation of TPMS in the Monte Carlo code OpenMC, enabling reactor physics modeling of TPMS. Here, the primary advantages over traditional methods using CAD files include reduced memory requirements for computations and high-fidelity implementation. This implementation has been tested against CAD files loaded in Serpent2, yielding promising results with low biases in the $k_{\textrm{eff}}$, comparable to biases in the material balance sheet. The implementation presented in this work will be used in future reactor physics computations related to new reactor designs involving TPMS-based fuels.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Investigating explainable transfer learning for battery lifetime prediction under state transitions

Battery lifetime prediction at early cycles is crucial for researchers and manufacturers to examine product quality and promote technology development. Machine learning has been widely utilized to construct data-driven solutions for high-accuracy predictions. However, the internal mechanisms of batteries are sensitive to many factors, such as charging/discharging protocols, manufacturing/storage conditions, and usage patterns. These factors will induce state transitions, thereby decreasing the prediction accuracy of data-driven approaches. Transfer learning is a promising technique that overcomes this difficulty and achieves accurate predictions by jointly utilizing information from various sources. Hence, we develop two transfer learning methods, Bayesian Model Fusion and Weighted Orthogonal Matching Pursuit, to strategically combine prior knowledge with limited information from the target dataset to achieve superior prediction performance. From our results, our transfer learning methods reduce root-mean-squared error by 41% through adapting to the target domain. Furthermore, the transfer learning strategies identify the variations of impactful features across different sets of batteries and therefore disentangle the battery degradation mechanisms and the root cause of state transitions from the perspective of data mining. These findings suggest that the transfer learning strategies proposed in our work are capable of acquiring knowledge across multiple data sources for solving specialized issues.

25 ENERGY STORAGE↗