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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Harnessing graph convolutional neural networks for identification of glassy states in metallic glasses

Graph Convolutional Neural Networks (GCNNs) have emerged as powerful tools for analyzing materials. In this study, we employ GCNNs to examine structural characteristics of CuZr metallic glasses (MGs) and identify their states. We use molecular dynamics to simulate the quenching process of CuZr, using cooling rates ranging from 10 9 to 10 15 K/s, to produce six unique glassy states. For each state, we create a dataset comprising 1,800 distinct samples. We evaluate the effectiveness of various GCNNs, including Graph Attention Neural Network (GANN), Graph Sample and AggreGatE (GraphSAGE), Graph Isomorphism Network (GIN), and Relational Graph Convolutional Neural Network (RGCN). GANN and GraphSAGE demonstrate comparable performance, achieving an overall accuracy of 81% in classifying the MG states. Furthermore, these results underscore the potential of GCNNs to detect subtle structural variances in disordered materials and point to broader application of deep learning in the analysis of MGs and other amorphous substances.

36 MATERIALS SCIENCE↗
DOE OSTI2026

AGFormer: Adaptive Spatiotemporal graph informed transformer for multi-reservoir inflow forecasting

Accurate reservoir inflow forecasting is crucial for effective water resource management, yet most machine learning models focus on single-reservoir prediction and overlook spatial dependencies among hydrologically connected reservoirs. Here, we propose AGFormer (Adaptive Graph-Informed Transformer), an end-to-end framework that integrates adaptive graph learning with temporal sequence modeling for multi-reservoir inflow forecasting. A shared encoder and graph attention mechanism generate reservoir-specific embeddings, which are then processed by the Transformer-based encoder–decoder for multi-step inflow forecasting. We also introduce a pretraining paradigm to learn robust temporal embeddings from misaligned historical records. Evaluated on 30 reservoirs in the Upper Colorado River Basin, AGFormer achieves superior seven-day-ahead forecasts, with NSE > 0.75 for 20 reservoirs—outperforming Encoder–Decoder LSTM, GCN+LSTM, and Transformer baselines. Adaptive graph learning captures dynamic inter-reservoir dependencies, and feature attribution aligns with snowmelt-driven hydrology. Incorporating forecasted meteorological inputs further enhances accuracy, demonstrating AGFormer’s potential to support reservoir management under dynamic hydrological conditions.

Adaptive graph learning↗
DOE OSTI2025

River Dissolved Oxygen Prediction Using Machine Learning Models and Wireless Sensor Measurements

Simultaneous flooding&heat and droughts&heat events can potentially destabilize hydro-meteorological conditions to deteriorate the water quality of Neches River. Machine learning (ML) models utilizing wireless sensor measurements have been applied to predict water quality and optimize various water management strategies. This study aims to develop ML models to predict dissolved oxygen (DO) prediction under various hydro-meteorological conditions and enhance water management decision-making. Wireless sensor measurements of DO, water temperature, sample depth, conductivity, turbidity, and pH, along with discharge from the United States Geological Survey stations, are collected for model inputs at the Pine Island Bayou C749 station (PIB-C749) and Neches River Saltwater Barrier (SWB). Multilayer perceptron neural networks, recurrent neural networks, long short-term memory (LSTM), and bidirectional LSTM (BiLSTM) with and without attention mechanism (AT) are tested to determine the best model, which is applied the rolling forecast method to predict 14-day DO. Traditional and recurrent transfer learning (TL and RTL) methods are adopted to overcome insufficient data at the SWB. The input feature importance analysis using the integrated gradients (IG) algorithm is applied to determine dominant inputs. The results show LSTM-based models are capable handling long sequential data. AT-BiLSTM and RTL-LSTM demonstrate the best performance at the PIB-C749 (RMSE=0.054) and the SWB (RMSE=0.028), respectively. TL and RTL methods significantly improve model performance at the SWB. DO, temperature, and pH show higher importance, consistent with hydrodynamics and water chemistry. Both best models are applied to predict 14-day DO and demonstrate reasonable performance for decision-making. Hydro-meteorological conditions of 2017 flood and 2012 drought events are simulated and reveal that possible hypoxia occurs after flooding due to increasing temperature and turbidity, and DO concentration decreases significantly under heat and drought conditions. In conclusion, LSTM-based models utilizing wireless sensor data can be a timely and effective approach to make appropriate decisions on water resource management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Load Profile Inpainting for Missing Load Data Restoration and Baseline Estimation

This paper introduces a Generative Adversarial Nets (GAN) based, Load Profile Inpainting Network (Load-PIN) for restoring missing load data segments and estimating the baseline for a demand response event. The inputs are time series load data before and after the inpainting period together with explanatory variables (e.g., weather data). Here, we propose a Generator structure consisting of a coarse network and a fine-tuning network. The coarse network provides an initial estimation of the data segment in the inpainting period. The fine-tuning network consists of self-attention blocks and gated convolution layers for adjusting the initial estimations. Loss functions are specially designed for the fine-tuning and the discriminator networks to enhance both the point-to-point accuracy and realisticness of the results. We test the Load-PIN on three real-world data sets for two applications: patching missing data and deriving baselines of conservation voltage reduction (CVR) events. We benchmark the performance of Load-PIN with five existing deep-learning methods. Our simulation results show that, compared with the state-of-the-art methods, Load-PIN can handle varying-length missing data events and achieve 15-30% accuracy improvement.

14 SOLAR ENERGY↗
NASA NTRS2003

Application of a data-mining method based on Bayesian networks to lesion-deficit analysis

Although lesion-deficit analysis (LDA) has provided extensive information about structure-function associations in the human brain, LDA has suffered from the difficulties inherent to the analysis of spatial data, i.e., there are many more variables than subjects, and data may be difficult to model using standard distributions, such as the normal distribution. We herein describe a Bayesian method for LDA; this method is based on data-mining techniques that employ Bayesian networks to represent structure-function associations. These methods are computationally tractable, and can represent complex, nonlinear structure-function associations. When applied to the evaluation of data obtained from a study of the psychiatric sequelae of traumatic brain injury in children, this method generates a Bayesian network that demonstrates complex, nonlinear associations among lesions in the left caudate, right globus pallidus, right side of the corpus callosum, right caudate, and left thalamus, and subsequent development of attention-deficit hyperactivity disorder, confirming and extending our previous statistical analysis of these data. Furthermore, analysis of simulated data indicates that methods based on Bayesian networks may be more sensitive and specific for detecting associations among categorical variables than methods based on chi-square and Fisher exact statistics.

NASA Discipline Neuroscience↗
NASA NTRS1999

Arctic cognition: a study of cognitive performance in summer and winter at 69 degrees N

Evidence has accumulated over the past 15 years that affect in humans is cyclical. In winter there is a tendency to depression, with remission in summer, and this effect is stronger at higher latitudes. In order to determine whether human cognition is similarly rhythmical, this study investigated the cognitive processes of 100 participants living at 69 degrees N. Participants were tested in summer and winter on a range of cognitive tasks, including verbal memory, attention and simple reaction time tasks. The seasonally counterbalanced design and the very northerly latitude of this study provide optimal conditions for detecting impaired cognitive performance in winter, and the conclusion is negative: of five tasks with seasonal effects, four had disadvantages in summer. Like the menstrual cycle, the circannual cycle appears to influence mood but not cognition.

Cognition↗
NASA NTRS2000

A dual-task investigation of automaticity in visual word processing

An analysis of activation models of visual word processing suggests that frequency-sensitive forms of lexical processing should proceed normally while unattended. This hypothesis was tested by having participants perform a speeded pitch discrimination task followed by lexical decisions or word naming. As the stimulus onset asynchrony between the tasks was reduced, lexical-decision and naming latencies increased dramatically. Word-frequency effects were additive with the increase, indicating that frequency-sensitive processing was subject to postponement while attention was devoted to the other task. Either (a) the same neural hardware shares responsibility for lexical processing and central stages of choice reaction time task processing and cannot perform both computations simultaneously, or (b) lexical processing is blocked in order to optimize performance on the pitch discrimination task. Either way, word processing is not as automatic as activation models suggest.

Attention↗
NASA NTRS1998

Word-identification priming for ignored and attended words

Three experiments examined contributions of study phase awareness of word identity to subsequent word-identification priming by manipulating visual attention to words at study. In Experiment 1, word-identification priming was reduced for ignored relative to attended words, even though ignored words were identified sufficiently to produce negative priming in the study phase. Word-identification priming was also reduced after color naming relative to emotional valence rating (Experiment 2) or word reading (Experiment 3), even though an effect of emotional valence upon color naming (Experiment 2) indicated that words were identified at study. Thus, word-identification priming was reduced even when word identification occurred at study. Word-identification priming may depend on awareness of word identity at the time of study.

Mental Recall↗
NASA NTRSDate not supplied

The Effects of Chronic Sleep Restriction on Multiple Object Tracking

The ability to simultaneously track numerous moving objects in the presence of irrelevant stimuli is essential for carrying out a variety of tasks. Sleep loss has been found to impair neurocognitive functioning and, as a result, attentional processing capacity is reduced. A common form of sleep loss is chronic sleep restriction (CSR), in which an inadequate amount of sleep is obtained over consecutive days. The objective of the current study was to determine if performance on the multiple object tracking (MOT) task was adversely impacted by a week of CSR. Twelve healthy participants (6 males, 6 females) kept a fixed sleep-wake schedule, with a constant waketime, at home for four weeks (activity monitors worn on the participant’s nondominant wrist were used to confirm compliance). Weeks one and three were deemed washout weeks, during which participants maintained a 9-hour sleep-wake schedule. Weeks two and four were deemed experimental weeks, during which participants were randomly assigned a 5-hour (CSR) and 9-hour (sleep satiation) sleep-wake schedule. Following night seven of each experimental week, participants completed a 13-hour laboratory visit under dim light (less than 15 lux) where they maintained a constant posture and were provided with hourly isocaloric snacks. MOT was presented at approximately 6 and 8 hours after waking. Participants were required to track four, five, or six moving targets in the presence of identical distractors (always 12 total objects). It was found that participants slept significantly less during the week of CSR compared to the week of sleep satiation. There was no difference in the overall proportion of correct MOT responses following the CSR and sleep satiation weeks. However, an additional analysis examining only the 6 target condition found that the proportion of correct responses was significantly lower following the week of CSR. These findings suggest that CSR has an adverse impact on tracking performance when the cognitive demand was higher. This has implications for individuals, such as air traffic controllers and truck drivers, who must visually track multiple moving objects under high workload situations, often while chronically sleep deprived.

sleep restriction↗
NASA NTRSDate not supplied

The Influence of Self-Reported Distractions on the Psychomotor Vigilance Task (PVT) in Laboratory and Field Environments

Introduction: The Psychomotor Vigilance Task (PVT) is a measure of vigilant attention that is commonly used in laboratory environments to assess the neurobehavioral impact of sleep loss and circadian misalignment. The PVT has been increasing in popularity for use in field environments; however, the potential for distraction is higher in the field compared to the lab. It is unclear how distractions experienced by individuals taking the PVT in the real world may influence reaction time metrics. We investigated the influence of self-reported distraction on PVT outcomes across laboratory and field environments. Methods: We examined PVT data from five studies including short (n=36 participants, 3799 PVTs) and long-haul (n=75 participants, 3283 PVTs) airline personnel, control center personnel (n=5 participants, 96 PVTs), and healthy individuals who participated in a study involving at-home and laboratory assessments (n=12 participants, 486 and 310 PVTs). Individuals in all of the studies were asked to complete the five-minute NASA PVT at least three times daily. Participants were asked to indicate the number of distractions they experienced immediately after each PVT. Mean PVT reaction time (RT) and number of distractions were computed for each study and overall. Results: Participants reported more distractions in field environments compared to the lab (short-haul=1.29 +/- 1.48, long-haul=0.66 +/- 1.07, control-center=1.20 +/- 1.37, at-home=0.86 +/- 1.36, laboratory=0.46 +/- 1.07) Across all studies, we found that PVT RT slowed as self-reported distractions increased (all studies combined: 0 distractions=PVT RT 274.4ms; 1=284.6ms; 2=302.9ms; 3=321.2ms; >4=409.3ms). These findings were similar for healthy participants completing PVTs at home (0 distractions=287.1ms; 1=309.9ms; 2=328.3ms; 3=369.8ms; >4=385.1ms) but were less consistent during in-lab assessments (0 distractions=278.7ms; 1=316.2ms; 2=396.2ms; 3=370.4ms; >4=354.4ms). These findings were similar for other PVT outcomes. Conclusion: Participants reported more distractions in field environments compared to the laboratory. Our findings suggest that the number of distractions that individuals report experiencing while taking a PVT increases the reaction time registered by the device. Researchers should collect information about distractions during the PVT and should be aware that distractions may influence the recorded PVT reaction time.

behavior↗
NASA NTRS2023

Neural-Based Compression Scheme for Solar Image Data

Studying the solar system and especially the Sun relies on the data gathered daily from space missions. These missions are data-intensive and compressing this data to make them efficiently transferable to the ground station is a twofold decision to make. Stronger compression methods, by distorting the data, can increase data throughput at the cost of accuracy which could affect scientific analysis of the data. On the other hand, preserving subtle details in the compressed data requires a high amount of data to be transferred, reducing the desired gains from compression. In this work, we propose a neural network-based lossy compression method to be used in NASA’s data-intensive imagery missions. We chose NASA’s Solar Dynamics Observatory (SDO) mission which transmits 1.4 terabytes of data each day as a proof of concept for the proposed algorithm. In this work, we propose an adversarially trained neural network, equipped with local and non-local attention modules to capture both the local and global structure of the image resulting in a better trade-off in rate-distortion (RD) compared to conventional hand-engineered codecs. The RD variational autoencoder used in this work is jointly trained with a channel-dependent entropy model as a shared prior between the analysis and synthesis transforms to make the entropy coding of the latent code more effective. We also studied how optimizing perceptual losses could help our neural compressor to preserve high-frequency details of the data in the reconstructed compressed image. Our neural image compression algorithm outperforms currently-in-use and state-of-the-art codecs such as JPEG and JPEG-2000 in terms of the RD performance when compressing extreme-ultraviolet (EUV) data. As a proof of concept for use of this algorithm in SDO data analysis, we have performed coronal hole (CH) detection using our compressed images, and generated consistent segmentations, even at a compression rate of ∼ 0.1 bits per pixel (compared to 8 bits per pixel on the original data) using EUV data from SDO.

Image coding↗
DOE OSTI2022

Monitoring Carbon Storage Sites with Time-Lapse Gravity Surveys

Monitoring technologies are deployed on carbon storage sites to demonstrate that the injected CO2 is contained within the storage complex during and long after the active life of the project and that the CO2 behavior is conforming to expectations. Detection of gravity variations accompanying mass redistributions in the subsurface caused by fluid movements in reservoirs provides a unique means for monitoring the dynamics of a carbon sequestration site. Time-lapse gravity surveys have a long history of effectively monitoring temporal density changes in the subsurface. However, monitoring of carbon storage sites with time-lapse gravity surveys using surface or borehole measurements remains limited, although it gained more attention during the last decade. With the recent progresses in instrument capabilities, and the increased number of fluid storage sites (e.g., wastewater, CO2, natural gas, etc.), the gravity method (with its unique ability to remotely estimate both the mass of an injected fluid and its location) has a promising future in monitoring. This chapter provides background information regarding the gravity method and discusses its modeling and application to various carbon storage sites.

Carbon Sequestration, time-lapse gravity, monitori↗
DOE OSTI2023

Ion Mobility Spectrometry-Mass Spectrometry for High-Throughput Analysis

Ion mobility spectrometry is a widely used analytical technique providing gas phase separation of molecules. It has received increasing attention in the recent years with the advancement in technology development and the availability of commercial instruments. In this chapter, we introduced the ion mobility fundamental theory and provided examples of IMS applications, especially for isomer separation, collision cross section database generation, high throughput analysis workflows, software tools for IMS data analysis, and ongoing high resolution SLIM IMS development. While IMS is not yet routinely utilized in drug discovery and pharmaceutical industry, there has been increased interest in high throughput library screening and antibody characterization. With all the ongoing development in IMS technology and informatics, we foresee more and more exciting applications of high throughput IMS analysis in different fields including omics studies, drug discovery and clinical applications in the near future.

Ross, Dylan H.↗
DOE OSTI2024

Non-Invasive Biophysical Techniques to Monitor the Structural Plasticity of the Photosynthetic Machinery of Live Diatom Cells

The photosynthetic performance of diatoms depends largely on the organization and structural flexibility of their thylakoid membranes, the densely packed, highly organized membrane vesicles in which light reactions of photosynthesis occur. Different regulatory mechanisms that fine tune the photosynthetic functions affect the organization of the photosynthetic machinery at different levels of structural complexity, from the level of individual protein complexes to the macroarray of membrane proteins and the remodeling of the entire thylakoid membrane system. To monitor these reorganizations, non-invasive techniques are of special value. In this chapter, we focus our attention on three of these techniques, which have been demonstrated to provide unique and useful information on the structure and structural and functional plasticity of live diatom cells: (i) circular dichroism (CD) spectroscopy, which has provided unique information on the chiral (macro-)organization of protein complexes and on their rapid, reversible reorganizations, fine-tuning the light-harvesting processes, as well as on variations in the short-range excitonic interactions in the antenna complexes; (ii) small-angle neutron scattering (SANS), which has been used to determine the periodic organization of the thylakoid membranes and to monitor reversible ultrastructural changes on the time-scale of minutes, induced by variations in the environmental conditions such as changes in temperature or light intensity; and (iii) electrochromic shift absorbance transients (ΔA ECS ), a spectroscopic tool which has been shown to be capable of identifying distinct functional groups of the light-harvesting carotenoid fucoxanthin in different diatoms and in cells exposed to different light intensities. Future use of these techniques will most certainly contribute to the deeper understanding of key regulatory mechanisms of photosynthesis in diatoms.

Szabo, Milan↗
DOE OSTI2024

Circular Economy Contributions to Decarbonizing the US Steel Sector

The potential benefits of the circular economy (CE) for decarbonization have recently attracted much attention in the academic and grey literature. The department of energy (DOE) industrial decarbonization roadmap highlight that in addition to the four pillars (energy efficiency, electrification, low-carbon fuels, feedstocks, and energy sources, and carbon capture, utilization, and storage (CCUS)) identified in the roadmap, "scenarios [...] will need to be developed that incorporate materials efficiency and circular economy strategies". Moreover, the roadmap identifies hard-to-abate CO2 emissions across the five industrial subsectors included in the analysis (iron & steel, chemical, food & beverage, refining, and cement manufacturing). Thus, additional options to decarbonize the industrial sector, like the CE, could prove beneficial to address those hard-to-abate emissions. Moreover, they may be less costly, require less R&D or incentives, and be more readily adopted than other strategies such as energy efficiency and CCUS. While the Industrial Decarbonization Roadmap includes some CE strategies (e.g., the increasing market share of steel from electric arc furnaces - which incorporate steel scrap), a deeper dive into what role the CE could play in the United States (US) industrial decarbonization is needed. In this work, we present several industrial decarbonization scenarios that incorporate materials efficiency and circular economy strategies for the iron & steel subsector. The scenarios identify barriers (including technical limits and constraints), opportunities, and R&D needs.Moreover, we estimate the contribution of those scenarios to reducing the industrial sector's CO2 emissions and their potential synergies with the Industrial Decarbonization Roadmap's four pillars. Iron & steel manufacturing was responsible for 90 million tons of industrial CO2 emissions in 2020 - 7% of the industrial sector's total process- related emissions. Besides recycling - which presents technical limitations due to trace contaminants - CE strategies on the demand side could lower iron & steel manufacturing emissions (e.g., the development of lighter products or business models that encourage a more intensive use). This study reviews barriers, opportunities, and trade-offs for 5 material efficiency strategies. Many topics explored in the review call for further research.

barriers↗
DOE OSTI2023

Chapter 7: Recent Advances in Electrocatalysts for Hydrogen Oxidation Reaction in Alkaline Electrolytes

The recent development in electrocatalysts for the hydrogen oxidation reaction (HOR) attracted significant attention. In this sense, this chapter provides a fundamental understanding of the state-of-the-art PGM and non-PGM-based electrocatalysts for the HOR in alkaline conditions. Moreover, a strong focus has been made on the strategies to overcome the slow kinetics of the HOR to commercialize anion-exchange membrane fuel cells.

anion-exchange membrane↗
DOE OSTI2023

Additive Manufacturing of Self‐Sensing Carbon Fiber Composites

Carbon fiber-reinforced polymer (CFRP) composites have gained substantial attention across various industries owing to their exceptional mechanical properties and lightweight nature. The emergence of additive manufacturing technologies brings new opportunities to the industry, offering advantages such as design freedom, rapid prototyping, and customization. However, the fabrication of CFRP composites through 3D printing techniques poses challenges pertaining to low resolution and limitations in complex geometry realization. This work introduces digital light processing printing as a versatile, high-resolution method ideal for CFRP composite fabrication. Furthermore, the development and characterization of CFRP are focused on and the manipulation of mechanical properties through variations in matrix resins and fiber loadings is investigated, showcasing the versatility of CFRP composites for tailored applications. Additionally, the integration of self-sensing capabilities in CFRP structures is explored, which opens up opportunities for applications in smart components for automotive and structural health monitoring.

carbon fiber composites↗
DOE OSTI2019

Broadband, High-Speed, and Large-Amplitude Dynamic Optical Switching with Yttrium-Doped Cadmium Oxide

Transparent conducting oxides, such as doped indium oxide, zinc oxide, and cadmium oxide (CdO), have recently attracted attention as tailorable materials for applications in nanophotonic and plasmonic devices such as low-loss modulators and all-optical switches due to their tunable optical properties, fast optical response, and low losses. Here, optically induced extraordinarily large reflection changes (up to 135%) are demonstrated in bulk CdO films in the mid-infrared wavelength range close to the epsilon near zero (ENZ) point. To develop a better understanding of how doping level affects the static and dynamic optical properties of CdO, the evolution of the optical properties with yttrium (Y) doping is investigated. An increase in the metallicity and a blueshift of the ENZ point with increasing Y-concentrations is observed. Broadband all-optical switching from near-infrared to mid-infrared wavelengths is demonstrated. The major photoexcited carrier relaxation mechanisms in CdO are identified and it is shown that the relaxation times can be significantly reduced by increasing the dopant concentration in the film. Our report could pave the way to practical dynamic and passive optical and plasmonic devices with doped CdO spanning wavelengths from the ultraviolet to the mid-infrared region.

42 ENGINEERING↗