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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Coupling Shared E-scooters and Public Transit: A Spatial and Temporal Analysis

The integration of shared e-scooters with public transit is a promising solution for urban mobility's first/last-mile challenge. This study explores spatiotemporal factors influencing this integration, using 35-day e-scooter trip data from Chicago. Employing a random-effect negative binomial approach, we modeled the frequency of e-scooter trips to access/egress to/from bus stops and train stations. Results indicate that weather conditions, design features like intersection density, and multimodal network density significantly influence usage. The transit system characteristics such as service frequency have a positive effect on the integration of e-scooters and trains while a similar effect for bus and e-scooter integration was not significant. Furthermore, safety-related variables such as accident and crime rates as well as demographic characteristics were also revealed to be significant factors in our study. These findings offer vital insights to urban planners and policymakers for infrastructure, safety enhancements, and interventions to encourage efficient e-scooter-public transit integration.

Chicago↗
DOE OSTI2024

An Analysis of the Spatial Variations in the Relationship Between Built Environment and Severe Crashes

Traffic crashes significantly contribute to global fatalities, particularly in urban areas, highlighting the need to evaluate the relationship between urban environments and traffic safety. This study extends former spatial modeling frameworks by drawing paths between global models, including spatial lag (SLM), and spatial error (SEM), and local models, including geographically weighted regression (GWR), multi-scale geographically weighted regression (MGWR), and multi-scale geographically weighted regression with spatially lagged dependent variable (MGWRL). Utilizing the proposed framework, this study analyzes severe traffic crashes in relation to urban built environments using various spatial regression models within Leon County, Florida. According to the results, SLM outperforms OLS, SEM, and GWR models. Local models with lagged dependent variables outperform both the global and generic versions of the local models in all performance measures, whereas MGWR and MGWRL outperform GWR and GWRL. Local models performed better than global models, showing spatial non-stationarity; so, the relationship between the dependent and independent variables varies over space. The better performance of models with lagged dependent variables signifies that the spatial distribution of severe crashes is correlated. Finally, the better performance of multi-scale local models than classical local models indicates varying influences of independent variables with different bandwidths. According to the MGWRL model, census block groups close to the urban area with higher population, higher education level, and lower car ownership rates have lower crash rates. On the contrary, motor vehicle percentage for commuting is found to have a negative association with severe crash rate, which suggests the locality of the mentioned associations.

Alisan, Onur (ORCID:0000000193113984)↗
DOE OSTI2022

Understanding the Seismic Ground Motion Spatial Variability Using Network Analysis Community Detection

This project is to explore ground motion spatial distribution using a new approach graph-based network analysis. In this study, we combine a large-N seismic array and graph analytics to explore spatial variability and correlation at a local scale using small local and regional earthquakes. In this method, each seismic station is modeled as a node and the similarities of the waveforms that represent ground motions between two stations are modeled as edges. By analyzing this graph network using the similarity matrices and community detection algorithm, we can group the stations spatially with similar patterns. A random forest algorithm is used to reveal the important features that affect the spatial grouping. The result suggests site conditions, and how they interact with the incident seismic wavefield, strongly condition the spatial correlation of ground motion. Future progress in characterizing ground motion spatial variability will require dense wavefield measurements, either through nodal deployments, or perhaps distributed acoustic sensing measurements of seismic wavefields.

58 GEOSCIENCES↗
DOE OSTI2022

Evaluation of macadamia felted coccid (Hemiptera: Eriococcidae) damage and cultivar susceptibility using imagery from a small unmanned aerial vehicle ( sUAV ), combined with ground truthing

Abstract BACKGROUND Macadamia felted coccid, Acanthococcus ioronsidei (Williams) (Hemiptera: Eriococcidae), is a significant pest of macadamia nut, Macadamia integrifolia Maiden & Betche (Protaceae), in Hawaii, and heavy infestations can kill branches, resulting in characteristic dead, copper‐colored leaves. Small Unmanned Aerial Vehicles (sUAV) or ‘drones,’ combined with spatial data analysis, can provide growers with accurate and high‐resolution detection of plant stress due to pest infestations. We investigated the feasibility of using RGB (red‐green‐blue) color images from sUAV to detect dieback caused by macadamia felted coccid infestation and compared sUAV estimates with ground‐based damage estimates (ground truthing). RESULTS Spatial analysis showed clustering of foliar damage that reflected cultivar susceptibility to macadamia felted coccid infestation, with cultivars 344 and 856 being susceptible, and cultivars 800 and 333 being tolerant. sUAV and ground‐based estimates of foliar damage were similar for the cultivar 344, but ground‐based assessments were higher than sUAV for cultivar 856, possibly due to the differences in canopy architecture and significant early dieback in the lower canopy. At foliar damage levels <10%, sUAV and ground truthing data were significantly positively correlated, suggesting sUAV may be useful in detecting early stages of macadamia felted coccid infestation. CONCLUSIONS Cultivars showed varying susceptibility to macadamia felted coccid infestation and the foliage damage appeared in clusters. sUAV was able to detect the foliage damage under high and low infestation scenarios suggesting that it can be effectively used for the early detection of infestations. © 2022 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Non-propagating structures and propagating waves in solar wind turbulence revealed by simulations and observations

Structures and waves are common features of solar wind turbulence at various scales. The interplay between structures and waves is important for processes such as the turbulent energy cascade, plasma heating, and particle scattering. Our understanding of turbulence has been advanced by not only new space missions and numerical simulations, but also techniques that have been developed to interpret the rapidly growing turbulence data. We review basic models of turbulence with a specific focus on the analysis methods for understanding magnetic structures and waves. MHD and kinetic waves in single-spacecraft time series measurements can be identified through mode decomposition or their characteristic polarization signatures. The structures in this paper are considered as zero-frequency, non-propagating or convected modes embedded in the solar wind. The synergy between observations and simulations is most evident in the application of spatial-temporal analysis to multi-spacecraft observation and turbulence simulations. The spatial-temporal analysis has greatly improved our understanding of structures and waves in turbulence. We conclude by discussing prospects for future research.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Robust two-colour pyrometry uncertainty analysis to acquire spatially-resolved measurements

Two-colour pyrometry (2CP) has been used over several decades to study engine-relevant combustion processes, but results are generally regarded as qualitative or semi-quantitative. In many current 2CP systems, large measurement errors are introduced by parallax because the two measured wavelengths are not from the same line of sight. Here this work presents a spatially-resolved 2CP system with the objective of quantifying and reducing measurement uncertainty. An optical setup that eliminates parallax in 2CP is used together with pixel-by-pixel calibration of the camera sensor to increase measurement accuracy. Primary uncertainty terms are identified, and an error propagation analysis is performed to compute uncertainties in the final results of soot temperature, soot concentration parameter, KL, and soot mass. These methodologies are applied to investigate an auto-igniting fuel spray in a constant pressure flow rig at diesel-like conditions of high ambient pressure and temperature. Results show bias uncertainty of around 200 K (≈10%) for temperature and about 40%–60% for KL. High uncertainty was found to occur on the diffusion flame front where both optical thickness and soot concentrations are small. However, these uncertain measurement zones with relatively low soot concentrations contribute minimally to the total soot mass present in the reacting jet during the temporal evolution of the flame.

42 ENGINEERING↗
DOE OSTI2025

Vision Foundation Models in Remote Sensing: A survey

Artificial intelligence (AI) technologies have profoundly transformed the field of remote sensing (RS), revolutionizing data collection, processing, and analysis. Traditionally reliant on manual interpretation and task-specific models, RS research has been significantly enhanced by the advent of foundation models (FMs)—large-scale pretrained AI models capable of performing a wide array of tasks with unprecedented accuracy and efficiency. This article provides a comprehensive survey of FMs in the RS domain. We categorize these models based on their architectures, pretraining datasets, and methodologies. Through detailed performance comparisons, we highlight emerging trends and the significant advancements achieved by those FMs. Additionally, we discuss technical challenges, practical implications, and future research directions, addressing the need for high-quality data, computational resources, and improved model generalization. Our research also finds that pretraining methods, particularly self-supervised learning (SSL) techniques like contrastive learning (CL) and masked autoencoders (MAEs), remarkably enhance the performance and robustness of FMs. This survey aims to serve as a resource for researchers and practitioners by providing a panorama of advances and promising pathways for the continued development and application of FMs in RS.

data models↗
DOE OSTI2021

Alkali-silica reaction products and cracks: X-ray micro-tomography-based analysis of their spatial-temporal evolution at a mesoscale

In this study, we propose a laboratory-scale methodology, based on X-ray micro-tomography and caesium (Cs) as a contrast agent, to advance the understanding of cracking due to alkali-silica reaction (ASR) in concrete. The methodology allows achieving a completely non-destructive and time-lapse characterization of the spatial-temporal patterns of both the cracks and the ASR products. While Cs addition slightly accelerated the ASR kinetics, the crack patterns, with and without Cs addition, were statistically equivalent. Cracks with ASR products appeared first in the aggregates, close to the interface with the cement paste. They propagated afterwards towards the aggregates interior. Some products were then extruded for several mm into air voids and cracks in the cement paste. This process suggests that, in the early stage, the ASR products may be a low-viscosity gel that can flow away from the source aggregate and may settle later elsewhere as a rigid phase, upon calcium uptake.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Spatial and Temporal Interpolation Analysis Process of Shock Loading

The following document serves to describe the current process that is utilized by W-13 analysts to approximate the pressure-time boundary condition seen by a test object from shock tube loading. The intent of this report is to capture the current capability and to encourage continued growth and development. Information on the required input variables, mapping process, and generated output information is covered in the following sections. Throughout the document, gaps and weaknesses of the mapping process are noted to encourage future development efforts.

42 ENGINEERING↗
DOE OSTI2023

A review on recent machine learning applications for imaging mass spectrometry studies

Imaging mass spectrometry (IMS) is a powerful analytical technique widely used in biology, chemistry, and materials science fields that continue to expand. IMS provides a qualitative compositional analysis and spatial mapping with high chemical specificity. The spatial mapping information can be 2D or 3D depending on the analysis technique employed. Due to the combination of complex mass spectra coupled with spatial information, large high-dimensional datasets (hyperspectral) are often produced. Therefore, the use of automated computational methods for an exploratory analysis is highly beneficial. The fast-paced development of artificial intelligence (AI) and machine learning (ML) tools has received significant attention in recent years. These tools, in principle, can enable the unification of data collection and analysis into a single pipeline to make sampling and analysis decisions on the go. There are various ML approaches that have been applied to IMS data over the last decade. Here, in this review, we discuss recent examples of the common unsupervised (principal component analysis, non-negative matrix factorization, k-means clustering, uniform manifold approximation and projection), supervised (random forest, logistic regression, XGboost, support vector machine), and other methods applied to various IMS datasets in the past five years. The information from this review will be useful for specialists from both IMS and ML fields since it summarizes current and representative studies of computational ML-based exploratory methods for IMS.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

A Review of Bayesian Networks for Spatial Data

We report Bayesian networks are a popular class of multivariate probabilistic models as they allow for the translation of prior beliefs about conditional dependencies between variables to be easily encoded into their model structure. Due to their widespread usage, they are often applied to spatial data for inferring properties of the systems under study and also generating predictions for how these systems may behave in the future. We review published research on methodologies for representing spatial data with Bayesian networks and also summarize the application areas for which Bayesian networks are employed in the modeling of spatial data. We find that a wide variety of perspectives are taken, including a GIS-centric focus on efficiently generating geospatial predictions, a statistical focus on rigorously constructing graphical models controlling for spatial correlation, as well as a range of problem-specific heuristics for mitigating the effects of spatial correlation and dependency arising in spatial data analysis. Special attention is also paid to potential future directions for integration of Bayesian networks with spatial processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Spatially resolved gas and stellar kinematics in compact starburst galaxies

The kinematics of galaxies provide valuable insights into their physics and assembly history. Kinematics are governed not only by the gravitational potential, but also by merger events and stellar feedback processes such as stellar winds and supernova explosions. We aim to identify what governs the kinematics in a sample of SDSS-selected nearby starburst galaxies, by obtaining spatially resolved measurements of the gas and stellar kinematics. We obtained near-infrared integral-field K-band spectroscopy with VLT/SINFONI for 15 compact starburst galaxies. We derived the integrated as well as spatially resolved stellar and gas kinematics. The stellar kinematics were derived from the CO absorption bands, and Paα and Brγ emission lines were used for the gas kinematics. Based on the integrated spectra, we find that the majority of galaxies have gas and stellar velocity dispersion that are comparable. A spatially resolved comparison shows that the six galaxies that deviate show evidence for a bulge or stellar feedback. Two galaxies are identified as mergers based on their double-peaked emission lines. In our sample, we find a negative correlation between the ratio of the rotational velocity over the velocity dispersion (v rot /σ) and the star formation rate surface density. We propose a scenario where the global kinematics of the galaxies are determined by gravitational instabilities that affect both the stars and gas. This process could be driven by mergers or accretion events. Effects of stellar feedback on the ionised gas are more localised and detected only in the spatially resolved analysis. The mass derived from the velocity dispersion provides a reliable mass even if the galaxy cannot be spatially resolved. The technique used in this paper is applicable to galaxies at low and high redshift with the next generation of infrared-focussed telescopes (JWST and ELT).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Farm Practice Typologies as a Strategy for Management-Relevant Land Use and Land Cover Mapping in the Great Lakes Region (Version 1) [Dataset]

Dataset overview and development This dataset provides spatially explicit agricultural land-use and land-management typologies developed for the Great Lakes Region (GLR) at the farm-parcel level. The typologies were designed to characterize not only the land-use and land-cover (LULC) associated with individual agricultural farm parcels, but also the land-management practices (LMPs), including irrigation, tile drainage, and conservation easements, occurring within those parcels and how these characteristics change through time. The dataset contains four related typology products: Annual integrated typology – describes the combined LULC and land-management characteristics for each farm parcel for individual years. LULC transition typology – describes the temporal pattern of LULC change for each farm parcel across the study period (2008-2023). LMP trend typology – describes the temporal pattern in the occurrence of LMPs for each farm parcel across the study period. Multi-year integrated typology – combines the LULC transition typology and LMP trend typology to provide an integrated characterization of long-term land-use and management patterns. Purpose of the dataset The purpose of these products is to provide a management-relevant integrated and consistent framework for evaluating the spatial and temporal organization of agricultural landscapes across the GLR. The resulting typologies can: support landscape-scale environmental and land-use analysis; provide spatial information relevant to land-management strategies, conservation planning, policy development, and program evaluation; characterize spatial patterns of agricultural land use and management; examine changes in agricultural landscapes through time; and identify persistent, transitional, and changing agricultural systems. Please refer to the README file provided in Files for more details.

Agriculture↗
DOE OSTI2026

Biorefinery siting and sizing to achieve the US Billion‐Ton Bioeconomy vision: A case study using a gasification–Fischer–Tropsch process

Achieving a secure, abundant, and affordable energy future requires a robust and adaptable energy strategy, with bioenergy playing a pivotal role. Biomass-based energy presents a promising pathway to use domestic resources while fostering economic opportunities in rural areas. Despite the potential to source more than 1 billion dry short tons of biomass annually in the US, significant infrastructure and economic barriers hinder full utilization for energy production. This study used the Biofuel Infrastructure, Logistics, and Transportation (BILT) model to assess biorefinery siting and scale and determine the number and size of facilities required to maximize use of the US biomass potential. A spatially agnostic approach first assessed the effects of facility capacity and transportation constraints on biomass use. Then, a spatially explicit analysis integrated county-level biomass availability from the US Department of Energy's 2023 Billion-Ton Report and technoeconomic assessments to evaluate different biorefinery deployment scenarios. The results indicate that an optimized mix of facility sizes is essential to leverage biomass resources fully across varying regional production densities to maximize use of the US biomass potential. Larger biorefineries or co-located smaller facilities significantly enhance biomass use while reducing costs through economies of scale. These findings underscore the importance of strategically balancing facility capacity and spatial distribution to optimize the bioenergy supply chain. In conclusion, this study provides critical insights for advancing the US bioenergy economy by aligning biorefinery deployment with biomass resource availability and economic viability.

BILT Model↗
DOE OSTI2024

Critical needs to close monitoring gaps in pan-tropical wetland CH 4 emissions

Global wetlands are the largest and most uncertain natural source of atmospheric methane (CH 4 ). The FLUXNET-CH 4 synthesis initiative has established a global network of flux tower infrastructure, offering valuable data products and fostering a dedicated community for the measurement and analysis of methane flux data. Existing studies using the FLUXNET-CH 4 Community Product v1.0 have provided invaluable insights into the drivers of ecosystem-to-regional spatial patterns and daily-to-decadal temporal dynamics in temperate, boreal, and Arctic climate regions. However, as the wetland CH 4 monitoring network grows, there is a critical knowledge gap about where new monitoring infrastructure ought to be located to improve understanding of the global wetland CH 4 budget. Here we address this gap with a spatial representativeness analysis at existing and hypothetical observation sites, using 16 process-based wetland biogeochemistry models and machine learning. We find that, in addition to eddy covariance monitoring sites, existing chamber sites are important complements, especially over high latitudes and the tropics. Furthermore, expanding the current monitoring network for wetland CH 4 emissions should prioritize, first, tropical and second, sub-tropical semi-arid wetland regions. Considering those new hypothetical wetland sites from tropical and semi-arid climate zones could significantly improve global estimates of wetland CH 4 emissions and reduce bias by 79% (from 76 to 16 TgCH 4 y -1 ), compared with using solely existing monitoring networks. Our study thus demonstrates an approach for long-term strategic expansion of flux observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Single molecule insights into interfacial molecular recognition for model electrochemical DNA biosensors

Electrochemical sensors that use surface-immobilized DNA to bind analytes and transduce the binding into electrochemical signals, have the potential for rapid, specific, and sensitive detection of bioanalytes via a compact and portable platform. However, accessing the structure of these surfaces/interfaces at the relevant spatial scale (< 10 nm), which determines the interfacial interactions and ultimately sensing performance, remains an unsolved challenge. Here, we review studies that have used high resolution atomic force microscope imaging and spatial statistical analysis tools to understand crowding interactions between thiolated DNA probes immobilized on gold electrodes and how such interactions impact target binding. We also review related studies that attempt to control the nanoscale spatial arrangement of the immobilized recognition elements to optimize sensing performance. Furthermore, these efforts have led to new advances in understanding of the structure-function relationships of DNA-based electrochemical biosensors to move the field toward rational engineering of these biosensing interfaces.

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

Development of a broadband hard x-ray radiography platform for pulsed-power experiments

In this article, we develop and demonstrate a broadband hard x-ray radiography platform at the Zebra Pulsed Power Laboratory that integrates point-projection radiography, bremsstrahlung measurements, and hard x-ray pinhole imaging, designed to diagnose current-driven, cylindrically compressed matter. Initial laser-pulsed-power coupled experiments revealed that intense background radiation generated during 1 MA Zebra current shots overwhelmed laser-produced hard x-rays, obscuring radiographic images. Using combined spectral and spatial diagnostics, we identify energetic electrons accelerated by return currents as the dominant source of background hard x-rays, with electron energies inferred to be 3–4 MeV based on Monte Carlo simulations, and demonstrate mitigation through modifications to the radiation shielding and return-current configuration. The diagnostic platform was validated using a wire-pinch hard x-ray source, allowing radiographs of static 1-mm-diameter aluminum wires to be obtained while simultaneously measuring x-ray source spectra and spatial emission distributions within a single shot. Measured wire transmission profiles were quantitatively reconstructed using radiation transport simulations that incorporate an experimentally inferred two-temperature exponential x-ray spectrum from bremsstrahlung signal analysis and spatially distributed emission sources identified by pinhole imaging. Agreement between measured and simulated transmission profiles demonstrates the validity of the radiographic and x-ray source characterization approach, establishing this diagnostic platform as a promising tool for diagnosing magnetically driven, high-density plasmas relevant to warm dense matter and inertial fusion energy research.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗