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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Ferromagnetic resonance in single vertices and 2D lattices macro-dipoles of elongated nanoelements: measurements and simulations

We report broadband ferromagnetic resonance measurements of the in-plane magnetic field response of three- and four-fold symmetric vertices formed by non-contacting permalloy nano-ellipses together with extended lattices constructed from them. Complementing the experimental data with simulations, we are able to show that, as far as the most intense FMR responses are concerned, the spectra of vertices and lattices can largely be interpreted in terms of a superposition of the underlying hysteretic responses of the individual ellipses, as elemental building blocks of the system. Here, this property suggest that it is possible to understand the orientation of the individual magnetic dipole moments in a dipole network in terms of dynamic measurements alone, thereby offering a powerful tool to analyze the alignment statistics in frustrated systems that are exposed to various magnetic histories.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Automating Traffic Microsimulation from SYNCHRO UTDF to SUMO

Modern transportation research relies on seamlessly integrating traffic signal data with robust network representation and simulation tools. This study presents utdf2gmns, an open-source Python tool that automates conversion of the Universal Traffic Data Format, including network representation, signalized intersections, and turning volumes into the General Modeling Network Specification (GMNS) Standard. The resulting GMNS-compliant network can be converted for microsimulation in SUMO. By automatically extracting intersection control parameters and aligning them with GMNS conventions, utdf2gmns minimizes manual preprocessing and data loss. utdf2gmns also integrates with the Sigma-X engine to extract and visualize key traffic control metrics, such as phasing diagrams, turning volumes, volume-tocapacity ratios, and control delays. This streamlined workflow enables efficient scenario testing, accurate model building, and consistent data management. Validated through case studies, utdf2gmns reliably models complex urban corridors, promoting reproducibility and standardization. Documentation is available on GitHub and PyPI, supporting easy integration and community engagement.

Luo, Roy [ORNL] (ORCID:0009000312909983)↗
DOE OSTI2018

The NSLS-II Components Relative Alignment Deviation Estimate

The alignment of NSLS-II components will be conducted under the constraint of survey control network. The control network system is categorized as outside, primary and secondary control networks, and all of them have random errors due to measurement random errors. In the error propagation point of view, they all will contribute to the final positioning accuracy of each component in global sense. However, as the global positioning requirement is relative loose (i.e. ± 3mm for storage ring), relative positioning accuracy is what we really concern. In this situation, only secondary control network will contribute as only secondary monuments will be employed to direct the adjustment of components. Other error sources include instrument setup error, adjustment residuals and slab movement etc. This article will analyze the maximum relative deviation of 3 positions in one cell’s region, which is a simulation of 1 ID and 2 Vacuum chamber positions.

43 PARTICLE ACCELERATORS↗
DOE OSTI2013

The NSLS-II Components Relative Alignment Deviation Estimate

The alignment of NSLS-II components will be conducted under the constraint of survey control network. The control network system is categorized as outside, primary and secondary control networks, and all of them have random errors due to measurement random errors. In the error propagation point of view, they all will contribute to the final positioning accuracy of each component in global sense. However, as the global positioning requirement is relative loose (i.e. ± 3mm for storage ring), relative positioning accuracy is what we really concern. In this situation, only secondary control network will contribute as only secondary monuments will be employed to direct the adjustment of components. Other error sources include instrument setup error, adjustment residuals and slab movement etc. This article will analyze the maximum relative deviation of 3 positions in one cell’s region, which is a simulation of 1 ID and 2 Vacuum chamber positions.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Discrete and continuous variables for measurement-device-independent quantum cryptography

In a recent Article in Nature Photonics, Pirandola et al.1 claim that the achievable secret key rates of discrete-variable (DV) measurementdevice- independent (MDI) quantum key distribution (QKD) (refs 2,3) are “typically very low, unsuitable for the demands of a metropolitan network” and introduce a continuous-variable (CV) MDI QKD protocol capable of providing key rates which, they claim, are “three orders of magnitude higher” than those of DV MDI QKD. We believe, however, that the claims regarding low key rates of DV MDI QKD made by Pirandola et al.1 are too pessimistic. Here in this paper, we show that the secret key rate of DV MDI QKD with commercially available high-efficiency single-photon detectors (SPDs) (for example, see http://www.photonspot.com/detectors and http://www.singlequantum.com) and good system alignment is typically rather high and thus highly suitable for not only long-distance communication but also metropolitan networks.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Rotational superstructure in van der Waals heterostructure of self-assembled C 60 monolayer on the WSe 2 surface

Hybrid van der Waals (vdW) heterostructures composed of two-dimensional (2D) layered materials and self-assembled organic molecules are promising systems for electronic and optoelectronic applications with enhanced properties and performance. Control of molecular assembly is therefore paramount to fundamentally understand the nucleation, ordering, alignment, and electronic interaction of organic molecules with 2D materials. Here, we report the formation and detailed study of highly ordered, crystalline monolayers of C 60 molecules self-assembled on the surface of WSe 2 in well-ordered arrays with large grain sizes (~5 μm). Using high-resolution scanning tunneling microscopy (STM), we observe a periodic 2 × 2 superstructure in the C 60 monolayer and identify four distinct molecular appearances. Using vdW-corrected ab initio density functional theory (DFT) simulations, we determine that the interplay between vdW and Coulomb interactions as well as adsorbate–adsorbate and adsorbate–substrate interactions results in specific rotational arrangements of the molecules forming the superstructure. The orbital ordering through the relative positions of bonds in adjacent molecules creates a charge redistribution that links the molecule units in a long-range network. Furthermore, this rotational superstructure extends throughout the self-assembled monolayer and opens a pathway towards engineering aligned hybrid organic/inorganic vdW heterostructures with 2D layered materials in a precise and controlled way.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Plan evaluation for heat resilience: complementary methods to comprehensively assess heat planning in Tempe and Tucson, Arizona

Abstract Escalating impacts from climate change and urban heat are increasing the urgency for communities to equitably plan for heat resilience. Cities in the desert Southwest are among the hottest and fastest warming in the U.S., placing them on the front lines of heat planning. Urban heat resilience requires an integrated planning approach that coordinates strategies across the network of plans that shape the built environment and risk patterns. To date, few studies have assessed cities’ progress on heat planning. This research is the first to combine two emerging plan evaluation approaches to examine how networks of plans shape urban heat resilience through case studies of Tempe and Tucson, Arizona. The first methodology, Plan Quality Evaluation for Heat Resilience, adapts existing plan quality assessment approaches to heat. We assess whether plans meet 56 criteria across seven principles of high-quality planning and the types of heat strategies included in the plans. The second methodology, the Plan Integration for Resilience Scorecard™ (PIRS™) for Heat, focuses on plan policies that could influence urban heat hazards. We categorize policies by policy tool and heat mitigation strategy and score them based on their heat impact. Scored policies are then mapped to evaluate their spatial distribution and the net effect of the plan network. The resulting PIRS™ for Heat scorecard is compared with heat vulnerability indicators to assess policy alignment with risks. We find that both cities are proactively planning for heat resilience using similar plan and strategy types, however, there are clear and consistent opportunities for improvement. Combining these complementary plan evaluation methods provides a more comprehensive understanding of how plans address heat and a generalizable approach that communities everywhere could use to identify opportunities for improved heat resilience planning.

Environmental Sciences & Ecology↗
DOE OSTI2023

Relationship between magnetoresistance behavior and magnetic states in intercalated compounds Fe x TiS 2

Here, temperature and field-dependent neutron powder diffraction (NPD) measurements have been performed to reveal the nature of the unusual evolution of the magnetoresistance behavior with increasing Fe content in the intercalated compounds Fe x TiS 2 (with x=0.25,0.33,0.50,0.55) synthesized by solid-phase reaction method with prolonged homogenization heat treatment. As derived from neutron diffraction measurements, both the Fe 0.25 TiS 2 and the Fe 0.50 TiS 2 compound exhibit an antiferromagnetic (AFM) order below their respective Néel temperatures T N ≈52 K and T N ≈140K, which results in the presence of a large magnetoresistance accompanying the field-induced phase transition from AFM to the ferromagnetic (FM) state. At low temperatures, this AFM-FM transition is irreversible, confirmed by the irreversibility of changes in the NPD patterns and the presence of remnant magnetoresistance. In contrast, Fe 0.33 TiS 2 shows short-range magnetic order at T f ≈44 K due to a triangular network of intercalated Fe atoms and frustrations of exchange interactions with a field-induced FM alignment of Fe magnetic moments in an applied magnetic field as revealed by NPD measurements. The field-induced transformations of the cluster glass magnetic state in this compound lead to a significant decrease in electrical resistivity. According to NPD data, a reduced impact of an external magnetic field on the electrical resistivity of the compound Fe 0.55 TiS 2 can be ascribed to the presence of ferromagnetic or ferrimagnetic order in compounds with the Fe concentrations above x=0.50. The results obtained indicate that the distribution of the Fe atoms, along with their concentration in Fe x TiS 2 layered compounds, plays a decisive role in the formation of the magnetic state and the behavior of the magnetoresistance.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Dynamic Model Validation with Governor Deadband on the Eastern Interconnection

This report documents the efforts to perform dynamic model validation on the Eastern Interconnection (EI) by modeling governor deadband. An on-peak EI dynamic model is modified to represent governor deadband characteristics. Simulation results are compared with synchrophasor measurements collected by the Frequency Monitoring Network (FNET/GridEye). The comparison shows that by modeling governor deadband the simulated frequency response can closely align with the actual system response.

dynamic↗
DOE OSTI2026

Mauka Energy FEVER Tool Dataset

Mauka Energy’s dataset, developed under the Forestry Electric Vehicle Energy Routing (FEVER) project and funded by the U.S. Department of Energy’s Small Business Innovation Research program, is a high-resolution geospatial resource designed to support energy modeling for electric log trucks in complex forestry environments. The dataset integrates detailed spatial and road network data to enable accurate simulation of vehicle performance across varied terrain. At its core, the dataset incorporates lidar-derived elevation models, road alignments, and surface classifications from Oregon State University’s McDonald-Dunn Research Forest. These data capture fine-scale variations in slope, curvature, and surface conditions across forest road systems, allowing for vehicle-level analysis of energy consumption and recovery. The dataset also includes data collected on the surrounding public and private road networks in Benton County, Oregon, used in real-world haul routes. These connecting segments provide critical context for modeling transitions between forest operations and regional transportation infrastructure, incorporating attributes such as grade profiles, elevation change, and speed constraints. This combined dataset underpins the development of Mauka Energy’s rolldown tool, which quantifies energy use and regenerative braking potential on downhill and variable-grade segments. By leveraging high-resolution terrain and road data, the FEVER project enables more accurate assessment of electric vehicle feasibility and performance in forestry applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Crossing the Cleft: Communication Challenges Between Neuroscience and Artificial Intelligence

Historically, neuroscience principles have heavily influenced artificial intelligence (AI), for example the influence of the perceptron model, essentially a simple model of a biological neuron, on artificial neural networks. More recently, notable recent AI advances, for example the growing popularity of reinforcement learning, often appear more aligned with cognitive neuroscience or psychology, focusing on function at a relatively abstract level. At the same time, neuroscience stands poised to enter a new era of large-scale high-resolution data and appears more focused on underlying neural mechanisms or architectures that can, at times, seem rather removed from functional descriptions. While this might seem to foretell a new generation of AI approaches arising from a deeper exploration of neuroscience specifically for AI, the most direct path for achieving this is unclear. Here we discuss cultural differences between the two fields, including divergent priorities that should be considered when leveraging modern-day neuroscience for AI. For example, the two fields feed two very different applications that at times require potentially conflicting perspectives. We highlight small but significant cultural shifts that we feel would greatly facilitate increased synergy between the two fields.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Patch-Based Convolutional Neural Networks for Multiple Microstructural Features Detection in FIB-SEM Micrographs of Irradiated Nuclear Fuel

Focused ion beam scanning electron microscopy (FIB-SEM) tomography has increasingly been utilized for acquiring three-dimensional (3D) microstructure features at the sub-micron scale in irradiated nuclear materials. This technique involves sequential ion beam slicing followed by electron beam imaging and compositional mapping using energy dispersive spectroscopy (EDS). Despite its growing use, several challenges persist. These include the time-intensive nature of data collection of EDS data, difficulties in distinguishing between various microstructures, and issues with image alignment. These challenges currently limit the broader application of FIB-SEM tomography in the field. To overcome these limitations, we propose using convolutional neural networks (CNNs) to automate microstructure identification in SEM images. Our study introduces a new framework for identifying microstructures in irradiated U-10Zr (wt. %) metallic fuel with limited annotated data. The framework includes the creation of a reliable annotated dataset with paired SEM and ground truth data from EDS maps, the applications of CNNs for microstructure identification, and the validation of model performance. Specifically, we employed the Segment Anything Model (SAM) to align SEM images with corresponding EDS maps and focused ion beam (FIB) tomography SEM data. We evaluate several models, including Patch-based U-Net, Attention U-Net, and Residual U-Net, finding that patch-based U-Net exhibits superior segmentation performance and consistency. This approach reduces reliance on EDS detectors and aids in accelerating nuclear material analysis process, highlighting the potential of advanced deep learning techniques to improve microstructural understanding in nuclear material. This is the first framework to integrate SAM and Patch-based CNN models for semantic segmentation of irradiated nuclear materials, with potential applicability to other tomography datasets.

36 - MATERIALS SCIENCE↗
DOE OSTI2019

Does geography matter? A study of technological learning and innovation in the U.S. Solar photovoltaic balance-of-systems (PV BOS) industry

Different learning channels (i.e., learning from upstream actors, local network, global codified knowledge, etc.) coexist in firms’ technological innovation processes. Although firms are exposed to globalization and convenient communication and transportation tools, researchers still argue about the role of geography in technology innovation. Geographic proximity can facilitate knowledge spillover and network-building. Localized learning and the significance of geography indicate the importance of local knowledge in innovative activities. Local knowledge tends to be more closely aligned with local firms’ technological demand and easier to understand and absorb, because they are created and applied in the same context. However, following Schumpeter, innovation researchers believe that diverse knowledge, especially knowledge from external industries and geographically distant places, is a valuable source for innovation, especially disruptive innovation. Therefore, firms must balance different learning channels, especially between localized learning and global learning. However, it is still unclear about the extent to which localized learning is important for technology innovations and which factors influence the degree of localized learning. Meanwhile, most studies in the area of localized learning usually take no account of the international dimension and the interdependent of knowledge at different geographic scales. Different learning channels (i.e., learning from upstream actors, local network, global codified knowledge, etc.) coexist in firms’ technological innovation processes. Although firms are exposed to globalization and convenient communication and transportation tools, researchers still argue about the role of geography in technology innovation. Geographic proximity can facilitate knowledge spillover and network-building. Localized learning and the significance of geography indicate the importance of local knowledge in innovative activities. Local knowledge tends to be more closely aligned with local firms’ technological demand and easier to understand and absorb, because they are created and applied in the same context. However, following Schumpeter, innovation researchers believe that diverse knowledge, especially knowledge from external industries and geographically distant places, is a valuable source for innovation, especially disruptive innovation. Therefore, firms must balance different learning channels, especially between localized learning and global learning. However, it is still unclear about the extent to which localized learning is important for technology innovations and which factors influence the degree of localized learning. Meanwhile, most studies in the area of localized learning usually take no account of the international dimension and the interdependent of knowledge at different geographic scales. To address this research gap, this paper empirically investigated firms’ learning and innovation processes in relation to the geography. The aim is to answer the following three questions: 1) how does geographic distance influence knowledge spillover; 2) how technological characteristics influence knowledge acquisition choices as regards local vs. non-local knowledge? and 3) how do local and non-local knowledge affect the quality of innovation? The subject of our empirical inquiry is photovoltaic balance-of-systems (PV BOS) components. This choice is primarily motivated by two factors: (1) large-scale deployment of distributed solar PV technologies is widely considered to be an important piece in addressing the environmental impacts of the electricity sector, making PV an important technology to study; furthermore, there is a general consensus that the biggest barrier for a wider deployment of PV is the high price of non-module components, and (2) technological aspects of non-module components of PV systems make local conditions (climate, regulation, learning networks, etc.) salient, thus affording a rich empirical setting to explore the geography-innovation-economy interactions at the local level. We find that the localization effect of technological learning in the solar PV BOS industry is at state level, the number of citations-based linkages decreases with distance, and we also find that international knowledge significantly contributes to the quality of patents, which suggests that state border might be a barrier of current knowledge spillover, and only focusing on local innovation networks is not sufficient for promoting PV BOS innovation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Real-Time Alignment and Reorientation of Polymer Chains in Liquid Crystal Elastomers

Liquid crystal elastomers (LCEs) exhibit soft elasticity due to the alignment and reorientation of mesogens upon mechanical loading, which provides additional mechanisms to absorb and dissipate energy. This enhanced response makes LCEs potentially transformative materials for biomedical devices, tissue replacements, and protective equipment. However, there is a critical knowledge gap in understanding the highly rate-dependent dissipative behaviors of LCEs due to the lack of real-time characterization techniques that probe the microscale network structure and link it to the mechanical deformation of LCEs. In this work, we employ in situ optical measurements to evaluate the alignment and reorientation degree of mesogens in LCEs. The data are correlated to the quantitative physical analysis using polarized Fourier-transform infrared spectroscopy. The time scale of mesogen alignment is determined at different strain levels and loading rates. The mesogen reorientation kinetics is characterized to establish its relationship with the macroscale tensile strain, and compared to theoretical predictions. Overall, this work provides the first detailed study on the time-dependent evolution of mesogen alignment and reorientation in deformed LCEs. It also provides an effective and more accessible approach for other researchers to investigate the structural-property relationships of different types of polymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Investigation of partitionless growth of ε -Al 60 Sm 11 phase in Al-10 at% Sm liquid

Recent experiments on devitrification of Al 90 Sm 10 amorphous alloys revealed an unusual polymorphic transformation to a complex cubic crystal structure called the ε-Al 60 Sm 11 phase. Molecular dynamics simulations of the growth of the stoichiometric ε-phase seed in contact with an undercooled Al-10 at% Sm liquid are performed to elucidate the microscopic process of transformation. The as-grown crystal and undercooled liquid possess similar local order around Al atoms whereas a rigid network defined by the Sm sub-lattice develops during the growth. Using a template-cluster alignment method, we define an order parameter to characterize the structural evolution in the system. Estimates of the attachment rate is ${R}_{{\rm{a}}}=8.70\times {10}^{-4}$ Å-2 ns-1 and detachment rate is ${R}_{{\rm{d}}}=3.83\times {10}^{-4}$ Å-2 ns-1 at the interface between ε-Al 60 Sm 11 and Al-10 at% Sm liquid at 800 K.

36 MATERIALS SCIENCE↗
DOE OSTI2011

The Rap1–Rgl–Ral signaling network regulates neuroblast cortical polarity and spindle orientation

A crucial first step in asymmetric cell division is to establish an axis of cell polarity along which the mitotic spindle aligns. Drosophila melanogaster neural stem cells, called neuroblasts (NBs), divide asymmetrically through intrinsic polarity cues, which regulate spindle orientation and cortical polarity. In this paper, we show that the Ras-like small guanosine triphosphatase Rap1 signals through the Ral guanine nucleotide exchange factor Rgl and the PDZ protein Canoe (Cno; AF-6/Afadin in vertebrates) to modulate the NB division axis and its apicobasal cortical polarity. Rap1 is slightly enriched at the apical pole of metaphase/anaphase NBs and was found in a complex with atypical protein kinase C and Par6 in vivo. Loss of function and gain of function of Rap1, Rgl, and Ral proteins disrupt the mitotic axis orientation, the localization of Cno and Mushroom body defect, and the localization of cell fate determinants. We propose that the Rap1–Rgl–Ral signaling network is a novel mechanism that cooperates with other intrinsic polarity cues to modulate asymmetric NB division.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

The Demand for a Domestic Offshore Wind Energy Supply Chain

In March of 2021, the Biden-Harris Administration established a National Offshore Wind Target to install 30 GW by 2030. This ambitious goal was not only intended to help reduce dependencies on fossil fuels, but also represents an opportunity to establish a new and sustainable industry in the United States. The announcement referenced the potential benefits of establishing a domestic supply chain, including the opportunity for existing suppliers to produce thousands of components while creating tens of thousands of jobs over the course of the decade. This vision by the Biden-Harris Administration aligns with the perspective of the offshore wind industry. At a Leadership 100 event hosted by the Business Network for Offshore wind in 2019, offshore wind developers and manufacturers identified the need for a roadmap outlining a pathway to a domestic supply chain as the top priority facing the industry. Building up domestic manufacturing capabilities will not only energize local industries but can potentially de-risk individual project by reducing reliance on importing resources from European or Asian markets. Although establishing a domestic supply chain will require significant investment, it has the potential to create substantial benefits throughout the industry and, by extension, on the decarbonization goals of the United States. This study characterizes the challenges and opportunities facing the growth of a domestic supply chain industry and evaluates the potential benefits that could be achieved through the creation of the supply chain. This report is the first of a two-part series which will describe the full supply chain roadmap and the associated benefits; the current report focuses on the high-level deployment, workforce, and component requirements that need to be met to achieve the National Offshore Wind Target. We will present: 1. A deployment pipeline that demonstrates the pathway to 30 GW, the associated demand for major fixed-bottom and floating offshore wind components (turbines, foundations, cables, substations), and the vessel and port requirements to support these installation activities. 2. A series of sensitivity analyses showing how the demand for components, ports, and vessels changes for different technology pathways and availability of the global supply chain. 3. An estimate of the total number of jobs that would be required to support these deployment scenarios under varying levels of assumed domestic content. 4. A comprehensive list of the Tier 1, 2, and 3 components (finished components, subassemblies, and subcomponents) required to construct fixed-bottom and floating offshore wind projects. 5. A discussion of critical path components that represent a significant challenge, bottleneck, or risk for a future domestic supply chain.

17 WIND ENERGY↗
DOE OSTI2015

Prior knowledge driven Granger causality analysis on gene regulatory network discovery

Our study focuses on discovering gene regulatory networks from time series gene expression data using the Granger causality (GC) model. However, the number of available time points (T) usually is much smaller than the number of target genes (n) in biological datasets. The widely applied pairwise GC model (PGC) and other regularization strategies can lead to a significant number of false identifications when n>>T. In this study, we proposed a new method, viz., CGC-2SPR (CGC using two-step prior Ridge regularization) to resolve the problem by incorporating prior biological knowledge about a target gene data set. In our simulation experiments, the propose new methodology CGC-2SPR showed significant performance improvement in terms of accuracy over other widely used GC modeling (PGC, Ridge and Lasso) and MI-based (MRNET and ARACNE) methods. In addition, we applied CGC-2SPR to a real biological dataset, i.e., the yeast metabolic cycle, and discovered more true positive edges with CGC-2SPR than with the other existing methods. In our research, we noticed a “ 1+1>2” effect when we combined prior knowledge and gene expression data to discover regulatory networks. Based on causality networks, we made a functional prediction that the Abm1 gene (its functions previously were unknown) might be related to the yeast’s responses to different levels of glucose. In conclusion, our research improves causality modeling by combining heterogeneous knowledge, which is well aligned with the future direction in system biology. Furthermore, we proposed a method of Monte Carlo significance estimation (MCSE) to calculate the edge significances which provide statistical meanings to the discovered causality networks. All of our data and source codes will be available under the link https://bitbucket.org/dtyu/granger-causality/wiki/Home.

60 APPLIED LIFE SCIENCES↗