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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Modeling Enhancements, Cross-Section Generation Updates, and Benchmarking with Shift

This technical report documents the modeling enhancements, cross-section generation updates, and bench marking with the Shift Monte Carlo code performed under the US Department of Energy Nuclear Energy Advanced Modeling and Simulation Program in FY 2024. The work performed included several modeling enhancements, such as integration of cross-section generation in Titan and the ability to produce microscopic multigroup cross sections with Shift. Benchmarking of the cross sections produced by Shift and the two-step workflow with Griffin was performed for three problems: the Advanced Breeder Test Reactor, a generic pebble bed reactor, and a TRISO heat pipe microreactor. Comparisons of results from these benchmark problems were done with Serpent, OpenMC, and Griffin. These enhancements provide a robust foundation for applying Shift for both reference and two-step neutronics analysis for advanced reactor simulation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A New Vehicle-to-Vehicle Communication System: Visual-Enhanced Cooperative Traffic Operations

The advent of Connected and Autonomous Vehicles (CAVs) has highlighted the necessity for robust communication systems between vehicles and their environment. This study introduces a novel vehicle-to-vehicle (V2V) communication system, termed the Visual-Enhanced Cooperative Traffic Operations (VECTOR) system. The VECTOR system addresses the need for robust communication by converting dynamic data (including velocity and yaw angle data) into binary code, which is displayed on an LED panel mounted on the top of the vehicle. Following vehicles detect this panel and decode the information using a camera, implementing a visual-based communication method. VECTOR system employs a comprehensive five-module process. Initially, polynomial fitting techniques are applied to velocity data over fixed time intervals using third-degree polynomials, with validation via R² and MSE metrics. The second module converts velocity and yaw angle data into binary form, thereby enhancing detection and processing efficiency. The third module focuses on improving detection stability across various environmental conditions to enhance traffic safety. The fourth module decodes the binary data back into trajectory information, ensuring the fidelity of velocity and yaw angles. The final module integrates eco-control through the VECTOR system, employing advanced control algorithms to minimize energy consumption in CAVs. Experimental evaluations conducted using a modified CAV test platform based on the Lincoln MKZ demonstrate the feasibility and efficiency of the VECTOR system, achieving a 75% R-squared accuracy rate in replicating original velocity data. This methodology not only highlights potential applications but also underscores significant implications for advancing CAV technology.

Ma, Ke↗
DOE OSTI2026

Stakeholder-guided holistic, Adaptive Framework for enhancing community Energy Resilience (SAFER) (Final Technical Report)

The Stakeholder-guided holistic, Adaptive Framework for enhancing community Energy Resilience (SAFER) project advances resilience science and engineering by addressing challenges in rural Kansas communities where aging infrastructure, extreme weather, and socioeconomic disparities heighten vulnerability to energy disruptions. Traditional approaches often focus on technical performance while overlooking community concerns and priorities. SAFER responds by integrating community perspectives with advanced analytical frameworks to create a holistic model for measuring and improving resilience. Project objectives included developing novel resilience metrics, advancing modeling frameworks that capture interdependencies across infrastructures, and embedding community-centric indicators directly into planning processes for distributed energy resources. The key technical innovations included the creation of self-organizing map (SOM)-based indices for objective resilience quantification, hetero-functional graph theory (HFGT) models linking power, water, transportation, and community assets, and graph neural network (GNN) tools for identifying critical nodes in complex systems. Community-centric energy planning was demonstrated through optimal siting and sizing of (photovoltaic) PV and battery storage, ensuring resilience enhancements also addressed energy burden and energy insecurity. SAFER engaged community partners in Dodge City and Ford County through surveys, focus groups, and workshops, generating more than 600 responses that established baseline measures of energy burden, financial insecurity, and willingness-to-pay to avoid outages. This data, organized in terms of a community capitals framework, informed the development of weighted reliability indices that better reflect community costs than traditional utility metrics. SAFER’s GNN-based critical node identification framework identified expert-labelled critical nodes with over 99% accuracy, while also uncovering additional functionalities essential for proactive resilience planning. The project’s models demonstrated that optimal PV and storage deployment could improve resilience indices by over 11 percent, with dispatch strategies further enhancing outcomes, confirming both the technical effectiveness and economic feasibility of these approaches. Through its combined emphasis on rigorous modeling, community-focused planning, and community engagement, SAFER advances the state of resilience research while delivering direct benefits to rural communities. The project provides tools, guidelines, and resilience heatmaps that help utilities, local governments, and residents better anticipate disruptions, prioritize investments, and strengthen the capacity to withstand and recover from energy-related hazards. Furthermore, the developed HFG and GNN frameworks are designed for transferability, allowing them to be adapted for resilience planning in other communities with minimal retraining. This inductive learning capability provides a scalable pathway to extend the SAFER project’s impact. Thus, creating a foundation for a nationally applicable model of infrastructure resilience. Additionally, the HFG can also be extended to include other FEMA community lifelines.

14 SOLAR ENERGY↗
DOE OSTI2026

Designing carbon wetting layers with inorganic salts as additives for enhanced adhesion and molten sodium wettability in electrochemical application

ß” alumina solid-state electrolyte (BASE) is considered one of the most promising materials for sodium batteries due to its high ionic conductivity and exceptional stability with molten sodium. However, at lower operating temperatures, the poor physical contact at the liquid-solid interface between molten sodium and BASE results in high interfacial resistance. In this study, we explore the optimization of carbon wetting layers with various salt additives to enhance adhesion and improve molten sodium wettability. Using a spray-coating method, carbon layers incorporating sodium hexametaphosphate (Na6(PO3)6, SHMP) as an additive demonstrate superior adhesion, mechanical stability, and enhanced wettability with molten sodium. Scanning Electron Microscopy (SEM) analysis reveals that SHMP preserves the porous carbon network required for efficient sodium transport while minimizing structural defects such as cracks and voids. Electrochemical testing using Na symmetric cells confirms that SHMP-modified layers significantly reduce interfacial resistance and overpotential, outperforming No salts counterparts. These findings highlight that the strategic incorporation of suitable additives, such as SHMP, in the design of microscale carbon layers could significantly enhance the performance of molten sodium batteries at lower operating temperatures.

Han, Henry H.↗
DOE OSTI2024

Solar enhanced oxygen evolution reaction with transition metal telluride

The photo-enhanced electrocatalytic method of oxygen evolution reaction (OER) shows promise for enhancing the effectiveness of clear energy generation through water splitting by using renewable and sustainable source of energy. However, despite benefits of photoelectrocatalytic (PEC) water splitting, its uses are constrained by its low efficiency as a result of charge carrier recombination, a large overpotential, and sluggish reaction kinetics. Here, we illustrate that Nickel telluride (NiTe) synthesized by hydrothermal methods can function as an extremely effective photo-coupled electrochemical oxygen evolution reaction (POER) catalyst. In this study, NiTe was synthesized by hydrothermal method at 145°C within just an hour of reaction time. In dark conditions, the NiTe deposited on carbon cloth substrate shows a small oxygen evolution reaction overpotential (261 mV) at a current density of 10 mA cm –2 , a reduced Tafel slope (65.4 mV dec -1 ), and negligible activity decay after 12 h of chronoamperometry. By virtue of its enhanced photo response, excellent light harvesting ability, and increased interfacial kinetics of charge separation, the NiTe electrode under simulated solar illumination displays exceptional photoelectrochemical performance exhibiting overpotential of 165 mV at current density of 10 mA cm -2 , which is about 96 mV less than on dark conditions. In addition, Density Functional Theory investigations have been carried out on the NiTe surface, the results of which demonstrated a greater adsorption energy for intermediate -OH on the catalyst site. Since the -OH adsorption on the catalyst site correlates to catalyst activation, it indicates the facile electrocatalytic activity of NiTe owing to favorable catalyst activation. DFT calculations also revealed the facile charge density redistribution following intermediate -OH adsorption on the NiTe surface. This work demonstrates that arrays of NiTe elongated nanostructure are a promising option for both electrochemical and photoelectrocatalytic water oxidation and offers broad suggestions for developing effective PEC devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Enhancement on selenium volatilization for phytoremediation: role of plant and soil microbe interaction

This study aimed at quantifying the potential effects of plant and soil microbial interaction on selenium (Se) volatilization, with the specific objectives of identifying soil bacteria associated with rabbitfoot grass (Polypogon monspeliensis) and demonstrating the enhancement of Se volatilization in the soil-Indian mustard (Brassica juncea) system through inoculation of the soil with the identified best Se-volatilizing bacterial strain. Soil bacteria were isolated from topsoil and rhizosphere soils of rabbitfoot grass, and the bacterial colonies were characterized via PCR-DGGE and DGGE band analysis prior to their identification using 16S rDNA sequencing technique.Bacillus cereusproduced over 500-fold more volatile Se in a culture medium treated with 15 µg Se/mL (equal mixture of SeO 4 2- , SeO 3 2- and selenomethionine) than any of the other eight identified bacterial strains. Inoculation of Indian mustard vegetated soil with the best Se volatilizing bacterial strainB. cereusresulted in a significant (p<0.05) increase in Se volatilization during a 7-day time period, compared to the soil-plant system without inoculation ofB. cereus. Thus, inoculation of the soil withB. cereussubstantially enhanced Se removal via biogenic volatilization in the soil-Indian mustard system. This study evaluated the role ofB. cereusin enhancing Se volatilization in soil-plant systems, and demonstrated the importance of plant and soil microbial interaction for Se phytoremediation.

Plant Sciences↗
DOE OSTI2023

Metal Mixing in the r -process Enhanced Ultrafaint Dwarf Galaxy Reticulum II

The ultrafaint dwarf galaxy Reticulum II was enriched by a single rare and prolific r-process event. The r-process content of Reticulum II thus provides a unique opportunity to study metal mixing in a relic first galaxy. Using multi-object high-resolution spectroscopy with VLT/GIRAFFE and Magellan/M2FS, we identify 32 clear spectroscopic member stars and measure abundances of Mg, Ca, Fe, and Ba where possible. We find $72^{+10}_{-12}$ of the stars are r-process-enhanced, with a mean $\langle$[Ba / H]$\rangle$ = -1.68 ± 0.07 and unresolved intrinsic dispersion σ [Ba/H] <0.20. The homogeneous r-process abundances imply that Ret II’s metals are well mixed by the time the r enhanced stars form, which simulations have shown requires at least 100 Myr of metal mixing in between bursts of star formation to homogenize. This is the first direct evidence of bursty star formation in an ultrafaint dwarf galaxy. The homogeneous dilution prefers a prompt and high-yield r-process site, such as collapsar disk winds or prompt neutron star mergers. We also find evidence from [Ba/H] and [Mg/Ca] that the r-enhanced stars in Ret II formed in the absence of substantial pristine gas accretion, perhaps indicating that ≈70% of Ret II stars formed after reionization.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Enhanced Star Formation and Black Hole Accretion Rates in Galaxy Mergers in IllustrisTNG50

Many theoretical and observational studies have suggested that galaxy mergers may trigger enhanced star formation or active galactic nucleus activity. We present an analysis of merging and nonmerging galaxies from 0.2 ≤ z ≤ 3 in the IllustrisTNG50 simulation. These galaxies encompass a range of masses (M ⋆ > 10 8 M ⊙ ) and mass ratios (≥1:10) and multiple merger stages. We examine the effect that galaxy mergers have on star formation and black hole accretion rates in the TNG50 universe. We additionally investigate how galaxy and black hole mass, merger stage, merger mass ratio, and redshift affect these quantities. Mergers in our sample show excess specific star formation rates (sSFR) at z ≤ 3 and enhanced specific black hole accretion rates (sBHAR) at z ≲ 2. The difference between sSFRs and sBHARs in the merging sample compared to the nonmerging sample increases as redshift decreases. Additionally, we show that these enhancements persist for at least ∼1 Gyr after the merger event. Investigating how mergers behave in the TNG50 simulation throughout cosmic time enables both a better appreciation of the importance of spatial resolution in cosmological simulations and a better basis to understand our high- z Universe with observations from JWST.

AGN host galaxies↗
DOE OSTI2022

The R -process Alliance: A Nearly Complete R -process Abundance Template Derived from Ultraviolet Spectroscopy of the R -process-enhanced Metal-poor Star HD 222925 *

We present a nearly complete rapid neutron-capture process (r-process) chemical inventory of the metal-poor ([Fe/H] = -1.46 ± 0.10) r-process-enhanced ([Eu/Fe] = +1.32 ± 0.08) halo star HD 222925. This abundance set is the most complete for any object beyond the solar system, with a total of 63 metals detected and seven with upper limits. It comprises 42 elements from 31 ≤ Z ≤ 90, including elements rarely detected in r-process-enhanced stars, such as Ga, Ge, As, Se, Cd, In, Sn, Sb, Te, W, Re, Os, Ir, Pt, and Au. We derive these abundances from an analysis of 404 absorption lines in ultraviolet spectra collected using the Space Telescope Imaging Spectrograph on the Hubble Space Telescope and previously analyzed optical spectra. A series of appendices discusses the atomic data and quality of fits for these lines. The r-process elements from Ba to Pb, including all elements at the third r-process peak, exhibit remarkable agreement with the solar r-process residuals, with a standard deviation of the differences of only 0.08 dex (17%). In contrast, deviations among the lighter elements from Ga to Te span nearly 1.4 dex, and they show distinct trends from Ga to Se, Nb through Cd, and In through Te. The r-process contribution to Ga, Ge, and As is small, and Se is the lightest element whose production is dominated by the r-process. The lanthanide fraction, log X La = -1.39 ± 0.09, is typical for r-process-enhanced stars and higher than that of the kilonova from the GW170817 neutron-star merger event. We advocate adopting this pattern as an alternative to the solar r-process-element residuals when confronting future theoretical models of heavy-element nucleosynthesis with observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Enhancing the representation of water management in global hydrological models

Abstract. This study enhances an existing global hydrological model (GHM), Xanthos, by adding a new water management module that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We remapped reservoirs in the Global Reservoir and Dam (GRanD) database to the 0.5∘ spatial resolution in Xanthos so that a single lumped reservoir exists per grid cell, which yielded 3790 large reservoirs. We implemented unique operation rules for each reservoir type, based on their primary purposes. In particular, hydropower reservoirs have been treated as flood control reservoirs in previous GHM studies, while here, we determined the operation rules for hydropower reservoirs via optimization that maximizes long-term hydropower production. We conducted global simulations using the enhanced Xanthos and validated monthly streamflow for 91 large river basins, where high-quality observed streamflow data were available. A total of 1878 (296 hydropower, 486 irrigation, and 1096 flood control and others) out of the 3790 reservoirs are located in the 91 basins and are part of our reported results. The Kling–Gupta efficiency (KGE) value (after adding the new water management) is ≥ 0.5 and ≥ 0.0 in 39 and 81 basins, respectively. After adding the new water management module, model performance improved for 75 out of 91 basins and worsened for only 7. To measure the relative difference between explicitly representing hydropower reservoirs and representing hydropower reservoirs as flood control reservoirs (as is commonly done in other GHMs), we use the normalized root mean square error (NRMSE) and the coefficient of determination (R2). Out of the 296 hydropower reservoirs, the NRMSE is > 0.25 (i.e., considering 0.25 to represent a moderate difference) for over 44 % of the 296 reservoirs when comparing both the simulated reservoir releases and storage time series between the two simulations. We suggest that correctly representing hydropower reservoirs in GHMs could have important implications for our understanding and management of freshwater resource challenges at regional-to-global scales. This enhanced global water management modeling framework will allow the analysis of future global reservoir development and management from a coupled human–earth system perspective.

13 HYDRO ENERGY↗
DOE OSTI2020

Analysis of zebrafish periderm enhancers facilitates identification of a regulatory variant near human KRT8/18

Genome-wide association studies for non-syndromic orofacial clefting (OFC) have identified single nucleotide polymorphisms (SNPs) at loci where the presumed risk-relevant gene is expressed in oral periderm. The functional subsets of such SNPs are difficult to predict because the sequence underpinnings of periderm enhancers are unknown. We applied ATAC-seq to models of human palate periderm, including zebrafish periderm, mouse embryonic palate epithelia, and a human oral epithelium cell line, and to complementary mesenchymal cell types. We identified sets of enhancers specific to the epithelial cells and trained gapped-kmer support-vector-machine classifiers on these sets. We used the classifiers to predict the effects of 14 OFC-associated SNPs at 12q13 near KRT18. All the classifiers picked the same SNP as having the strongest effect, but the significance was highest with the classifier trained on zebrafish periderm. Reporter and deletion analyses support this SNP as lying within a periderm enhancer regulating KRT18/KRT8 expression.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Bi-fated tendon-to-bone attachment cells are regulated by shared enhancers and KLF transcription factors

The mechanical challenge of attaching elastic tendons to stiff bones is solved by the formation of a unique transitional tissue. Here, we show that murine tendon-to-bone attachment cells are bi-fated, activating a mixture of chondrocyte and tenocyte transcriptomes, under regulation of shared regulatory elements and Krüppel-like factors (KLFs) transcription factors. High-throughput bulk and single-cell RNA sequencing of humeral attachment cells revealed expression of hundreds of chondrogenic and tenogenic genes, which was validated by in situ hybridization and single-molecule ISH. ATAC sequencing showed that attachment cells share accessible intergenic chromatin areas with either tenocytes or chondrocytes. Epigenomic analysis revealed enhancer signatures for most of these regions. Transgenic mouse enhancer reporter assays verified the shared activity of some of these enhancers. Finally, integrative chromatin and motif analyses and transcriptomic data implicated KLFs as regulators of attachment cells. Indeed, blocking expression of both Klf2 and Klf4 in developing limb mesenchyme impaired their differentiation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Dynamic Distribution System Restoration Strategy for Resilience Enhancement: Preprint

In electric power distribution systems, distributed energy re-sources (DERs) can act as controllable power sources and support utility operators to minimize power outages after ex-treme weather events (e.g., hurricane, earthquake, wildfire) and thus help enhance the grid's resilience. Meanwhile, the influ-ences of extreme events and the capabilities of DERs are dy-namic and difficult to predict. Hence, the desired distribution system restoration strategy should be able to evolve according to real-time fault/disturbance information and the availabil-ity of DERs. In this paper, we propose a new dynamic distribu-tion system restoration strategy to enhance system resilience against potential hazards. An efficient reconfiguration algo-rithm is developed to eliminate the use of integer variables to relieve the computational burden. Model predictive control is implemented to adjust the system topology and DER opera-tion setpoints based on the updated fault information and DER forecasts. The effectiveness of the proposed restoration model in enhancing distribution system resilience is validated through an IEEE 123-bus test system. Simulation results also validate that the proposed restoration model can mitigate the occurrence of unexpected events and the fluctuations of DERs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Dynamic Restoration Strategy for Distribution System Resilience Enhancement

In electric power distribution systems, distributed energy resources (DERs) can act as controllable power sources and support utility operators to minimize power outages after extreme weather events (e.g., hurricane, earthquake, wildfire) and thus help enhance the grid's resilience. Meanwhile, the influences of extreme events and the capabilities of DERs are dynamic and difficult to predict. Hence, the desired distribution system restoration strategy should be able to evolve according to real-time fault/disturbance information and the availability of DERs. In this paper, we propose a new dynamic restoration strategy for distribution systems to enhance system resilience against potential hazards. An efficient reconfiguration algorithm is developed to eliminate the use of integer variables to relieve the computational burden. Model predictive control is implemented to adjust the system topology and DER operation set points based on the updated fault information and DER forecasts. The effectiveness of the proposed restoration model in enhancing distribution system resilience is validated through an IEEE 123-bus test system. Simulation results also validate that the proposed restoration model can mitigate the occurrence of unexpected events and the fluctuations of DERs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Study of Foam Fracturing for Enhanced Geothermal Systems Using Model Material

Foam fracturing is considered a potential approach to address water concerns with hydraulic fracturing in the development of enhanced geothermal systems (EGS). In many EGS sites, water required for hydraulic fracturing is either unavailable, extremely costly or environmentally unsustainable. This paper presents work performed in the first year of a project sponsored by the U.S. DOE GTO Waterless Stimulation Initiative that investigates foam fracturing as an alternative approach to hydraulic fracturing. One element of this work explores foam formulations that are suitable for EGS conditions. A separate paper submitted to this workshop covers the relevant work on foam characterization (Thakore et al., 2020).This paper describes the development of an experimental setup for studying the applicability of foam fluids for hydraulic fracturing along with enhanced fracture efficiency through cyclic pressurization. The system is equipped with pulse rate automation, dual pulse valves for enhanced pulsation amplitudes, and other functions. It can perform static injection at a given pressurization rate, and pulsed injection at specified pulse size and rate. Both single-phase and foamed fluids can be used. Initial experimental results of foam fracturing using cement as a model material are reported. The observations from the experimental work will be presented and discussed.

Wang, Hong↗
DOE OSTI2020

Performance and usability enhancements for continuous subgraph matching queries on graph-structured data

A query graph, which includes vertices and edges, represents a query on graph-structured data. The query graph is decomposed into query subgraphs. A network analysis tool performs continuous subgraph matching queries to facilitate analysis of computer network traffic, social media events, or other streams of data represented as a dynamic data graph (graph-structured data). This can help identify emerging trends in the data. Some features of the network analysis tool enhance performance by effectively utilizing distributed computing resources (including processing cores and memory at different nodes of a cluster) to speed up the process of updating the dynamic data graph and detecting matches of query subgraphs. Features of a query graph building tool enhance usability by providing intuitive ways to specify query graphs and their subgraphs. Features of a results visualization tool enhance usability by providing an intuitive way to present the results of continuous subgraph matching queries.

Choudhury, Sutanay↗
DOE OSTI2021

Enhanced Air-Cooling System with Optimized Asynchronously-Cooled Thermal Energy Storage

A transformative dry-cooling system that includes two primary dry-cooling components has been developed in this project. The two main features of this new technology consist of: (a) an ultra-enhanced air-cooled condenser (ACC), with novel swirl-producing and boundary-layer disrupting enhanced surface on the air-side of the condenser that substantially increase the heat transfer coefficients, and (b) a novel daytime peak-load shifting system that reduces the ambient air inlet temperature for air cooling, consisting of a highly compact and enhanced air pre-cooler that transfers the heat load to a unique thermal energy storage (TES), and which is coupled to the ACC in (a).

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

A Data-Fusion Method using Bayesian Approach to Enhance Raw Data Accuracy of Position and Distance Measurements for Connected Vehicles

Accurate positioning of vehicles is a critical element of autonomous and connected vehicle systems. Most of other studies heavily focused on enhancing simultaneous localization and mapping (SLAM) methods, i.e., constructing or updating a map of an unknown environment and tracking an object within the map. This paper provides a method that can, in addition to existing SLAM or relevant methods, enhance the raw measurements of position and distance. The basic idea of this study is to identify and update the error distribution of each data source by combining all available information. A Bayesian approach was incorporated to estimate and update the error distribution of individual data sources or sensors. The proposed method can be conducted in real-time environments, and a self-learning scheme determines whether enough data has been collected to further improve the accuracy of such measurements. The simulated experiments show that the proposed model noticeably improves the accuracy of position and distance measurements. Especially, the estimated biases of position coordinates and distance measures are very close to the biases of true error distributions, with the R-squared over 0.98. A similar approach can also be utilized to enhance accuracy of other sensors or measurements in connected vehicle or relevant systems, where multi-data sources are available.

Lim, Hyeonsup↗