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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Bridging the Gap between Analytical and Microbial Sciences in Microbiome Research

Metabolites from the microbiome influence human, animal, and environmental health, but the diversity and functional roles of these compounds have only begun to be elucidated. Comprehensively characterizing these molecules are significant challenges, as it requires expertise in analytical methods, such as mass spectrometry and nuclear magnetic resonance spectroscopy, skills that not many traditional microbiologists or microbial ecologists possess. This creates a gap between microbiome scientists that want to understand the role of microbial metabolites in microbiome systems and the skills required to generate and interpret complex metabolomics data sets. To bridge this gap, microbiome scientists should engage analytical chemists to best understand the underlying chemical principles of the data. Conversely, analytical scientists are encouraged to engage with microbiome scientists to better understand the biological questions being asked with metabolomics and to best communicate its intricacies. Better communication across the chemistry/biology disciplines will further reveal the “dark matter” within microbiomes that maintain healthy humans and environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Stability and Convergence of Solutions to Stochastic Inverse Problems Using Approximate Probability Densities

Data-consistent inversion is designed to solve a class of stochastic inverse problems where the solution is a pullback of a probability measure specified on the outputs of a quantities of interest (QoI) map. Here, this work presents stability and convergence results for the case where finite QoI data result in an approximation of the solution as a density. Given their popularity in the literature, separate results are proven for three different approaches to measuring discrepancies between probability measures: f-divergences, integral probability metrics, and L p metrics. In the context of integral probability metrics, we also introduce a pullback probability metric that is well-suited for data-consistent inversion. This fills a theoretical gap in the convergence and stability results for data-consistent inversion that have mostly focused on convergence of solutions associated with approximate maps. Numerical results are included to illustrate key theoretical results with intuitive and reproducible test problems that include a demonstration of convergence in the measure-theoretic "almost" sense.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Street context of various demographic groups in their daily mobility

Abstract We present an urban science framework to characterize phone users’ exposure to different street context types based on network science, geographical information systems (GIS), daily individual trajectories, and street imagery. We consider street context as the inferred usage of the street, based on its buildings and construction, categorized in nine possible labels. The labels define whether the street is residential, commercial or downtown, throughway or not, and other special categories. We apply the analysis to the City of Boston, considering daily trajectories synthetically generated with a model based on call detail records (CDR) and images from Google Street View. Images are categorized both manually and using artificial intelligence (AI). We focus on the city’s four main racial/ethnic demographic groups (White, Black, Hispanic and Asian), aiming to characterize the differences in what these groups of people see during their daily activities. Based on daily trajectories, we reconstruct most common paths over the street network. We use street demand (number of times a street is included in a trajectory) to detect each group’s most relevant streets and regions. Based on their street demand, we measure the street context distribution for each group. The inclusion of images allows us to quantitatively measure the prevalence of each context and points to qualitative differences on where that context takes place. Other AI methodologies can further exploit these differences. This approach presents the building blocks to further studies that relate mobile devices’ dynamic records with the differences in urban exposure by demographic groups. The addition of AI-based image analysis to street demand can power up the capabilities of urban planning methodologies, compare multiple cities under a unified framework, and reduce the crudeness of GIS-only mobility analysis. Shortening the gap between big data-driven analysis and traditional human classification analysis can help build smarter and more equal cities while reducing the efforts necessary to study a city’s characteristics.

Salgado, Ariel (ORCID:0000000177015372)↗
DOE OSTI2020

Autoignition of CRC diesel surrogates at low temperature combustion conditions: Rapid compression machine experiments and modeling

As federal programs require increasingly stringent engine emissions and fuel economy standards, these ambitions can only be met if next-generation combustion technology is developed focusing on high-efficiency and low-emissions engines. Recent research has indicated the need to operate engines at higher compression ratios and with low temperature combustion (LTC) to achieve the needed gains in engine efficiency and reductions in emissions. Because there is a lack of understanding of the chemistry of diesel fuel components and their mixtures at these LTC conditions, this limits the ability to develop predictive chemical kinetic models that can be used to optimize engine combustion. The current study aims to fill in gaps in fundamental combustion data on surrogate fuel mixtures relevant to diesel fuels. Specifically, four multicomponent diesel surrogates formulated by the Coordinating Research Council (CRC) to emulate an ultra-low-sulfur research-grade #2 certification diesel fuel (CFA), namely V0a (4 components), V0b (5 components), V1 (8 components), and V2 (9 components), have been investigated in a rapid compression machine (RCM) through determination of total and first-stage ignition delay times. Autoignition characteristics of lean to rich fuel/O 2 /N 2 mixtures, for the four CRC surrogates and CFA, have been measured using an RCM at LTC relevant pressures and temperatures, in the ranges of 10–20 bar and 650–1000 K, respectively. The equivalence ratios have been varied by independently changing the oxygen mole fraction and the fuel mole fraction in the test mixtures, thereby illustrating the individual effects of oxygen concentration and fuel loading on diesel autoignition. Autoignition results of these four CRC surrogates are compared among them and with those of CFA. Some degree of agreement in autoignition response between each CRC surrogate and CFA is observed, while discrepancies are also identified and discussed. In addition, a detailed chemical kinetic model for diesel surrogates has been developed and validated against these newly-acquired RCM data. We find that this model shows reasonable agreement with the overall ignition delay time results of the current RCM experiments. Chemical kinetic analyses of the developed model were further conducted to help identify the reactions controlling the autoignition processes and the consumption of fuel components in CRC surrogates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Water intensity of photovoltaic module manufacturing at the terawatt scale

As the U.S. ramps photovoltaic (PV) manufacturing to the terawatt scale and emphasizes re-shoring manufacturing, potential regional impacts on the U.S. water supply should be considered, particularly since many PV companies rely almost exclusively on public water supplies for manufacturing. This work surveys the academic literature and PV manufacturer reports to estimate the water intensity of monocrystalline silicon, multicrystalline silicon, and cadmium telluride modules manufactured at the terawatt scale, determining that on average, cadmium telluride manufacturing is less water intensive on a per megawatt scale – this is anticipated to be true for all thin film PV manufacturing. While much lower than the water intensity of thermoelectric (e.g., coal) energy generation, significant issues and gaps with PV manufacturing data quality in academic studies are identified which cause estimates to vary by over 1000x (0.04 – 49 trillion liters/terawatt). Data issues are discussed and the need for accurate accounting of water resources (e.g., via continuous, updated information during PV manufacturing) is highlighted. The opportunity to reconfigure decommissioned thermoelectric sites to PV manufacturing is also explored. Finally, factors that influence PV manufacturing water intensity, from individual manufacturing steps to trends across the PV industry, are examined and water conservation opportunities are presented.

14 SOLAR ENERGY↗
DOE OSTI2021

Stratigraphic Identification with Airborne Electromagnetic Methods at the Hanford Site, Washington

Stratigraphic units can influence the fate and transport of subsurface contaminants within groundwater. Units having coarse-grained sediments act as preferential flow pathways, and therefore can accelerate the transport of contaminants to reach human and ecological receptors. At legacy waste sites, detailed knowledge of subsurface stratigraphy can be used for effective monitoring and remediation planning to help minimize risk to human health and the environment. Airborne electromagnetic (AEM) methods can non-invasively provide information on kilometer-scale or larger subsurface stratigraphic features and fill informational gaps in directly sampled data from sparsely located boreholes. In this paper, we present inversion results of a 412 line-km frequency-domain AEM survey to delineate subsurface stratigraphic features at the Hanford Site, located in southeastern Washington State. The inversion was performed using a massively parallel 3D electromagnetic modeling and inversion code, where the modeling is based on solving frequency-domain Maxwell’s equations using an unstructured-mesh finite-element method and the inversion employs a Gauss-Newton optimization scheme. The results are compared to an underlying geologic framework model (GFM), built by interpolating contact depths of stratigraphic units interpreted from site borehole datasets. In areas with good borehole coverage, the inversion results show a good match with the GFM to a depth of about 60 m. Outside of these areas, the inversion results exhibit inconsistencies from the assumptions made to create the GFM, demonstrating that the AEM survey results can be used to improve the understanding of the geological conceptual model.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Unlocking nighttime mobility: Land use and accessibility in public transit for night commuters

Night commuters are integral to urban transportation systems. Essential services such as healthcare and manufacturing rely on workers who travel at night, and reliable mobility options are crucial for them. A gap exists in understanding how land use and accessibility influence public transportation use among night commuters. This study addresses this gap by using public data to explore land use and accessibility factors that affect night commuters' public transportation use in New York State. We investigated (1) the demographic characteristics of night commuters; (2) the influence of land use and accessibility on nighttime public transportation use; and (3) potential improvements to increase public transportation use and their impact. We combined data from the National Household Travel Survey with the Smart Location Database to link home locations with land use characteristics. Using logistic regression, we found that although females are generally less likely to be night commuters, they are more likely to use public transportation. Longer commute distances are associated with higher use of public transportation. Increasing job density along fixed-guideway transit routes and improving overall job accessibility via public transportation significantly enhances public transportation use among night commuters. In conclusion, this research provides actionable insights for public transportation agencies and urban planners to support night commuters, improving access and encouraging nighttime employment.

Job accessibility↗
DOE OSTI2020

Multifunctional transition metal doped titanium dioxide reduced graphene oxide composites as highly efficient adsorbents and photocatalysts

A series of novel, mesoporous transition metal doped titanium dioxide-reduced graphene oxide composites are synthesized where the dopants were Mn, Co, Ni, Mo, and W. An in-situ synthesis method was used which involves a green, economic, hydrothermal process which can be scaled up easily. The composites show high surface areas (165–245 m 2 /g) and monomodal pore size distributions with average pore diameters in the range of 3–5 nm. The homogeneous distributions of the dopants are seen in SEM-EDX mapping data. The indirect band gaps of the composite materials are in the range of 2.20–2.48 eV. The composites show excellent dye adsorption activities where 100% adsorption of Rhodamine B occurs within 5 min when Mo–TiO 2 /rGO composite is used. Furthermore, the adsorbed dye can either be desorbed or photocatalytically degraded and the composite is reusable up to 10 cycles without losing significant activity. Mo–TiO 2 /rGO composite showed excellent adsorption of both cationic and anionic dyes at neutral pH.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Optimizing Carbon Cycle Parameters Drastically Improves Terrestrial Biosphere Model Underestimates of Dryland Mean Net CO 2 Flux and its Inter-Annual Variability

Drylands occupy ~40% of the land surface and are thought to dominate global carbon (C) cycle inter-annual variability (IAV). Therefore, it is imperative that global terrestrial biosphere models (TBMs), which form the land component of IPCC earth system models, are able to accurately simulate dryland vegetation and biogeochemical processes. However, compared to more mesic ecosystems, TBMs have not been widely tested or optimized using in situ dryland CO 2 fluxes. Here, for this work, we address this gap using a Bayesian data assimilation system and 89 site-years of daily net ecosystem exchange (NEE) data from 12 southwest US Ameriflux sites to optimize the C cycle parameters of the ORCHIDEE TBM. The sites span high elevation forest ecosystems, which are a mean sink of C, and low elevation shrub and grass ecosystems that are either a mean C sink or “pivot” between an annual C sink and source. We find that using the default (prior) model parameters drastically underestimates both the mean annual NEE at the forested mean C sink sites and the NEE IAV across all sites. Our analysis demonstrated that optimizing phenology parameters are particularly useful in improving the model's ability to capture both the magnitude and sign of the NEE IAV. At the forest sites, optimizing C allocation, respiration, and biomass and soil C turnover parameters reduces the underestimate in simulated mean annual NEE. Our study demonstrates that all TBMs need to be calibrated for dryland ecosystems before they are used to determine dryland contributions to global C cycle variability and long-term carbon-climate feedbacks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Measurement and microscopic description of odd–even staggering of charge radii of exotic copper isotopes

Nuclear charge radii globally scale with atomic mass number A as A 1/3 , and isotopes with an odd number of neutrons are usually slightly smaller in size than their even-neutron neighbours. This odd–even staggering, ubiquitous throughout the nuclear landscape1, varies with the number of protons and neutrons, and poses a substantial challenge for nuclear theory. Here, we report measurements of the charge radii of short-lived copper isotopes up to the very exotic 78 Cu (with proton number Z = 29 and neutron number N = 49), produced at only 20 ions s –1 , using the collinear resonance ionization spectroscopy method at the Isotope Mass Separator On-Line Device facility (ISOLDE) at CERN. We observe an unexpected reduction in the odd–even staggering for isotopes approaching the N = 50 shell gap. To describe the data, we applied models based on nuclear density functional theory and A-body valence-space in-medium similarity renormalization group theory. Through these comparisons, we demonstrate a relation between the global behaviour of charge radii and the saturation density of nuclear matter, and show that the local charge radii variations, which reflect the many-body polarization effects, naturally emerge from A-body calculations fitted to properties of A ≤ 4 nuclei.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Electricity use in big area additive manufacturing of fiber-reinforced polymer composites

In recent years, additive manufacturing (AM), especially large-format additive manufacturing (LFAM), has gained momentum in the manufacturing industry. While LFAM offers benefits over conventional manufacturing processes, such as minimizing material waste and providing vast geometric freedom, assessing its sustainability remains challenging due to limited data, particularly on energy consumption. Most existing data pertain to small-scale or desktop AM and are not directly applicable to LFAM. In this study, we conducted real-time measurements of electricity usage for a type of LFAM known as big area additive manufacturing (BAAM), which typically uses fiber-reinforced polymer pellets as feedstock. We collected electricity usage data from fifteen printing jobs over two months in an industrial production setting. These data fill the existing gap and can be reused to enhance the community’s understanding of LFAM electricity usage, support further research, and promote sustainable development in advanced manufacturing technologies.

ecology↗
DOE OSTI2025

Spatial Impacts of Electric Vehicle Charging on Power Grid Stability: A Downtown Atlanta Case Study

The rapid increase in electric vehicle (EV) charging demand poses a potential risk to power grid stability, particularly as the spatial distribution of this demand remains underexplored. Existing research often focuses on technical optimization models while overlooking the geographic and human dynamics that affect energy consumption. This study addresses this gap by incorporating mobility data to estimate both building energy use and EV charging demand while also considering geographic factors for a better understanding of grid load. Using agent-based simulations and the Open-Source Distribution System Simulator, the study evaluates the effect of various EV penetration scenarios on grid voltage and unbalance. The results show that, although voltage remains within acceptable limits at lower EV penetration rates, significant voltage drop and unbalance occur as EV penetration exceeds 40%, particularly in residential areas with high charging demand. This study offers a framework for integrating spatial analysis and mobility data in power network simulations, providing insights for future EV infrastructure planning.

Pan, Melrose [ORNL] (ORCID:000000031627448X)↗
DOE OSTI2021

The very forward CASTOR calorimeter of the CMS experiment

The physics motivation, detector design, triggers, calibration, alignment, simulation, and overall performance of the very forward CASTOR calorimeter of the CMS experiment are reviewed. The CASTOR Cherenkov sampling calorimeter is located very close to the LHC beam line, at a radial distance of about 1 cm from the beam pipe, and at 14.4 m from the CMS interaction point, covering the pseudorapidity range of $-$6.6 $\lt\eta\lt$ $-$5.2. It was designed to withstand high ambient radiation and strong magnetic fields. The performance of the detector in measurements of forward energy density, jets, and processes characterized by rapidity gaps, is reviewed using data collected in proton and nuclear collisions at the LHC.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Using a Large Language Model as a Building Block to Generate Usable Validation and Verification Suite for OpenMP

In the HPC area, both hardware and software move quickly. Often new hardware is developed and deployed, the corresponding software stack, including compilers and other tools, are under active development while leading edge software developers are working to port and tune their applications, all at the same time. While the software ecosystem is in flux, one of the key challenges for users is obtaining insight into the state of implementation of key features in the programming languages and models their applications are using – whether they have been implemented, and whether the implementation conforms to the specification, especially for newly implemented features (less tested by widespread use). OpenMP is one of the most prominent shared memory programming models used for on-node programming in HPC. With the shift towards accelerators (such as GPUs and FPGAs) and heterogeneous programming OpenMP features are getting more complex. It is natural to ask whether generative AI approaches, and large language models (LLMs) in particular, can help in producing validation and verification test suites to allow users better and faster insights into the availability and correctness of OpenMP features of interest. In this work, we explore the use of ChatGPT-4 to generate a suite of tests for OpenMP features. We have chosen a set of directives and clauses, a total of 78 combinations, which first appeared in OpenMP 3.0 (released in May 2008) but are also relevant for accelerators. We prompted ChatGPT to generate tests in the C and Fortran languages, for both host (CPU) and device (accelerator). On the Summit super-computer using the GNU implementation, we found that, of the 78 generated tests 67 C tests and 43 Fortran tests compiled successfully and fewer than those executed to completion. On further analysis we show that not all generated tests are valid. We document the process, results, and provide detailed analysis regarding the quality of tests generated. With the aim of providing input to a production quality validation and verification suite, we manually implement the corrections required to make the tests valid according to the current OpenMP specification. We quantify this effort as small, medium, or large, and record the lines of code changed to correct the invalid tests. With the corrected tests we validate recent implementations from HPE, AMD, and GNU on the Frontier supercomputer. Our experiment and subsequent analysis show that although LLMs are capable of producing HPC specific codes, they are limited by their understanding of the deeper semantics and restrictions of programming models such as OpenMP. Unsurprisingly more commonly used features have better support, while some OpenMP 3.0 directives such as sections and tasking are not universally supported on accelerators. We demonstrate that successful compilation and execution to completion are inadequate metrics for evaluating generated code and that, at this time, commodity LLMs require expert intervention for code verification. This points to gaps in the training data that is currently available for HPC. We demonstrate that with "small" effort 37% of generated invalid C tests and 63% of generated invalid Fortran tests could be corrected. This improves productivity of test generation as we circumvent writing from scratch and the common programming errors associated with it.

Pophale, Swaroop [ORNL] (ORCID:0000000185446367)↗
DOE OSTI2020

SFAI and Related Technologies

Researchers at Los Alamos have developed Swept-Frequency Acoustic Interferometry (SFAI) a technology that enables businesses to have greater control over their industrial processes, allowing for significantly improved efficiency by giving operators vital composition and flow rate information in real-time. This technology fills the information gap by providing this data non-invasively and at a fraction of the cost of traditional composition meters. SFAI uses engineered ultrasound and advanced signal processing to characterize multiphase flow (e.g., liquids, gasses, multi-phase mixtures) and other flows inside sealed containers and flow pipes. By being both low-cost and non-invasive, this technology is set to disrupt the current oil field monitoring state-of-the-art by providing individual well-level composition data, compared to the multi-well macroscopic information that is currently available. We are seeking a commercialization partner to license the technology or fund us through a Cooperative Research and Development Agreement (CRADA) to a further develop a usable product for commercial purposes.

03 NATURAL GAS↗
DOE OSTI2021

National Ignition Facility 192 beam 3w power sensor

NIF requires additional laser diagnostic capabilities to support existing missions and drive performance of the laser to higher powers and energies. There exists a current gap in laser power data on the facility due to the subset of beamlines that are measurement capable. Additionally, as NIF increases the radiation yield on target experiments, the performance of the current laser diagnostic 3w power sensor system has been steadily decreasing. The degradation of the current system is not well understood and needs to be characterized to facilitate any new system design.

42 ENGINEERING↗
DOE OSTI2022

Clean Energy Education and Training Resources and Opportunities in New York's Southern Tier Region

New York's Southern Tier Region is experiencing high growth and investment in the clean energy sector and is anticipating more jobs to come in energy efficiency, renewable energy, and manufacturing in the coming years. The Network for a Sustainable Tomorrow (NEST) is a nonprofit network of programs working to develop a regional backbone system for education and training programs as well as curricula to support the workforce needed for the region's growing industries to succeed. Through its participation in the US Department of Energy's Better Buildings Workforce Accelerator, NEST requested technical assistance in conducting a landscape and needs assessment of the region's existing clean energy education and workforce development assets. This report supports NEST's efforts by providing a baseline of clean energy employment data, an inventory and gap analysis of the education and workforce development assets currently available and serving the Southern Tier Region, and case studies of innovative and successful regional clean energy education and workforce coalitions from around the county.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Investigations into the Ternary NaF-KF-UF4 Salt System – Phase A

A knowledge gap exists in the data and understanding of fresh fuel salt and irradiated multicomponent fuel salt systems thermophysical properties. Quantifying these properties is necessary for the design and construction of test reactors, as well as the licensing of future commercial molten-salt reactors. To facilitate thermal property determination on a proposed fuel salt composition for Seaborg Technologies, several samples containing depleted uranium tetrafluoride (UF4), sodium fluoride (NaF), and potassium fluoride (KF) were blended, and a melt temperature analysis was performed. From the melting temperature analysis, it was determined that sample Seaborg-7, a ternary salt composition of 26.4UF4-24.7KF-48.9NaF (mol%), was very near a ternary eutectic point. Therefore, thermal properties such as melting temperature, salt stability, density, heat capacity, thermal diffusivity, and viscosity were experimentally determined on the Seaborg-7 salt. These measurements document the baseline properties of fresh fuel salt as a function of temperature, where future experiments on irradiated fuel salt will provide a holistic perspective on the change of thermophysical properties during reactor operations. Several precision instruments were used to collect property data, and instrument calibrations and data collection were performed and documented in a standardized and reproducible manner with meticulous detail. This process ensured that the measurement procedures and resulting data can readily be duplicated elsewhere. The Seaborg-7 salt was shown to be stable at temperatures up to 900°C, as no mass change was observed upon repeated heating and cooling. The peak melting temperature was determined to be 547°C (557°C endset). The enthalpy of fusion (??H?_fus^o) was determined to be 167.5 ± 2.7 J/g while the enthalpy of crystallization (??H?_c^o) was determined to be -147.8 ± 13.3 J/g. In addition to the eutectic melting peak, upon heating, several pre eutectic peaks were observed, occurring at 470°C (onset) and 499°C (peak). Specific heat capacity measurements showed a slightly increasing trend with respect to temperature in the solid phase, while the liquid-specific heat capacity showed a somewhat flat trend with an average value of 106.1 ± 1.24 J/mol·K between 600 to 800°C. Three independent trials using the Seaborg-7 salt determined the density to be ?(T) = 4.908 – 0.000363·T(°C), validated between 32 to 200°C, and ?(T) = 4.808 – 0.00113·T(°C), validated between ~575 to 850°C. Thermal diffusivity was determined for the liquid state and is represented by the linear equation y = 0.1581 + 0.000207·T(°C) between 550 to 850°C. The viscosity was determined from 600 to 800°C and is represented by the exponential fit equation, ? (mPa·s) = 736.58e^(-0.006·T(°C)). This report documents the conclusion of fuel salt thermophysical property measurements for the Seaborg SPP, Phase A project.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗