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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A Spatial-Temporal Analysis of Travel Time Gap and Inequality between Public Transportation and Personal Vehicles

The increased use of personal vehicles presents environmental challenges, prompting the exploration of public transportation as an affordable, eco-friendly alternative. However, obstacles like fixed schedules, limited routes, and extended travel times impede widespread adoption. This study investigates the temporal evolution of spatial inequality in the travel time gap between public transportation and personal vehicles, reflecting disparities across states and time periods. Analyzing Census Transportation Planning Program data for six northeastern states in 2010 and 2016 reveals no significant increase in the travel time gap, but notable growth in inequality in a few urban and disadvantaged communities. Comprehending these trends is vital for fostering equitable advancements in transportation infrastructure and enhancing public transportation competitiveness.

Pan, Melrose↗
DOE OSTI2020

Optical properties of cubic boron arsenide

The ultrahigh thermal conductivity of cubic boron arsenide (BAs) makes it a promising material for next-generation electronics and optoelectronics. Here, we report measured optical properties of BAs crystals, including the complex dielectric function, refractive index, and absorption coefficient in the ultraviolet, visible, and near-infrared wavelength range. In this work, the data were collected at room temperature using spectroscopic ellipsometry and transmission and reflection spectroscopy. We further calculated the optical response using density functional theory and many-body perturbation theory, considering quasiparticle and excitonic corrections. The computed values for the direct and indirect bandgaps (4.25 eV and 2.07 eV) agree well with the measured results (4.12 eV and 2.02 eV). Our findings pave the way for using BAs in future electronic and optoelectronic applications that take advantage of its demonstrated ultrahigh thermal conductivity and predicted high ambipolar carrier mobility.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2017

Unsteady drag following shock wave impingement on a dense particle curtain measured using pulse-burst PIV

High-speed, time-resolved particle image velocimetry with a pulse-burst laser was used to measure the gas-phase velocity upstream and downstream of a shock wave–particle curtain interaction at three shock Mach numbers (1.22, 1.40, and 1.45) at a repetition rate of 37.5 kHz. The particle curtain was formed from free-falling soda-lime particles resulting in volume fractions of 9% or 23% at mid-height, depending on particle diameter (106–125 and 300–355 μm, respectively). Following impingement by a shock wave, a pressure difference was created between the upstream and downstream sides of the curtain, which accelerated flow through the curtain. Jetting of flow through the curtain was observed downstream once deformation of the curtain began, demonstrating a long-term unsteady effect. Using a control volume approach, the unsteady drag on the curtain was estimated from velocity and pressure data. The drag imposed on the curtain has a strong volume fraction dependence with a prolonged unsteadiness following initial shock impingement. Additionally, the data suggest that the resulting pressure difference following the propagation of the reflected and transmitted shock waves is the primary component to curtain drag.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Point-particle drag, lift, and torque closure models using machine learning: Hierarchical approach and interpretability

Developing deterministic neighborhood-informed point-particle closure models using machine learning has garnered interest recently from the dispersed multiphase flow community. The robustness of neural models for this complex multibody problem is hindered by the availability of particle-resolved data. Here, the present work addresses this unavoidable limitation of data paucity by implementing two strategies: (1) by using a rotation and reflection equivariant neural network and (2) by pursuing a physics-based hierarchical machine learning approach. The resulting machine-learned models are observed to achieve a maximum accuracy of 85% and 96% in the prediction of neighbor-induced force and torque fluctuations, respectively, for a wide range of Reynolds number and volume fraction conditions considered. Furthermore, we pursue force and torque network architectures that provide universal prediction spanning a wide range of Reynolds number (0.25 ≤ Re ≤250) and particle volume fraction (0 ≤ φ ≤0.4). The hierarchical nature of the approach enables improved prediction of quantities such as streamwise torque, by going beyond binary interactions to include trinary interactions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Static metrology of the meter-scale deformable heliostat

This paper describes a heliostat metrology system which is developed based on deflectometry, utilizing a static perforated panel instead of conventional dynamic monitor displays to provide incident rays. The developed method is named static screen deflectometry (SSD). This robust and scalable method is especially valuable for outdoor tests of large reflectors used in concentrating solar-thermal power (CSP) systems. The developed method has been successfully demonstrated on a 2.4 𝑚 × 3.3 𝑚 float glass deformable reflector bent to focus sunlight at 113 𝑚 distance throughout a day. From images obtained from a camera at 50𝑚 distance, the reflector surface was measured to an accuracy of less than 1 𝑚𝑟𝑎𝑑 rms slope error in the full test scope.

14 SOLAR ENERGY↗
DOE OSTI2019

Extracting Non-propagating Oscillatory Fields in Concrete to Detect Distributed Cracking

We introduce work to find and locate distributed subsurface cracks in concrete by extracting non-propagating oscillatory fields. The medium of interest is concrete, but the approach also applies to other types of inhomogeneous media. The theoretical basis of the work is first presented through a one-dimensional point-scatterer model that considers the wavefield set up by multiple distinct scatterers. More complex scattering scenarios are then investigated using numerical simulation. The numerical models consider two types of scatterers: elliptic large-scale particles distributed throughout a medium and small-sized cracks localized within a damage zone. The theoretical and numerical analyses show that forward propagating waves undergo distinct scattering behavior within the crack damaged zone: non-propagating resonance-like oscillatory fields are set up within the cracked zone that are distinct from the scatter caused by the large-scale particles. We employ frequency-wavenumber (f-k) domain analysis to extract the energy of non-propagating oscillatory fields and thus to detect and locate zones of distributed cracking. The proposed approach is evaluated using numerical simulation and experimental data collected from concrete specimens that contain simulated distributed cracks. The results reflect the location of distributed crack zones in discrete random media such as concrete can be successfully detected.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine learning-driven predictive resource management in complex science workflows

Here, the collaborative efforts of large communities in science experiments, often comprising thousands of global members, reflect a monumental commitment to exploration and discovery. Recently, advanced and complex data processing has gained increasing importance in science experiments. Data processing workflows typically consist of multiple intricate steps, and the precise specification of resource requirements is crucial for each step to allocate optimal resources for effective processing. Estimating resource requirements in advance is challenging due to a wide range of analysis scenarios, varying skill levels among community members, and the continuously increasing spectrum of computing options. One practical approach to mitigate these challenges involves initially processing a subset of each step to measure precise resource utilization from actual processing profiles before completing the entire step. While this two-staged approach enables processing on optimal resources for most of the workflow, it has drawbacks such as initial inaccuracies leading to potential failures and suboptimal resource usage, along with overhead from waiting for initial processing completion, which is critical for fast-turnaround analyses. In this context, our study introduces a novel pipeline of machine learning models within a comprehensive workflow management system, the Production and Distributed Analysis (PanDA) system. These models employ advanced machine learning techniques to predict key resource requirements, overcoming challenges posed by limited upfront knowledge of characteristics at each step. Accurate forecasts of resource requirements enable informed and proactive decision-making in workflow management, enhancing the efficiency of handling diverse, complex workflows across heterogeneous resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Benchmarking Demand Flexibility in Commercial Buildings and Flattening the Duck – Addressing Baseline and Commissioning Challenges

With the transition from our traditional electric grid to a cleaner grid with renewable power generation, there is a need to enable building loads to be flexible. Load shedding and shifting will be essential for flattening the “Duck” for decarbonization. This paper explored the trend in the timing of DR events as a reflection of the grid’s needs using recent four years of event data from 203 retail stores in 11 states. The events are becoming significantly shorter with 2-hour duration being the most popular; shifting to late afternoon and early evening is another trend beyond California. Benchmarking will be essential for accounting DF as a reliable grid resource. This paper addresses a challenging aspect of benchmarking – inaccuracies in counterfactual baseline methods can introduce significant DF metrics variations in addition to weather and building characteristics related factors. The conventional “10/10” with adjustment baseline method has inherent limitation by design for load shifting applications. Therefore, it is imperative to identify alternative methods. This study compared three hourly regression baseline methods with “10/10” methods using two groups of commercial buildings that participated in DR programs: (1) 121 big-box retail stores, and (2) 11 office buildings in CA. The 14-day hourly outdoor temperature regression method was found to produce least error in the tested datasets and is promising for load shifting. The paper also pointed out that commissioning issues can also be a significant barrier for achieving consistent DF performance, which building managers and utilities should be aware of.

Liu, Jingjing↗
DOE OSTI2018

DWPF Melter Off-Gas Chemistry Model for the Nitric-Glycolic Acid Flowsheet

The Defense Waste Processing Facility (DWPF) melter off-gas flammability model for the current nitric- formic acid (NFA) flowsheet was revised in Revision 0 to reflect the upcoming reductant change from formic to glycolic acid. The necessary data for the model revision was obtained during Phase 2 of the Cold-cap Evaluation Furnace (CEF) test, which was completed on March 21, 2014 after 24 days of round-the-clock feeding and pouring, including a total downtime of ~20 hours due to equipment failures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

DWPF Melter Off-Gas Chemistry Model for the Nitric-Glycolic Acid Flowsheet

The Defense Waste Processing Facility (DWPF) melter off-gas flammability model for the current nitricformic acid (NFA) flowsheet was revised in Revision 0 to reflect the upcoming reductant change from formic to glycolic acid. The necessary data for the model revision was obtained during Phase 2 of the Coldcap Evaluation Furnace (CEF) test, which was completed on March 21, 2014 after 24 days of round-theclock feeding and pouring, including a total downtime of ~20 hours due to equipment failures. Specifically, the CEF was run to generate steady state melter off-gas data during the first 10 days using two nitricglycolic acid (NGA) flowsheet feeds prepared at 100% and 125% acid stoichiometry from the same Sludge Batch 6 (SB6) simulant used during the Phase 1 CEF test in 2013. Each feed was fed under both bubbled and non-bubbled conditions, while maintaining the CEF vapor space temperature constant for at least two hours approximately at 700, 600, 500, 400, 350, and <300 °C for a total of 26 steady state runs. During the next 8 days, the CEF was run at the nominal vapor space temperature of 700 °C to generate the off-gas surge data using the 100% acid-stoichiometry feed under bubbled and non-bubbled conditions. The remaining days were spent on producing additional data using the 100% acid-stoichiometry feed spiked with excess antifoam as well as re-running some of the earlier steady state runs.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

China Green Low-Carbon City Index

The main purpose of the new China Green Low-Carbon City Index (CGLCI, or City Index) is to evaluate the status of green and low-carbon development for a large number of Chinese cities. Chinese cities are formulating plans and implementing programs to address national initiatives, especially the national low-carbon pilot program for 36 cities and six provinces. However, there is not yet an established framework for measuring, reporting, or evaluating low-carbon progress across Chinese cities, despite the development of a number of Chinese and international city indicator systems. The new China City Index highlights energy use and carbon emissions of Chinese cities, along with environmental and socio-economic indicators. We selected metrics (indicators) that have data available in China’s statistical system, are commonly used internationally, and reflect Chinese policy goals. We then benchmarked and ranked Chinese cities in terms of their status and progress, identified potential areas for improvement, and identified top-runners.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

China Green Low-Carbon City Index: Report on the Performance of 100+ Cities (2010-2015)

The main purpose of the new China Green Low-Carbon City Index (CGLCI, or City Index ) is to evaluate the status of green and low-carbon development for a large number of Chinese cities. Chinese cities are formulating plans and implementing programs to address national initiatives, especially the national low-carbon pilot program for 36 cities and six provinces. However, there is not yet an established framework for measuring, reporting, or evaluating low-carbon progress across Chinese cities, despite the development of a number of Chinese and international city indicator systems. The new China City Index highlights energy use and carbon emissions of Chinese cities, along with environmental and socio-economic indicators. We selected metrics (indicators) that have data available in China’s statistical system, are commonly used internationally, and reflect Chinese policy goals. (See Table ES-1.) We then benchmarked and ranked Chinese cities in terms of their status and progress, identified potential areas for improvement, and identified top-runners. By “low-carbon” we mean reducing emissions of carbon dioxide (CO 2 ) and methane (CH 4 ), with a focus on energy-related CO 2 . The term “carbon” is used as short-hand for these GHGs throughout the report. By “green” we recognize multiple environmental parameters related to urbanization and climate change: air quality, water use, solid waste, transport networks, and urban green space. In early 2017, China expanded its low-carbon pilot cities program, adding 45 more cities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

ARM FY2026 Radar Plan

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility maintains a suite of advanced atmospheric radar systems that serve as critical tools in ARM’s mission to provide continuous, high-quality observations for advancing the understanding and modeling of atmospheric processes. These radar systems enable detailed characterization of clouds, precipitation, and dynamic structures in the atmosphere, supporting a broad range of scientific applications. The number of deployed systems exceeds what current staffing levels can fully support for continuous 24/7/365 operation. As such, it is essential to have a clearly defined and community-informed plan that prioritizes radar operations and communicates ARM’s strategy for sustaining and evolving these observational assets. This FY2026 Radar Plan outlines ARM’s approach to managing its radar portfolio—balancing scientific impact, operational feasibility, and long-term sustainability. It reflects ARM’s continued commitment to delivering calibrated, well-documented radar data products that enable process-level studies and support the development and evaluation of weather and climate models. Through this plan, ARM aims to ensure transparency in decision-making, alignment with user needs, and support for innovative science across the facility’s fixed and mobile observatories. Given uncertainties around the Fiscal Year (FY) 2026 budget, this plan was developed to assume business as usual and will be updated as budgets and plans may change. It should be noted that, given the limited timeframe involved, this plan will be more succinct than previous plans.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Comparison of Zirconium Redistribution in BISON EBR-II Models Using FIPD and IMIS Databases with Experimental Post Irradiation Examination

Metallic fuels have seen increased interest for future sodium fast reactors due to their material properties: high thermal conductivities and advantageous neutronic properties allow for greater fission densities. One drawback to typical metallic fuels is zirconium redistribution, which impacts this advantageous material and its neutronic properties. Unfortunately, the processes behind zirconium migration behavior are understood using first principles, so before these fuels are implemented in future fast reactors, characterization and fuel qualification regimes must be completed. These activities can be supported through the use of robust modeling using the most accurate empirical models currently available to fuel researchers around the world. The tool that allows researchers to model this complex coupled thermo-mechanical behavior and nuclear properties is BISON. Additionally, BISON model parameters need to be compared against PIE measurements. The current work utilizes two fuel pins from EBR-II experiment X441 to optimize various model parameters, including porosity correction factor, thermal conductivity, phase transition temperature, and diffusion coefficient multipliers, before implementing the final model for seven fuel pins with differing characteristics. To properly evaluate the BISON simulations, the results are compared to PIE metallography data for each fuel pin, to ensure the zirconium redistribution is properly reflected in the simulation results. Six out of seven analyzed fuel pins demonstrate good agreement between the metallography images and BISON results, showing alignment of the Zr-rich, Zr-depleted, and moderately Zr-enriched zones at various axial heights along the fuel pins. Further work is needed to refine the model parameters for general pin use.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

KAZRARSCL-c0-Cloud Boundaries subset

The KAZR-ARSCL VAP provides cloud boundaries and best-estimate time-height fields of radar moments. The VAP merges corrected, but uncalibrated, KAZR moments from all active radar modes with cloud base and cloud mask observations from the micropulse lidar (MPL), cloud base from the ceilometer, as well information from soundings, rain gauge, and microwave radiometer instruments to produce two data streams, one with best-estimate cloud base and cloud layer boundaries, and another which also includes best-estimate time-height fields of radar moments. This DOI is for the data stream that contain cloud layer boundaries only. Please note that the reflectivity used in this level c0 product is uncalibrated.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Initial-Boundary Value Problem for the Non-Stationary Radiative Transfer Equation with Fresnel Reflection and Refraction Conditions

We consider the initial-boundary value problems for the nonstationary radiative transfer equation in a system of semitransparent bodies with boundary and initial data in the complete scale of Lebesgue spaces. We establish the unique solvability of the first initial-boundary value problem, the problem with “shooting conditions,” and the problem with Fresnel reflection and refraction conditions on the body boundaries.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Data & Code from Phoenix CPPP Phase 2 Analysis

This data and code package supports the analysis presented in “Beyond Surface Cooling: Comprehensive Field Assessment of Reflective Pavement Thermal Performance in Phoenix, Arizona” and provides fully reproducible workflows for evaluating the thermal performance of cool pavement treatments in a hot urban environment. The dataset integrates multi-modal field measurements collected across residential and nonresidential settings, including mobile air temperature traverses, stationary air temperature monitoring, residential mean radiant temperature (MRT) measurements, subsurface temperature profiles, and controlled testbed observations. The data package contains raw and processed datasets in comma-separated value (CSV) format, accompanying metadata files describing site characteristics and measurement protocols, and R scripts (.R files) used for data cleaning, time synchronization, spatial and temporal matching, quality control filtering, statistical comparison, and figure generation. All analyses were conducted using R (version ≥ 4.2.0) with commonly available packages (e.g., tidyverse, lubridate, data.table, ggplot2). No proprietary software is required to reproduce results. Field campaigns were designed to quantify the effects of high-reflectance pavement coatings on surface temperature, near-surface air temperature, subsurface heat propagation, and radiative heat exposure. Temporal alignment procedures include standardized timestamp conversion and nearest-neighbor matching of high-frequency sensor measurements to stop-based metadata within defined tolerance windows to ensure comparability across instruments. The workflows generate summary statistics, treatment–control contrasts, depth-dependent thermal gradients, and time-series visualizations used in the associated publication. By integrating mobile, stationary, radiative, and subsurface measurements within a unified and transparent processing framework, this package enables comprehensive evaluation of cool pavement performance across multiple thermal exposure pathways and supports reuse in future urban heat mitigation and climate resilience studies.

AIR TEMPERATURE↗