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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Construction of St. Benedict

The Superallowed Transition Beta-Neutrino Decay Ion Coincidence Trap (St. Benedict) is currently under construction at the University of Notre Dame Nuclear Science Laboratory. It is designed to measure the beta-neutrino angular correlation parameter in superallowed mixed beta-decay transitions between mirror nuclei in order to extract the Fermi-to-Gamow Teller mixing ratio and test theoretical corrections entering in the determination of the V ud element of the Cabibbo–Kobayashi–Maskawa quark mixing matrix. St. Benedict includes a large volume gas catcher to thermalize the radioactive ion beam from TwinSol, a differentially-pumped extraction system, a radiofrequency quadrupole ion cooler and buncher, and a Paul trap for the observation and detection of the β decay products. Finally, the status of each of these components will be presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Framework for Modeling 3D-Printed Concrete Construction to Assess Energy Efficiency and Backup Power Trade-Offs in a Mixed-Use, New Construction Neighborhood Development

This paper presents a framework that expands the URBANopt(TM) modeling platform to include 3D-printed concrete wall assemblies and assess energy efficiency and backup power trade-offs in new housing developments. Applied to a planned mixed-use neighborhood in Oil City, Pennsylvania, the workflow integrates building energy and distributed energy resource (DER) modeling to evaluate envelope and equipment upgrades alongside DER operations. Results show that advanced 3D-printed envelopes combined with efficient systems and onsite photovoltaics (PV) and storage reduce energy use intensity and sustain critical loads during outages. The framework supports planning for emerging construction technologies by quantifying key trade-offs between energy efficiency and backup power performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Stabilizing Anionic Redox Chemistry in a Mn-Based Layered Oxide Cathode Constructed by Li-Deficient Pristine State

Li-rich cathode materials are of significant interest for coupling anionic redox with cationic redox chemistry to achieve high-energy-density batteries. However, lattice oxygen loss and derived structure distortion would induce serious capacity loss and voltage decay, further hindering its practical application. Herein, a novel Li-rich cathode material, O3-type Li 0.6 [Li 0.2 Mn 0.8 ]O 2 , is developed with the pristine state displaying both a Li excess in the transition metal layer and a deficiency in the alkali metal layer. Benefiting from stable structure evolution and Li migration processes, not only can high reversible capacity (≈329 mAh g -1 ) be harvested but also irreversible/reversible anionic/cationic redox reactions are comprehensively assigned via the combination of in/ex situ spectroscopies. Furthermore, irreversible lattice oxygen loss and structure distortion are effectively restrained, resulting in long-term cycle stability (capacity drop of 0.045% per cycle, 500 cycles). Altogether, tuning the Li state in the alkali metal layer presents a promising way for modification of high-capacity Li-rich cathode candidates.

anionic redox reactions↗
DOE OSTI2023

Joint analysis of Dark Energy Survey Year 3 data and CMB lensing from SPT and Planck . I. Construction of CMB lensing maps and modeling choices

Joint analyses of cross-correlations between measurements of galaxy positions, galaxy lensing, and lensing of the cosmic microwave background (CMB) offer powerful constraints on the large-scale structure of the Universe. In a forthcoming analysis, we will present cosmological constraints from the analysis of such cross-correlations measured using Year 3 data from the Dark Energy Survey (DES), and CMB data from the South Pole Telescope (SPT) and Planck. Here we present two key ingredients of this analysis: (1) an improved CMB lensing map in the SPT-SZ survey footprint and (2) the analysis methodology that will be used to extract cosmological information from the cross-correlation measurements. Relative to previous lensing maps made from the same CMB observations, we have implemented techniques to remove contamination from the thermal Sunyaev Zel’dovich effect, enabling the extraction of cosmological information from smaller angular scales of the cross-correlation measurements than in previous analyses with DES Year 1 data. We describe our model for the cross-correlations between these maps and DES data, and validate our modeling choices to demonstrate the robustness of our analysis. We then forecast the expected cosmological constraints from the galaxy survey-CMB lensing auto and cross-correlations. We find that the galaxy-CMB lensing and galaxy shear-CMB lensing correlations will on their own provide a constraint on S 8 =σ 8 $\sqrt{Ωm/0.3}$ at the few percent level, providing a powerful consistency check for the DES-only constraints. Finally, we explore scenarios where external priors on shear calibration are removed, finding that the joint analysis of CMB lensing cross-correlations can provide constraints on the shear calibration amplitude at the 5% to 10% level.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Dynamic Construction, Perception, and Remodeling of Plant Cell Walls

Plant cell walls are dynamic structures that are synthesized by plants to provide durable coverings for the delicate cells they encase. They are made of polysaccharides, proteins, and other biomolecules and have evolved to withstand large amounts of physical force and to resist external attack by herbivores and pathogens but can in many cases expand, contract, and undergo controlled degradation and reconstruction to facilitate developmental transitions and regulate plant physiology and reproduction. Recent advances in genetics, microscopy, biochemistry, structural biology, and physical characterization methods have revealed a diverse set of mechanisms by which plant cells dynamically monitor and regulate the composition and architecture of their cell walls, but much remains to be discovered about how the nanoscale assembly of these remarkable structures underpins the majestic forms and vital ecological functions achieved by plants.

biofuels (including algae and biomass), bio-inspir↗
DOE OSTI2026

Synthesis of ARM User Facility Surface Rainfall Datasets to Construct a Best Estimate Value Added Product (PrecipBE)

Surface precipitation measurements are essential for Earth system model (ESM) evaluation and understanding cloud processes. An ever-growing need for robust, temporally evolving, and easy-to-use statistical datasets provides motivation for a baseline ground-based precipitation properties data product. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility operates an extensive suite of precipitation instruments with various sensitivities and operating mechanisms, which render the decision of which instrument to use based on one or more fixed thresholds challenging and prone to errors and bias. Using a long-term instrument inter-comparison from a unique per-precipitation event perspective, rather than instantaneous sample comparison, we demonstrate that ARM rainfall-measuring instruments are generally consistent with each other at the statistical level. Inter-instrument deviations at the single event level can be large, especially for specific rainfall event properties such as maximum precipitation rates. A machine-learning (ML) analysis using a random forest regressor indicates that in some cases, depending on instrument, local site climatology, and/or specific deployment configuration, certain atmospheric state variables influence the measured quantities in an unpredictable manner. Thus, a-priori weighting of different instruments does not necessarily lead to more accurate and less biased synthesis of instrument data. These results motivate the design of the ARM precipitation best-estimate (PrecipBE) value-added product, which incorporates all valid precipitation data while considering data quality and other instrument limitations. PrecipBE consists of time series and tabular statistics datasets in an easy-to-use and insightful per-precipitation event format. It provides a large set of precipitation event properties supplemented with ancillary data from ARM datasets that correspond to the detected precipitation events. We describe the PrecipBE algorithm and demonstrate its use via the examination of a single-day output as well as a long-term trend analysis of precipitation events at the ARM Southern Great Plains (SGP) site, covering more than 30 years of data. The trend analysis tentatively suggests a long-term temporal tendency for mainly shorter and less intense precipitation events at the SGP site, but a long-term increase in annual rainfall by more than 36 mm (5 %) per decade. This rainfall trend is catalyzed primarily by more extreme event properties of relatively rare, intense precipitation events, with event total and 1 min maximum precipitation rate at a 1 year timeframe increasing up to 5 mm and 9 mm h −1 (several percent) per decade, respectively. While the currently available PrecipBE datasets (at https://adc.arm.gov/discovery/, last access: 8 December 2025) cover rainfall from multiple ARM deployments up to March 2025, PrecipBE is planned to be expanded to include solid-phase precipitation and will soon become an operational product with a several-day lag from real-time. We invite the ARM user community to leverage this new product and welcome user feedback to further enhance the dataset.

Silber, Israel [Pacific Northwest National Laborat↗
DOE OSTI2025

Construction Methodology Transformation for the Benefit of Workforce Development

Construction is a key economic engine driving both national and global economies. While manual, onsite construction methods dominate the U.S. construction industry, a major shift towards offsite methods has been underway due to its efficiency, speed, and potential cost savings. The workforce necessary for offsite construction growth does not exist in its current form because of the focus on onsite methodologies and the lack of exposure to offsite building methods at all levels of a student’s learning journey. The growth of the U.S. construction industry and competitiveness in an increasingly global construction market over the coming decades can only be supported by a dramatic increase in the use of offsite methods, which requires ramping up workforce training for certain skillsets. The goal of Construction Methodology Transformation for the Benefit of Workforce Development was to understand the opportunities and barriers in both education and industry and to identify best practices for offering curriculum and training to educators, industry, and students that would support skills needed for careers in offsite construction. Our team proposed combining three offsite construction workforce development needs: content development, exposure and training, and job placement - under a single Platform model that would increase experience and career opportunities for students and help match them with potential industry members. Through our proposed solution we expected to see: developed offsite curriculum being utilized by educators and students; an increase in the identification of construction technology and offsite construction methods; an average increase in knowledge gain of at least 25% after participation in pilots; better equipped candidates who are prepared for jobs in offsite construction; and a beta workforce development platform that helps build more pathways for students looking for careers in offsite construction. The two pilots included almost 250 students and resulted in an average knowledge gain of 37 percent. Our research has identified areas of opportunity, for both education and industry to make collaborative training programs more efficient and successful. The chosen techniques for this program are extremely effective when both the school and factory have solid processes and cultures in place to accept students into training programs. This project serves as an important stepping stone to industrywide collaboration to move workforce development for offsite construction forward across the country. With continued collaboration programs like this can provide much needed early exposure and training in offsite construction and we can begin to fill important positions for the future of construction.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Bottleneck Detection in Modular Construction Factories Using Computer Vision

The construction industry is increasingly adopting off-site and modular construction methods due to the advantages offered in terms of safety, quality, and productivity for construction projects. Despite the advantages promised by this method of construction, modular construction factories still rely on manually-intensive work, which can lead to highly variable cycle times. As a result, these factories experience bottlenecks in production that can reduce productivity and cause delays to modular integrated construction projects. To remedy this effect, computer vision-based methods have been proposed to monitor the progress of work in modular construction factories. However, these methods fail to account for changes in the appearance of the modular units during production, they are difficult to adapt to other stations and factories, and they require a significant amount of annotation effort. Due to these drawbacks, this paper proposes a computer vision-based progress monitoring method that is easy to adapt to different stations and factories and relies only on two image annotations per station. In doing so, the Scale-invariant feature transform (SIFT) method is used to identify the presence of modular units at workstations, and the Mask R-CNN deep learning-based method is used to identify active workstations. This information was synthesized using a near real-time data-driven bottleneck identification method suited for assembly lines in modular construction factories. This framework was successfully validated using 420 h of surveillance videos of a production line in a modular construction factory in the U.S., providing 96% accuracy in identifying the occupancy of the workstations and an F-1 Score of 89% in identifying the state of each station on the production line. The extracted active and inactive durations were successfully used via a data-driven bottleneck detection method to detect bottleneck stations inside a modular construction factory. The implementation of this method in factories can lead to continuous and comprehensive monitoring of the production line and prevent delays by timely identification of bottlenecks.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Assessment of small mechanical wastewater treatment plants: Relative life cycle environmental impacts of construction and operations

Many slow growing and shrinking rural communities struggle with aging or inadequate wastewater treatment plants (WWTPs), and face challenges in constructing and operating such facilities. Although existing literature has provided insight into the environmental sustainability of large facilities, including both the construction and operational phases, these studies have not examined small, rural facilities treating less than 7,000 m 3 /d (1.8 MGD) of wastewater in adequate depth and breadth. In this study, a detailed inventory of the construction and operational data for 16 case studies of small WWTPs was developed to elucidate their environmental life cycle impacts. Conventional LCA framework was followed. The results show that the environmental impacts of both the construction and operational phases are considerable. Operational impacts are highly related to energy usage. Improving energy efficiency of a plant may reduce the environmental impacts related to operations. Construction impacts can vary considerably between facilities. Process-related factors (e.g., concrete and reinforcing steel used in basins) are typically sized using the design flow; thus much of the variability in construction impacts among plants stems from the non-process related infrastructure. Multiple regression analysis was used as an exploratory tool to identify which non-process related plant aspects contribute to the variable environmental impact of small WWTPs. These factors include aluminum, cast iron, and the capacity utilization ratio (defined as the ratio of average flow to design flow). Furthermore, industry practitioners should consider these factors when aiming to reduce the environmental impacts of a small WWTP related to construction. Scenario sensitivity analyses found that the environmental impact of construction became smaller with longer design life, and the end-of-life consideration does not heavily influence the environmental sustainability of a WWTP.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Heating and Cooling Energy Modeling of 3D-Printed Concrete Construction of Residential Buildings [Slides]

3D printed concrete construction (C3DP) is an emerging technology that comes with the associated benefits of high thermal mass walls. We investigated using BEopt software the heating and cooling energy use of a single story C3DP-constructed house. We then compared the heating/cooling energy use of the C3DP house against the corresponding energy use in a traditional timber (wood) frame construction (WFC), as well as a concrete masonry unit (CMU) construction. Both peak energy use for heating and cooling (Btu), as well as base energy use for both heating and cooling (MMBtu/yr) of the C3DP design were compared against the WFC and CMU baseline construction of identical geometries and orientations across all eight climate zones defined in the International Energy Conservation Code (IECC). The BEopt models for all three constructions (C3DP, WFC, and CMU) were built to comply with the 2018 IECC code. Results indicate that C3DP construction has significant heating and cooling energy benefits in certain climate zones, with the highest peak cooling energy savings (9% compared to WFC, and 5% compared to CMU) in the IECC Climate Zone 1 in the month of July. The peak heating and cooling energy demand reduction of C3DP were found to be more significant than the base heating and cooling energy demand in all IECC climate zones. The 2018 IECC compliance-related U-Factor adjustments of all models also resulted in more peak energy savings of the C3DP design in the cooling-dominated climate zones (climate zones 1 and 2), moderate energy savings in moderate climate zones (climate zones 3, 4, and 6), and little to no change in energy savings in very cold climates (climate zones 7 and 8).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗