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At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Moisture Stability of Sulfide Solid-State Electrolytes

In this report we detail a comprehensive study on the moisture stability of sulfide solid-state electrolytes in dry room environments. Although sulfide SSEs have many favorable attributes, this class of materials suffers from poor stability with water. Sulfide SSEs react with water to form gaseous H 2 S and a variety of solid byproducts like Li 3 PO 4 and LiOH, which go on to increase the interfacial impedance of solid-state batteries. Lab-scale research typically utilizes gloveboxes with <1 ppm water, however, the large-scale manufacturing of Li-ion batteries occurs in –40°C dewpoint dry rooms with around 126 ppm water. Consequently, the moisture stability of sulfide SSEs must be addressed if the manufacture of solid-state batteries based on sulfide SSEs is to be scaled up. Here, we are the first to characterize the moisture stability of sulfide SSEs according to both H 2 S and the degradation of ionic conductivity at different moisture setpoints ranging from –76°C to –40°C dewpoint. A variety of different SSE compositions are studied; namely, (Li 2 S) 75 (P 2 S 5 ) 25 , (Li 2 S) 70 (P 2 S 5 ) 30 , (Li 2 O) 7 (Li 2 S) 68 (P 2 S 5 ) 25 , (Li 2 O) 7 (Li 2 S) 63 (P 2 S 5 ) 30 , and (Li 2 S) 75 (P 2 S 5 ) 25 + 20 mol% LiI. We find that moisture stability improves with 75 mol% Li2S modifier content and the introduction of a Li 2 O co-modifier. After a 30 min exposure in a –40°C dewpoint dry room environment we found that (Li 2 S) 75 (P 2 S 5 ) 25 + 20 mol% LiI powder generated 0.1 cc/g H 2 S and its ionic conductivity decreased by over 50%. However, when SSE powder was exposed as a slurry in a dodecane carrier the same SSE composition generated 0 cc/g H 2 S and its ionic conductivity only dropped by 14%. Our results show that sulfide SSEs have acceptable moisture stability when appropriately processed in a dry room environment.

25 ENERGY STORAGE↗
DOE OSTI2023

Extended Low Load Boiler Operation to Improve Performance and Economics of an Existing Coal Fired Power Plant (Final Report)

The overall goal is to improve the performance and economics of existing coal fired power plants by extending low load boiler operation to lower loads than is currently achievable. The objective of this program is to develop and validate sensor hardware and analytical algorithms to lower plant operating expenses (OPEX) for the currently operating pulverized coal utility boiler fleet. Coal fired utility boilers are increasingly under grid dispatch pressure. In some cases, the coal fired cost of generation is noncompetitive with respect to natural gas generation and subsidized renewable sources. To remain profitable and remain fully compliant with existing environmental regulations, the installed coal fired fleet must find technologies which allow it to move into a more flexible cyclic load dispatch model. Today the installed coal fired utility fleet must be cost of generation competitive, fully emissions compliant, and responsive to the variability inherent in renewable energy generation sources. In the Phase I of the project, GE Steam Power, Inc. (GE) performed modeling of different operating scenarios for low load operation using an existing full plant dynamic model developed for a 660MW steam power plant. Sensors and analytic algorithms to enable a stable and steady coal supply for low load pulverizer operation were identified and tested at the Pulverizer Development Facility (PDF) at GE’s Clean Energy Center in Bloomfield, Connecticut. Sensors and analytic algorithms to enable stable combustion for low load operation were identified and tested at the 15 MWth Industrial Scale Burner facility (ISBF) at GE’s Clean Energy Center. A concept was developed to test the sensors and control algorithms, down selected after testing, at a full-scale coal fired power plant. A budget estimate was then developed, and the concept was implemented at an existing utility power plant. The specific objectives of the experimental work were to: • Identify and select sensors and analytic algorithms for monitoring coal pulverizer operation at lower loads to provide stable operation and appropriate coal fineness at lower coal throughput; Identify and select sensors and analytic algorithms for a Boiler Flame Stability Monitor to better balance air and fuel at each burner. This enables a reduction in a coal boiler’s safe low load power level while maintaining stable flame characteristics; Develop a concept in Phase I for low load operation of a full-scale power plant and develop a budget estimate for testing and execute the test plan at an existing plant in Phase II; Validate the capability of the extended low load boiler system to extend the minimum load operating point in a safe and reliable manner on an existing full-scale utility boiler. At the completion of this experimental study, GE has developed a set of sensors and analytic algorithms, down selected after testing, that have the potential to enable safe low load operation of a utility boiler. GE has also identified a host site for testing these identified sensors and analytic algorithms. GE has generated a full set of deliverables that provide sufficient information to proceed with the next step of testing at a host site. This includes a potential host site and budget estimate for concept testing at host site. In the Phase II of the project, a series of field tests were completed to validate the extended low load boiler operation, which consisted of detailed engineering, installation, commissioning, and testing the additional sensors and analytics for the coal-fired combustion system on an existing full-scale utility boiler. The optimization work has been supported by the host plant and endorsed by their engineering and operation staff.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Stable isotopes contain substantial additive information about terrestrial carbon and water cycling

Abstract Stable isotope ratios of H ( δ 2 H ), O ( δ 18 O), and C ( δ 13 C) are linked to key biogeochemical processes of the water and carbon cycles; however, the degree to which isotope-associated processes are reflected in macroscale ecosystem flux observations remains unquantified. Here through formal information assessment, new measurements of δ 13 C of net ecosystem exchange ( NEE ) as well as δ 2 H and δ 18 O of latent heat ( LH ) fluxes across the United States National Ecological Observation Network (NEON) are used to determine conditions under which isotope measurements are informative of environmental exchanges. We find all three isotopic datasets individually contain comparable amounts of information about NEE and LH fluxes as wind speed observations. Such information from isotope measurements, however, is largely unique. Generally, δ 13 C provides more information about LH as aridity increases or mean annual precipitation decreases. δ 2 H provides more information about LH as temperatures or mean annual precipitation decreases, and also provides more information about NEE as temperatures decrease. Overall, we show that the stable isotope datasets collected by NEON contribute non-trivial amounts of new information about bulk environmental fluxes useful for interpreting biogeochemical and ecohydrological processes at landscape scales. However, the utility of this new information varies with environmental conditions at continental scales. This study provides an approach for quantifying the value adding non-traditional sensing approaches to environmental monitoring sites and the patterns identified here are expected to aid in modeling and data interpretation efforts focused on constraining carbon and water cycles’ mechanisms.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Experimental longitudinal emittance manipulation using laser-based photoionization in the Fermilab Linac

A series of simulations and beam studies were conducted at Fermilab’s linear accelerator to evaluate the effectiveness of longitudinal emittance control via laser-induced photoionization. While similar laser techniques have been employed at Fermilab to enhance injection and extraction efficiency into the Booster, the work presented here focuses on extending these methods to bunch-by-bunch manipulation. This approach utilizes fine-scale correction of the H- bunches’ longitudinal spatial distribution. In theory, loosely confined particles in longitudinal phase space contribute to emittance growth during acceleration. By selectively removing these outlying particles through laser scraping (H- + γ → H + e-), this growth can be reduced. This report presents experimental results from both symmetric and asymmetric longitudinal scraping of H⁻ bunches in the Fermilab linac, which were subsequently injected into Booster, and evaluates the broader applicability of this method for future high-intensity accelerator operations.

Landon, Parker [Boston U.; Fermilab]↗
DOE OSTI2020

Probabilistic evolution of stochastic dynamical systems: A meso-scale perspective

Stochastic dynamical systems arise naturally across nearly all areas of science and engineering. Typically, a dynamical system model is based on some prior knowledge about the underlying dynamics of interest in which probabilistic features are used to quantify and propagate uncertainties associated with the initial conditions, external excitations, etc. From a probabilistic modeling standing point, two broad classes of methods exist, i.e. macro-scale methods and micro-scale methods. Classically, macro-scale methods such as statistical moments-based strategies are usually too coarse to capture the multi-mode shape or tails of a non-Gaussian distribution. Micro-scale methods such as random samples-based approaches, on the other hand, become computationally very challenging in dealing with high-dimensional stochastic systems. In view of these potential limitations, a meso-scale scheme is proposed here that utilizes a meso-scale statistical structure to describe the dynamical evolution from a probabilistic perspective. The significance of this statistical structure is twofold. First, it can be tailored to any arbitrary random space. Second, it not only maintains the probability evolution around sample trajectories but also requires fewer meso-scale components than the micro-scale samples. To demonstrate the efficacy of the proposed meso-scale scheme, a set of examples of increasing complexity are provided. Connections to the benchmark stochastic models as conservative and Markov models along with practical implementation guidelines are presented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Direct numerical simulations of three-component Rayleigh–Taylor mixing and an improved model for multicomponent reacting mixtures

We present direct numerical simulations of a three-layer Rayleigh–Taylor instability (RTI) problem with a configuration based on the experiments of Suchandra & Ranjan ( J. Fluid Mech. , vol. 974, 2023, A35) and Jacobs & Dalziel ( J. Fluid Mech. , vol. 542, 2005, pp. 251–279). The problem consists of a layer of light fluid between two layers of heavy fluid with an Atwood number of 0.3. These simulations are first validated through comparison with available experimental data. The validated simulations are then utilized to analyse statistics in this three-component flow. First, length scales are examined utilizing spectra and two-point spatial correlations of velocity and species concentration fluctuations. Next, joint probability density functions (p.d.f.s) of species concentration are compared against several model p.d.f.s representing generalizations of the bivariate beta distribution. Notably, the joint p.d.f.s do not appear to be accurately described by a Dirichlet distribution, indicating the marginal distributions do not conform to a beta distribution. Finally, similarity of the present configuration to three-component mixing found in inertial confinement fusion (ICF) applications is exploited to develop and validate an improved model for the impact of multicomponent mixing on thermonuclear (TN) reaction rates. A single time instant from the present simulations is chosen for a TN burn calculation under the hypothetical assumption of ICF materials and temperatures. Total TN output from this second calculation is then compared against the prediction of the improved model. The new model is found to accurately predict TN reaction rates in both premixed and non-premixed configurations.

42 ENGINEERING↗
DOE OSTI2024

Improved digital construction binder solution utilizing calcium aluminate additive for Infrastructure Scale Additive Manufacturing

Oak Ridge National Laboratory (ORNL) worked with Kerneos Inc., a Division of Imerys USA, Inc. to develop an improved digital construction binder using calcium aluminate additives produced by Kerneos along with locally available concrete materials. When the two-stage (2K) binder formulation was added to commercially available Portland Limestone Cement and sand to form a digital construction mortar, its strength exceeded a number of commercially available digital construction binders both at early and later ages. At the same time, the developed formulation showed precise control of setting time after addition and showed very low shrinkage making it a strong, reliable, and easily customizable alternative to fixed proprietary digital construction binders.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Improved digital construction binder solution utilizing calcium aluminate additive for Infrastructure Scale Additive Manufacturing

Oak Ridge National Laboratory (ORNL) worked with Kerneos Inc., a Division of Imerys USA, Inc. to develop an improved digital construction binder using calcium aluminate additives produced by Kerneos along with locally available concrete materials. When the two-stage (2K) binder formulation was added to commercially available Portland Limestone Cement and sand to form a digital construction mortar, its strength exceeded a number of commercially available digital construction binders both at early and later ages. At the same time, the developed formulation showed precise control of setting time after addition and showed very low shrinkage making it a strong, reliable, and easily customizable alternative to fixed proprietary digital construction binders.

36 MATERIALS SCIENCE↗
NASA NTRS1985

Utilization of satellite data and regional scale numerical models in short range weather forecasting

Overwhelming evidence was developed in a number of studies of satellite data impact on numerical weather prediction that it is unrealistic to expect satellite temperature soundings to improve detailed regional numerical weather prediction. It is likely that satellite data over the United States would substantially impact mesoscale dynamical predictions if the effort were made to develop a composite moisture analysis system. The horizontal variability of moisture, most clearly depicited in images from satellite water vapor channels, would not be determined from conventional rawinsondes even if that network were increased by a doubling of both the number of sites and the time frequency.

Kreitzberg, C. W.↗
DOE OSTI2023

EVs@Scale Next-Gen Profiles - Fleet Utilization 2023

As U.S. fleet operators begin transitioning to electric vehicles (EVs), critical questions arise regarding how to manage this shift without disrupting fleet operations or placing undue stress on the electric grid. A major challenge for fleets is maintaining effective operational schedules while accommodating charging requirements, particularly with high-power charging (HPC) infrastructure, which presents grid stability concerns for utilities. Proposed solutions such as charging substations, megawatt charging systems (MCS), and smart charge management systems (SCMS) offer potential pathways forward, but their effectiveness depends on alignment with real-world fleet behavior and operational constraints. This report investigates the charging and utilization behavior of EV and EVSE fleets actively employing HPC technologies by conducting detailed case study analyses based on telematics data. A suite of predefined metrics—covering charging, routing, and other operational behaviors—is developed to evaluate the impact of fleet activities on grid infrastructure and identify opportunities for optimization. Results highlight variations in charging behavior across fleets, such as weekday versus weekend usage, diurnal charging trends, and the role of operational predictability in enabling SCMS effectiveness. While SCMS can help lower costs and improve energy efficiency for fleets with stable schedules, they may be insufficient for fleets with highly variable or long-haul operations, which may require more robust solutions like MCS. Visualization of aggregated hourly energy metrics reveals that while fleet behaviors are diverse, there are common temporal patterns that could inform infrastructure planning and energy management. These insights emphasize the need for fleet-specific charging strategies that minimize grid impact while supporting reliable fleet operations. Additionally, the report underscores the broader economic stakes of electrification, particularly in high-value markets such as freight, where misaligned transitions could stall EV adoption. By examining current EV and EVSE fleet deployments using predetermined standardized metrics, this study offers a foundation for developing technologies and operational frameworks that support scalable, grid-compatible electrification across a variety of fleet types while establishing a baseline understanding of operational behaviors. In doing so, we aim to ensure that future charging solutions reflect actual fleet needs and grid constraints—an essential step toward maintaining operational continuity and achieving a successful transition to electric fleet operations.

Charging↗
DOE OSTI2025

Self-Lubricating Bushing Testing for John Day Dam Ka plan Turbine Replacement

The goal of this testing program is to find suitable self-lubricated alternatives to replace the in use oil lubricated grooved-bronze bushing in Kaplan runner hubs that are currently being utilized at John Day Dam operated by the U.S. Army Corps of Engineers (USACE). Scaled bushing testing utilizing a Pacific Northwest National Laboratory designed, fabricated, and operated Bushing Test Stand under the direction and funding from USACE determined that self-lubricated bushings tested appear capable of outperforming the current oil-lubricated bronze bushings. Performance tests are focused on durability which includes cyclic load bearing properties and articulations under various loading scenarios. This testing was performed at nominally 1/5 th scale. A scaling sensitivity analysis determined that scaled testing results apply to prototypic size. This report shows the direct comparison between the current baseline bronze bushing to self-lubricated bushings from various manufacturers. Bushing data like coefficient of friction and bushing material loss (associated with bushing wear) was determined. Self-lubricated bushings tested include products manufactured specifically for hydropower applications by the following companies: Trelleborg, Kamatics, Anonymous, and Tenneco/Deva.

42 ENGINEERING↗
NASA NTRS1986

Sharply curved turn around duct flow predictions using spectral partitioning of the turbulent kinetic energy and a pressure modified wall law

Computational predictions of turbulent flow in sharply curved 180 degree turn around ducts are presented. The CNS2D computer code is used to solve the equations of motion for two-dimensional incompressible flows transformed to a nonorthogonal body-fitted coordinate system. This procedure incorporates the pressure velocity correction algorithm SIMPLE-C to iteratively solve a discretized form of the transformed equations. A multiple scale turbulence model based on simplified spectral partitioning is employed to obtain closure. Flow field predictions utilizing the multiple scale model are compared to features predicted by the traditional single scale k-epsilon model. Tuning parameter sensitivities of the multiple scale model applied to turn around duct flows are also determined. In addition, a wall function approach based on a wall law suitable for incompressible turbulent boundary layers under strong adverse pressure gradients is tested. Turn around duct flow characteristics utilizing this modified wall law are presented and compared to results based on a standard wall treatment.

Santi, L. Michael↗
DOE OSTI2022

Using soil library hyperspectral reflectance and machine learning to predict soil organic carbon: Assessing potential of airborne and spaceborne optical soil sensing

Soil organic carbon (SOC) is a key variable to determine soil functioning, ecosystem services, and global carbon cycles. Spectroscopy, particularly optical hyperspectral reflectance coupled with machine learning, can provide rapid, efficient, and cost-effective quantification of SOC. However, how to exploit soil hyperspectral reflectance to predict SOC concentration, and the potential performance of airborne and satellite data for predicting surface SOC at large scales remain relatively underknown. Here, this study utilized a continental-scale soil laboratory spectral library (37,540 full-pedon 350–2500 nm reflectance spectra with SOC concentration of 0–780 g·kg –1 across the US) to thoroughly evaluate seven machine learning algorithms including Partial-Least Squares Regression (PLSR), Random Forest (RF), K-Nearest Neighbors (KNN), Ridge, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) along with four preprocessed spectra, i.e. original, vector normalization, continuum removal, and first-order derivative, to quantify SOC concentration. Furthermore, by using the coupled soil-vegetation-atmosphere radiative transfer model, we simulated twelve airborne and spaceborne hyper/multi-spectral remote sensing data from surface bare soil laboratory spectra to evaluate their potential for estimating SOC concentration of surface bare soils. Results show that LSTM achieved best predictive performance of quantifying SOC concentration for the whole data sets (R 2 = 0.96, RMSE = 30.81 g·kg –1 ), mineral soils (SOC ≤ 120 g·kg –1 , R 2 = 0.71, RMSE = 10.60 g·kg –1 ), and organic soils (SOC > 120 g·kg –1 , R 2 = 0.78, RMSE = 62.31 g·kg –1 ). Spectral data preprocessing, particularly the first-order derivative, improved the performance of PLSR, RF, Ridge, KNN, and ANN, but not LSTM or CNN. We found that the SOC models of mineral and organic soils should be distinguished given their distinct spectral signatures. Finally, we identified that the shortwave infrared is vital for airborne and spaceborne hyperspectral sensors to monitor surface SOC. This study highlights the high accuracy of LSTM with hyperspectral/multispectral data to mitigate a certain level of noise (soil moisture <0.4 m 3 ·m –3 , green leaf area < 0.3 m 2 ·m –2 , plant residue <0.4 m 2 ·m –2 ) for quantifying surface SOC concentration. Forthcoming satellite hyperspectral missions like Surface Biology and Geology (SBG) have a high potential for future global soil carbon monitoring, while high-resolution satellite multispectral fusion data can be an alternative.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1974

Short forms of the Texas Social Behavior Inventory /TSBI/, an objective measure of self-esteem

Two short (16 item) forms of the Helmreich, Stapp, and Ervin (1974) Texas Social Behavior Inventory, a validated, objective measure of self-esteem or social competence are presented. Normative data and other statistics are described for males and females. Correlations between each short form and long (32-item) scale were .97. Factor analysis and part-whole correlations verified the similarity of the two forms. The utility of the scale in research is described.

Helmreich, R.↗
NASA NTRS1979

Full scale LANDSAT-D antenna pattern measurements

The design verification of the LANDSAT-D antenna subsystem is addressed. In particular, the analysis of the antenna radiation patterns utilizing a full scale mockup of the LANDSAT-D satellite is discussed. Test antennas included two S-Band shaped beam antennas, two S-Band omni unit radiators (to operate in array), a GPS antenna, an X-Band shaped beam antenna, and one S-Band high-gain parabolic antenna.

Source record↗