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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Calorimetric metal vapor adsorption energies for characterizing industrial catalyst support materials

Many important catalysts and electro-catalysts for energy and environmental technologies involve late transition metal atoms and nanoparticles dispersed across the surface of high-surface-area support materials, for example, oxide nanoparticles. The surface reactivity and long-term stability against deactivation of these materials depends strongly on the strength of bonding of the metal atom and nanoparticles to the support surface. Here, we introduce a novel calorimetry method that implements a LiTaO 3 crystal for heat detection and extends metal adsorption calorimetry to surfaces of nanomaterials that are deposited from liquid solutions, as is typical when preparing industrial catalyst supports. This allows one to measure the strength of metal atom bonding and the adhesion energy of metal nanoparticles to clean surfaces of technologically relevant catalyst supports. We demonstrate that here by measuring the heat of Ag vapor adsorption onto the clean surfaces of high-surface-area TiO 2 anatase powdered support materials (i.e., 5-nm-diameter nanoparticles). These heats of adsorption show a pulse-to-pulse standard deviation of only 9.3 kJ/mol (3.3%) for pulses containing only 1% of a monolayer of Ag vapor, with potential for a 5-fold reduction in noise if used with another metal evaporator. The calorimeter was also proven to work well measuring heats of Ag adsorption on calcium niobate(001) nanosheets deposited as thin films from liquid solution. Here, this calorimeter opens up the possibility of using metal adsorption calorimetry to screen catalyst support materials for the strengths with which they bind metal atoms and metal nanoparticles, offering new opportunities to discover better support materials or for basic scientific study of structure / function relationships with respect to metal / support bond energies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

HPLC-Parallel accelerator and molecular mass spectrometry analysis of 14 C-labeled amino acids

Accelerator mass spectrometry (AMS) is the method of choice for quantitation of low amounts of 14 C-labeled biomolecules. Despite exquisite sensitivity, an important limitation of AMS is its inability to provide structural information about the analyte. This limitation is not critical when the labeled compounds are well-characterized prior to AMS analysis. However, analyte identity is important in other experiments where, for example, a compound is metabolized and the structures of its metabolites are not known. We previously described a moving wire interface that enables direct AMS measurement of liquid sample in the form of discrete drops or HPLC eluent without the need for individual fraction collection, termed liquid sample-AMS (LS-AMS). Here, we now report the coupling of LS-AMS with a molecular mass spectrometer, providing parallel accelerator and molecular mass spectrometry (PAMMS) detection of analytes separated by liquid chromatography. The repeatability of the method was examined by performing repeated injections of 14 C-labeled tryptophan, and relative standard deviations of the 14 C peak areas were ≤10.57% after applying a normalization factor based on a standard. Five 14 C-labeled amino acids were separated and detected to provide simultaneous quantitative AMS and structural MS data, and AMS results were compared with solid sample-AMS (SS-AMS) data using Bland-Altman plots. To demonstrate the utility of the workflow, yeast cells were grown in a medium with 14 C-labeled tryptophan. The cell extracts were analyzed by PAMMS, and 14 C was detected in tryptophan and its metabolite kynurenine.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2023

Integrated system to reduce emissions from natural gas-fired reciprocating engines

Natural gas-fired reciprocating engines (NGFRE), which are naturally aspirated are used in the oil and gas industry for the production, storage, processing, and transmission of natural gas extracted from wells. These stationary, lean-burn engines exhibit increased combustion instability and higher emissions of methane (CH 4 ) and Volatile Organic Compounds (VOCs) at lower loads, resulting in restrictions on the operational envelope of such engines. Here, in this paper, some of the emission reduction technologies for NGFREs are reviewed. Also, an experimental investigation of an integrated system to reduce emissions and improve the combustion performance of an NGFRE is presented. The integrated system consists of an air management package and integrated sensors including an amperometric NOx/O 2 sensor, exhaust temperature thermocouple, pressure transducers and vibration sensors. Experiments were carried out using natural gas and natural gas/propane blends at different load steps, and combustion performance, as well as emissions, were analyzed. For natural gas fuel, the results show that the standard deviation of peak pressure and indicated thermal efficiency (ITE) improved by 67.4 psi and 4.2% respectively, at 40% load. Similarly, CH 4 and Nitrogen Oxides (NOx) emissions reduced considerably by 84% and 63% respectively. However, CO emissions increased from 8 ppm to 152 ppm. At 60% load, the ITE improved by 3.8% and the CH 4 emissions were reduced by 68%. The reduction in VOCs emissions was 63% at 40% load and 69% at 60% load. The findings of this research provide evidence of the effectiveness of the integrated air management system in improving the sustainability of NGFREs. In general, this technology can be implemented on air-assisted combustion applications to improve combustion performance and, consequently, reduce emissions.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Techno-economic and environmental assessment of solar-based electrical vehicles charging stations integrated with hydrogen production

With the growing interest in adopting both commercial and residential electric vehicles (EVs) utilizing green renewable energy, the techno-economic assessment of EV charging stations with solar energy is a critical aspect of the transition to sustainable transportation. However, battery storage capacity for variable solar energy production is becoming increasingly less cost-effective due to the higher Lithium price. Complementing solar energy production and battery storage, the potential of combining green hydrogen fuel energy integration with solar energy for EV charging stations can provide a sustainable alternative to reach carbon-free transportation. This research aims to assess the technical and economic viability of grid-connected Photovoltaic (PV)-based EV charging stations across Kentucky based on existing EV load profiles. The study considers different PV system sizes, locations, orientations, and the impact of net metering policies and electricity tariffs on the profitability of the PV systems. Further, using excess PV energy, Lithium-Ion batteries will be charged, which will be assessed considering the techno-economic and environmental impacts of green EV charging stations. To further enhance green renewable energy production, we also investigated the feasibility of on-grid PV-based EV charging stations with Lithium-Ion batteries charged during low tariff periods. In comparison with these scenarios, we studied the potential benefits of integrating green hydrogen energy production using excess solar energy with and without governmental incentives. Furthermore, we assess the environmental impact of these green EV charging stations across Kentucky. Our study indicates that EV stations across Kentucky have similar techno-economic feasibility with insignificant deviation in the levelized cost of electricity, payback period, and PV energy fraction with/without Li-Ion batteries. However, with green hydrogen production, the marginal H 2 production cost indicates the first scenario (on-grid PV energy charging) is the most significant, roughly 10 USD/Kg, which is similar to 1.5 times higher than the second (charging during low-tariff periods) and the third scenarios (power grid to satisfy the demand deficit - approximately 6.5 USD/Kg). From the environmental perspective, the second scenario would be a good option for either hybrid EV/H 2 or EV charging stations in Kentucky, factoring in the hydrogen production cost and the carbon footprint.

08 HYDROGEN↗
DOE OSTI2020

Non-local model for surface tension in fluid-fluid simulations

We propose a non-local model for surface tension obtained in the form of an integral of a molecular-force-like function with support 3.5e added to the Navier-Stokes momentum conservation equation. We demonstrate analytically and numerically that with the non-local model interfaces with a radius of curvature larger than the support length behave macroscopically and microscopically, otherwise. For static droplets, the pressure difference Pe,in - Pe,out satisfies the Young-Laplace law for droplet radius greater than 3.5e and other- wise deviates from the Young-Laplace law. The latter indicates that the surface tension in the proposed model decreases with decreasing radius of curvature, which agrees with molecular dynamics and experimental studies of nanodroplets. Using the non-local model we perform numerical simulations of droplets under dynamic conditions, including a rising droplet, a droplet in shear flow, and two colliding droplets in shear flow, and compare results with a standard Navier-Stokes model subject to the Young-Laplace boundary condition at the fluid-fluid interface implemented via the Conservative Level Set (CLS) method. We find good agreement with existing numerical methods and analytical results for a rising macroscopic droplet and a droplet in a shear flow. For colliding droplets in shear flow, the non- local model converges (with respect to the grid size) to the correct behavior, including sliding, coalescence, and merging and breaking of two droplets depending on the capillary number. In contrast, we find that the results of the CLS model are highly grid-size dependent.

Howard, Amanda A.↗
DOE OSTI2022

GenMod: A generative modeling approach for spectral representation of PDEs with random inputs

Here, we propose a method for quantifying uncertainty in high-dimensional PDE systems with random parameters, where the number of solution evaluations is small. Parametric PDE solutions are often approximated using a spectral decomposition based on polynomial chaos expansions. For the class of systems we consider (i.e., high dimensional with limited solution evaluations) the coefficients are given by an underdetermined linear system in a regression formulation. This implies additional assumptions, such as sparsity of the coefficient vector, are needed to approximate the solution. Here, we present an approach where we assume the coefficients are close to the range of a generative model that maps from a low to a high dimensional space of coefficients. Our approach is inspired be recent work examining how generative models can be used for compressed sensing in systems with random Gaussian measurement matrices. Using results from PDE theory on coefficient decay rates, we construct an explicit generative model that predicts the polynomial chaos coefficient magnitudes. The algorithm we developed to find the coefficients, which we call GenMod, is composed of two main steps. First, we predict the coefficient signs using Orthogonal Matching Pursuit. Then, we assume the coefficients are within a sparse deviation from the range of a sign-adjusted generative model. This allows us to find the coefficients by solving a nonconvex optimization problem, over the input space of the generative model and the space of sparse vectors. We obtain theoretical recovery results for a Lipschitz continuous generative model and for a more specific generative model, based on coefficient decay rate bounds. We examine three high-dimensional problems and show that, for all three examples, the generative model approach outperforms sparsity promoting methods at small sample sizes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

A hybrid Monte Carlo-deterministic second moment method with efficient variance reduction

In this work, we present a hybrid method that combines Monte Carlo with deterministic finite element methods to solve a linear Boltzmann transport equation. Our hybrid method runs orders of magnitude faster than Monte Carlo, without sacrificing accuracy, for a proxy problem from radiative transfer that contains both optically-thick and optically-thin material. We believe that this is the first demonstration of a hybrid Second Moment Method in more than one spatial dimension, the first to consider more than one material, and the first to use variance reduction. Our variance reduction approach arises from an asymptotic analysis in which we show that the magnitude of the scattering source grows without bound. We transform the problem to compute the deviation of the radiation intensity from isotropy. The magnitude of the source in the transformed problem is bounded, and the quality of the hybrid method solution is dramatically improved by a substantial reduction in the variance.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

The impact of feedstock size and composition on the hydrothermal growth of (U,Th)O 2

Bulk crystal growth of refractory oxides requires unique approaches, and the U x Th 1-x O 2 solid solution is no exception. Hydrothermal transport reactions of U 0.1 Th 0.9 O 2 onto ThO 2 seeds with feedstocks comprised of U 0.1 Th 0.9 O 2 or mixtures of UO 2 and ThO 2 with a nominal composition of U 0.1 Th 0.9 O 2 are investigated with µ-Raman spectroscopy and X-ray fluorescence. In each case, the trends in stoichiometry as a function of distance from the seed are analyzed and the deviations from the nominal stoichiometry discussed. When the feedstock is composed of mixed oxides, the particle surface area’s influence on the feedstock dissolution rate is the dominant factor and can produce U-rich stoichiometries as high as U 0.75 Th 0.25 O 2 . When composed solely of U 0.1 Th 0.9 O 2 , the total growth amount depends on the particle size, but the obtained stoichiometry is the product of differing solubilities. Although the U0.1Th0.9O 2 feedstock produces the most homogeneous growth, the ending growth of the smallest particle UO 2 /ThO 2 mixture yields the stoichiometry closest to U 0.1 Th 0.9 O 2 . How to obtain desired stoichiometries from mixtures by matching the particle size and solubilities is briefly discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Gas diffusion through variably-water-saturated zeolitic tuff: Implications for transport following a subsurface nuclear event

Noble gas transport through geologic media has important applications in the characterization of underground nuclear explosions (UNEs). Without accurate transport models, it is nearly impossible to distinguish between xenon signatures originating from civilian nuclear facilities and UNEs. Understanding xenon transport time through the earth is a key parameter for interpreting measured xenon isotopic ratios. One of the most challenging aspects of modeling gas transport time is accounting for the effect of variable water saturation of geological media. In this study, we utilize bench-scale laboratory experiments to characterize the diffusion of krypton, xenon, and sulfur hexafluoride (SF6) through intact zeolitic tuff under different saturations. Here, we demonstrate that the water in rock cores with low partial saturation dramatically affects xenon transport time compared to that of krypton and SF6 by blocking sites in zeolitic tuff that preferentially adsorb xenon. This leads to breakthrough trends that are strongly influenced by the degree of the rock saturation. Xenon is especially susceptible to this phenomenon, a finding that is crucial to incorporate in subsurface gas transport models used for nuclear event identification. We also find that the breakthrough of SF6 diverges significantly from that of noble gases within our system. When developing field scale models, it is important to understand how the behavior of xenon deviates from chemical tracers used in the field, such as SF6 (Carrigan et al., 1996). These new insights demonstrate the critical need to consider the interplay between rock saturation and fission product sorption during transport modeling, and the importance of evaluating specific interactions between geomedia and gases of interest, which may differ from geomedia interactions with chemical tracers.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

Pressureless sintering of lithium hydride

Lithium Hydride is a material of growing importance for addressing technological challenges related to nuclear fusion, long-term human space travel, and thermal energy storage. Pressureless sintering provides a straightforward, scalable approach to produce dense LiH parts of all sizes and shapes. Pressed LiH green compacts were sintered at heating rates from 2.5 to 20 °C/min to 650 °C, yielding densities up to 96 ± 1.4 %, with densification initiating at 500 °C. Here, a validated master sintering curve was constructed with a sintering apparent activation energy of 135 kJ/mol. X-ray diffraction and simultaneous thermal analysis revealed Li 2 O formation from 300 – 550 °C and decomposition of LiH into Li metal at 550 °C, each reflected as deviations in the master sintering curve. Computed tomography after thermal treatment to 550 °C showed the formation of corrosion products, and after thermal treatment to 650 °C LiH reduction to Li most significantly at exposed surfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Numerical simulation of vortex-induced vibration response of a single IEA 10-MW wind turbine blade

Three-dimensional simulation of vortex-induced vibration (VIV) of a single International Energy Agency (IEA) 10-MW reference wind turbine blade with a length of 97.325 m is performed using the ExaWind stack, an open-source suite of codes. This study aims to illustrate the spanwise VIV response characteristics and cross-validate the results with an existing commercial framework. Five near-body meshes and three time steps are selected for the convergence study. To improve computational efficiency, several VIV triggering methods are also compared to shorten the VIV development period. The ExaWind-based VIV simulation strategy for a single IEA 10-MW blade is determined. First, the modal shape is validated against published results. Then, spanwise VIV responses of four blade configurations under a fixed and varied incoming flow velocity are analyzed. Results show that the VIV response is dominated by the first edgewise (second overall) mode. Little first-mode contributions appear near the second-mode node, producing a pi phase jump, and a higher harmonics response occurs near the blade root. Rotational degrees of freedom are minor compared with translational motion. The response versus reduced velocity is analyzed, showing a two-branch behavior similar to that of VIV for a bluff cylinder. Across all tested cases, the dominant frequency remains locked to the natural frequency of the second mode with no observed desynchronization. A mild deviation is observed for the case of 90-degree pitch and 310-degree azimuth rotation near a reduced velocity of 6, which will be examined with additional cases in future work. These findings indicate that severe VIV responses can arise under specific configurations and flow conditions, thereby increasing the potential for VIV fatigue damage and requiring greater attention during operation.

17 WIND ENERGY↗
DOE OSTI2021

Comparison of fiber-optic distributed temperature sensing and high-sensitivity sensor spatial surveying of stream temperature

Measuring surface water temperature spatial variability is needed to estimate the interaction between surface water and groundwater, evaluate fish habitat and thermal inertia, and to estimate streamflow frequency and duration. Fiber optic distributed temperature sensing (FO-DTS) has been used in rivers and lakes, providing high-resolution and sensitive temperature monitoring over large temporal and spatial scales. However, in streams with cobbly or bedrock-lined streambeds and variable bathymetry, use of FO-DTS to measure temperature close to the surface water and groundwater interface can be challenging if even feasible. FO-DTS can also be costly, involve difficult installations, and require an advanced understanding of the technology, calibration, and data processing. In this study, we compared FO-DTS stream temperature survey results to an alternative temperature survey method employing a towed transect of high-resolution temperature loggers spaced at 1-m and transported in the stream along the study reach, to measure the spatial distribution of stream-water temperature in East Fork Poplar Creek near Oak Ridge, Tennessee, USA. We assessed the applicability and limitations of the two methods, and quantitatively compared in-situ temperature survey results measured simultaneously with each method. Regression results showed strong temporal and spatial correlation between the two methods. Differences were only elevated near the stream banks in areas that were coincident with correlation slope deviations from unity, which was attributed to shallower water and lower data density. Kriging standard errors were also low at channel center with minor increases near the stream banks. Furthermore, the results suggested that the array of the individual temperature sensors can provide a practical alternative to FO-DTS for thermal characterization of surface water, providing slightly lower spatial and temporal resolution, but with higher accuracy of temperature measurement, with greater simplicity, and with a broader range of conditions where it may be applied.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Characterizing the impact of finite matrix block size on conservative particle transport through three-dimensional fracture networks

Mass transfer of solutes between fractures and the surrounding rock matrix exerts a noticeable signature on the tail of travel time distributions. When the width of the matrix is assumed to be infinite and advective transport through the fracture is sufficiently fast, the tails of the travel time distributions exhibit a classically expected slope of ψ(t) ∝ t -3/2 . However, studies have yet to characterize how solute transfer between fractures via diffusion through finite matrix blocks influences the tail’s slope in three-dimensional fractured media. Here, in this study, we assess the impact of finite matrix block size on breakthrough curve shape at different spatio-temporal scales by con ducting particle tracking simulations in three-dimensional discrete fracture networks. We consider a variety of hydrodynamic and geostructural proper ties to determine their relative impact on the resulting travel time distributions. We observe that the impact of matrix diffusion through a finite block on travel time distributions is similar to that of an infinite matrix block when the fracture spacing is sufficiently large, matrix diffusion is relatively weak, or transport is considered at an early control plane distance. We observe that the converse of these conditions, results in deviations from the classical ψ(t) ∝ t -3/2 scaling. These results provide a first step toward developing a metric to assess when finite block size effects are expected to significantly influence transport.

58 GEOSCIENCES↗
DOE OSTI2025

Substituent size versus metal binding of inhibitors with variants of influenza endonuclease

The influenza virus causes a significant burden of illness each year. Although vaccination is the most effective method to prevent seasonal influenza infection, viral escape mechanisms make vaccine composition difficult to predict. Antivirals are crucial for decreasing rates of morbidity and mortality from influenza viral infection. The newest anti-influenza drugs target the RNA-dependent RNA polymerase acidic N-terminal (PA N ) endonuclease, a critical component of influenza viral replication machinery. This study examines the structure of inhibitors of PAN that utilize a hydroxypyridinone-based metal-binding pharmacophore (MBP). Specifically, this report explores how the size of substituent groups impacts the binding conformation and affinity of a series of compounds against both wild-type (WT) and resistance mutant strains, I38T and E23K. Co-crystal structures revealed that the distance between compounds and enzyme residue 38 was conserved to maintain strong interactions, resulting in deviations from ideal coordination geometries at the active site metal centers. This suggests the interactions with residue 38 with each compound is important and can impact inhibitor potency as a consequence of distortions in the metal binding geometry of the compounds.

Endonuclease↗
DOE OSTI2021

Sensitivity of additively manufactured AA7075 to variation in feedstock composition and print parameters

Here we developed a combined finite element and CALPHAD based model of the Laser Powder Bed Fusion (LPBF) process for AA7075 alloy that considers the effect of feedstock composition and print parameters. A single-pass of a laser on a layer of AA7075 alloy powder has been considered. Sensitivity of temperature evolution and melt pool geometry to variation in the stoichiometry of the feedstock powder and laser source characteristics have been studied. Our results indicate that deviation (up to 10%) of the feedstock composition from the AA7075 raises the maximum temperature and increases melt pool size. Excess Cu content shows the largest melt pool width and depth among all the cases. The peak temperature is higher than the standard feedstock composition in all cases, except when the Cu concentration is reduced. Increasing the scan power also results in a higher peak temperature and a larger melt pool size. Furthermore, the temperature's rise time increases by lowering the scan speed.

42 ENGINEERING↗
DOE OSTI2022

Physics-based modeling and information-theoretic sensor and settings selection for tool wear detection in precision machining

Precision machining of metals is an energy intensive process with applications and impacts across the manufacturing industry. The energy efficiency, product yield, and maintenance of the precision machine require a digital twin that can assist with prognostics and health management. In this report a physics-based model is developed and validated against face milling data, and then used for the timely and precise inference of machining faults that cannot be measured directly. Computer numerical control (CNC) measurements of power and force are used through this physics-based machining model to predict deviations of the outputs of power consumption and cutting forces during normal operation. A model-based fault detection and isolation methodology is applied to determine the optimal (traditional and available) sensor suite and the test settings (admissible input values) that improve the inference of tool wear in face milling. The optimal sensor suite and input test settings are obtained by solving a mixed integer non-linear program that optimizes information-theoretic metrics relevant to the detection and isolation of tool wear from steady-state or transient machining measurements. Dynamic time warping and k—NN classification are then used to validate the robustness of the optimal design for fault detection test design, including the optimal sensor suite.

42 ENGINEERING↗
DOE OSTI2025

Characterization and prediction of the electromechanical wear of contact tips during wire arc additive manufacturing of 316L stainless steel

Here, this study seeks to better understand the degradation of the contact tip with respect to WAAM for a 316L wire electrode as well as explore methods of monitoring the contact tip state from process data. The contact tip, a consumable component, positions the wire and serves as the electrical contact surface between the wire electrode and the welding power supply. The wear of the contact tip was characterized in terms of material loss and material contamination for a set of tips worn to discrete levels as measured by the amount of wire fed or arc time. Geometrical characterization found a 49% increase in the bore exit area at 180 meters of wire fed. Machine learning models were developed to predict the relative bore exit area of the contact tip from arc-based process data and a random forest classifier exhibited favorable performance with a cross-validated f1-score of 0.84. The regression architecture implemented a multi-layer perceptron with the ability to predict the relative exit area with an $R^2$ score of 0.75. Key features used in the prediction include the standard deviation of the voltage and the time between shorts.

Contact tip wear↗
DOE OSTI2025

Effect of threshold parameters on infrared segmentation methods for porosity detection in electron beam powder bed fusion

In-situ process monitoring has seen significant interest in additive manufacturing to address qualification and certification goals. This is especially prevalent in metal powder bed fusion processes such as electron beam powder bed fusion (PBF-EB), with layer-wise infrared imaging being commonly used to detect defects. Here, this work compares two different segmentation methods (static thresholding and statistical thresholding) used for detecting porosity from in-situ infrared imaging data for PBF-EB. Samples were manufactured at a variety of focus offset values to induce porosity. Then, the segmented infrared images were compared to ex-situ X-ray computed tomography scans, which served as a ground-truth reference for objective evaluation. Through this analysis framework, the influential parameters, static threshold and N-value (number of standard deviations above the mean pixel value), respectively, for both image segmentation methods were analyzed and compared for their effects on porosity detection. With optimal parameter settings, the two methods had similar porosity detection performance, but the statistical method performed better under a larger variety of parameter settings.

Infrared imaging↗