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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Graphene nanoplatelets reinforced cement as a solution to leaky wellbores reinforcing weak points in hydrated Portland cement with graphene nanoparticles improves mechanical and chemical durability of wellbore cements

In this work, to improve the performance of wellbore cement under subsurface environments, graphene nanoplatelets (GNPs) were added in various percentages to Class-H cement slurry. Microstructural characterization of cement slurries cured at 90 °C and 95% RH indicates that GNP modifies the microstructure of hydrated cement by reinforcing pore spaces. As a result, the mechanical properties, such as Young's modulus and axial peak stress, obtained from high temperature triaxial compression tests are significantly improved based on different percentages of added GNPs. Furthermore, the hydrated 1 × 2 inch GNP- Portland cement cores tested under simulated deep wellbore conditions of high-temperature and high pressure (HTHP), appear to have a ductile-like behavior, when compared to a typical brittle nature of Portland cement pastes. From our observations and published data on graphene resistance to fracture, GNP addition to cement is evidently enhancing the flexibility of cement. These improvements would reduce the risks associated with wellbore cement deterioration and a consequent leakage in fossil fuel production, geothermal energy production, CO 2 storage as well as long-term sealing materials in plugging and abandonment of all wellbores at the end of their service life.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A time-dependent chloride diffusion model for predicting initial corrosion time of reinforced concrete with slag addition

The effect of granulated blast furnace slag (GBFS) addition on the threshold chloride concentration (TCC) for rebar corrosion was investigated. A modified diffusion model, coupled with a time-dependent effective diffusion coefficient and surface chloride concentration, was proposed to predict the chloride profile. The corrosion initiation time was estimated based on the model predictions and the measured TCC. The results indicate that adding GBFS decreases the TCC by lowering the pH value of the pore solution. The evolution of corrosion potential and current density is found to obey a 3-parameter Weibull distribution. MIP tests show that adding GBFS contributes to refinement of pore structure by decreasing the fraction of large capillary pores. The time-dependent model exhibits good predictive strength and helps understand how GBFS addition delays the corrosion initiation by retarding the chloride diffusion, though a lower TCC is obtained.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Effects of polycarboxylate ether (PCE)-based superplasticizer on the dissolution and subsequent hydration of calcium oxide (CaO)

Calcium oxide (CaO) is an important construction material, used as expansive agent, alkaline activator, and repairing material for historical structures. In this study, the dissolution and subsequent hydration of CaO, with or without the polycarboxylate ether (PCE)-based superplasticizer, were investigated to elucidate the complex crystallization process of calcium hydroxide (Ca(OH){sub 2}). On contact with pure water, CaO particles began to hydrate within a few minutes, while releasing a significant amount of heat. Adding superplasticizer slowed such rapid heat evolution by forming a dormant period and significantly affected the crystallinity of the hydration product. The dormant period was controllable by the amount of added superplasticizer. Moreover, as a result of early nucleation of Ca(OH){sub 2}, the formation of 10–100-nm spherical nanoparticles was observed via an in-situ transmission electron microscope, which completely differed from the final crystal shape of the hydration product.

36 MATERIALS SCIENCE↗
DOE OSTI2025

An adaptive and stability-promoting layerwise training approach for sparse deep neural network architecture

This work presents a two-stage adaptive framework for progressively developing deep neural network (DNN) architectures that generalize well for a given training data set. In the first stage, a layerwise training approach is adopted where a new layer is added each time and trained independently by freezing parameters in the previous layers. We impose desirable structures on the DNN by employing manifold regularization, sparsity regularization, and physics-informed terms. We introduce a ε – δ – stability-promoting concept as a desirable property for a learning algorithm and show that employing manifold regularization yields a ε – δ stability-promoting algorithm. Further, we also derive the necessary conditions for the trainability of a newly added layer and investigate the training saturation problem. In the second stage of the algorithm (post-processing), a sequence of shallow networks is employed to extract information from the residual produced in the first stage, thereby improving the prediction accuracy. Numerical investigations on prototype regression and classification problems demonstrate that the proposed approach can outperform fully connected DNNs of the same size. Moreover, by equipping the physics-informed neural network (PINN) with the proposed adaptive architecture strategy to solve partial differential equations, we numerically show that adaptive PINNs not only are superior to standard PINNs but also produce interpretable hidden layers with provable stability. As a result, we also apply our architecture design strategy to solve inverse problems governed by elliptic partial differential equations.

42 ENGINEERING↗
DOE OSTI2020

Combustion of metal powder with dinitrogen tetroxide

In this work, we present analysis of a novel reactive material system that employs dinitrogen tetroxide (N 2 O 4 ) as a liquid oxidizer with metal powder fuels. The oxidizer was added to micron scale aluminum and zirconium powders by a remote injection system. When ignited with a high voltage spark, the mixtures were observed to possess reactivity comparable to nanocomposite reactive materials, with open-tube flame expansion velocities from 500 to 1400 m/s depending on fuel/oxidizer ratio and tube diameter. Temperatures were observed to range from 3000 to 3500 K as measured with gray body fits to 16-channel time-resolved pyrometer and time-integrated spectrometer data. These values were significantly below calculated adiabatic flame temperatures, which we attribute to local deviations from stoichiometry and kinetic/energetic limitations similar to those observed in studies of particles burning in high pressures gaseous oxidizers. Al/N 2 O 4 reactivity was found to be most likely limited by the vaporization of the metal from the particle surface and Zr/N 2 O 4 was limited by slower burning and complex interactions involving the solubility of nitrogen and oxygen in the molten Zr. We also discuss the potential for these materials to be used to create an “on/off” reactive material, since N2O4 can be added remotely and driven to evaporate via vacuum or purge of inert gases to return it to a safe condition.

36 MATERIALS SCIENCE↗
DOE OSTI2024

In-situ two-dimensional temperature measurements using x-ray fluorescence spectroscopy in laminar flames with high silica particle concentrations

X-ray fluorescence spectroscopy (XRF) was used to measure temperatures and study mixing, for the first time in silica particle synthesis flames. Hexamethyldisiloxane (HMDSO) and trimethylsilanol (TMSO) were the particle precursors. A multi-element diffusion burner was used to produce a flat methane flame, and the precursors, dilute in inert gas, were injected via a central jet. Krypton was the fluorescent medium at 3.2 % concentration by volume. Scans with Kr in the central flow and not in the main flow were made to assess the mixing effects between the central and main gas flows. When HMDSO or TMSO were added, a secondary diffusion flame formed between the jet and the main methane flame. The results revealed a dramatic change in the centerline temperature profile of the jet gases when HMDSO or TMSO were added. The main methane flame stoichiometry also affected the temperature profiles. The results show HMDSO and TMSO reactions are initiated in a lowtemperature and low-oxygen concentration region of the jet where thermal decomposition is not expected to be significant. Reaction of the particle precursors is therefore attributed to radical transport from the main methane flame. In the current work, particle number densities of up to 270 g/m 3 locally are estimated. Thus, the study also demonstrates the capability of the XRF technique for high spatial fidelity measurements in flames with high concentrations of condensed-phase particles, leveraging the attribute that the XRF signal is generally not impacted by condensed-phase interferences. In conclusion, the observations and data obtained in this study inform likely reaction pathways for this important class of siloxane compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Effect of excess Mg to control corrosion in molten MgCl 2 and KCl eutectic salt mixture

Structural alloys may experience corrosion when exposed to molten chloride salts due to selective dissolution of active alloying elements. One way to prevent this is to make the molten salt reducing. For the KCl + MgCl 2 eutectic salt mixture, pure Mg can be added to achieve this. However, Mg can form intermetallic compounds with nickel at high temperatures, which may cause alloy embrittlement. This work shows that an optimum level of excess Mg could be added to the molten salt which will prevent corrosion of alloys like 316 H, while not forming any detectable Ni-Mg intermetallic phases on Ni-rich alloy surfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Locally developed electronic conduction in a yttria stabilized zirconia (YSZ) electrolyte for durable solid oxide fuel cells

In the present work, anode supported solid oxide fuel cells (SOFCs) based on a YSZ single layer electrolyte with locally developed electronic conduction near the anode side is fabricated to improve the cell durability under negative voltage conditions. A ceria added Ni/YSZ anode functional layer is utilized and co-sintered with the YSZ electrolyte at 1460 °C. Cerium ions are diffused into a limited region of the YSZ layer (~6 μm), establishing a ceria doped YSZ region. We successfully demonstrate that this YSZ single layer cell can operate stably for ~100 h without anode/electrolyte interface delamination, even under a negative voltage. The embedded probe technique and theoretical calculation reveal that the internal oxygen partial pressure (near the anode) is substantially reduced (stabilized) by means of local electronic conduction of ceria doped YSZ. Thus, we propose an SOFC electrolyte design comprising a YSZ single layer with a ceria added anode functional layer (locally formed ceria doped YSZ in the electrolyte) as a protection method against fuel cell stack degradation caused by negative voltage operation.

25 ENERGY STORAGE↗
DOE OSTI2021

Towards robust autonomous impedance spectroscopy analysis: A calibrated hierarchical Bayesian approach for electrochemical impedance spectroscopy (EIS) inversion

Distribution-based analyses, such as the distribution of relaxation times (DRT) and the distribution of diffusion times (DDT), present model-free alternatives to equivalent circuit modeling for analysis of electrochemical impedance spectroscopy (EIS) data. However, reconstructing such distributions from noisy impedance data is an ill-posed problem that must be solved with specialized inversion algorithms, requiring careful control and tuning. Furthermore, most inversion algorithms developed to date can only solve problems of limited complexity. Herein, we present a new hierarchical Bayesian method for EIS inversion, leveraging efficient algorithms for optimization and Hamiltonian Monte Carlo (HMC) sampling to solve models of arbitrary complexity. We overcome the challenge of ad-hoc parameter tuning by encoding intrinsic characteristics of the DRT and DDT into flexible prior distributions and “pre-calibrating” the model to simulated data. This approach is versatile, highly robust to noise, and provides quantitative estimates of both the error structure of the data and the uncertainty in the recovered distributions. The model is validated with simulated data to demonstrate accurate recovery of the DRT and the DDT. The method also shows promise for simultaneous recovery of multiple distributions, raising the intriguing possibility of semi-autonomous EIS analysis and ad-hoc model construction. Finally, the practical utility of the method is illustrated with experimental data. Throughout, we draw comparisons to several recently published EIS inversion methodologies.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Application of a deep learning semantic segmentation model to helium bubbles and voids in nuclear materials

Imaging nanoscale radiation-induced defects using the transmission electron microscope (TEM) is a key factor in the successful implementation of materials for nuclear energy structural applications. Analyzing each defect in a TEM micrograph is currently a manual task. To identify the defects in a single image can take anywhere from 15 min to an hour and a project can require the analysis of anywhere from tens to ≥ 100 images. Here, we use artificial intelligence (AI) models to automate this task. For simplification, we evaluated images with only a single type of defect; helium bubbles. Additionally, we performed semantic segmentation of these helium bubble defects in electron microscopy images of irradiated FeCrAl alloys using a deep learning DefectSegNet model. This model, which was previously used to classify crystal defects, is inspired by the classic DenseNet and U-Net image segmentation models. It claims high spatial resolution, but has poor performance at object boundaries. Our paper improves the DefectSegNet model’s application by adding two new features. First, the DefectSegNet model is applied not only to perform calculation pixel-wise but also object (or feature) wise. Because object-wise metrics are directly relevant to our final goal of detecting bubbles, whereas pixel-wise classification is only an intermediate step, it is an important part of our study. Second, a distance map loss (DML) function has been added to increase its performance at object boundaries. It is crucial to accurately represent defects boundaries, especially bubbles, in order to track the bubble-induced swelling caused by irradiation. The boundary-focused DML function is also compared to other loss functions like Cross-entropy, Weighted Binary Cross Entropy (WBCE), Dice and Intersection over Union (IOU). Finally, by incorporating new features, we found a marked improvement on segmentation quality and better shape preservation at the boundaries and areas of the bubbles.

42 ENGINEERING↗
DOE OSTI2024

An OpenStreetMaps based tool to study the energy demand and emissions impact of electrification of medium and heavy-duty freight trucks

In this paper, we present the mathematical formulation of an OpenStreetMaps (OSM) based tool that compares the costs and emissions of long-haul medium and heavy-duty (M&HD) electric and diesel freight trucks, and determines the spatial distribution of added energy demand due to M&HD EVs. The optimization utilizes a combination of information on routes from OSM, utility rate design data across the United States, and freight volume data, to determine these values. In order to deal with the computational complexity of this problem, we formulate the problem as a convex optimization problem that is scalable to a large geographic area. In our analysis, we further evaluate various scenarios of utility rate design (energy charges) and EV penetration rate across different geographic regions and their impact on the operating cost and emissions of the freight trucks. Our approach determines the net emissions reduction benefits of freight electrification by considering the primary energy source in different regions. Such analysis will provide insights to policy makers in designing utility rates for electric vehicle supply equipment (EVSE) operators depending upon the specific geographic region and to electric utilities in deciding infrastructure upgrades based on the spatial distribution of the added energy demand of M&HD EVs. To showcase the results, a case study for the U.S. state of Texas is conducted.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Decontamination of urban surfaces contaminated with radioactive materials and consequent onsite recycling of the waste water

Enhancing rapid remediation strategies is paramount for recovery after a large-scale nuclear contamination event in an urban environment. Some current strategies recommend use of readily available equipment, materials, and facilities to expedite recovery. For example, applying pressurized water to contaminated surfaces may effectively remove radioactive contamination. In this study, a commercial power washer removes soluble forms of 152 Eu 3 + , 85 Sr 2 + , and 137 Cs + contamination from common porous building materials, and computer simulations characterize the recycling of the resultant contaminated wash water. Pressure washing the porous building materials under spray conditions typical with do-it-yourself units improved decontamination factors (DFs) for 152 Eu compared to low-pressure application of tap water (majority of two-tailed t-test p-values < 0.1), but pressure did not improve DFs for 137 Cs or 85 Sr. For both pressurized and low-pressure applications, adding potassium ions (K + ) to promote ion exchange reactions produced significantly higher DFs for tested radionuclides on asphalt, brick, and concrete. The resultant contaminated wash water can be processed through self-prepared chemical filtration beds of clay and sand. Modeled in a prior study, the beds yielded linear trends (R 2 > 0.98) in sensitivity analyses between most bed configuration variables and bed performance variables, permitting flexible ad-hoc bed design. The experimental and simulation results led to estimates of the remediation rate and waste generated after cleaning 250 m 2 of cesium-contaminated concrete from the combined deployment of a power washer and two different mobile treatment beds. Furthermore, the first treatment bed was designed to reduce treatment time and processed 1900 L of wash solution in 70 min using 880 kg of clay/sand infill material. Designed to reduce the solid waste generated, the second bed processed the same solution volume in 1040 min (17 h) using 170 kg of clay/sand infill material. The results of this analysis warrant further investigation of power washing with recycled salt solution as an effective rapid decontamination method with manageable waste.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Enhancing methane production of anaerobic digested sewage sludge by advanced wet oxidation & steam explosion pretreatment

Anaerobic digestion (AD) of sewage sludge is associated with lowered carbon conversion efficiency due to the recalcitrance of microbial cells in the Waste Activated Sludge (WAS) from the aeration basins with 50 % of the dry weight still being present after AD. The effect of Advanced Wet Oxidation & Steam Explosion (AWOEx) pretreatment on the methane yield at variable temperatures (165–200 °C), residence time (15–45 min) and oxygen dosage (1%–10% based on VS concentration) was tested in this study. Central composite design (JMP® Pro) was used to design 19 runs of AWOEx pretreatment of dewatered digested sewage sludge (DADSS) at different operating conditions. After pretreatment the DADSS was fed to 19 bioreactors operated in semi-continues mode at 37 °C. The highest average methane yields of 183 mL/g VS and 170 mL/g VS were found for the bioreactors receiving DADSS pretreated at 165 °C with a retention time of 15 min and 10 % O2 (Condition 1), and, 182.5 °C with a retention time of 15 min and 5.5 % O2 (Condition 2). This corresponds to an increase in the methane production of 156.2 % and 140.5 % compared to the methane production from the DADSS without pretreatment.

09 BIOMASS FUELS↗
DOE OSTI2025

Innovative use of CaO in combination with amino acid salt to convert CO 2 as CaCO 3 nanoparticles under mild pH and low temperature

The increasing demand for sustainable CO 2 management has driven the development of innovative methods that can convert point source CO 2 into value-added products. In this study, CaO in combination with amino acid salt was used to convert CO 2 into CaCO 3 nanoparticles. Different from the conventional method where CO 2 diffusion presents a major challenge in reacting with CaO to form CaCO 3 , amino acid salt solvent was applied to absorb CO 2 first and then rapidly reacted with CaO to form CaCO 3 nanoparticles (∼50 nm) at a low temperature (e.g., 60 °C). Our experiments showed that at a glycine (Gly)/NaOH ratio of 2:1 or 3:1, the solution pH values during the CO 2 absorption and conversion were about 8–9 at 60 °C, while at a ratio of 1:1, the solution pH values were about 9–11; without Gly, the solution pH values were about 12. Gly-optimized solvent substantially reduced corrosion risk to reactors. In addition, the use of amino acid (i.e., Gly) led to much smaller CaCO 3 particles, distinctly different chemical phases, and fundamentally different chemical reactions. Moreover, in the presence of Gly, the solution pH was completely reversed and the solution was regenerated for cyclic use when CaO was added. The solvent was recyclable and reusable, highlighting the cost-effectiveness and sustainability of this approach. The Gly-modulated CaCO 3 nanoparticles may have significant potential for industrial applications in the biomedicine, construction, plastics, and rubber industries.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Economic and Environmental Analysis to Evaluate the Potential Value of Co-Optima Diesel Bioblendstocks to Petroleum Refiners

The U.S. petroleum refining sector is undergoing a period of historic transformation, catalyzed by the decarbonization of the U.S. economy. Diesel-boiling-range bioblendstocks have gained traction, owing to their superior fuel properties and environmental performance as compared to traditional petroleum fuels. This work couples refinery linear programming models with life cycle assessment to quantify the potential economic and environmental benefits, and trade-offs, of blending diesel-boiling-range bioblendstocks at petroleum refineries. Linear programming models were developed in Aspen Process Industry Modeling Systems (PIMS) for three representative petroleum refinery configurations of differing complexity. Seven diesel-boiling-range bioblendstocks: 4-butoxyheptane, 5-ethyl-4-propylnonane, soy biodiesel, sludge hydrothermal liquefaction diesel, polyoxymethylene ethers, renewable diesel, and hexyl hexanoate, were investigated to identify key fuel properties that influence refineries' economics and to track the effect of adding bioblendstocks on refinery-wide cradle-to-gate greenhouse gases (GHG) emissions. These analyses considered blending levels from 10 to 30 vol% and fuel demand projections over the period 2040 to 2050. This analysis determines that bioblendstock sulfur content and cetane number are the primary fuel attributes with the potential to provide value to refiners. Life cycle assessment results indicate that the use of diesel-boiling-range bioblendstocks can reduce cradle-to-gate refinery GHG emissions by up to ~ 40 % relative to conventional refinery operations when considering carbon uptake in the supply chain of the bioblendstock. Refinery-wide marginal GHG abatement costs range from 120 to 3,600 USD2016/metric tons carbon dioxide equivalent avoided across the scenarios evaluated. Reducing the price of bioblendstocks is identified as a key to their adoption.

ADVANCED PROPULSION SYSTEMS,BIOMASS FUELS,ENERGY P↗
DOE OSTI2023

Bioleaching to produce clean loblolly pine for thermochemical conversion

Lignocellulosic biomass contains inorganic elements that could induce slagging, ash fouling, and emission of both corrosive and harmful gases during thermochemical conversion. Water leaching pretreatment can remove most of the water-soluble elements, while it has limited industrial applicability due to its ineffectiveness in removing water-insoluble elements. Bioleaching is an alternative pretreatment method which has not been fully studied. In this work, bioleaching by Aspergillus niger strains NRRL 2001, NRRL 3122, and NRRL 567 was conducted to pretreat loblolly pine biomass with added water. Further, the removal of inorganic elements (K, Ca, Mg, and S) by bioleaching with A. niger NRRL 2001 was compared with chemical leaching with citric acid, HCl, and NaOH. It was shown that the pH reduction during bioleaching greatly improved K and Mg leaching to the point comparable to acid leaching, whereas Ca was precipitated by oxalic acid produced by the fungus. Bioleaching was also conducted in manually separated needles and chips components of loblolly pine. Glucose was added at different levels to assist bioleaching. At higher glucose levels, gluconic acid and citric acid were produced during bioleaching, leading to the increase of K, Ca, and Mg removal rates by more than 35%, 180%, and 390%, respectively, from the feedstock as compared with water leaching. Overall, bioleaching greatly improved removal of K, Ca, Mg, but not S from loblolly pine feedstocks as compared with water leaching.

09 BIOMASS FUELS↗
DOE OSTI2023

Experimental results of embedded phase change material capsules for increasing the performance of a wrapped heat pump water heater

Herein, to enhance the capacity of heat pump water heaters (HPWHs), high energy density phase change material (PCM) was used in two experimentally tested HPWH prototypes. Preliminary findings showed that in the PCM, stored thermal potential energy could be effectively converted into sensible heat in the water over a slight temperature difference, with the goal of increasing the capacity of hot water above a desired cut-off temperature. This exchange of energy results in an increased amount of hot water necessary for mixing at the location utilized by the consumer. It was also found that for a wrapped HPWH, there was an average increase of 27.7% in the capacity when 7.6 L of novel PCM filled spherical capsules were added to a 22.7 L tank downstream of the HPWH. In the second design (Beta), PCM capsules were immersed into the tank with a cylindrical geometry, and the UEF was increased by 5.5% over the baseline HPWH test result. As for the Beta design, heat loss was reduced significantly compared to a typical HPWH and the Alpha design. Food-grade PCMs were selected as appropriate. The novel encapsulation design increased the effective conductivity inside the capsule by adding metal fins.

42 ENGINEERING↗
DOE OSTI2021

Bulk Storage of Hydrogen

The technical aspects and economics of bulk hydrogen storage in underground pipes, lined rock caverns (LRC) and salt caverns are analyzed. Hydrogen storage in underground pipes is more economical than in geological caverns for useable amounts <20-t-H2. However, because the pipe material is a major cost factor, the capital and operating costs for this storage method do not decrease appreciably with an increase in the amount of stored H2. Unlike underground pipes, the installed capital cost of salt caverns decreases appreciably from ~$95/kg-H2 at 100 t-H2 stored to <$19/kg-H2 at 3000 t-H2 stored. Over the same scale, the annual storage cost decreases from ~$17/kg-H2 to ~$3/kg-H2. Like salt caverns, the installed capital cost of lined rock caverns decreases from ~$160/kg-H2 at 100 t-H2 stored to <$44/kg-H2 at 3000 t-H2 stored. Storing >750-t useable H2 requires multiple caverns. The cost of salt caverns scales more favorably with size because the salt caverns are larger than lined rock caverns and need to be added at a slower rate as the storage capacity is increased.

Bulk storage of hydrogen↗