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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 91 records · Page 5

Sulphate resistance of low‐clinker engineered cementitious composites examined by MicroXRF imaging

Abstract Engineered cementitious composites (ECC) are a class of high‐performing fibre‐reinforced cementitious materials recognised for their increased ductility and durability compared to conventional cement‐based materials, owing to their autogenously controlled tight crack widths, even when subjected to high strains. To reduce ECC's environmental impact, this research examines the use of a low‐clinker binder − limestone‐calcined clay cement (LC3) − as an alternative to portland cement (PC), along with fly ash to further reduce the clinker proportion and the embodied CO 2 of the formulations. In conventional concrete, LC3 hydrates to a denser microstructure resulting from the synergistic reaction between limestone and calcined clay. At the lower water contents typical of ECC and with the presence of fly ash, the influence of the binder composition on the microstructure is difficult to anticipate. To examine the influence of these compositional variables on microstructure, permeability and durability, the sulphate resistance of LC3‐based ECC is explored. Specifically, the ECC‐LC3 blends are designed with high clinker replacement rate of 75% by mass of binder and contain either conventional fly ash or reclaimed fly ash at 50% by mass of binder. Expansion of ECC‐LC3 samples subjected to standard sodium sulphate test conditions was measured up to 12 months and the depth of penetration of sulphates into the ECC‐LC3 of varying compositions was quantified using micro‐X‐Ray Fluorescence (microXRF) imaging and modelling. The expansion results show that the ECC‐LC3 formulations performed better than the PC samples and can provide adequate resistance to external sulphate attack, even when reclaimed fly ashes are used in place of the conventional ash. In addition, the shallow penetration of sulphate into these cementitious composites demonstrates the low diffusion coefficients values that were determined using the quantitative data from MicroXRF imaging.

Microscopy↗

End-gas autoignition fraction and flame propagation rate in laser-ignited primary reference fuel mixtures at elevated temperature and pressure

Knock in spark-ignited (SI) engines is initiated by autoignition of the unburned gasses upstream of spark-ignited, propagating, turbulent premixed flames. Knock propensity of fuel/air mixtures is typically quantified using research octane number (RON), motor octane number (MON), or methane number (MN; for gaseous fuels), which are measured using single-cylinder, variable compression ratio engines. In this study, knock propensity of SI fuels was quantified via observations of end-gas autoignition (EGAI) in unburned gasses upstream of laser-ignited, premixed flames at elevated pressures and temperatures in a rapid compression machine. Stoichiometric primary reference fuel (PRF; n-heptane/isooctane) blends of varying reactivity (50 ≤ PRF ≤ 100) were ignited using an Nd:YAG laser over a range of temperatures and pressures, all in excess of 545 K and 16.1 bar. Laser ignition produced outwardly-propagating premixed flames. High-speed pressure measurements and schlieren images indicated the presence of EGAI. The fraction of the total heat release attributed to EGAI (i.e., EGAI fraction) varied with fuel reactivity (i.e., octane number) and the time-integrated temperature of the end-gas prior to ignition. Flame propagation rates, which were measured using schlieren images, were only weakly correlated with octane number but were affected by turbulence caused by variation in piston timing. Under conditions of low turbulence, measured flame propagation rates approached one-dimensional premixed laminar flame speed computations performed at the same conditions. Experiments were simulated with a three-dimensional CONVERGE™ model using reduced chemical kinetics (121 species, 538 reactions). The simulations accurately captured the measured flame propagation rates, as well as the variation in EGAI fraction with fuel reactivity and time-integrated end-gas temperature. The simulations also revealed low-temperature heat release as well as formaldehyde and hydrogen peroxide formation in the end-gas upstream of the propagating flame, which increased the temperature and degree of chain branching in the end-gas, ultimately leading to EGAI.

42 ENGINEERING↗

Experimental study of sprays with isooctane, hexane, ethanol and their binary mixtures under different flash boiling intensities

Here, this work investigated the behaviors of single-component and multi-component sprays under different flash boiling intensities. Isooctane, hexane, ethanol, isooctane-hexane, and isooctane-ethanol mixtures were used as test fuels. The pressure ratio of the ambient pressure over saturation pressure (R p ) was used as the indicator of the superheated degree. Macroscopic characteristics of the sprays under 10 different R p (1.1 to 0.14) were investigated using Diffused Backlight Imaging (DBI). Droplet size, which revealed the spray microscopic characteristics, was also investigated using Phase Doppler Anemometry (PDA). The results showed that ambient pressure reduction led to higher flash boiling intensity, significant spray morphology changes, and smaller droplet size, regardless of the fuel type and blend ratios. Spray collapse was observed for all sprays once R p dropped below 0.3. Spray collapse dramatically increased the liquid penetration length, expanded the near-field spray angle, and reduced the far-field angle for sprays with all the fuels. Fuel properties, such as the latent heat of vaporization, had significant impacts on droplet size distributions, thus altering the liquid penetration length and spray angles. Under similar R p , sprays with high ethanol content generally had larger droplet sizes compared with other fuel sprays due to the high latent heat of vaporization. The larger droplets caused the high ethanol content sprays to be more resistant to the plume trajectory change, resulting in a smaller far-field spray angle variations with changing R p . At R p of 0.28, sprays with the similar latent heat of vaporization have similar droplet sizes, and droplet sizes at different radial locations were also similar to each other.

42 ENGINEERING↗

Flame Characterization and Flashback Studies of Hydrogen-Enriched Low-Swirl Flames Using High-Speed OH-PLIF

Here, this paper reports the flashback mechanism observed in hydrogen-enriched flames stabilized in a low-swirl burner (LSB) at atmospheric temperature and pressure. The fundamentals of hydrogen-rich stable flames and spatiotemporal information of flashback phenomena were observed experimentally using a high-repetition-rate nanosecond (ns)-duration hydroxyl planar laser-induced fluorescence (OH-PLIF) diagnostic. Testing was conducted for methane and hydrogen (50–90%H 2 , by mole) blends in an optically accessible LSB premixer with measured swirl numbers varying from 0.43 to 0.49. The flashback propensity showed dependence on liftoff length, which was dependent on premixer velocity (𝑉), hydrogen content (𝑋 H 2 ), and equivalence ratio (𝜙) at constant temperature and pressure. High-speed OH-PLIF images revealed that lifted flames were first observed at low 𝜙/𝑋 H 2 conditions, which were changed to an M-shaped flame attached to the burner rim with an increase in 𝜙/𝑋 H 2 . A further increase in 𝜙/𝑋 H 2 triggered the flame flashback into the premixer. Flame 𝜙 at flashback (𝜙 FB ) showed an expected linearly increasing trend with increasing 𝑉 and decreasing 𝑋 H 2 , and the conclusions drawn aligned well with detailed liftoff length investigations. The 𝜙 FB increased with increasing perforated plate hole diameter and increasing swirler vane angle. The vane angle had little effect on flashback tendency at high premixer velocity.

combustion↗

Physical model and experimental validation of a high temperature proton exchange membrane electrochemical hydrogen pump cell for efficient single-stage extraction of low concentration hydrogen gas

There is interest in valorization of existing natural gas infrastructure to facilitate the co-transportation of hydrogen via blending of hydrogen gas initially at limited concentrations of 1–20 vol% H2 and to subsequently extract hydrogen at fuel cell quality standards (SAE J2719/ISO14687-2). High temperature proton exchange membrane electrochemical hydrogen pump (HT-PEM EHP) based on phosphoric acid doped polybenzimidazole (PA-PBI) exhibits good performance at elevated temperatures (>120 °C), which provides desirable tolerance to non-methane natural gas constituents that are problematic for lower temperature based EHP. To better understand the suitability of the HT-PEM EHP for such gas separation processes, a two-dimensional model of EHP based on PA-PBI was developed. The model is validated for several relevant operating conditions and across cells with differing amounts of phosphoric acid content in the electrodes. Operando micro x-ray computed tomography (CT) imaging of an HT-PEM EHP was used to further validate physical parameters and assumptions of the model. The impacts of pressure, relative humidity of the anode feed, and concentration of feed gases on separation performance are investigated. This study shows that a specific energy of separation of 5.1 kWh/kg H2 at a hydrogen recovery factor (HRF) of 50 vol% can be achieved in a single stage with the EHP, producing fuel cell quality hydrogen purity of 99.99 vol% H2 from a 2 vol% H2/CH4 feed blend, while pressurizing the product H2 at a pressure ratio of 1.3 relative to feed pressure.

Stansberry, John M↗

Incorporating a naphthalene diimide polymer into a fullerene electron-transport layer to improve the fracture energy of perovskite solar cells

By blending a naphthalene diimide polymer into C 60 , we made a solution-processed electron-transport layer (ETL) for perovskite solar cells with fracture energies of 1.25 J m −2 , over 3× higher than that of thermally evaporated C 60 . Fracture energies were measured in a double cantilever beam configuration, and fracture surface images showed a fracture location near the ETL/perovskite interface, indicating a toughening of the interface between the ETL and Ag. We show that this modification to the ETL has no adverse effect on solar cell performance, and highlight the additional benefit of reduced parasitic absorption; a finding relevant for tandem solar cells.

electron transport layer↗

Tutorial: Machine-Learning-Based CREASE-2D Analysis of 2D SAXS Profiles to Characterize Anisotropic Nanostructures in Soft Materials

We present a tutorial to guide users on how to extend the Computational Reverse Engineering Analysis of Scattering Experiments-2D (CREASE-2D) framework to interpret their experimental two-dimensional small-angle scattering (SAS) data from soft materials (e.g., polymers, peptide amphiphiles, biomolecular fibrils). Unlike most traditional SAS analysis approaches, which typically rely on azimuthally averaged onedimensional (1D) profiles, CREASE-2D utilizes the complete 2D scattering profile to reveal information about anisotropy in the structure. In past applications, CREASE has provided insights into complex structural features, including the cross-sectional shapes of assembled nanostructures and dispersity in these features, which are difficult to discern with existing analytical models. While (1D- ) CREASE has been applied to SANS and SAXS data, this tutorial shares the steps for implementing CREASE-2D using an example of a dipeptide solution system, for which we have SAXS data. We present details for these steps involved in using CREASE-2D to interpret SAXS profiles: how to preprocess SAXS data, define relevant structural features, generate three-dimensional real-space structures for specific values of these features, train a machine learning (ML) surrogate model to predict scattering profiles for given structural features, and optimize these features using genetic algorithms (GA). Then, we use these steps to interpret complex 2DSAXS data collected from dipeptide solutions that, in microscopy images, exhibit nanoscale structures that could be elliptical tubes/ flat tapes/cylinders or a combination of these cross sections. Open-source codes, computational hardware, and software requirements, as well as the strengths and limitations of this protocol, are also presented. We expect researchers working with (soft) biomaterials, peptide amphiphiles, amphiphilic polymer solutions, polymer nanocomposites, and blends of particles/polymers will find this CREASE-2D method and this tutorial of use.

CREASE↗

Experimental study of flash boiling spray with isooctane, hexane, ethanol and their binary mixtures

The main goal of this study is to understand the effect of fuel properties on flash boiling sprays using multi-hole injectors. Macroscopic characteristics were investigated using Diffused Backlight Imaging (DBI) and quantified with liquid penetration length and spray angles. Phase Doppler Anemometry (PDA) was applied to measure the droplet size and velocity. Behaviors of spray with three single-component fuels (isooctane, hexane, ethanol) under different ambient pressures (100 kPa, 80 kPa, 50 kPa, 22 kPa) as well as their binary mixtures under different blend ratios (10%, 30%, 85%) were reported and analyzed. The results showed that adding ethanol or hexane to isooctane could both promote flash boiling by increasing the overall vapor pressure thus superheated degree. Under mixing ratios of 10% and 30%, sprays with ethanol blends showed more severe flash boiling comparing with sprays with hexane blends. Shorter liquid penetration and better liquid dispersion have been observed. Comparing with isooctane spray, the droplet size continuously decreased with increasing hexane content due to enhanced breakup and faster vaporization. Sprays with ethanol mixtures showed a different trend. Noticeable droplet size reduction has been observed for spray with 10% ethanol content. However, droplet size did not further decrease with higher blend ratios regardless of higher flash boiling intensity. Such observations indicate severe vaporization inhibition caused by the high latent heat of vaporization of ethanol. For sprays with all test fuels, collapsing occurred when the pressure ratio of ambient pressure to saturation pressure (Rp) dropped below 0.3.

03 NATURAL GAS↗

MagAO Observations of the Binary Microlens OGLE-2014-BLG-1050 Prefer the Higher-mass Solution

We report adaptive optics (AO) follow-up imaging of OGLE-2014-BLG-1050, which is the second binary microlensing event with space-based parallax measurements. The degeneracy in microlens parallax π {sub E} led to two sets of solutions, either a ∼ (0.9, 0.35)M {sub ⊙} binary at ∼3.5 kpc or a ∼(0.2, 0.07)M {sub ⊙} binary at ∼1.1 kpc. We measure the flux blended with the microlensed source by conducting Magellan AO observations, and find that the blending is consistent with the predicted lens flux from the higher-mass solution. From the combination of the AO flux measurement together with previous lensing constraints, it is estimated that the lens system consists of a 1.05{sub −0.07}{sup +0.08} M{sub ⊙} primary and a 0.38{sub −0.06}{sup +0.07}M{sub ⊙} secondary at 3.43{sub −0.21}{sup +0.19} kpc.

47 OTHER INSTRUMENTATION↗

Automated Defect Identification for Tri-structural Isotropic Fuels (AUDIT)

During the manufacture of tri-structural isotropic (TRISO)-coated nuclear fuel particles, the potential exists for the formation of internal fissure defects in the uranium oxycarbide (UCO) kernels. These fissures result in a defective fuel particle that can fracture during subsequent fuel processing. Therefore, it is necessary to detect the presence of fissured kernels in a batch to determine if the batch meets specification prior to blending with other batches and upgrading processes. Previous attempts at identifying fissures involved manual inspection of micrographs of UCO fuel kernel cross-sections. This process is tedious, time-consuming and may introduce counting errors making it a good candidate for automation. This work presents a method for the automated detection of fissures in UCO kernels. Image segmentation is used for the extraction of relevant features in the micrographs which then serve as the input to a convolutional neural network used to automatically distinguish between fissured and non-fissured kernels.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Shrinkage-induced deformations and creep of structural concrete: 1-year measurements and numerical prediction

Highlights: • Extensive experimental study on drying shrinkage, creep and microcracking of concrete • All specimens prepared from a single batch of ordinary-strength structural concrete • 1st year of measurements of (not only) non-uniformly drying beams with span up to 3-m • The database is downloadable from free-to-use research data repository • Modified MPS model for concrete creep used in blind prediction of all experiments The material models for creep and shrinkage operating on the material point level in FEM are usually intended for challenging complex applications and structures, where the average cross-sectional approach does not suffice. The identification of the growing number of material parameters induced by increasing model capabilities relies on very specific and narrow-oriented yet interconnected experiments which are scarce. The presented comprehensive experiments aim to provide a clearer image of the complicated interaction among the basic phenomena: drying, shrinkage, creep, and microcracking. The cornerstone of this ongoing research is a unique set of 30 partially-sealed unreinforced concrete beams with span 1.75–3.0 m subjected to drying. To minimize material variation, all specimens in this study were cast from a single batch of ordinary strength structural concrete with slag-blended binder. The resulting experimental database will be suitable both for validation and development of the constitutive models.

36 MATERIALS SCIENCE↗

Vaporizable endoskeletal droplets via tunable interfacial melting transitions

Liquid emulsion droplet evaporation is of importance for various sensing and imaging applications. The liquid-to-gas phase transformation is typically triggered thermally or acoustically by low–boiling point liquids, or by inclusion of solid structures that pin the vapor/liquid contact line to facilitate heterogeneous nucleation. However, these approaches lack precise tunability in vaporization behavior. Here, we describe a previously unused approach to control vaporization behavior through an endoskeleton that can melt and blend into the liquid core to either enhance or disrupt cohesive intermolecular forces. This effect is demonstrated using perfluoropentane (C 5 F 12 ) droplets encapsulating a fluorocarbon (FC) or hydrocarbon (HC) endoskeleton. FC skeletons inhibit vaporization, whereas HC skeletons trigger vaporization near the rotator melting transition. Our findings highlight the importance of skeletal interfacial mixing for initiating droplet vaporization. Tuning molecular interactions between the endoskeleton and droplet phase is generalizable for achieving emulsion or other secondary phase transitions, in emulsions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A small-volume, high-throughput approach for surface tension and viscosity measurements of liquid fuels

An ongoing effort looking at bioblendstocks to function as drop-in replacements or blending components for gasoline has identified a large number of candidate fuels. This work documents an approach for rapid screening of candidate fuels using relatively small sample sizes ($\mathcal{O}(\mu\,{\rm l})$) that targets two key physical properties of liquid fuels—surface tension and viscosity. The approach utilizes shape oscillation dynamics of single droplets generated by a piezo-electric device and their decay over time. Strobed imaging of the oscillation process is used along with image processing, edge detection, and data analysis to capture the decay of the oscillation over time. The time constant of the decay process along with oscillation frequency are then used to estimate viscosity and surface tension using a theory for small amplitude droplet oscillations. Measurements are obtained for primary reference fuels (isooctane and n-heptane) as well as candidate fuels from four bio-derived functional groups of interest. Measurement results for surface tension and viscosity are correlated with literature data as well as measurements from standard reference instruments. The measurement results show that the droplet oscillation based approach is capable of reproducing surface tension and viscosity values for the tested fuels within deviations of 7% and 13% respectively from literature data. Results are obtained using an average of 5 µl per fuel within about 20 s, thus demonstrating a small-volume, high-throughput approach that can be used for screening of candidate bioblendstock fuels.

09 BIOMASS FUELS↗

TiNi-Based Bi-Metallic Shape-Memory Alloy by Laser-Directed Energy Deposition

In this study, laser-directed energy deposition was applied to build a Ti-rich ternary Ti–Ni–Cu shape-memory alloy onto a TiNi shape-memory alloy substrate to realize the joining of the multifunctional bi-metallic shape-memory alloy structure. The cost-effective Ti, Ni, and Cu elemental powder blend was used for raw materials. Various material characterization approaches were applied to reveal different material properties in two sections. The as-fabricated Ti–Ni–Cu alloy microstructure has the TiNi phase as the matrix with Ti2Ni secondary precipitates. The hardness shows no high values indicating that the major phase is not hard intermetallics. A bonding strength of 569.1 MPa was obtained by tensile testing, and digital image correlation reveals the different tensile responses of the two sections. Differential scanning calorimetry was used to measure the phase-transformation temperatures. The austenite finishing temperature of higher than 80 °C was measured for the Ti–Ni–Cu alloy section. For the TiNi substrate, the austenite finishing temperature was tested to be near 47 °C at the bottom and around 22 °C at the upper substrate region, which is due to the repeated laser scanning that acts as annealing on the substrate. Finally, the multiple shape-memory effect of two shape-memory alloy sides was tested and identified.

36 MATERIALS SCIENCE↗

Flame Flashback Investigations in Hydrogen-Enriched Low Swirl Flames using High-Speed Hydroxyl (OH) Planar Laser-Induced Fluorescence

This study reports flashback mechanism observed in hydrogen-enriched flames stabilized in a low swirl burner (LSB) at atmospheric pressure and temperature conditions. The fundamentals of hydrogen-rich stable flames and spatiotemporal investigation of flashback phenomena were observed experimentally using a high-repetition-rate nanosecond (ns)-duration hydroxyl radical planar laser-induced fluorescence (OH-PLIF) diagnostic. Testing was conducted in an optically accessible pre-mixing section of LSB with inlet pre-mixing velocities from 5 to 10 m/s for methane and hydrogen (50 – 90% by mole) blends. Swirlers with two different turning angles, 26 and 33 degrees and three different perforated plate hole diameters, 1.08, 1.12 and 1.16 mm with measured swirl numbers varying from 0.43 to 0.49 were used. The flashback propensity showed dependence on the proximity of the lifted flame to the burner exit which was dependent on pre-mixer velocity, hydrogen content and equivalence ratio (ER). High-speed OH-PLIF images revealed lifted flames were first observed at low ER condition which is changed to M-shaped flame attached to burner rim with the increase in ER. Then, further increase in ER triggers flame flashback into the nozzle. Flashback occurred when burning occurs in the outer shear layer and the leading flame brush ingresses into the nozzle.

Parajuli, Pradeep↗

Automated and High-Throughput Phase Separation Control for Supramolecular Polymer Blends Enabled by Machine Learning

Supramolecular polymer blends (SPBs) offer tunable morphologies that dictate their macroscopic properties, yet their rational design is limited by the absence of predictive structure−morphology models. Here, we introduce a data-driven highthroughput workflow that integrates modular polymer synthesis, robotic formulation, automated morphology characterization, and machine learning (ML) for accelerated SPB discovery. Using a plug-and-play synthetic strategy, 33 hydrogen-bonding endfunctional homopolymers were prepared and orthogonally combined to generate 260 SPBs in 1 day. A fully automated atomic force microscopy (AFM) pipeline enabled systematic imaging, producing 2340 morphology data sets with minimal human intervention. Domain spacings were extracted through complementary imageprocessing methods and used to train ML models. A support vector regression (SVR) model accurately predicted target phase-separation sizes (50, 100, and 150 nm), which were experimentally validated. This work demonstrates the power of coupling high-throughput experimentation with ML to accelerate morphology discovery and provides one of the first large-scale experimental data sets for supramolecular polymer systems.

ML-guided polymer design↗

DeepShadows: Separating low surface brightness galaxies from artifacts using deep learning

Searches for low-surface-brightness galaxies (LSBGs) in galaxy surveys are plagued by the presence of a large number of artifacts (e.g., objects blended in the diffuse light from stars and galaxies, Galactic cirrus, star-forming regions in the arms of spiral galaxies, etc.) that have to be rejected through time consuming visual inspection. In future surveys, which are expected to collect hundreds of petabytes of data and detect billions of objects, such an approach will not be feasible. We investigate the use of convolutional neural networks (CNNs) for the problem of separating LSBGs from artifacts in survey images. We take advantage of the fact that we have available a large number of labeled LSBGs and artifacts from the Dark Energy Survey, that we use to train, validate, and test a CNN model. That model, which we call DeepShadows , achieves a test accuracy of 92.0%, a significant improvement relative to feature-based machine learning models. We also study the ability to use transfer learning to adapt this model to classify objects from the deeper Hyper-Suprime-Cam survey, and we show that after the model is retrained on a very small sample from the new survey, it can reach an accuracy of 87.6%. Finally, these results demonstrate that CNNs offer a very promising path in the quest to study the low-surface-brightness universe.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗