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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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A model is formulated in order to simulate the development of a sheer layer between a flow of air and a flow of fuel drops in a carrier gas. A characteristic feature of this type of flow is the interaction between the drop and the large-scale vortices produced the shear layer.
LES of a circular jet laden with evaporating liquid drops are conducted to assess computational-drop modeling and three different SGS-flux models: the Scale Similarity model (SSC), using a constant coefficient calibrated on a temporal mixing layer DNS database, and dynamic-coefficient Gradient and Smagorinsky models.
Shapes and stability of rotating molten metal drops carrying net surface electric charges are experimentally investigated, and the feasibility of measureing surface tension based on drop rotation is examined.
Ground based microgravity facilities are an important proving ground for space experiments, ground-based research and space hardware risk mitigation. An overview of existing platforms will be discussed with an emphasis on drop tower capabilities. The potential for extension to partial gravity conditions will be discussed. Input will be solicited from attendees for their potential to use drop towers in the future and the need for enhanced capabilities (e.g. partial gravity)
A model describing external sheet combustion of a cluster of drops has been developed for clusters of binary-fuel drops.
A fundamental and very general model of fluid drop behavior at all pressures is presented and is shown to reproduce microgravity experimental data for heptane drops in nitrogen.
The differences between subcritical liquid drop and supercritical fluid drop behavior are discussed.
A model has been developed, which describes the evaporation, ignition, combustion of a drop cluster embedded in a large vortex. The purpose of this model is to simulate the behavior of drops in large coherent vortices produced in the shear layer of a jet.
We present results for the soft drop groomed jet radius R g at next-to-leading logarithmic accuracy. The radius of a groomed jet which corresponds to the angle between the two branches passing the soft drop criterion is one of the characteristic observables relevant for the precise understanding of groomed jet substructure. We establish a factorization formalism that allows for the resummation of all relevant large logarithms, which is based on demonstrating the all order equivalence to a jet veto in the region between the boundaries of the groomed and ungroomed jet. Non-global logarithms including clustering effects due to the Cambridge/Aachen algorithm are resummed to all orders using a suitable Monte Carlo algorithm. We perform numerical calculations and find a very good agreement with Pythia 8 simulations. We provide theoretical predictions for the LHC and RHIC.
We compute the soft-drop jet-mass distribution from pp collisions to NNLL accuracy while including nonperturbative corrections through a field-theory based formalism. Using these calculations, we assess the theoretical uncertainties on an αs precision measurement due to higher order perturbative effects, nonperturbative corrections, and PDF uncertainty. We identify which soft-drop parameters are well-suited for measuring αs, and find that higher-logarithmic resummation has a qualitatively important effect on the shape of the jet-mass distribution. We find that quark jets and gluon jets have similar sensitivity to αs, and emphasize that experimentally distinguishing quark and gluon jets is not required for an αs measurement. We conclude that measuring αs to the 10% level is feasible now, and with improvements in theory a 5% level measurement is possible. Getting down to the 1% level to be competitive with other state-of-the-art measurements will be challenging.
The apparent simplicity of the drop-weight apparatus for explosive sensitivity testing hides the reality that it is actually a complex integrated test of both ignition and growth of deflagration. Although the drop-weight test is undeniably a useful screening test for explosive properties, a misunderstanding of the technique’s limitations has blinded many researchers to its limited wider applicability. This monograph discusses how the test actually works, the significant engineering difficulties with standardization between machines, which types of explosives are suited to the test and which are not, and finally offers a few suggestions for alternatives when a more quantified understanding of a material’s response is required for other applications.
The future flexible sensor technology will require low-temperature, fast processing 3D printing techniques that will rely heavily on the quality of nanoparticle (NP) Ink. Electrical conductivity has been found to decrease in majority of the 3D printed electronic wires. Herein we report a fundamental understanding in drop of electrical conductivity in processed silver (Ag) line wire, generally found in Ag-nanoparticle Ink, using large-scale atomistic molecular dynamics (MD) simulations of sintering of five different sizes of Ag NPs. To preserve the high conductivity of pure silver wires, the integrity of the pristine face-centered cubic (FCC) crystalline structure must be retained in the processed line wires. Simulations show that the pristine Ag FCC structures of the nanoparticles are not recovered after melting and resolidification, instead, the resolidified material is paracrystaline. The breakdown of pristine FCC structures might be the cause of the drop in conductivity of processed Ag wires. Simulation results suggest that the intermediate size nanoparticles retain highest percentage of the pristine silver face-centered cubic (FCC) structure after pulse treatments. Our results show that the most promising Ag-Ink should contain smaller to medium size silver NPs that can retain FCC structure after 3D printing of the Ag-Ink.
Current sustainable aviation fuels (SAFs) require blending with petroleum-derived fuels due to incomplete hydrocarbon distributions, most notably a lack of aromatics. Lignin, the most abundant renewable source of aromatics, is a promising feedstock for addressing this limitation. Here, we demonstrate a sequential reductive catalytic fractionation and continuous hydrodeoxygenation process that converts multiple woody feedstocks into aromatic hydrocarbons at up to 93% of the theoretical carbon yield. Blending these products with commercial SAFs produces drop-in compatible fuels with elastomer swell performances equivalent to conventional aviation fuels. The process is adaptable across multiple biomass sources, yielding aromatic hydrocarbons with consistent enthalpic efficiencies and fuel properties. These findings establish a scalable route to 100% drop-in SAFs, leveraging lignin-derived aromatics within the existing biofuels infrastructure.
Mechanisms for surface pattern formation from evaporation of a reactive nanofluid sessile drop are not well understood. In contrast to the coffee-ring effect from inert particles, rapid chemical and morphological transformation of reactive nanoparticles upon rapid evaporative drying are challenging to probe experimentally. Here, using grazing-incidence X-ray surface scattering, the nanostructure of nascent surface patterns has been probed as a ZnO nanofluid sessile drop rapidly dries. The high temporal resolution enabled by the high flux of synchrotron X-rays allows the observation of the emergence of Zn(OH) 2 surface crystals from the onset of evaporation and their rapid evolution into the final residual surface pattern, via transient layered complexes evident from the temporary appearance of X-ray diffraction peaks preceding Zn(OH) 2 formation. The results offer mechanistic insights of morphogenesis of surface patterns from evaporation-induced self-assembly and self-organization of reactive nanofluids, previously untenable using other experimental methods.
This study examines how different structural choices in bulk microphysics schemes impact the simulation of warm rain initiation. A single liquid category (SLC) approach prognosing up to four moments of a single drop size distribution (DSD) is compared to the traditional two-category, two-moment approach with separate DSDs for cloud and rain (four total prognostic variables). Different methods for calculating tendencies of the prognostic variables from drop collision-coalescence are also tested: a discretized numerical-integration approach, machine learning via neural networks, lookup tables, and traditional power law fits. Relative to simulations using a bin microphysics model, SLC gives smaller error overall than the two-category approach when numerical integration is used to calculate the collision-coalescence tendencies for both. Replacing the numerical integration with a pre-computed lookup table reduces computational cost with little loss of accuracy. However, using fitted power laws with SLC to represent the collision-coalescence tendencies substantially reduces accuracy and leads to an order of magnitude increase in error. It is also demonstrated that with SLC, reasonably accurate solutions are obtained using only three prognostic moments, while a two-moment SLC scheme leads to substantial error. Overall, both the choice of prognostic moments (e.g., SLC vs. two-category) and method to calculate the collision-coalescence tendencies are important to consider for minimizing errors in bulk schemes. SLC with a sufficiently detailed calculation of the collision-coalescence tendencies provides accurate solutions for a reasonable computational cost, providing a viable alternative to the traditional two-category, two-moment approach for bulk microphysics.
Microbe-mineral interactions are ubiquitous and can facilitate major biogeochemical reactions that drive dynamic Earth processes such as rock formation. One example is microbially induced calcium carbonate precipitation (MICP) in which microbial activity leads to the formation of calcium carbonate precipitates. A majority of MICP studies have been conducted at the mesoscale but fundamental questions persist regarding the mechanisms of cell encapsulation and mineral polymorphism. Here, we are the first to investigate and characterize precipitates on the microscale formed by MICP starting from single ureolytic E. coli MJK2 cells in 25 µm diameter drops. Mineral precipitation was observed over time and cells surrounded by calcium carbonate precipitates were observed under hydrated conditions. Using Raman microspectroscopy, amorphous calcium carbonate (ACC) was observed first in the drops, followed by vaterite formation. ACC and vaterite remained stable for up to 4 days, possibly due to the presence of organics. The vaterite precipitates exhibited a dense interior structure with a grainy exterior when examined using electron microscopy. Autofluorescence of these precipitates was observed possibly indicating the development of a calcite phase. The developed approach provides an avenue for future investigations surrounding fundamental processes such as precipitate nucleation on bacteria, microbe-mineral interactions, and polymorph transitions.
Spatial proteomics holds great promise for revealing tissue heterogeneity in both physiological and pathological conditions. However, one significant limitation of most spatial proteomics workflows is the requirement of large sample amounts that blurs cell-type-specific or microstructure-specific information. In this study, we developed an improved sample preparation approach for spatial proteomics and integrated it with our previously-established laser capture microdissection (LCM) and microfluidics sample processing platform. Specifically, we developed a hanging drop (HD) method to improve the sample recovery by positioning a nanowell chip upside-down during protein extraction and tryptic digestion steps. Compared with the commonly-used sitting-drop method, the HD method keeps the tissue pixel away from the container surface, and thus improves the accessibility of the extraction/digestion buffer to the tissue sample. The HD method can increase the MS signal by 7 fold, leading to a 66% increase in the number of identified proteins. An average of 721, 1489, and 2521 proteins can be quantitatively profiled from laser-dissected 10 μm-thick mouse liver tissue pixels with areas of 0.0025, 0.01, and 0.04 mm 2 , respectively. The improved system was further validated in the study of cell-type-specific proteomes of mouse uterine tissues.
Packed beds play an important role in several engineering fields, with their applications in nuclear energy being driven by the development of next-generation reactors utilizing pebble fuel. The random nature of a packed pebble bed creates a flow field that is complex and difficult to predict. Porous media models are an attractive option for modeling pebble-bed reactors (PBRs), as they provide intermediate fidelity results and are computationally efficient. Porous media models, however, rely on the use of correlations to estimate the effect of complicated flow features on the pressure drop and heat transfer in the system. Existing correlations were developed to predict the average behavior of the bed, but they are inaccurate in the near-wall region where the presence of the wall affects the pebble packing. This work aims to investigate the accuracy of a porous media model using the Kerntechnischer Ausschuss (KTA) correlation, the most common pressure drop correlation for PBRs compared to the high-fidelity large eddy simulation (LES). A bed of 1568 pebbles is investigated at Reynolds numbers from 625 to 10 000. The bed is divided into five concentric subdomains to compare the average velocity, friction losses, and form losses between the porous media and LES codes. The comparison between the LES simulation and the KTA correlation revealed that the KTA correlation largely underpredicts the form losses in the near-wall region, leading to an overprediction of the velocity near the wall by nearly 30%. An investigation of the form losses across the range of Reynolds numbers in the LES results provided additional insight into how the KTA correlation may be improved to better predict these spatial effects in a pebble bed. These data suggest that the form coefficient near the wall must be increased by 48% while decreasing the form coefficient of the inner bulk region of the bed by 15%. The implementation of these improvements to the KTA correlation in a porous media model produced a radial velocity profile that saw significantly improved agreement with the LES results.