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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

In-situ qualification and physics-based process design for aerosol jet printing via spatially correlated light scattering measurements

Aerosol jet printing is a contactless, digital, and additive technique broadly used for manufacturing flexible, hybrid, and conformal electronics. However, batch-to-batch variability has hindered widespread industry adoption and scaling to production volumes. Recently, light scattering measurements have emerged as a tool to measure aerosol volume fraction – a key parameter determining deposition rate – and have proven an effective feedback source for closed loop control on timescales ranging from minutes to hours. Here, the efficacy of light scattering measurements as a tool for in-situ qualification over shorter time durations is explored. To be the linear relationship between light scattering measurements and deposition rate was validated at 500 ms time intervals, allowing deposition rate to be mapped to positional coordinates and providing a new data stream to drive quality control assessments. Because light scattering measurements are indicative of a physical process parameter, they were substituted into equations for resistance and sheet resistance, resulting in predictions nominally within 10% of measured values for sets of printed devices. Finally, to highlight a more active utility, light scattering measurements were employed in a print repair framework, successfully repairing prints with randomly induced defects to within 5% of the measured resistance of a control set.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Static versioning in the polyhedral model

An approach is presented to enhancing the optimization process in a polyhedral compiler by introducing compile-time versioning, i.e., the production of several versions of optimized code under varying assumptions on its run-time parameters. We illustrate this process by enabling versioning in the polyhedral processor placement pass. We propose an efficient code generation method and validate that versioning can be useful in a polyhedral compiler by performing benchmarking on a small set of deep learning layers defined for dynamically-sized tensors.

Meister, Benoit J.↗
DOE OSTI2024

Resonant inelastic x-ray scattering in the topological semimetal FeSi

The energy spectrum of topological semimetals contains protected degeneracies in reciprocal space that correspond to Weyl, Dirac, or multifold fermionic states. To exploit the unconventional properties of these states, one has to access the electronic structure of the three-dimensional bulk. In this paper, we present a joint theory-experiment study of the electronic structure of a candidate topological semimetal with resonant inelastic x-ray scattering (RIXS). We resolve the bulk electronic states of FeSi using momentum-dependent RIXS at the Fe L 3 edge. We observe a broad excitation continuum devoid of sharp features, consistent with particle-hole scattering in an underlying electronic band structure. Using density functional theory (DFT), we calculate the electronic structure of FeSi and derive a band theory formulation of RIXS in the fast collision approximation to model the scattering process with zero adjustable parameters. While band theory predicts an excitation continuum with broad spectral features similar to the observed ones, discrepancies between theory and experiment suggest the presence of low-energy processes that DFT alone does not account for. Further, this study of RIXS in a topological semimetal shows that RIXS is a useful tool for revealing unanticipated behavior of bulk electronic states in this class of materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

In Operando Monitoring of Dynamic Recovery in 3D-Printed Thermoset Nanocomposites by XPCS

Extrusion-based additive manufacturing methods, such as direct-write of carbon fiber-reinforced epoxy inks, have become an attractive route toward development of structural composites in recent years, because of emerging techniques such as big area additive manufacturing. The development of improved materials for these methods has been a major focus area; however, an understanding of the effects of the printing process on the structural and dynamic recovery in printed materials remains largely unexplored. Here, our goal is to capture multiscale and temporal morphology and dynamics within thermosetting composite inks to determine the parameters during the printing process that influence the recovery of the printed material. Herein, we use X-ray photon correlation spectroscopy in small-angle scattering geometry to reveal both morphology and recovery dynamics of a nanoparticle (layered-silicate Cloisite 30B) in a thermoset epoxy resin (EPON 826) during the printing process in real time. Our results show that the dynamics of the layered silicate particles during recovery are anisotropic and slow down to behavior which is characteristic of aging in colloidal clay suspensions around tage ≈ 12 s. The dynamics and alignment of the particles during recovery were tempo-spatially mapped, and the recovery post printing was shown to be strongly influenced by the deposition onto the build plate in addition to the extrusion through the print head. Finally, our in operando results provide insight into the parameters that must be considered when optimizing materials and methods for precisely tailored local properties during 3D printing.

36 MATERIALS SCIENCE↗
DOE OSTI2018

A comparative research on bobbin tool and conventional friction stir welding of Al-Mg-Si alloy plates

Highlights: • 6061-T4 Al plates were successfully welded by both BT-FSW and C-FSW processes. • A wide welding parameter window was achieved with bobbin tool. • BT-FSW joints showed good strength similar to that of C-FSW joints. • Proving that an Isothermal Dissolution Layer Theory also apply to BT-FSW - Abstract: In our work, Al-Mg-Si (6061Al -T4) plates of 6 mm thickness were subjected to bobbin tool friction stir welding (BT-FSW) and conventional friction stir welding (C-FSW) for comparison. How the welding process affects the microstructure and mechanical properties was investigated at various rotation rates and welding speeds. The results showed that butt FSW joints with high quality could be produced at the selected parameters. The joint strength rose with enhanced welding speed and was nearly independent of the rotation rate in both the welding processes. The strength of the joints produced with BT-FSW reached the same level as that of the C-FSW. The maximum joint strength of 229 MPa was 93% of the base material (BM), which is superior to the reported results of 60–80% for the T4 condition. The fracture position of most of the joints was in the heat-affected zone (HAZ), which is the lowest hardness zone.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The interplay between vapour, liquid, and solid phases in laser powder bed fusion

The capability of producing complex, high performance metal parts on demand has established laser powder bed fusion (LPBF) as a promising additive manufacturing technology, yet deeper understanding of the laser-material interaction is crucial to exploit the potential of the process. By simultaneous in-situ synchrotron x-ray and schlieren imaging, we probe directly the interconnected fluid dynamics of the vapour jet formed by the laser and the depression it produces in the melt pool. The combined imaging shows the formation of a stable plume over stable surface depressions, which becomes chaotic following transition to a full keyhole. We quantify process instability across several parameter sets by analysing keyhole and plume morphologies, and identify a previously unreported threshold of the energy input required for stable line scans. The effect of the powder layer and its impact on process stability is explored. These high-speed visualisations of the fluid mechanics governing LPBF enable us to identify unfavourable process dynamics associated with unwanted porosity, aiding the design of process windows at higher power and speed, and providing the potential for in-process monitoring of process stability.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A Sensitivity-informed, Process Model Abstraction Implementation in GoldSim - 19276

System-level Performance Assessment (PA) models rely on specific process-level models for some inputs, such as infiltration. For probabilistic PA, the value of the process model output depends on the values of input parameters that also vary in the PA model. The simplest conceptual solution is to draw values of the input parameters from their distributions, run the process level model at these values, and then use the output where it is needed in the PA model. However, this solution is unsatisfactory for multiple reasons: (1) the PA Model should be set up such that new runs can be made and distributions can change without having to go back and run the process-level model; (2) calling the process model from the system model is possible, but technical difficulties remain in coupling the two; and (3) there is no uncertainty in the process-level model results incorporated into the PA model. Therefore, it is desirable to develop a method that maps the values of PA Model inputs to process level model outputs, in real time within the PA Model set up and using as simple of a map as possible without losing key features of the relationship. This has been termed 'model abstraction' because information about process model runs is 'abstracted' to be used with the PA model. A simple 'nearest neighbor' abstraction approach is presented to overcome these problems, in which a sensitivity-informed distance metric is used to map a system-level model realization to the most appropriate process-level model realization. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Exploring the relationship between solvent-assisted ball milling, particle size, and sintering temperature in garnet-type solid electrolytes

Garnet-type solid electrolytes, such as Li 6.4 La 3 Zr 1.4 Ta 0.6 O 12 (LLZTO), are promising materials for solid-state batteries, but processing remains a challenge, in part due to the high sintering temperature required for densification. Additionally, this temperature can be lowered by decreasing the initial particle size via solvent-assisted ball milling, but the relationship between solvent choice, particle properties, sintering behavior, and ionic conductivity is not well understood. In this work, we systematically explore these parameters, showing that milling in commonly used protic solvents, such as alcohols, effectively decreases the particle size but results in lithium loss (through Li + /H + exchange) that leads to poor sintering. By contrast, milling in aprotic solvents with surfactant reduces the particle size to ~220 nm without lithium loss, enabling the fabrication of dense samples (5.1 g/cm 3 ) with good ionic conductivity (0.43 mS/cm at 25 °C) at a lower sintering temperature (1000 °C). We compare ionic conductivities and activation energies for samples prepared with different particle sizes and sintering temperatures and use multiphase-field simulations to identify the mass transport and microstructural mechanisms responsible for the observed sintering dependence on particle size. These results further clarify the relationship between processing parameters and performance and represent important progress toward overcoming fabrication challenges for these materials.

25 ENERGY STORAGE↗
DOE OSTI2018

BioPuSL: Material & Process Optimization

Stereolithography is an advantages bioprinting technique that can be used to develop 3 dimensional printed structures. In the field, prints of structures with cells seeded after the printing process has completed has shown to generate high cell viability [ Hang Lin]. Currently, a technique is needed to print with cells already in the resin so that the cells print inside the structures as the layers are curing. For this to occur, the resin and the parameters of the printing process need to print high-resolution structures while still able to achieve high cell viability. This technique can have significant impact many biological fields like tissue and drug application studies. In this study, various concentrations of Poly (ethylene glycol) diacrylate (PEGDa) and Gelatin Methacryloyl (GelMA) are used as prepolymers with the photoinitiator Lithium phenyl- 2,4,6-trimethylbenzoylphosphinate (LAP) at 1% weight concentration is used in the resin. The mechanical properties, biocompatibility, and impacts of the parameters are tested. From this study we were able to conclude that the amount of PEGDa makes a significant impact on storage modulus of the gel. Likewise, the various amounts of PEGDa, when printing, will affect the amount of polymerization that occurs. Therefore, if less than the needed amount of PEGDa is used when printing, the 3D structure will not fully form or form at all. Also, when studying various GelMa concentrations in resin, the study resulted in the amount of GelMa is directly proportional to cell variability. These results will help determine the concentration of PEGDa and GelMa that can be tested in future studies to ultimately print cells in 3D structures with high cell viability.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Aging power spectrum of membrane protein transport and other subordinated random walks

Single-particle tracking offers detailed information about the motion of molecules in complex environments such as those encountered in live cells, but the interpretation of experimental data is challenging. One of the most powerful tools in the characterization of random processes is the power spectral density. However, because anomalous diffusion processes in complex systems are usually not stationary, the traditional Wiener-Khinchin theorem for the analysis of power spectral densities is invalid. Here, we employ a recently developed tool named aging Wiener-Khinchin theorem to derive the power spectral density of fractional Brownian motion coexisting with a scale-free continuous time random walk, the two most typical anomalous diffusion processes. Using this analysis, we characterize the motion of voltage-gated sodium channels on the surface of hippocampal neurons. Our results show aging where the power spectral density can either increase or decrease with observation time depending on the specific parameters of both underlying processes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

BEYONDPLANCK XIII. Intensity foreground sampling, degeneracies, and priors

We present the intensity foreground algorithms and model employed within the BEYONDPLANCK analysis framework. The BEYONDPLANCK analysis is aimed at integrating component separation and instrumental parameter sampling within a global framework, leading to complete end-to-end error propagation in the Planck Low Frequency Instrument (LFI) data analysis. Given the scope of the BEYONDPLANCK analysis, a limited set of data is included in the component separation process, leading to foreground parameter degeneracies. In order to properly constrain the Galactic foreground parameters, we improve upon the previous Commander component separation implementation by adding a suite of algorithmic techniques. These algorithms are designed to improve the stability and computational efficiency for weakly constrained posterior distributions. These are: (1) joint foreground spectral parameter and amplitude sampling, building on ideas from MIRAMARE; (2) component-based monopole determination; (3) joint spectral parameter and monopole sampling; and (4) application of informative spatial priors for component amplitude maps. We find that the only spectral parameter with a significant signal-to-noise ratio using the current BEYONDPLANCK data set is the peak frequency of the anomalous microwave emission component, for which we find $ν$ p = 25.3 ± 0.5 GHz; all others must be constrained through external priors. Future works will be aimed at integrating many more data sets into this analysis, both map and time-ordered based, thereby gradually eliminating the currently observed degeneracies in a controlled manner with respect to both instrumental systematic effects and astrophysical degeneracies. When this happens, the simple LFI-oriented data model employed in the current work will need to be generalized to account for both a richer astrophysical model and additional instrumental effects. This work will be organized within the Open Science-based COSMOGLOBE community effort.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

CFD-Guided Evaluation of Spark-Assisted Gasoline Compression Ignition for Cold Idle Operation

A closed-cycle, three-dimensional (3D) computational fluid dynamics (CFD) analysis campaign was conducted to evaluate the performance of using spark plugs to assist gasoline compression ignition (GCI) combustion during cold idle operations. A conventional spark plug using single-sided J-strap design was put at a location on the cylinder head to facilitate spray-guided spark assistance. Ignition was modeled with an L-type energy distribution to depict the breakdown and the arc-to-glow phases during the energy discharge process. Several key design parameters were investigated, including injector clocking, number of nozzle holes, spray inclusion angle, number of fuel injections, fuel split ratio, and fuel injection timings. The study emphasized the region around the spark gap, focusing on flame kernel formation and development and local equivalence ratio distribution. Flame kernel development and the ignition process were found to correlate strongly with the fuel stratification and the flow velocity near the spark gap. The analysis results showed that the flame kernel development followed the direction of the local flow field. In addition, the local fuel stratification notably influenced early-stage flame kernel development due to varying injection spray patterns and the fuel injection strategies. Among these design parameters, the number of nozzle holes and fuel injection timing had the most significant effects on the engine combustion performance.

42 ENGINEERING↗
DOE OSTI2020

Sequential Bayesian Parameter Estimation of Stochastic Dynamic Load Models

In this paper we focus on the parameter estimation of dynamic load models with stochastic terms-in particular, load models where protection settings are uncertain, such as in aggregated air conditioning units. We show how the uncertainty in the aggregated protection characteristics can be formulated as a stochastic differential equation with process noise. We cast the parameter inversion within a Bayesian parameter estimation framework, and we present methods to include process noise. We demonstrate the benefits of considering stochasticity in the parameter estimation and the risks of ignoring it.

Bayesian Statistics↗
DOE OSTI2024

Machine Learning Driven Sensitivity Analysis of E3SM Land Model Parameters for Wetland Methane Emissions

Methane (CH 4 ) is globally the second most critical greenhouse gas after carbon dioxide, contributing to 16%–25% of the observed atmospheric warming. Wetlands are the primary natural source of methane emissions globally. However, wetland methane emission estimates from biogeochemistry models contain considerable uncertainty. One of the main sources of this uncertainty arises from the numerous uncertain model parameters within various physical, biological, and chemical processes that influence methane production, oxidation, and transport. Sensitivity Analysis (SA) can help identify critical parameters for methane emission and achieve reduced biases and uncertainties in future projections. This study performs SA for 19 selected parameters responsible for critical biogeochemical processes in the methane module of the Energy Exascale Earth System Model (E3SM) land model (ELM). The impact of these parameters on various CH 4 fluxes is examined at 14 FLUXNET- CH 4 sites with diverse vegetation types. Given the extensive number of model simulations needed for global variance-based SA, we employ a machine learning (ML) algorithm to emulate the complex behavior of ELM methane biogeochemistry. We found that parameters linked to CH 4 production and diffusion generally present the highest sensitivities despite apparent seasonal variation. Comparing simulated emissions from perturbed parameter sets against FLUXNET-CH 4 observations revealed that better performances can be achieved at each site compared to the default parameter values. This presents a scope for further improving simulated emissions using parameter calibration with advanced optimization techniques.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Data for Physiological Controls on Carbon Fluxes and Biomass Production in Miscanthus: Insights From a Process- Based Agroecosystem Model

Biomass crops serve as essential feedstocks for renewable energy and bioproducts and play a critical role in achieving lower emissions in the transportation sector. However, dedicated perennial biomass crops such as Miscanthus × giganteus (Miscanthus) remain underrepresented in process- based agroecosystem models, limiting robust evaluation of their economic and environmental performance. In this study, we developed a data- constrained representation of the sterile triploid Miscanthus (IL clone) within the process- based model ecosys, integrating global sensitivity analysis, ensemble simulation, and parameter calibration. Planting, harvesting, and fertilization practices consistent with field management were incorporated, and phenology was constrained using PhenoCam- derived Green Chromatic Coordinate (GCC) data. Using the Morris global sensitivity analysis method, we identified 11 key physiological parameters governing plant carbon, water, and nutrient relations, particularly processes associated with CO2 assimilation. We then conducted ensemble simulations by perturbing these parameters and calibrated the model against eddy covariance fluxes and field- measured biomass. Building on the calibrated operating state, parameter- response analyses show that different photosynthetic processes influence productivity in different ways. Protein allocation determines whether productivity increases toward a higher level, whereas electron transport capacity controls additional gains once protein allocation approaches saturation. These findings demonstrate that parameter importance depends on physiological context and on which photosynthetic processes remain limiting. Calibration and validation against observations show that ecosys can reliably reproduce carbon and water fluxes, as well as both above- and belowground biomass, with post- calibration GPP R2 improving from 0.67 to 0.95 during the calibration period and remaining high during validation (R2 = 0.95). These results provide a mechanistic foundation for regional simulations and sustainable bioenergy assessments.

Carbon↗
DOE OSTI2022

Microbiome assembly in thawing permafrost and its feedbacks to climate

Abstract The physical and chemical changes that accompany permafrost thaw directly influence the microbial communities that mediate the decomposition of formerly frozen organic matter, leading to uncertainty in permafrost–climate feedbacks. Although changes to microbial metabolism and community structure are documented following thaw, the generality of post‐thaw assembly patterns across permafrost soils of the world remains uncertain, limiting our ability to predict biogeochemistry and microbial community responses to climate change. Based on our review of the Arctic microbiome, permafrost microbiology, and community ecology, we propose that Assembly Theory provides a framework to better understand thaw‐mediated microbiome changes and the implications for community function and climate feedbacks. This framework posits that the prevalence of deterministic or stochastic processes indicates whether the community is well‐suited to thrive in changing environmental conditions. We predict that on a short timescale and following high‐disturbance thaw (e.g., thermokarst), stochasticity dominates post‐thaw microbiome assembly, suggesting that functional predictions will be aided by detailed information about the microbiome. At a longer timescale and lower‐intensity disturbance (e.g., active layer deepening), deterministic processes likely dominate, making environmental parameters sufficient for predicting function. We propose that the contribution of stochastic and deterministic processes to post‐thaw microbiome assembly depends on the characteristics of the thaw disturbance, as well as characteristics of the microbial community, such as the ecological and phylogenetic breadth of functional guilds, their functional redundancy, and biotic interactions. These propagate across space and time, potentially providing a means for predicting the microbial forcing of greenhouse gas feedbacks to global climate change.

54 ENVIRONMENTAL SCIENCES↗