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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Pulling thin single crystal silicon wafers from a melt: The new leading-edge solar substrate

The Floating Silicon Method (FSM) has been established as a viable, stable method for growing single crystal ribbons directly from a silicon melt. With intense helium jet cooling to drive the linear progress of a [111] facet, pulled in the <110> direction, ribbons in the 0.6 – 3.0 mm thickness range can be grown at linear growth rates from 0.3 mm/s to >6 mm/s as reported in the literature. We report on recent progress towards growing (100) oriented ribbons with a net thickness of less than 200 microns and a ribbon width up to 18 cm using a stable, continuous process in the Leading Edge prototype furnace. Here, the 3D details of the single crystal growth are explained by extending the mechanics of the Limit Cycle Theory, with novel Internal Side Effect morphology described by a proposed Facet Flow Theory. Grown-in crystalline defect distributions are described as well as values of critical impurities like oxygen, carbon, dopants, and metals that are relevant for use as wafers for solar cells.

14 SOLAR ENERGY↗
DOE OSTI2021

A Case Study on Pathogen Transport, Deposition, Evaporation and Transmission: Linking High-Fidelity Computational Fluid Dynamics Simulations to Probability of Infection

A high-fidelity, low-Mach computational fluid dynamics simulation tool that includes evaporating droplets and variable-density turbulent flow coupling is well-suited to ascertain transmission probability and supports risk mitigation methods development for airborne infectious diseases such as COVID-19. A multi-physics large-eddy simulation-based paradigm is used to explore droplet and aerosol pathogen transport from a synthetic cough emanating from a kneeling humanoid. For an outdoor configuration that mimics the recent open-space social distance strategy of San Francisco, maximum primary droplet deposition distances are shown to approach 8.1 m in a moderate wind configuration with the aerosol plume transported in excess of 15 m. In quiescent conditions, the aerosol plume extends to approximately 4 m before the emanating pulsed jet becomes neutrally buoyant. A dose–response model, which is based on previous SARS coronavirus (SARS-CoV) data, is exercised on the high-fidelity aerosol transport database to establish relative risk at eighteen virtual receptor probe locations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Magnetic flux emergence in binary neutron star remnants

Using high-resolution AthenaK simulations of a twisted toroidal flux tube, we study the flux emergence of magnetic structures in the shear layer of a hot massive neutron star typical of a binary neutron star remnant. High-resolution simulations demonstrate that magnetic buoyant instabilities allow for emergence only for extremely large magnetic fields significantly exceeding 10 17 G, and more typical fields around 10 16 G are instead dominated by hydrodynamic effects. Because merger remnants tend to be stable against hydrodynamic convection, our work places strong limitations on the mechanisms by which massive binary neutron star remnants can produce the magnetically-driven outflows needed to power jets.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

End-to-end codesign of Hessian-aware quantized neural networks for FPGAs

Here, we develop an end-to-end workflow for the training and implementation of co-designed neural networks (NNs) for efficient field-programmable gate array (FPGA) hardware. Our approach leverages Hessian-aware quantization of NNs, the Quantized Open Neural Network Exchange intermediate representation, and the hls4ml tool flow for transpiling NNs into FPGA firmware. This makes efficient NN implementations in hardware accessible to nonexperts in a single open sourced workflow that can be deployed for real-time machine-learning applications in a wide range of scientific and industrial settings. We demonstrate the workflow in a particle physics application involving trigger decisions that must operate at the 40-MHz collision rate of the CERN Large Hadron Collider (LHC). Given the high collision rate, all data processing must be implemented on FPGA hardware within the strict area and latency requirements. Based on these constraints, we implement an optimized mixed-precision NN classifier for high-momentum particle jets in simulated LHC proton-proton collisions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Deconvoluting sources of variability in aerosol jet printing using light scattering measurements

Aerosol jet printing (AJP) is a digital additive manufacturing technique for hybrid and conformal electronics, where its contactless deposition readily enables patterning over 3D surface topography. However, the complexity of aerosol transport physics makes deposition rate sensitive to process variability, hindering widespread industry adoption. Light scattering measurements have recently been demonstrated as a capable tool for measuring deposition rate in real time, enabling closed-loop control and in-situ qualification frameworks. These inline optical measurements present further opportunity as a diagnostic tool to understand the effects of secondary process parameters affecting vapor–liquid equilibrium and heat and mass transport during deposition. Downstream of the optical measurement cell, changing the sheath gas flow rate, increasing temperature via an inline heater, and adding solvent vapor via a sheath gas bubbler were observed to alter drying physics during sheath-collimation, and thereby impaction efficiency. Further upstream, the introduction of solvent vapor via a carrier gas bubbler and liquid build-up in the printhead were seen to affect the quantitative relationship between the light scattering data and the true deposition rate. Using this information, tightened controls of meaningful secondary parameters were implemented to improve the batch-to-batch consistency for printing a dielectric ink. In addition to advancing AJP process reliability for production, this work demonstrates the capability of inline optical measurements to provide insight into process physics and deconvolute competing mechanisms that underly variability.

additive manufacturing↗
DOE OSTI2025

Radiation GRMHD Models of Accretion onto Stellar-mass Black Holes. I. Survey of Eddington Ratios

We summarize results from a survey of radiation-dominated black hole accretion flows across a wide range of mass accretion rates, as well as two values of black hole spin and initial magnetic field geometry. These models apply an algorithm targeting direct solutions to the radiation transport equation in full general relativity and have been enabled by access to modern exascale computing systems. Super-Eddington accretion flows form geometrically thick radiation-pressure-supported disks that drive powerful equatorial outflows. A narrow funnel-shaped photosphere in the inner region results in very low radiative efficiencies in this regime. The structure of near- and sub-Eddington accretion depends on whether there is net vertical magnetic flux at the midplane of the disk. With net flux, the disk forms a thin, dense layer at the midplane surrounded by a magnetically dominated corona, whereas without net flux, the disk remains magnetically dominated everywhere. Although none of our models achieve the magnetically arrested disk regime, those with net vertical flux and a rapidly spinning black hole still produce powerful relativistic jets. Our calculations adopt simple opacity models (with scalings appropriate to stellar-mass black hole accretion). We discuss the application of our results to observations of X-ray binaries and ultraluminous X-ray sources such as Cyg X-3 and SS433. We also speculate on the application of our super-Eddington models to the interpretation of little red dots recently discovered by JWST.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Probability of Hydrogen Ignition: A Landscape Review and Gaps Assessment

The primary hazard of a leak from a hydrogen system is due to the immediate or delayed ignition of the fuel leading to a jet flame or explosion. Therefore, understanding the hydrogen ignition probability is critical for analyzing the risk of hydrogen systems. This report reviews the current understanding of hydrogen ignition mechanisms and methods for modeling their probability. The stoichiometry, ignition strength, and ignition source temperature are all important characteristics that can affect both the probability of ignition and the outcome of the subsequent combustion event. A brief review of diffusion ignition demonstrates that ignition probability models must account for seemingly spontaneous ignition of hydrogen in addition to scenarios where the ignition source is readily identified. State-of-the art models for both immediate and delayed ignition probabilities are presented, including different physical aspects of the scenarios (e.g., flow rate, ignition source characteristics) that are considered in the different modeling approaches. Current models often fail to account for the unique properties of hydrogen compared to other fuels, and most lack rigorous validation with hydrogen as a fuel. A fault tree framework is proposed to systematically evaluate the probability of ignition by integrating various ignition mechanisms and their uncertainties. Furthermore, this type of framework could enable additional insights into the most important mechanisms and would enable uncertainty quantification in risk assessment modeling. Recommendations for future research include the need for experimental validation of ignition models and the development of comprehensive methodologies that incorporate the specifics of hydrogen behavior in real-world scenarios.

hydrogen↗
DOE OSTI2026

Energy Storage Technologies and U.S. Department of War Requirements

This report offers an overarching primer the energy storage market and assessment of each technology’s suitability for U.S. Department of War applications. We find that battery energy storage is the most promising technology for energy storage applications in terms of energy density and cost. While commercial and advanced Li-ion can satisfy certain electric mobility (e.g., cars, midsized vehicles) and electric flight application (e.g. drones) needs of DoW, next generation technologies like Li metal solid-state batteries, and conversion chemistries (Li-air, Li-CFx) are needed for heavy duty applications like armored vehicles, tanks, airplanes, jets. Batteries should also be supplemented with technologies such as supercapacitors and next-generation flywheels to provide short bursts of high energy for high power needs. Thermal energy storage solutions (sensible and latent heat) and fuel cells (PEMFC and SOFC) are good alternatives for supporting energy storage applications in terms of energy density and cost. Gravity-based energy storage promises the least energy density with high cost and should only be used for niche DoW applications. Of the different gravity energy storage technologies reviewed, pumped-hydro, flywheel, and compressed air energy storage are promising, while solid gravity energy storage appears the least attractive. Military facilities requiring 14 days of energy storage can benefit from flow batteries, especially commercial VFRBs and next generation iron-air systems. While VFRBs are expensive, iron-air batteries have the coupled benefits of low cost and good energy density promising 100+ hours of storage.

25 ENERGY STORAGE↗
DOE OSTI2024

Propelling sustainable energy: Multi-omics analysis of pennycress FATTY ACID ELONGATION1 knockout for biofuel production

Abstract The aviation industry’s growing interest in renewable jet fuel has encouraged the exploration of alternative oilseed crops. Replacing traditional fossil fuels with a sustainable, domestically sourced crop can substantially reduce carbon emissions, thus mitigating global climate instability. Pennycress (Thlaspi arvense L.) is an emerging oilseed intermediate crop that can be grown during the offseason between maize (Zea mays) and soybean (Glycine max) to produce renewable biofuel. Pennycress is being domesticated through breeding and mutagenesis, providing opportunities for trait enhancement. Here, we employed metabolic engineering strategies to improve seed oil composition and bolster the plant's economic competitiveness. FATTY ACID ELONGATION1 (FAE1) was targeted using CRISPR-Cas 9 gene editing to eliminate very long chain fatty acids (VLCFAs) from pennycress seed oil, thereby enhancing its cold flow properties. Through an integrated multiomics approach, we investigated the impact of eliminating VLCFAs in developing and mature plant embryos. Our findings revealed improved cold-germination efficiency in fae1, with seedling emergence occurring up to 3 d earlier at 10 °C. However, these alterations led to a tradeoff between storage oil content and composition. Additionally, these shifts in lipid biosynthesis were accompanied by broad metabolic changes, such as the accumulation of glucose and ADP-glucose quantities consistent with increased starch production. Furthermore, shifts to shorter FA chains triggered the upregulation of heat shock proteins, underscoring the importance of VLCFAs in stress signaling pathways. Overall, this research provides crucial insights for optimizing pennycress seed oil while preserving essential traits for biofuel applications.

Rasoul, Amira (ORCID:0009000714290175)↗
DOE OSTI2024

The persistent shadow of the supermassive black hole of M 87. I. Observations, calibration, imaging, and analysis

In April 2019, the Event Horizon Telescope (EHT) Collaboration reported the first-ever event-horizon-scale images of a black hole, resolving the central compact radio source in the giant elliptical galaxy M 87. These images reveal a ring with a southerly brightness distribution and a diameter of ∼42 μas, consistent with the predicted size and shape of a shadow produced by the gravitationally lensed emission around a supermassive black hole. These results were obtained as part of the April 2017 EHT observation campaign, using a global very long baseline interferometric radio array operating at a wavelength of 1.3 mm. Here, we present results based on the second EHT observing campaign, taking place in April 2018 with an improved array, wider frequency coverage, and increased bandwidth. In particular, the additional baselines provided by the Greenland telescope improved the coverage of the array. Multiyear EHT observations provide independent snapshots of the horizon-scale emission, allowing us to confirm the persistence, size, and shape of the black hole shadow, and constrain the intrinsic structural variability of the accretion flow. We have confirmed the presence of an asymmetric ring structure, brighter in the southwest, with a median diameter of 43.3−3.1+1.5 μas. The diameter of the 2018 ring is remarkably consistent with the diameter obtained from the previous 2017 observations. On the other hand, the position angle of the brightness asymmetry in 2018 is shifted by about 30° relative to 2017. The perennial persistence of the ring and its diameter robustly support the interpretation that the ring is formed by lensed emission surrounding a Kerr black hole with a mass ∼6.5 × 109 M⊙. The significant change in the ring brightness asymmetry implies a spin axis that is more consistent with the position angle of the large-scale jet.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

A regularized-interface method as a unified formulation for simulations of high-pressure multiphase flows

The injection of multi-species fluids into high-pressure and high-temperature environments beyond the species' critical points is commonly found in engineering applications. At these conditions, for immiscible species, both subcritical interfacial dynamics and supercritical mixing can coexist due to variations in temperature around the mixture critical point. The modeling of these complex transcritical phenomena for large-scale configurations is so far not possible. To address this issue, we propose the Regularized-Interface Method (RIM) as a unified formulation that can describe both sub- and supercritical processes as well as the transition between them. The proposed method is derived via filtering of the nanoscale interface-resolving formulation based on van der Waals' linear gradient theory. Thus, this approach allows for the consistent modeling of interfacial dynamics that vanishes at supercritical conditions, while significantly reducing the temporal and spatial resolution constraints of the original nanoscale formulation. The resulting RIM formulation is examined in interface-capturing simulations of sub-, trans-, and supercritical fuel injection processes, involving droplets and jets. Furthermore, these results highlight the importance of resolving spatio-temporal transitions from subcritical interfacial dynamics to supercritical mixing in high-pressure multiphase simulations, in contrast to commonly employed diffused-interface methods, where interfacial dynamics are often neglected.

Interface capturing↗
DOE OSTI2021

CFD investigation of low-attrition air-reactor designs for the NETL chemical-looping combustion reactor

The flow dynamics and attrition characteristics of three air reactor designs are investigated using an Eulerian-Eulerian kinetic theory model. The hydrodynamics of these reactor designs are analyzed to identify the flow regimes in which the particles evolve. The investigated designs which differ in the configuration of the secondary flow ports, exhibit bubbling and turbulent fluidization regimes within similar spatial regions. The most substantial differences in the hydrodynamics was in the turbulent fluidized regions near the secondary inlets. The mechanical stressing environments undergone by the particles were analyzed using the collision energy spectra, reconstructed using the local flow field properties (i.e. granular temperature and particle number density). The spectra show that the increase in the specific collision power is associated with the regions near the secondary injector ports, which are in a turbulent fluidization regime. A simple analysis relying upon a partitioning of the energy spectrum, showed that attrition rate may be reduced by about 34% over the original design.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2026

Data-based filtered dissipation rate modelling for multi-modal turbulent combustion: evaluating a priori model generalizability

Manifold-based models offer a computationally efficient alternative to directly transporting the thermochemical state in computational simulations of turbulent reacting flows, projecting the high-dimensional thermochemical state-space onto a low-dimensional manifold. Recent efforts have yielded a manifold-based model applicable to multi-modal combustion, enabling reconstruction of the thermochemical state from solutions to two-dimensional manifold equations in mixture fraction and generalized progress variable that are parameterised by three scalar dissipation rates. In coarse-grained simulations such as Large Eddy Simulation (LES), closure of the multi-modal manifold equations and subfilter variances/covariance requires closure of three filtered scalar dissipation rates. Here, the present work adopts a data-based approach, providing closure for the three filtered scalar dissipation rates via deep neural networks (DNNs). High-fidelity datasets corresponding to an autoigniting n-dodecane jet flame and a bluff body swirl-stabilized confined lifted spray flame of two aviation fuels (Jet-A and C1) with different ignition propensities are leveraged to generate training data that spans a diverse range of thermodynamic conditions and combustion modes, including low- and high-temperature ignition regimes in addition to premixed and nonpremixed behaviour. A final DNN model is trained to enforce inherent physical constraints by learning nonlinear functional transformations of the three filtered scalar dissipation rates. The generalizability of this constrained DNN model is demonstrated a priori via conditional statistics evaluated on the lifted spray flame with C1–a configuration that had not been included in the training data. Excellent DNN agreement with conditional DNS statistics is observed, and integrated gradients are computed to identify the most sensitive input variables. The similarity of the marginal PDFs of the most informative input variables and outputs across configurations are quantified via the Wasserstein metric, demonstrating that data-based models may successfully generalize to unseen parametric conditions so long as the most informative input variables share similar distributions across training and testing datasets.

Data-based modelling↗
DOE OSTI2026

Analytical model for balling defects in laser melting using rivulet theory and solidification

In laser welding and additive manufacturing communities, the balling (humping) defect is primarily attributed to the Plateau–Rayleigh fluid instability (PRI) with a few authors suggesting fluid jetting and volume conservation as alternative mechanisms. As analytical descriptions of these mechanisms are unavailable, combining them into a single formalism is unfeasible. We present a new model of PRI with higher accuracy, accounting for competition with solidification, to compare the expected behavior with known experimental trends when fluid jetting is neglected. We adapt a rivulet instability model from fluid physics to account for the stabilizing effects of the substrate, which the traditional cylindrical-jet geometry does not account for and estimate the instability growth rate. Our model yields a continuous transition from non-balling to balling hitherto lacking in current literature and predicts instability growth at higher wavelengths with strong sensitivity to the solidification front curvature. While the fluid surface is most unstable for shallow melt pools, the absolute magnitude of balling relevant to printing defects scales with melt pool depth and has a maximum for a given melt pool geometry. Synchrotron-based x-ray radiography of thin samples indicates that PRI growth rates and solidification can be comparable in magnitude and thus compete, as we find in this work. We predict that deviations between model predictions and our experimental results demonstrate the importance of fluid flows and heat transport in the balling process. Our experiments further demonstrate at least one mechanism by which the melt pool length and the balling wavelength are not equivalent, as commonly claimed.

Taylor, Z. [Stanford Univ., CA (United States); SL↗
DOE OSTI2022

Elucidating size effects on the yield strength of single-crystal Cu via the Richtmyer–Meshkov instability

Capturing the dynamic response of a material under high strain-rate deformation often demands challenging and time consuming experimental effort. While shock hydrodynamic simulation methods can aid in this area, a priori characterizations of the material strength under shock loading and spall failure are needed in order to parameterize constitutive models needed for these computational tools. Moreover, parameterizations of strain-rate-dependent strength models are needed to capture the full suite of Richtmyer–Meshkov instability (RMI) behavior of shock compressed metals, creating an unrealistic demand for these training data solely on experiments. Herein, we sweep a large range of geometric, crystallographic, and shock conditions within molecular dynamics (MD) simulations and demonstrate the breadth of RMI in Cu that can be captured from the atomic scale. In this work, yield strength measurements from jetted and arrested material from a sinusoidal surface perturbation were quantified as Y RMI = 0.787 ± 0.374 GPa, higher than strain-rate-independent models used in experimentally matched hydrodynamic simulations. Defect-free, single-crystal Cu samples used in MD will overestimate Y RMI , but the drastic scale difference between experiment and MD is highlighted by high confidence neighborhood clustering predictions of RMI characterizations, yielding incorrect classifications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Design, Analysis, and Experimental Testing of Hydrogen Lean Direct Injection Nozzles at Elevated Pressure

Abstract There are many challenges of commissioning a hydrogen combustor into future gas turbine engines; especially regarding achieving emissions goals. Previously, Escudero et al. and Tran et al. conducted a study to adapt the liquid fuel Lean Direct Injection (LDI) concept from Jet-A to gaseous natural gas-hydrogen blends and pure hydrogen [1], [2]. Experimental data was collected at atmospheric conditions using a Box Behnken design of experiments. The design of experiments suggested that biasing the air split in favor of the inner air circuit and increasing the swirl strength of this inner air passage resulted in improved NOx emissions, while the inverse was true for stability, which was quantified by studying the lean blowoff point (LBO) [1], [2]. The trends revealed by the original experiment [1], [2] provided a design direction for further iterations of the experimental hardware. The study presented herein describes the further investigation of such LDI injectors through experimental methods and computational fluid dynamic (CFD) simulations at atmospheric conditions, which were used to identify potential flow behaviors driving enhanced emissions performance. Further evaluation of select injectors from both studies was then conducted at elevated pressures up to 6 atmospheres. The results from both experiments are presented in this study, which include flame observations, emissions measurements, and operational challenges. NOx emissions results are reported on a volume basis in ppmvd corrected to 15% O2 and corrected for fuel. A predictive model for relating NOx emissions to test conditions at atmospheric conditions show high significance to adiabatic flame temperature while little to no significance to fuel composition for the best performing configurations. The results illustrate the connection between atmospheric testing and testing elevated pressures. The design direction indicated by the initial tests and CFD results in promising configurations for implementation into a Multi-point LDI array.

08 HYDROGEN↗
DOE OSTI2024

The Evolution of Galaxies and Clusters at High Spatial Resolution with Advanced X-ray Imaging Satellite (AXIS)

Stellar and black hole feedback heat and disperse surrounding cold gas clouds, launching gas flows off circumnuclear and galactic disks, producing a dynamic interstellar medium. On large scales bordering the cosmic web, feedback drives enriched gas out of galaxies and groups, seeding the intergalactic medium with heavy elements. In this way, feedback shapes galaxy evolution by shutting down star formation and ultimately curtailing the growth of structure after the peak at redshift 2–3. To understand the complex interplay between gravity and feedback, we must resolve both the key physics within galaxies and map the impact of these processes over large scales, out into the cosmic web. The Advanced X-ray Imaging Satellite (AXIS) is a proposed X-ray probe mission for the 2030s with arcsecond spatial resolution, large effective area, and low background. AXIS will untangle the interactions of winds, radiation, jets, and supernovae with the surrounding interstellar medium across the wide range of mass scales and large volumes driving galaxy evolution and trace the establishment of feedback back to the main event at cosmic noon. This white paper is part of a series commissioned for the AXIS Probe mission concept; additional AXIS white papers can be found at the AXIS website.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Automated and efficient local adaptive regression for principal component-based reduced-order modeling of turbulent reacting flows

Principal Component Analysis can be used to reduce the cost of Computational Fluid Dynamics simulations of turbulent reacting flows by reducing the dimensionality of the transported variables through projection of the thermochemical state onto a lower-dimensional manifold. However, because of the nonlinearity of the principal component source terms, nonlinear regression techniques must be utilized for the source terms in terms of the principal components. Unfortunately, widely available and utilized nonlinear regression techniques can have prohibitive computational requirements and/or accuracy that is highly dependent on user experience in ad hoc tuning of model architecture and hyperparameters. Here, in this work, a new nonlinear regression approach is proposed that is both computationally efficient and automated so does not require any user input. The approach is evaluated through a priori prediction of principal component source terms using data from a Direct Numerical Simulation of a turbulent nonpremixed n-heptane/air jet flame. In particular, the proposed framework consists of local regressions whose complexity is adapted according to the local nonlinearity of the data: local linear regression when accurate enough and local Artificial Neural Networks when nonlinear regression is required. The number of local clusters for local regression is determined automatically using the Davies-Bouldin index. In addition, Bayesian optimization is utilized for model training (i.e., to select the best architectures and hyperparameters of the nonlinear regressions in an unsupervised fashion), eliminating ad hoc hand-tuning and/or expensive grid searches. Overall, compared to a single, global neural network, the new local adaptive regression approach is shown to have comparable accuracy but 69% less training time due to the utilization of local linear regression and faster training of local neural networks.

42 ENGINEERING↗