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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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23 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The Patterson function as auto-hologram and graph enables the direct solution to the phase problem for coherently illuminated atomistic structures

Abstract The coherent Patterson function, derived from the coherent scattering intensities from N atoms, can be described as an auto-holographic image, i.e., the superposition of N -holographic images in which each atom serves as the source of a different reference wave, and as a mathematical graph of the unknown structure. These insights indicate that the unknown structure is significantly over-determined by the inherent information content in a Patterson function by a factor of ( N − 1)/2 (for non-degenerate structures), independent of dimensionality. However, we also show that the ability to resolve the distinct features in the Patterson function depends strongly on details of the experimental design (data range, sampling frequency, and dimensionality). This re-interpretation suggests that the coherent Patterson function provides a natural context for describing the information content in coherent scattering, reveals that there is no inherent phase problem for coherently illuminated pointwise structures, and enables the design of an algorithm which retrieves the structure directly from the Patterson function, without using error metric minimization.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

AUTOIGNITION DELAY TIMES FOR REFORMATE GAS MIXTURES FROM METHANE GAS ENGINES

Methane slip is a prominent issue in natural gas reciprocating engines that are used in transportation and marine applications. The incomplete combustion that results in methane slip can be resolved with the introduction of hydrogen within the combustion mixture to improve methane oxidation and further enable combustion within the engine crevices where methane has previously remained unreacted. Steam methane reforming (SMR) is a common method used to produce hydrogen and can be used to design an onboard device to reduce methane slip from reciprocating engines. The development of this reformer device requires the validation of high-fidelity chemical kinetic models at the low temperatures of the crevice volumes of these engines. In this work, auto-ignition data is obtained using a shock tube at lean (φ—0.714 or λ—1.4) and stoichiometric (φ, λ = 1) equivalence ratios spanning a temperature range of 1042–1234 K at the 80-bar operating pressure of the test engine. Blends of methane, hydrogen, and reformate products from the SMR reaction are shock-heated in synthetic air, with the ignition delay time measured using an OH* chemiluminescence detector at 310 nm and a CH* detector at 430 nm. The experimental results are compared to several state-of-the-art chemical kinetic mechanisms from the literature. In general, most of the mechanisms show very good agreement with experiments at higher temperatures, with simulation results showing little deviation from experiments at lower temperatures. A sensitivity analysis was conducted, and the results reveal that the reaction H2 + CH3O2 = H + CH3O2H has a very significant role in determining low-temperature ignition delay times (IDTs) of SMR mixtures. These findings provide valuable insights into the chemical kinetics governing methane reformate combustion and contribute to the optimization of onboard reformer designs aimed at mitigating methane slip in natural gas-fueled engines.

Fraze, Matthew↗
DOE OSTI2022

On the initiation and evolution of dielectric breakdown in auto-magnetizing liner experiments

Auto-magnetizing (AutoMag) liners are cylindrical tubes composed of discrete metallic helices encapsulated in insulating material; when driven with a ~2 MA, ~100-ns prepulse on the 20 MA, 100-ns rise time Z accelerator, AutoMag targets produced >150 T internal axial magnetic fields. Once the current rise rate of the pulsed power driver reaches sufficient magnitude, the induced electric fields in the liner cause dielectric breakdown of the insulator material and, with sufficient current, the cylindrical target radially implodes. The dielectric breakdown process of the insulating material in AutoMag liners has been studied in experiments on the 500–900 kA, ~100-ns rise time Mykonos accelerator. Multi-frame gated imaging enabled the first time-resolved observations of photoemission from dynamically evolving plasma distributions during the breakdown process in AutoMag targets. Using magnetohydrodynamic simulations, we calculate the induced electric field distribution and provide a detailed comparison to the experimental data. We find that breakdown in AutoMag targets does not primarily depend on the induced electric field in the gaps between conductive helices as previously thought. Finally, to better control the dielectric breakdown time, a 12–32 mJ, 170 ps ultraviolet (λ = 266 nm) laser was implemented to irradiate the outer surface of AutoMag targets to promote breakdown in a controlled manner at a lower internal axial field. Here, the laser had an observable effect on the time of breakdown and subsequent plasma evolution, indicating that pulsed UV lasers can be used to control breakdown timing in AutoMag.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Large eddy simulation of a supersonic lifted hydrogen flame: Impacts of Lewis, turbulent Schmidt and Prandtl numbers

Parametric large eddy simulations (LES) of a supersonic lifted hydrogen flame are reported. The emphases are on two aspects: impacts of (1) Lewis number (Lei of the ith species) and (2) turbulent Schmidt and Prandtl numbers (⁠Sc t and Pr t ⁠) on supersonic turbulent flame and flow structures. Five cases are considered: species-specific Le i , Sc t = Sr t = 1.0 (C 0 ); unity Le i , Sc t = Pr t = 1.0 (C 1 ); species-specific Sc t = 0.5, Pr t = 1.0 (C 2 ); species-specific Le i , Sc t = 1.0, Pr t = 0.5 (C 3 ); and species-specific Le i , Sc t = Pr t = 0.5 (C 4 ). Numerical results of instantaneous and/or time-averaged species mole fractions, mixture fraction, heat release rate, flame base location, and mixed modes of premixed and diffusion combustion are compared between cases C 0 and C 1 . Differences in auto-ignition locations and strengths and flame structures and stabilization specify the impacts of Lewis number. They are triggered by different predictions of species mass and thermal diffusions at fuel-coflow and/or coflow-ambient air mixing layers. These differences are rationalized by a scale analysis of mass/thermal diffusion and convection for case C 0 , which suggests the relatively low but non-negligible former against the latter. Cases C 0 and C 2 –C 4 barely see differences in terms of instantaneous and/or time-averaged temperature, velocity, and mixed combustion modes except for further downstream areas where combustion occurs. Both Sc t and Pr t impose less significant influences than Lewis number, as sub-grid scale mass/thermal diffusion is subordinate to its resolved counterpart according to their scale analysis for case C 4 .

42 ENGINEERING↗
DOE OSTI2021

MACHINE LEARNING BASED CHEMICAL EXPLOSIVE MODE ANALYSIS (ML-CEMA)

The software consists of a Machine Learning based Chemical Explosive Mode Analysis (ML-CEMA) tool for advanced computational flame diagnostics. CEMA, originally based on eigen-analysis of the local thermochemical system, is capable of identifying reaction fronts and limit phenomena such as auto-ignition and extinction in practical combustion systems, such as internal combustion engines and gas turbine combustors. However, the original CEMA is computationally expensive for large reaction mechanisms that are typically needed to describe fuel chemistry of practical large-hydrocarbon fuels. This novel ML-CEMA tool employs a ML technique to accelerate the eigen-analysis of the basic CEMA approach by orders of magnitude, thus making it suitable for practical fuels. In ML-CEMA,zero-dimensional (0D) reactors and one-dimensional (1D) premixed flames are first used to generate a large number of data points for neural network based ML training. The trained ML model is then used to perform CEMA prediction. This ML-CEMA tool has been demonstrated in canonical 0D and 1D configurations as well as highly-transient three-dimensional spray flames exhibiting multi-mode turbulent combustion, showing promising results. ML-CEMA, as a standalone tool, can be used for computationally-efficient diagnostics of massive datasets generated from both experiments and simulations. For example, based on spatially resolved measurements of a small set of reactive scalars(such as temperature, hydroxyl radical and formaldehyde), ML-CEMA can effectively identify flame fronts and rare events. ML-CEMA also provides a robust online or offline flame feature detection tool. When used for on-the-fly simulations, ML-CEMA further enables zone-adaptive combustion modeling, in which the predicted eigenvalue is used as a robust mode indicator for judicious assignment of locally-valid combustion models. This ML-CEMA based zone-adaptive model can lead to substantial computational cost savings when used for large-scale simulations of multi-mode combustion systems. Third Party Code Web Page to Download Code Web Page Location of Third Party License

Xu, Chao↗