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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Effects of residual kinetic energy on yield degradation and ion temperature asymmetries in inertial confinement fusion implosions

The study of Rayleigh–Taylor instability in the deceleration phase of inertial confinement fusion implosions is carried out using the three-dimensional (3-D) radiation-hydrodynamic Eulerian parallel code DEC3D. In this paper, we show that the yield-over-clean is a strong function of the residual kinetic energy (RKE) for low modes. Our analytical models indicate that the behavior of larger hot-spot volumes observed in low modes and the consequential pressure degradation can be explained in terms of increasing the RKE. These results are derived using a simple adiabatic implosion model of the deceleration phase as well as through an extensive set of 3-D single-mode simulations using the code DEC3D. The effect of the bulk velocity broadening on ion temperature asymmetries is analyzed for different mode numbers ℓ = 1 -12. The jet observed in low mode ℓ = 1 is shown to cause the largest ion temperature variation in the mode spectrum. Finally, the vortices of high modes within the cold bubbles are shown to cause lower ion temperature variations than low modes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Simulation of adiabatic quantum computing for molecular ground states

Quantum computation promises to provide substantial speedups in many practical applications with a particularly exciting one being the simulation of quantum many-body systems. Adiabatic state preparation (ASP) is one way that quantum computers could recreate and simulate the ground state of a physical system. In this paper, we explore a novel approach for classically simulating the time dynamics of ASP with high accuracy and with only modest computational resources via an adaptive sampling configuration interaction scheme for truncating the Hilbert space to only the most important determinants. We verify that this truncation introduces negligible error and use this new approach to simulate ASP for sets of small molecular systems and Hubbard models. Furthermore, we examine two approaches to speeding up ASP when performed on quantum hardware: (i) using the complete active space configuration interaction (CASCI) wave function instead of the Hartree–Fock initial state and (ii) a nonlinear interpolation between the initial and target Hamiltonians. We find that starting with a CASCI wave function with a limited active space yields substantial speedups for many of the systems examined, while nonlinear interpolation does not. In additional, we observe interesting trends in the minimum gap location (based on the initial state) as well as how state preparation time can depend on certain molecular properties, such as the number of valence electrons. Importantly, we find that the required state preparation times do not show an immediate exponential wall that would preclude an efficient run of ASP on actual hardware.

Kremenetski, Vladimir↗
DOE OSTI2023

Evaporation and scattering of neon, methane, and water from a dodecane flat liquid jet

The evaporation and scattering of Ne, CD 4 , and D 2 O from a dodecane flat liquid jet are investigated in a molecular beam apparatus. The experiment yields translational energy distributions as a function of scattering angle by means of a rotatable mass spectrometer. In the evaporation experiments, one observes a Maxwell–Boltzmann distribution with a cos $θ$ angular distribution superimposed on a weak, isotropic background. The scattering experiments show contributions from impulsive scattering and thermal desorption. At select incident angles for the three systems, angular distributions show super-specular scattering for the impulsive scattering channel, an effect attributed to anisotropic momentum transfer to the liquid surface. The impulsive scattering channel is analyzed with a soft-sphere model to explore energy transfer between the scatterer and liquid as a function of deflection angle. In conclusion, compared to Ne scattering, the polyatomic gases exhibit more thermal desorption and, in the impulsive scattering channel, a higher degree of internal excitation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Dissociation of gas-phase anisole induced by low-energy electron interactions: understanding patterns of aromatic bond cleavage

Abstract In view of elucidating the fragmentation patterns of aromatic systems induced by low-energy electron interactions, dissociative electron attachment (DEA) to gas-phase anisole was performed. Anionic fragments resulting from this DEA process were detected by a quadrupole mass spectrometer, and ion yields of those fragments as a function of incident electron energy were rendered. Our study showed the formation of CH 3 − , HCC − , and OCH 3 − fragments, suggesting that various dissociation channels proceed out of DEA to anisole. We employed density functional theory to compute thermodynamic threshold energies for each potential dissociation channel. Those theoretical calculations supported the prediction that the CH 3 − and OCH 3 − fragments form via mechanisms of single-bond cleavage; the HCC − fragments may form through two-, three-, or four-body dissociation channels that entail hydrogen transfers and the cleavage of multiple aromatic bonds. The experimental resonance energies that form the CH 3 − , HCC − , and OCH 3 − fragments were 6.0 eV, 5.8 and 9.7 eV, and 9.8 eV, respectively. Given the classification of anisole as a monosubstituted aromatic species, our results explain generalizable patterns of electron-mediated dissociation in aromatic systems.

Finley, Jacob (ORCID:0009000606963352)↗
DOE OSTI2020

ANNIE: Neutron multiplicity in neutrino interactions and new technologies

The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) aims at measuring the neutron abundance in the final state of neutrino-nucleus interactions. This measurement will have a direct impact on our understanding of neutrino interactions and will lead to a better reduction of systematic errors and an improvement of signal-background discrimination in future large neutrino detectors, thus impacting long baseline oscillation experiments as well as proton decay searches and supernova detection. With a volume of about 26 tons of pure water doped with gadolinium to enhance neutron tagging efficiency, ANNIE will provide a measurement of the neutron yield of neutrino interactions as a function of the neutrino energy in the well-characterized Booster Neutrino Beam at Fermilab. The modularity of ANNIE will allow it to perform several fundamental tests of new technologies to be used in neutrino detectors, such as a novel kind of photodetectors called Large Area Picosecond Photodetectors and Water-based Liquid Scintillator, a newly-developed detection medium. The technology behind the ANNIE detector will have a noticeable impact on the development of future large water Cherenkov detectors as well as on detection techniques for neutrino physics.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

A global perspective on bacterial diversity in the terrestrial deep subsurface

While recent efforts to catalogue Earth’s microbial diversity have focused upon surface and marine habitats, 12–20% of Earth’s biomass is suggested to exist in the terrestrial deep subsurface, compared to ~1.8% in the deep subseafloor. Metagenomic studies of the terrestrial deep subsurface have yielded a trove of divergent and functionally important microbiomes from a range of localities. However, a wider perspective of microbial diversity and its relationship to environmental conditions within the terrestrial deep subsurface is still required. Our meta-analysis reveals that terrestrial deep subsurface microbiota are dominated by Betaproteobacteria, Gammaproteobacteria and Firmicutes, probably as a function of the diverse metabolic strategies of these taxa. Evidence was also found for a common small consortium of prevalent Betaproteobacteria and Gammaproteobacteria operational taxonomic units across the localities. This implies a core terrestrial deep subsurface community, irrespective of aquifer lithology, depth and other variables, that may play an important role in colonizing and sustaining microbial habitats in the deep terrestrial subsurface. An in silico contamination-aware approach to analysing this dataset underscores the importance of downstream methods for assuring that robust conclusions can be reached from deep subsurface-derived sequencing data. Understanding the global panorama of microbial diversity and ecological dynamics in the deep terrestrial subsurface provides a first step towards understanding the role of microbes in global subsurface element and nutrient cycling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Ferrimagnetic Order in Tetragonal Antiperovskite Mn3GeN

The crystal and magnetic structures of the nitride antiperovskite M⁢n3⁢GeN reveals ferrimagnetic order stemming from a distorted kagome-derived lattice of the Mn atoms. Polycrystalline M⁢n3⁢GeN was synthesized via a solid-state reaction and characterized using neutron powder diffraction, dc magnetometry, and first-principles calculations. Rietveld refinement reveals near-stoichiometric composition (M⁢n3⁢Ge⁢N0.992⁢(7)) adopting a tetragonal 𝐼⁢4/𝑚⁢𝑐⁢𝑚 structure at 𝑇=500K and below, featuring axially distorted and tilted [NM⁢n6] octahedra that result in a buckled Mn kagome lattice. On heating, the tetragonal distortion and octahedral tilt angle decrease continuously before transitioning to the cubic 𝑃⁢𝑚⁢3⁢𝑚 antiperovskite phase at 𝑇≈527K. Neutron diffraction and magnetometry together reveal noncollinear ferrimagnetic ordering. For 30K≤𝑇≤500K, the magnetic structure is described by magnetic space group 𝐼⁢𝑏⁢𝑎′⁢𝑚′ (72.544), with inequivalent Mn1 and Mn2 sublattices that couple antiferromagnetically to yield a net moment. Density-functional theory-based calculations show that the different local moments originate from the bandwidths associated with distinct Mn–N bond lengths. Temperature-dependent refinements reveal distinct differences in the thermal disordering profiles of the Mn1 and Mn2 sublattices. These findings reveal a subtlety in the magnetic and structural behavior of M⁢n3⁢GeN, highlighting the interplay between structural distortions, magnetic ordering, and electronic structure in kagome-derived antiperovskite materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Applications of a Rayleigh-Taylor model to direct-drive laser fusion

Here, this paper presents a simple physics-based model for the interpretation of key metrics in laser direct drive. The only input parameters required are target scale, in-flight aspect ratio, and beam-to-target radius, and the importance of each has been quantified with a tailored set of cryogenic implosion experiments. These analyses lead to compact and accurate predictions of the fusion yield and areal density as a function of hydrodynamic stability, and suggest new ways to take advantage of direct drive. To provide examples, we will discuss how the inferred mix width behaves relative to theory, then show how it could be exploited to perform a direct drive implosion with a Lawson metric or Χ no α of 0.24 ± 0.02—using a novel parameter space at high velocities and beam radii on the OMEGA laser—that projects to ignition at a laser energy ≤2.0 MJ.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach

Gasoline compression ignition (GCI) engines are considered an attractive alternative to traditional spark-ignition and diesel engines. Here, a Machine Learning-Grid Gradient Ascent (ML-GGA) approach was developed to optimize the performance of internal combustion engines. ML offers a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. The developed ML-GGA model was compared with a recently developed Machine Learning-Genetic Algorithm (ML-GA). Detailed investigations of optimization solver parameters and variable limit extension were performed in the present ML-GGA model to improve the accuracy and robustness of the optimization process. Detailed descriptions of the different procedures, optimization tools, and criteria that must be followed for a successful output are provided here. The developed ML-GGA approach was used to optimize the operating conditions (case 1) and the piston bowl design (case 2) of a heavy-duty diesel engine running on a gasoline fuel with a research octane number (RON) of 80. The ML-GGA approach yielded >2% improvements in the merit function, compared with the optimum obtained from a thorough computational fluid dynamics (CFD) guided system optimization. The predictions from the ML-GGA approach were validated with engine CFD simulations. This study demonstrates the potential of ML-GGA to significantly reduce the time needed for optimization problems, without loss in accuracy compared with traditional approaches.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Thermal Aging Effects on the Yield and Tensile Strength of 9Cr-1Mo-V (Grade 91)

The long-term exposure of 9Cr-1Mo-V (Grade 91) steel to elevated temperature can have a significant effect on reducing its yield and tensile strength. The yield and tensile strength changes, in turn, have potential implications to the assurance of the integrity of components constructed with this material over their design or intended lifetime. The ASME Boiler & Pressure Vessel Code (BPVC), Section III, Division 5 (III-5, high temperature reactors) provides tabulated reduction factors for Grade 91 yield and tensile strength as a function of exposure temperature and time up to 300,000 h. ASME BPVC III-5 s intent to extend these factors to an exposure duration of 500,000 h, the lack of available historic information to support the existing factors, and the recent development of a physics-based prediction model for ASME BPVC application are prime motivation for this study. This work describes results of the conventional time-temperature Hollomon–Jaffe parameter, strength reduction ratio prediction method using an updated, extensive Grade 91 unaged and related aged material strength database. The method, previously used by Oak Ridge National Laboratory in its evaluation of Grade 91 and likely used in development of the existing BPVC III-5 reduction factors, provides strength reduction ratio predictions useful for general component fitness-for-service assessments and for computing BPVC III-5 reduction factors as defined. Specific reduction factors to 500,000 h at 650 °C applicable to ASME BPVC III-5 are computed from the strength reduction ratios.

36 MATERIALS SCIENCE↗
DOE OSTI2020

ENGINE COMBUSTION SYSTEM OPTIMIZATION USING CFD AND MACHINE LEARNING: A METHODOLOGICAL APPROACH

Gasoline compression ignition (GCI) engines are considered an attractive alternative to traditional spark-ignition and diesel engines. In this work, a Machine Learning-Grid Gradient Algorithm (ML-GGA) approach was developed to optimize the performance of internal combustion engines. Machine learning (ML) offers a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. The developed ML-GGA model was compared with a recently developed Machine learning Genetic Algorithm (ML-GA). Detailed investigations of optimization solver parameters and variables limits extension were performed in the present ML-GGA model to improve the accuracy and robustness of the optimization process. Detailed descriptions of the different procedures, optimization tools and criteria that must be followed for a successful output are provided here. The developed ML-GGA approach was used to optimize the operating conditions (case 1) and the piston bowl design (case 2) of a heavy-duty diesel engine running on a gasoline fuel with a Research Octane Number (RON) of 80. The ML-GGA approach yielded > 2% improvements in the merit function, compared to the optimum obtained from a thorough computational fluid dynamics (CFD) guided system optimization. The predictions from the ML-GGA approach were validated with engine CFD simulations. This study demonstrates the potential of ML-GGA to significantly reduce the time needed for optimization problems, without loss in accuracy compared to traditional approaches.

engine optimization↗
DOE OSTI2017

Ring current Atmosphere interactions Model with Self-Consistent Magnetic field

The Ring current Atmosphere interactions Model with Self-Consistent magnetic field (B) is a unique code that combines a kinetic model of ring current plasma with a three dimensional force-balanced model of the terrestrial magnetic field. The kinetic portion, RAM, solves the kinetic equation to yield the bounce-averaged distribution function as a function of azimuth, radial distance, energy and pitch angle for three ion species (H+, He+, and O+) and, optionally, electrons. The domain is a circle in the Solar-Magnetic (SM) equatorial plane with a radial span of 2 to 6.5 RE. It has an energy range of approximately 100 eV to 500 KeV. The 3-D force balanced magnetic field model, SCB, balances the JxB force with the divergence of the general pressure tensor to calculate the magnetic field configuration within its domain. The domain ranges from near the Earth’s surface, where the field is assumed dipolar, to the shell created by field lines passing through the SM equatorial plane at a radial distance of 6.5 RE. The two codes work in tandem, with RAM providing anisotropic pressure to SCB and SCB returning the self-consistent magnetic field through which RAM plasma is advected.

Jordanova, Vania↗
DOE OSTI2024

Electron cloud simulations in the Fermilab booster

As part of Fermilab's Proton Improvement Plan-II (PIP-II),the Fermilab Booster synchrotron will operate at a higherintensity, increasing from 4.5 x 10^12 to 6.7 x 10^12 protons per pulse (ppp). A potential challenge for achievinghigh intensity performance arises from rapid transverse instabilities induced by electron cloud (EC). This researchpresents EC simulations using PyECLOUD, which is anadvanced computational tool that incorporates measurements of the secondary electron yield (SEY) from theBooster's combined function magnet material. By systematically varying beam parameters in PyECLOUD, such asbunch structure, SEY, bunch length, and intensity, it becomes possible to forecast the impact of EC effects on thebeam stability of the PIP-II era Booster.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Precision calibration of calorimeter signals in the ATLAS experiment using an uncertainty-aware neural network

The ATLAS experiment at the Large Hadron Collider explores the use of modern neural networks for a multi-dimensional calibration of its calorimeter signal defined by clusters of topologically connected cells (topo-clusters). The Bayesian neural network (BNN) approach not only yields a continuous and smooth calibration function that improves performance relative to the standard calibration but also provides uncertainties on the calibrated energies for each topo-cluster. The results obtained by using a trained BNN are compared to the standard local hadronic calibration and to a calibration provided by training a deep neural network. The uncertainties predicted by the BNN are interpreted in the context of a fractional contribution to the systematic uncertainties of the trained calibration. They are also compared to uncertainty predictions obtained from an alternative estimator employing repulsive ensembles.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Energetic Particles - microturbulence interaction thrust (EP SIWG white paper)

This white paper outlines a strategic approach to Energetic Particle (EP) research, aimed at applying the results to the future burning devices such as ITER. Our goal is to deepen the understanding of the interplay between plasma background microturbulence and diverse species of energetic ions in burning plasma devices. This strategy is poised to advance our knowledge in this crucial area of fusion science, paving the way for significant breakthroughs in plasma physics and fusion energy research. We consider the confinement of energetic ions such as auxiliary heating beams, minority ions from ICRH, and fusion product alpha particles. Recent studies have underscored the potential of EP/microturbulence interaction to enhance fusion plasma performance by stabilizing microturbulence. However, it also poses risks, such as exacerbating Alfvénic eigenmode (AE) instabilities, which could compromise the sustainability of plasma discharge by causing EP losses. The interplay between EPs and background microturbulence is evident in the phenomenon of effective pitch angle scattering, a crucial aspect of the quasilinear (QL) theory. This theory is integral to developing numerically-efficient yet comprehensive and self-consistent approaches, recently employed to investigate the relaxation of energetic particle populations in the holistic modeling of fusion-grade plasmas. The primary objectives of this white paper thrust encompass a dual focus. First is that we need to delve into the mechanisms through which microturbulence engenders effective pitch angle scattering. This investigation will entail the examination of the presence of known or self-consistently predicted spectra of modes accountable for microturbulence. In these explorations, electrostatic microturbulence serves as a logical initial stride towards achieving the trust's objectives. This endeavor is anticipated to yield formulations expressing the parametric dependencies of the effective pitch angle scattering frequency on variables such as thermal plasma electron and/or ion temperatures, as well as their respective thermal conductivities. In our second objective, we aim to delve into the intricate formation of zonal flow (ZF) structures amidst the complex interplay of microturbulence and Alfvenic eigenmodes (AEs). This endeavor poses greater challenges as we seek to unravel the macroscopic manifestations influenced by microturbulence, termed as zonal structures (ZS), stemming from microturbulence-induced ZF. Drawing from this understanding, we anticipate employing a QL approach to yield comprehensive insights into the distribution function of energetic particles (EP) within phase space. This method entails resolving the dominant multidimensional phase space diffusion processes while effectively averaging over the rapid ballistic responses. Nevertheless, substantial strides remain imperative to realize a comprehensive whole-device modeling framework. This entails meticulous verification and validation exercises against experimental observations, as well as rigorous benchmarking against theoretical frameworks and numerical simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Magnetic Fields in the Massive Dense Cores of the DR21 Filament: Weakly Magnetized Cores in a Strongly Magnetized Filament

We present Submillimeter Array 880 μ m dust polarization observations of six massive dense cores in the DR21 filament. The dust polarization shows complex magnetic field structures in the massive dense cores with sizes of 0.1 pc, in contrast to the ordered magnetic fields of the parsec-scale filament. The major axes of the massive dense cores appear to be aligned either parallel or perpendicular to the magnetic fields of the filament, indicating that the parsec-scale magnetic fields play an important role in the formation of the massive dense cores. However, the correlation between the major axes of the cores and the magnetic fields of the cores is less significant, suggesting that during the core formation, the magnetic fields below 0.1 pc scales become less important than the magnetic fields above 0.1 pc scales in supporting a core against gravity. Our analysis of the angular dispersion functions of the observed polarization segments yields a plane-of-sky magnetic field strength of 0.4–1.7 mG for the massive dense cores. We estimate the kinematic, magnetic, and gravitational virial parameters of the filament and the cores. The virial parameters show that the gravitational energy in the filament dominates magnetic and kinematic energies, while the kinematic energy dominates in the cores. Our work suggests that although magnetic fields may play an important role in a collapsing filament, the kinematics arising from gravitational collapse must become more important than magnetic fields during the evolution from filaments to massive dense cores.

79 ASTRONOMY AND ASTROPHYSICS↗