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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Resonant di-Higgs production at gravitational wave benchmarks: a collider study using machine learning

We perform a complementarity study of gravitational waves and colliders in the context of electroweak phase transitions choosing as our template the xSM model, which consists of the Standard Model augmented by a real scalar. We carefully analyze the gravitational wave signal at benchmark points compatible with a first order phase transition, taking into account subtle issues pertaining to the bubble wall velocity and the hydrodynamics of the plasma. In particular, we comment on the tension between requiring bubble wall velocities small enough to produce a net baryon number through the sphaleron process, and large enough to obtain appreciable gravitational wave production. For the most promising benchmark models, we study resonant di-Higgs production at the high-luminosity LHC using machine learning tools: a Gaussian process algorithm to jointly search for optimum cut thresholds and tuning hyperparameters, and a boosted decision trees algorithm to discriminate signal and background. The multivariate analysis on the collider side is able either to discover or provide strong statistical evidence of the benchmark points, opening the possibility for complementary searches for electroweak phase transitions in collider and gravitational wave experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

In silico investigation of blast-induced intracranial fluid cavitation as it potentially leads to traumatic brain injury

In this paper, we conducted computational macroscale simulations predicting blast-induced intracranial fluid cavitation possibly leading to brain injury. To further understanding of this problem, we developed microscale models investigating the effects of blast-induced cavitation bubble collapse within white matter axonal fiber bundles of the brain. We model fiber tracks of myelinated axons whose diameters are statistically representative of white matter. Nodes of Ranvier are modeled as unmyelinated sections of axon. Extracellular matrix envelops the axon fiber bundle, and gray matter is placed adjacent to the bundle. Cavitation bubbles are initially placed assuming an intracranial wave has already produced them. Pressure pulses, of varied strengths, are applied to the upper boundary of the gray matter and propagate through the model, inducing bubble collapse. Simulations, conducted using the shock wave physics code CTH, predict an increase in pressure and von Mises stress in axons downstream of the bubbles after collapse. This appears to be the result of hydrodynamic jetting produced during bubble collapse. Interestingly, results predict axon cores suffer significantly lower shear stresses from proximal bubble collapse than does their myelin sheathing. Finally, simulations also predict damage to myelin sheathing, which, if true, degrades axonal electrical transmissibility and general health of the white matter structures in the brain.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

X-RAY EMISSION FROM STRONGLY ASYMMETRIC CIRCUMSTELLAR MATERIAL IN THE REMNANT OF KEPLER'S SUPERNOVA

Kepler's supernova remnant resulted from a thermonuclear explosion, but is interacting with circumstellar material (CSM) lost from the progenitor system. We describe a statistical technique for isolating X-ray emission due to CSM from that due to shocked ejecta. Shocked CSM coincides well in position with 24 {mu}m emission seen by Spitzer. We find most CSM to be distributed along the bright north rim, but substantial concentrations are also found projected against the center of the remnant, roughly along a diameter with position angle {approx}100 Degree-Sign . We interpret this as evidence for a disk distribution of CSM before the supernova, with the line of sight to the observer roughly in the disk plane. We present two-dimensional hydrodynamic simulations of this scenario in qualitative agreement with the observed CSM morphology. Our observations require Kepler to have originated in a close binary system with an asymptotic giant branch star companion.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1977

The ionospheres and plasma tails of comets

The paper reviews the current state of knowledge about cometary plasma (type I) tails and ionospheres. Observational statistics for type I tails are examined along with spectroscopic observations of plasma tails, identified ion species in such tails, and the morphology of cometary plasma tails and ionospheres. Evidence for a strong interaction between comets and the solar wind is evaluated on the basis of observations of plasma-tail orientations, large accelerations of tail structures, and correlations between disturbances in type I tails and solar-wind or geomagnetic disturbances. The use of comets as solar-wind probes is discussed, the nature of comet-solar-wind interactions is investigated, and ionization sources for cometary gases are considered. Hydrodynamic models of comet-solar-wind interaction are summarized, and the structure and ion chemistry of cometary ionospheres are studied. Observations suggesting that significant magnetic fields are associated with comets are briefly reviewed and interpreted.

Mendis, D. A.↗
DOE OSTI2015

Coupling DAEM and CFD for simulating biomass fast pyrolysis in fluidized beds

We report results from computational simulations of an experimental, lab-scale bubbling bed biomass pyrolysis reactor that include a distributed activation energy model (DAEM) for the kinetics. In this study, we utilized multiphase computational fluid dynamics (CFD) to account for the turbulent hydrodynamics, and this was combined with the DAEM kinetics in a multi-component, multi-step reaction network. Our results indicate that it is possible to numerically integrate the coupled CFD–DAEM system without significantly increasing computational overhead. It is also clear, however, that reactor operating conditions, reaction kinetics, and multiphase flow dynamics all have major impacts on the pyrolysis products exiting the reactor. We find that, with the same pre-exponential factors and mean activation energies, inclusion of distributed activation energies in the kinetics can shift the predicted average value of the exit vapor-phase tar flux and its statistical distribution, compared to single-valued activation-energy kinetics. Perhaps the most interesting observed trend is that increasing the diversity of the DAEM activation energies appears to increase the mean tar yield, all else being equal. As a result, these findings imply that accurate resolution of the reaction activation energy distributions will be important for optimizing biomass pyrolysis processes.

09 BIOMASS FUELS↗
DOE OSTI2019

Storm Surge Modeling in Puget Sound

In this study, a storm surge model of the Salish Sea was developed to simulate storm surge in Puget Sound using the unstructured-grid, finite-volume community ocean model (FVCOM). The model was based on Pacific Northwest National Laboratory’s existing Puget Sound hydrodynamic model that has been validated extensively in previous studies. To identify representative historical storm events, a systematic approach was developed based on the non-tidal residual (NTR) method. A total of 34 storm surge events between 1980 and 2016 were identified and simulated. To accurately simulate storm surge inside Puget Sound, the model was driven by reanalyzed Climate Forecast System Reanalysis meteorological forcing and observed water levels at the entrance of Strait of Juan de Fuca and southern entrance of Johnstone Strait. Model results for total water level, storm surge, and currents were compared with field measurements. A series of error statistics parameters were also calculated to quantify the model’s skills in simulating storm surge in the Salish Sea. Error statistics of model performance metrics demonstrated that the Salish Sea storm surge model was able to simulate storm surge inside Puget Sound with high accuracy. Storm surge propagation into Puget Sound is a nonlinear process that cannot be simply determined by a static approach for the entire domain. The maximum NTR distribution inside Puget Sound suggests that storm surge tends to be amplified in several sub-basins and inlets of the Puget Sound, such as Bellingham Bay, Hood Canal, south Puget Sound, and the multi-inlet basin behind Agate and Rich Passages. Therefore, future study is necessary to further quantify the storm surge level in these high-risk areas through field measurements and refined model simulations.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Tracer Particles for Core-collapse Supernova Nucleosynthesis: The Advantages of Moving Backward

Abstract After decades, the theoretical study of core-collapse supernova explosions is moving from parameterized, spherically symmetric models to increasingly realistic multidimensional simulations. However, obtaining nucleosynthesis yields based on such multidimensional core-collapse supernova simulations is not straightforward. Frequently, tracer particles are employed. Tracer particles may be tracked in situ during the simulation, but often they are reconstructed in a post-processing step based on the information saved during the hydrodynamic simulation. Reconstruction can be done in a number of ways, and here we compare the approaches of backward and forward integration of the equations of motion to the results based on inline particle trajectories. We find that both methods agree reasonably well with the inline results for isotopes for which a large number of particles contribute. However, for rarer isotopes that are produced only by a small number of particle trajectories, deviations can be large. For our setup, we find that backward integration leads to better agreement with the inline particles by more accurately reproducing the conditions following freeze-out from nuclear statistical equilibrium, because the establishment of nuclear statistical equilibrium erases the need for detailed trajectories at earlier times. Based on our results, if inline tracers are unavailable, we recommend backward reconstruction to the point when nuclear statistical equilibrium was last applied, with an interval between simulation snapshots of at most 1 ms for nucleosynthesis post-processing.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

The pre-merger impact velocity of the binary cluster A1750 from X-ray, lensing, and hydrodynamical simulations

Since the discovery of the 'Bullet Cluster', several similar cases have been uncovered that suggest relative velocities well beyond the tail of high speed collisions predicted by the concordance ΛCDM model. However, quantifying such post-merger events with hydrodynamical models requires a wide coverage of possible initial conditions. Here, we show that it is simpler to interpret pre-merger cases, such as A1750, where the gas between the colliding clusters is modestly affected, so that the initial conditions are clear. We analyze publicly available Chandra data confirming a significant increase in the projected X-ray temperature between the two cluster centers in A1750 consistent with our expectations for a merging cluster. We model this system with a self-consistent hydrodynamical simulation of dark matter and gas using the FLASH code. Our simulations reproduce well the X-ray data and the measured redshift difference between the two clusters in the phase before the first core passage viewed at an intermediate projection angle. The deprojected initial relative velocity derived using our model is 1460 km s{sup –1}, which is considerably higher than the predicted mean impact velocity for simulated massive haloes derived by recent ΛCDM cosmological simulations, but is within the allowed range. Our simulations demonstrate that such systems can be identified using a multi-wavelength approach and numerical simulations, for which the statistical distribution of relative impact velocities may provide a definitive examination of a broad range of dark matter scenarios.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

ATMOSPHERIC CIRCULATION OF HOT JUPITERS: INSENSITIVITY TO INITIAL CONDITIONS

The ongoing characterization of hot Jupiters has motivated a variety of circulation models of their atmospheres. Such models must be integrated starting from an assumed initial state, which is typically taken to be a wind-free, rest state. Here, we investigate the sensitivity of hot-Jupiter atmospheric circulation to initial conditions with shallow-water models and full three-dimensional models. Those models are initialized with zonal jets, and we explore a variety of different initial jet profiles. We demonstrate that, in both classes of models, the final, equilibrated state is independent of initial condition-as long as frictional drag near the bottom of the domain and/or interaction with a specified planetary interior are included so that the atmosphere can adjust angular momentum over time relative to the interior. When such mechanisms are included, otherwise identical models initialized with vastly different initial conditions all converge to the same statistical steady state. In some cases, the models exhibit modest time variability; this variability results in random fluctuations about the statistical steady state, but we emphasize that, even in these cases, the statistical steady state itself does not depend on initial conditions. Although the outcome of hot-Jupiter circulation models depend on details of the radiative forcing and frictional drag, aspects of which remain uncertain, we conclude that the specification of initial conditions is not a source of uncertainty, at least over the parameter range explored in most current models.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1994

TOPEX/POSEIDON tides estimated using a global inverse model

Altimetric data from the TOPEX/POSEIDON mission will be used for studies of global ocean circulation and marine geophysics. However, it is first necessary to remove the ocean tides, which are aliased in the raw data. The tides are constrained by the two distinct types of information: the hydrodynamic equations which the tidal fields of elevations and velocities must satisfy, and direct observational data from tide gauges and satellite altimetry. Here we develop and apply a generalized inverse method, which allows us to combine rationally all of this information into global tidal fields best fitting both the data and the dynamics, in a least squares sense. The resulting inverse solution is a sum of the direct solution to the astronomically forced Laplace tidal equations and a linear combination of the representers for the data functionals. The representer functions (one for each datum) are determined by the dynamical equations, and by our prior estimates of the statistics or errors in these equations. Our major task is a direct numerical calculation of these representers. This task is computationally intensive, but well suited to massively parallel processing. By calculating the representers we reduce the full (infinite dimensional) problem to a relatively low-dimensional problem at the outset, allowing full control over the conditioning and hence the stability of the inverse solution. With the representers calculated we can easily update our model as additional TOPEX/POSEIDON data become available. As an initial illustration we invert harmonic constants from a set of 80 open-ocean tide gauges. We then present a practical scheme for direct inversion of TOPEX/POSEIDON crossover data. We apply this method to 38 cycles of geophysical data records (GDR) data, computing preliminary global estimates of the four principal tidal constituents, M(sub 2), S(sub 2), K(sub 1) and O(sub 1). The inverse solution yields tidal fields which are simultaneously smoother, and in better agreement with altimetric and ground truth data, than previously proposed tidal models. Relative to the 'default' tidal corrections provided with the TOPEX/POSEIDON GDR, the inverse solution reduces crossover difference variances significantly (approximately 20-30%), even though only a small number of free parameters (approximately equal to 1000) are actually fit to the crossover data.

Egbert, Gary D.↗
DOE OSTI2014

Hydrodynamic equations for electrons in graphene obtained from the maximum entropy principle

The maximum entropy principle is applied to the formal derivation of isothermal, Euler-like equations for semiclassical fermions (electrons and holes) in graphene. After proving general mathematical properties of the equations so obtained, their asymptotic form corresponding to significant physical regimes is investigated. In particular, the diffusive regime, the Maxwell-Boltzmann regime (high temperature), the collimation regime and the degenerate gas limit (vanishing temperature) are considered.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

DARHT Multi-intelligence Seismic and Acoustic Data Analysis

The purpose of this report is to document the analysis of seismic and acoustic data collected at the Dual-Axis Radiographic Hydrodynamic Test (DARHT) facility at Los Alamos National Laboratory for robust, multi-intelligence decision making. The data utilized herein is obtained from two tri-axial seismic sensors and three acoustic sensors, resulting in a total of nine data channels. The goal of this analysis is to develop a generalized, automated framework to determine internal operations at DARHT using informative features extracted from measurements collected external of the facility. Our framework involves four components: (1) feature extraction, (2) data fusion, (3) classification, and finally (4) robustness analysis. Two approaches are taken for extracting features from the data. The first of these, generic feature extraction, involves extraction of statistical features from the nine data channels. The second approach, event detection, identifies specific events relevant to traffic entering and leaving the facility as well as explosive activities at DARHT and nearby explosive testing sites. Event detection is completed using a two stage method, first utilizing signatures in the frequency domain to identify outliers and second extracting short duration events of interest among these outliers by evaluating residuals of an autoregressive exogenous time series model. Features extracted from each data set are then fused to perform analysis with a multi-intelligence paradigm, where information from multiple data sets are combined to generate more information than available through analysis of each independently. The fused feature set is used to train a statistical classifier and predict the state of operations to inform a decision maker. We demonstrate this classification using both generic statistical features and event detection and provide a comparison of the two methods. Finally, the concept of decision robustness is presented through a preliminary analysis where uncertainty is added to the system through noise in the measurements.

58 GEOSCIENCES↗
DOE OSTI2016

THE MASS AND SIZE DISTRIBUTION OF PLANETESIMALS FORMED BY THE STREAMING INSTABILITY. I. THE ROLE OF SELF-GRAVITY

We study the formation of planetesimals in protoplanetary disks from the gravitational collapse of solid over-densities generated via the streaming instability. To carry out these studies, we implement and test a particle-mesh self-gravity module for the Athena code that enables the simulation of aerodynamically coupled systems of gas and collisionless self-gravitating solid particles. Upon employment of our algorithm to planetesimal formation simulations, we find that (when a direct comparison is possible) the Athena simulations yield predicted planetesimal properties that agree well with those found in prior work using different numerical techniques. In particular, the gravitational collapse of streaming-initiated clumps leads to an initial planetesimal mass function that is well-represented by a power law, dN/dM{sub p}∝M{sub p}{sup −p}, with p≃1.6±0.1, which equates to a differential size distribution of dN/dR{sub p}∝R{sub p}{sup −q}, with q≃2.8±0.1. We find no significant trends with resolution from a convergence study of up to 512{sup 3} grid zones and N{sub par}≈1.5×10{sup 8} particles. Likewise, the power-law slope appears indifferent to changes in the relative strength of self-gravity and tidal shear, and to the time when (for reasons of numerical economy) self-gravity is turned on, though the strength of these claims is limited by small number statistics. For a typically assumed radial distribution of minimum mass solar nebula solids (assumed here to have dimensionless stopping time τ=0.3), our results support the hypothesis that bodies on the scale of large asteroids or Kuiper Belt Objects could have formed as the high-mass tail of a primordial planetesimal population.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Lagrangian large eddy simulations via physics-informed machine learning

High-Reynolds number homogeneous isotropic turbulence (HIT) is fully described within the Navier–Stokes (NS) equations, which are notoriously difficult to solve numerically. Engineers, interested primarily in describing turbulence at a reduced range of resolved scales, have designed heuristics, known as large eddy simulation (LES). LES is described in terms of the temporally evolving Eulerian velocity field defined over a spatial grid with the mean-spacing correspondent to the resolved scale. This classic Eulerian LES depends on assumptions about effects of subgrid scales on the resolved scales. Here, we take an alternative approach and design LES heuristics stated in terms of Lagrangian particles moving with the flow. Our Lagrangian LES, thus L-LES, is described by equations generalizing the weakly compressible smoothed particle hydrodynamics formulation with extended parametric and functional freedom, which is then resolved via Machine Learning training on Lagrangian data from direct numerical simulations of the NS equations. The L-LES model includes physics-informed parameterization and functional form, by combining physics-based parameters and physics-inspired Neural Networks to describe the evolution of turbulence within the resolved range of scales. The subgrid-scale contributions are modeled separately with physical constraints to account for the effects from unresolved scales. We build the resulting model under the differentiable programming framework to facilitate efficient training. We experiment with loss functions of different types, including physics-informed ones accounting for statistics of Lagrangian particles. We show that our L-LES model is capable of reproducing Eulerian and unique Lagrangian turbulence structures and statistics over a range of turbulent Mach numbers.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

One-dimensional pion, kaon, and proton femtoscopy in Pb-Pb collisions at s NN = 2.76 TeV

The size of the particle emission region in high-energy collisions can be deduced using the femtoscopic correlations of particle pairs at low relative momentum. Such correlations arise due to quantum statistics and Coulomb and strong final state interactions. In this paper, results are presented from femtoscopic analyses of π ± π ± , K ± K ± , K$0\atop{S}$K$0\atop{S}$, pp , and $\overline{p}$ $\overline{p}$ correlations from Pb-Pb collisions at s NN = 2.76 TeV by the ALICE experiment at the LHC. One-dimensional radii of the system are extracted from correlation functions in terms of the invariant momentum difference of the pair. The comparison of the measured radii with the predictions from a hydrokinetic model is discussed. The pion and kaon source radii display a monotonic decrease with increasing average pair transverse mass m T which is consistent with hydrodynamic model predictions for central collisions. Lastly, the kaon and proton source sizes can be reasonably described by approximate m T scaling.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Code for the manuscript "Lagrangian Large Eddy Simulations vis Physics-informed Machine Learning"

We would like to disclose a code based on Python/Pytorch, in which we implement the data-driven physics-informed machine learning of Lagrangian large eddy simulation that is described in the manuscript "Lagrangian Large Eddy Simulations via Physics-Informed Machine Learning ". High-Reynolds number Homogeneous Isotropic Turbulence is fully described within the Navier-Stokes (NS) equations, which are notoriously difficult to solve numerically. Engineers, interested primarily in describing turbulence at a reduced range of resolved scales, have designed heuristics, known as Large Eddy Simulation (LES). LES is described in terms of the temporally evolving Eulerian velocity field defined over a spatial grid with the mean-spacing correspondent to the resolved scale. This classic Eulerian LES depends on assumptions about the effects of sub-grid scales on the resolved scales. Here, we take an alternative approach and design novel LES heuristics stated in terms of Lagrangian particles moving with the flow. Our \emph{Lagrangian LES}, thus L-LES, is described by equations generalizing the weakly compressible Smoothed Particle Hydrodynamics formulation with extended parametric and functional freedom, which is then resolved via Machine Learning training on Lagrangian data from Direct Numerical Simulations of the NS equations. The L-LES model includes physics-informed parameterization and functional form, by combining physics-based parameters and physics-inspired Neural Networks to describe the evolution of turbulence within the resolved range of scales. The sub-grid scale contributions are modeled separately with physical constraints to account for the effects from un-resolved scales. We build the resulting model under the Differentiable Programming framework to facilitate efficient training. We experiment with loss functions of different types, including physics-informed ones accounting for statistics of Lagrangian particles. We show that our Lagrangian LES model is capable of reproducing Eulerian and unique Lagrangian turbulence structures and statistics over a range of turbulent Mach numbers.

Tian, Yifeng↗
DOE OSTI2020

Static and dynamic properties of multi-ionic plasma mixtures

Complex plasma mixtures with three or more components are often encountered in astrophysics or in inertial confinement fusion (ICF) experiments. For mixtures containing species with large differences in atomic number Z, the modeling needs to consider at the same time the kinetic theory for low-Z elements combined with the theory of strongly coupled plasma for high-Z elements, as well as all the intermediate situations that can appear in multi-component systems. For such cases, we study the pair distribution functions, self-diffusions, mutual diffusion and viscosity for ternary mixtures at extreme conditions. These quantities can be produced from first principles using orbital free molecular dynamics at the computational expense of very intensive simulations to reach good statistics. Utilizing the first-principles results as reference data, we assess the merit of a global analytic model for transport coefficients, "Pseudo-Ions in Jellium" (PIJ), based on an iso-electronic assumption (iso-n e ). With a multi-component hypernetted-chain integral equation, we verify the quality of the iso-n e prescription for describing the static structure of the mixtures. This semi-analytical modeling compares well with the simulation results and allows one to consider plasma mixtures not accessible to simulations. Further, applications are given for the mix of materials in ICF experiments. A reduction of a multicomponent mixture to an effective binary mixture is also established in the hydrodynamic limit and compared with PIJ estimations for ICF relevant mixtures.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

The Thermal Proximity Effect: A New Probe of the He ii Reionization History and Quasar Lifetime

Despite decades of effort, the timing and duration of He ii reionization and the properties of the quasars believed to drive it are still not well constrained. We present a new method to study both via the thermal proximity effect—the heating of the intergalactic medium (IGM) around quasars when their radiation doubly ionizes helium. We post-process hydrodynamical simulations with 1D radiative transfer and study how the thermal proximity effect depends on the He ii fraction, x{sub HeII,0}, which prevailed in the IGM before the quasar turned on, and the quasar lifetime t{sub Q}. We find that the amplitude of the temperature boost in the quasar environment depends on x{sub HeII,0}, with a characteristic value of ΔT≃10{sup 4} K for x{sub HeII,0}=1.0, whereas the size of the thermal proximity zone is sensitive to t{sub Q}, with typical sizes of ≃100 cMpc for t{sub Q}=10{sup 8} yr. This temperature boost increases the thermal broadening of H i absorption lines near the quasar. We introduce a new Bayesian statistical method based on measuring the Lyα forest power spectrum as a function of distance from the quasar, and demonstrate that the thermal proximity effect should be easily detectable. For a mock data set of 50 quasars at z≃4, we predict that one can measure x{sub HeII,0} to an (absolute) precision ≈0.04 and t{sub Q} to a precision of ≈0.1 dex. By applying our formalism to existing high-resolution Lyα forest spectra, one should be able to reconstruct the He ii reionization history, providing a global census of hard photons in the high-z universe.

79 ASTRONOMY AND ASTROPHYSICS↗