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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Bayesian reconstruction of anisotropic flow fluctuations at fixed impact parameter

The cumulants of the distribution of anisotropic flow are measured accurately in Pb+Pb collisions at the LHC as a function of centrality classifiers (charged multiplicity and/or transverse energy). Using Bayesian inference, we reconstruct from these measurements the probability distribution of anisotropic flow in the ``theorists' frame'' where the impact parameter has a fixed magnitude and orientation, up to ∼70% centrality. The variation of flow fluctuations with impact parameter displays direct evidence of viscous damping, which is larger for higher Fourier harmonics, in line with expectations from hydrodynamics. We use intensive measures of non-Gaussian flow fluctuations, which have reduced dependence on centrality. Here, we infer from ATLAS data the magnitude of these intensive non-Gaussianities in each Fourier harmonic. They provide data-driven estimates of response coefficients to initial anisotropies, without resorting to any specific microscopic model of initial conditions. These estimates agree with viscous hydrodynamic calculations.

Bayesian methods↗
DOE OSTI2024

Assessment of Bird Strike Likelihood to Refine Bird Strike Risk Models

In its most basic form, bird strike risk is comprised of a frequency component that reflects the likelihood of a collision and a severity component that reflects the cost (monetary or otherwise) of the incident. The bird strike risk model currently used by United State Department of Agriculture (USDA) Wildlife Services to evaluate the risk posed by individual bird species at airports and establish priorities for management was developed in 2018. The model uses airport-specific data on the number of reported strikes for a species recorded in the Federal Aviation Administration (FAA)’s National Wildlife Strike Database as a measure of frequency and the species’ relative hazard score as a measure of severity. The model was tested against independent data, found to perform well overall, and is being implemented widely across the United States. However, the model has limitations, including that species known to pose risk to aircraft locally, but not present in the strike record database, are not reflected as a major component of risk. Standard bird survey methodology commonly used at airports (e.g. point counts or transects) potentially can be used to complement wildlife strike records to calculate frequency or relative abundance of species. However, these methods generally focus on airport-wide population estimation and often ignore vital information that contributes to the true likelihood of a strike, such as use of runway protection zones and other critical areas, and spatial and temporal overlap with departing or approaching aircraft. As such, a more detailed understanding of space use by birds across landcovers and population fluctuations across the year is needed to accurately estimate the likelihood of bird strikes at airports. In this manuscript, we will review the extant risk model, including a discussion on its limitations. We then discuss approaches for refining our understanding of strike likelihood and briefly touch on needs for estimating probability of strike severity (cost).

bird strike, aircraft collision, damage by wildlif↗
DOE OSTI2024

Semi-Lagrangian nodal discontinuous Galerkin method for the BGK model

In this work, we propose a semi-Lagrangian (SL) nodal discontinuous Galerkin (DG) solver for the BGK equation. The BGK model was introduced by Bhatnagar, Gross, and Krook [1] as a relaxation model for the fundamental Boltzmann equation [5], which describes the kinetic dynamic of rarefied gases with a probability distribution function. The challenges of designing efficient numerical schemes for the Boltzmann equation mainly come from its high dimensionality and complicated nonlinear collision operator. The BGK model gains interests since it has much lower computational cost, due to the relatively simple structure of the relaxation operator in replacement of the collision operator, while simultaneously preserving several important physical properties, such as macroscopic quantities and dissipation of entropy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Entanglement entropy, single-particle occupation probabilities, and short-range correlations

For quantum many-body systems with short-range correlations (SRCs), the intimate relationship between their magnitude, the behavior of the single-particle occupation probabilities at momenta larger than the Fermi momentum, and the entanglement entropy is a new qualitative aspect not studied and exploited yet. A large body of recent condensed matter studies indicates that the time evolution of the entanglement entropy describes the nonequilibrium dynamics of isolated and strongly interacting many-body systems, in a manner similar to the Boltzmann entropy, which is strictly defined for dilute and weakly interacting many-body systems. Both theoretical and experimental studies in nuclei and cold atomic gases have shown that the fermion momentum distribution has a generic behavior n(k)=C/k 4 at momenta larger than the Fermi momentum, due to the presence of SRCs, with approximately 20% of the particles having momenta larger than the Fermi momentum. Further, the presence of the long momentum tails in the presence of SRCs changes the textbook relation between the single-particle kinetic energy and occupation probabilities, n mf ⁡(k) = 1/{1+ exp ⁡β[ϵ⁡(k)-μ]} for momenta very different form the Fermi momentum, particularly for dynamics processes. SRCs induced high-momentum tails of the single-particle occupation probabilities increase the entanglement entropy of fermionic systems, which in its turn affects the dynamics of many nuclear reactions, such as heavy-ion collisions and nuclear fission.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Prioritizing Uncertainties in Hydrogen Contribution to Risk in Post-Crash Outcomes for Rail

This report presents analysis from Sandia National Laboratories predicting contributions to risk associated with the use of hydrogen technology for rail. Event sequence diagrams are used to describe possible accident scenarios and progressions. Initiating event frequencies and branch event probabilities for each scenario are quantified with uncertainty using distributions fit to Federal Railroad Administration and U.S. Department of Transportation Pipeline and Hazardous Materials Safety Administration data on applicable accidents from 2000 to 2020. Uncertainty is propagated through the event sequence diagram to estimate the frequency and conditional probability of accident end states. The analysis identifies four scenarios with significant contributions to risk from hydrogen that are predicted to occur relatively frequently, which may inform priorities for reducing uncertainty. These scenarios are 1) overpressure events resulting from collisions with hydrogen release due to mechanical damage and delayed ignition, 2) jet fire events resulting from collisions with hydrogen release due to mechanical damage and immediate ignition, 3) jet fires resulting from fire or explosion initiating events involving the hydrogen tank and correct operation of the thermally-activated pressure relief device (TPRD) subsequent to the thermal insult, and 4) pressure burst resulting from fire or explosion initiating events involving the hydrogen tank and failure of the TPRD. Delayed and immediate hydrogen ignition probabilities are identified as being highly uncertain and potential candidates for reducing conservatism in the predicted frequencies for these two scenarios.

08 HYDROGEN↗
DOE OSTI2025

Assessing the Impact of Measurement Precision on Metabolite Identification Probability in Multidimensional Mass Spectrometry-Based, Reference-Free Metabolomics

Identification of compounds with minimal ambiguity remains a central challenge in mass spectrometry-based metabolomics. Conventional compound identification relies on comparing analytical signatures (e.g., mass-to-charge ratio, collision cross section, tandem mass spectra) against reference data obtained from measurements of authentic chemical standards. The breadth of annotatable compounds using this approach is necessarily limited by availability of authentic standards, analytical throughput, and resolving power of the separations that underly the measurements. The maturation of computational methods, both theory-driven and artificial intelligence/machine learning-based, for prediction of various molecular properties relevant to multidimensional mass spectrometry measurements has opened the door to a new “reference-free” paradigm of compound annotation. Through augmenting existing reference data for molecular properties with computational predictions, the universe of identifiable chemical species can be expanded significantly beyond its current limits. An unexplored aspect of this novel approach is understanding how to gauge confidence in resulting annotations, especially as the compound search space is expanded. Intuitively, the confidence of a compound annotation is related to the inherent discriminatory power of the molecular properties used for identification, as well as the precision with which the properties are measured or predicted. In this work, we characterize this relationship between measurement precision and identification probability in a systematic and quantitative fashion for a defined region of chemical space that includes organic small molecule metabolites. Importantly, this work establishes a framework for conducting metabolite identification probability analysis that enables others to quantify this relationship for their own compounds and properties of interest.

Metabolite Identification↗
DOE OSTI2025

Caught in headlights: Captive white-tailed deer responses to variations in vehicle lighting during imminent collision scenarios

Vehicle collisions with deer (Odocoileus spp.) cause billions of dollars in damages and injure thousands of drivers every year in the United States, and few mitigation methods have proven effective. However, recent research suggests that vehicle lighting might influence white-tailed deer (Odocoileus virginianus; hereafter, deer) responses to oncoming vehicles. Most new vehicles are manufactured with light emitting diode (LED) headlights which differ in total radiance and wavelength of light emitted compared to the previous industry standard of tungstenhalogen (halogen) headlights. Also, frontal vehicle illumination through rear-facing lighting has shown promise in enhancing deer responses to vehicles, but its effectiveness has not been tested under various headlight conditions (headlight type or intensity). As such, it remains unclear how these aspects of vehicle lighting affect deer responses to an approaching vehicle. We used 23 captive, wild-type deer to investigate how variations in vehicle lighting affect deer responses to an approaching vehicle at night, when most collisions occur. We released deer into a 95 m long, 3 m wide chute and approached them from the opposite end with an electric golf cart outfitted with two versions of stock 2017–2020 Ford Fusion headlights (LED and halogen) and a 51 cm rear-facing lightbar to test how vehicle lighting affected deer avoidance behaviors in an imminent, head-on collision scenario. Each deer received eight lighting treatments consisting of unique combinations of headlight type (LED vs. halogen), light intensity (low vs. high beam), and rear-facing lighting (lightbar off vs. on). We measured deer alert and flight behavior using infrared videography. We found that the halogen, high beam, lightbar off treatment had the greatest probability of evoking an alert response. Furthermore, when the lightbar was off, high beams appeared to increase alert probability for halogen headlights. Also, we found evidence that high beam, halogen headlights tend to increase alert probability over high beam, LED headlights, when the lighbar was off. We found no effect of our lighting treatments on deer alert distance, flight probability, or flight initiation distance. Across all behavioral responses, the random effect deer ID explained 0.86–9.19 × more variation than our lighting treatments, reflecting large differences in responses among deer. Overall, we found that vehicle lighting can impact deer behavior during an imminent, head-on collision scenario, although lighting was ineffective at increasing favorable flight behaviors. Future research should investigate how vehicle lighting treatments affect free-ranging, wild deer in a variety of real-world scenarios and at longer approach distances.

White-tailed deer (Odocoileus virginianus) Deer be↗
DOE OSTI2022

Angular correlation between the fission fragment intrinsic spins

The generation of fission fragment (FF) spins and of their relative orbital angular momentum has been debated for more than six decades, and no consensus has been achieved so far. The interpretation of recent experimental results of Wilson et al., has been challenged by several recent theoretical studies, which are not in agreement with one another. According to the interpretation by Wilson et al., the FF spins emerge very long after scission occurrs. Randrup and Vogt, while agreeing that the FF spins are uncorrelated, conclude on the basis of a phenomenological model that these spins are uncorrelated already before scission. Bulgac et al., in a fully microscopic study demonstrate that the primordial FF spin final values are defined before the emission of prompt neutrons and statistical gammas and are strongly correlated with a relative angle between spins close to 2π/3, a result in full agreement with the present independent analysis. The prompt neutrons and statistical gammas carry a significant amount of angular momentum according to the study of Stetcu et al. which can lead to a decorrelation of the FF bandhead spins of the yrast lines measured by Wilson et al., and which also provides arguments why these measured spins are so different from the primordial FF spins evaluated by Bulgac et al.. Here, I show that the unexpected character of the angular correlation between the primordial FF intrinsic spins, recently evaluated by Bulgac et al., which favors FF intrinsic spins pointing predominantly in opposite directions, can be understood by using simple general phase space arguments. The observation by Wilson et al., that the FF spins are uncorrelated follows from both the results of the microscopic calculation of Bulgac et al., and the present analysis of the full correlated probability distribution of the FF spins together with the relative orbital angular momentum of the primordial FFs. These arguments may apply also to heavy-ion collisions and, since there is no use of specifics of the particle interactions, the present results might apply to atomic and molecular systems as well.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Effects of Size and Shape on the Tolerances for Misalignment and Probabilities for Successful Oriented Attachment of Nanoparticles

Oriented attachment (OA) of nanoparticles is an important pathway of crystal growth, but tools for quantitatively modeling OA are lacking. Here we present several simple models that relate the probability of achieving OA to basic geometric parameters such as particle size, shape, and lattice periodicity. A Moiré-domain model is applied to understand twist-misorientations between parallel surfaces, and it predicts that the range of twist angles yielding perfect OA is inversely related to the width of the contact area. This is confirmed and further developed using a surface functional model, which predicts how crystallographic registration forces drive the emergence of complex orientational energy landscapes. The energy landscapes are predicted to possess local minima that can trap particles in imperfect alignments, and these local minima become deeper and more numerous as the contact area increases, making OA more challenging for large particles. Further, a second set of models is presented to understand the sequence of events by which two crystallographic faces become co-planer after collision. We use a ‘central force approximation’ to quantitatively predict the odds of attaining coalignment between various faces when particles collide with random misalignments, and we show that in the absence of biasing forces, the probability of attaining alignment on a given face is roughly proportional to its solid angle as viewed from the center of the particle. The model predicts that OA is most favorable between well-faceted particles and becomes exceedingly unlikely for large spherical particles that express many microfacets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Nucleon clustering at kinetic freezeout of heavy-ion collisions via path-integral Monte Carlo

Clustering of the four-nucleon system at kinetic freezeout conditions is studied using path-integral Monte Carlo techniques. This method seeks to improve upon previous calculations which relied on approximate semiclassical methods or few-body quantum mechanics. Estimates are given for the decay probabilities of the 4N system into various light nuclei decay channels and the strength of spatial correlations is characterized. Additionally, a simple model is presented to describe the impact of this clustering on nucleon multiplicity distributions. Additionally, the effects of a possible modification of the inter-nucleon interaction due to the close critical line (and hypothetical QCD critical point) on the clustering are also studied.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Multi-agent motion planning with sporadic communications for collision avoidance

Here, a novel multi-vehicle motion planning and collision avoidance algorithm is proposed and analyzed. The algorithm aims to reduce the amount of onboard calculations and inter-agent communications needed for each vehicle to successfully navigate through an environment with static obstacles and reach their goals. To this end, each agent first calculates a path to the goal by means of an asymptotically optimal rapidly-exploring random tree (RRT*) with respect to the static obstacles. Then, other agents are treated as dynamic obstacles and potential collisions are determined by means of collision cones. Collision cones depend on the position and velocity from other agents and are grown conservatively between inter-agent communications. Based on the available information, each agent determines if a deconfliction maneuver is needed, if it can continue along its current path, or if communication is needed to make a decision about a conflict. With probability one, our algorithm guarantees that the agents keep from colliding with each other. Under an assumption on the existence of a solution for a vehicle to its goal, this algorithm also solves the planning problem with probability one. Simulations illustrate a group of agents successfully reaching their goal configurations and examine how the uncertainty affects the communication frequency of the multi-agent system.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Pre-equilibrium evolution of conserved charges with initial conditions in the ICCING Monte Carlo event generator

Heavy-ion collisions can be well described through relativistic viscous hydrodynamics, but questions still remain when hydrodynamics is applicable because the initial state may begin very far from equilibrium. Thus, a pre-equilibrium evolution phase is used to bridge the gap between the initial state and hydrodynamics. KøMPøST is one such pre-equilibrium model that propagates the energy-momentum tensor by decomposing it into the background and fluctuations around that background, whose evolution is captured by Green's functions. We extend this formalism to include conserved charges and calculate the corresponding nonequilibrium Green's functions in the relaxation-time approximation. The ICCING algorithm initializes conserved charges in the initial state by sampling $g$ → $q$$\overline{q}$ splitting probabilities and is, thus, perfectly positioned to implement Green's functions for charge propagation. As a result, we show that this method alters the initial-state charge geometries and is applicable in central to mid-central collisions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Ephemeral Learning - Augmenting Triggers with Online-Trained Normalizing Flows

The large data rates at the LHC require an online trigger system to select relevant collisions. Rather than compressing individual events, we propose to compress an entire data set at once. We use a normalizing flow as a deep generative model to learn the probability density of the data online. The events are then represented by the generative neural network and can be inspected offline for anomalies or used for other analysis purposes. We demonstrate our new approach for a toy model and a correlation-enhanced bump hunt.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Neutron Next-Event Estimators Kinematics (Rev.2)

This paper reviews the kinematics of neutron elastic and inelastic scattering with moving and stationary targets for contributions to Neutron Next-Event Estimators (NEEs). NEEs are often used for simulating the response of detectors in locations that have few random walk particles. Contributions to NEEs from collisions differ from sampling the outgoing particle state in standard random walk collisions as the location of the estimator is fixed, thus fixing the scattering angle between the incoming and outgoing directions. To calculate contributions to NEEs, the outgoing energies and probabilities of scatter toward the estimator must be calculated. This paper presents the most general case that encompasses elastic and inelastic scattering with both moving and stationary targets. The moving target case is required for elastic scattering with thermal motion due to the free gas thermal treatment approximation. The stationary target equations are presented for comparison with equations presented in canonical Monte Carlo texts.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Fourier coefficients of noninterdependent collective motions in heavy-ion collisions

Here, we present a scenario in heavy-ion collisions where different modes of collective motions are noninterdependent, driven by factorized actions in the created nuclear medium. Such physics mechanisms could each dominate at a distinct evolution stage, or coexist simultaneously. If the probability of particle emission is modulated by each nondependent collective motion with a single-harmonic Fourier expansion, the particle azimuthal distribution should be the product of all these expansions. Consequently, nonleading cross terms between collectivity modes appear, and their contributions to experimental observables could be significant. In particular, we argue that the chiral magnetic effect (CME) and elliptic flow can develop separately, with their convolution affecting the observable that is sensitive to the shear-induced CME. We will use the event-by-event anomalous-viscous fluid dynamics model to illustrate the effects of this scenario. Besides giving insights into searches for the CME, we also propose feasible experimental tests based on conventional flow harmonics, and demonstrate the emergence of nonleading cross terms with a multiphase transport model.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Characterization of uncertainties in electron-argon collision cross sections

Abstract The predictive capability of a plasma discharge model depends on accurate representations of electron-impact collision cross sections, which determine the corresponding reaction rates and electron transport properties. The values of cross sections can be known only approximately either through experiments or simulations and are thus subject to uncertainties. Quantifying the uncertainties in plasma simulations allows us to assess the reliability of simulations and to provide a basis for interpreting discrepancies between simulations and experiments. For such uncertainty quantification of plasma simulations, it is essential to quantify the uncertainties of the underlying cross sections. Although much effort has been committed to calibrate the cross section values, their uncertainties are not well investigated. We characterize uncertainties in electron-argon atom collision cross sections using a Bayesian framework. Six collision processes—elastic momentum transfer, ionization, and four excitations—are characterized with semi-empirical models, which effectively capture the features important to the macroscopic properties of the plasma. A probability model for the uncertain parameters of these semi-empirical models is developed. Specifically, a Gaussian-process likelihood model is proposed to capture discrepancies among data sets, as well as the model-form inadequacies of the semi-empirical models. Two other likelihood models are compared with the proposed Gaussian-process model, to illustrate the importance of the choice of the likelihood model. The cross section models are calibrated using the electron-beam experiments and ab-inito quantum simulations. The resulting calibrated uncertainties capture well the scattering among the data sets. The calibrated cross section models are further validated against swarm-parameter experiments and zero-dimensional Boltzmann equation simulations of widely used cross section datasets.

Chung, Seung Whan (ORCID:0000000302501549)↗
DOE OSTI2021

Radial Segregation between Red Giant Populations in the Globular Cluster NGC 3201: Tentative Role of Varying Mass among the Progenitors

In the globular cluster NGC 3201, we study subpopulations (SPs) of red giants (RGs) distinguished by their characteristics. In its central part, we make use of multicolor Hubble Space Telescope (HST) photometry coupled with spectroscopy of RGs spread out over a larger cluster face. In the color index CI = (m{sub F275W}−m{sub F336W}) −(m{sub F336W}−m{sub F439W}), RGs commonly referred to as first generation, SPI, are separated from the counterparts of the second generation, SPII. The latter are more concentrated than the former at a confidence level of P = 93%, within the field covered by the HST photometry. Their minor portion (∼28%, SPIIm) located in the middle of the δCI range spanned across the split RG branch relative to the splitting edge is more centrally concentrated at a higher confidence level of 96%. We found, from the [Na/Fe]−δCI plot for RGs in common between the photometry and spectroscopy, that SPI and SPII are mostly overlapped within 0.0 < [Na/Fe] < 0.3. RGs with [Na/Fe] < 0.0 (mostly SPI) keep their lower concentration to the cluster center over larger radial distance as compared to SPII RGs with [Na/Fe] > 0.3 at a confidence level of P = 88%. It is increased up to 95% if a restriction on [O/Fe] is imposed. The obtained result, particularly in the central part, is a clue that SPIIm RGs are the descendants of more massive progenitors. Of these RGs approximately one-third probably originate from recent (within the past 1.5 Gyr) blue stragglers with M {sub BS} > 1.1M {sub ☉}, and the other two-thirds could be accounted for by older collision products of primordial MS stars.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Silica nanocluster binding rate coefficients from molecular dynamics trajectory calculations

Oxide nanoparticle growth from vapor phase precursors occurs in high temperature aerosol reactors first via the formation of nanoclusters, which are nanometer-scale condensed-phase species composed of 10 1 -10 2 atoms. The binding rate for nanoclusters, defined as the rate at which two nanoclusters collide with and stick to one another, is hence of interest in predicting nanoparticle size distribution functions in an aerosol. We have utilized molecular dynamics (MD) trajectory calculations to determine the homogeneous (equal-sized) and heterogeneous (disparate-sized) binding rate coefficients of SiO2 (silica) nanoclusters composed of 18, 144, and 333 atoms. MD trajectory calculations incorporated all-atom models of nanoclusters using a combined Born–Huggins–Mayer-Lennard-Jones potential model, which accounts for both short range interactions and electrostatic interactions. MD calculations were utilized to determine the binding probability as a function of both initial relative velocity and impact parameter; integration of the binding probability across impact parameter and relative velocity space yields the binding rate coefficient. MD trajectory calculations reveal that the most common type of encounters between nanoclusters leading to binding are grazing collisions, i.e. instances where collision would not occur without induced dipole potential influences. The resulting binding rate coefficients are found to be extremely weakly dependent on system temperature, which is in contrast to the use of rate coefficient models which are the product of a hard-sphere collision rate coefficient and a constant enhancement factor (leading to a rate coefficient proportional to the square root of temperature). Enhancement factors defined from MD trajectory calculations fall in the range of 3–9 as system temperature decreases from 1500 K to 300 K. Furthermore, such large values suggest that potential interactions need to be considered when modeling oxide nanocluster growth in the gas phase.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗