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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Statistical Distributions of Optical Flares from Gamma-Ray Bursts

We statistically study gamma-ray burst (GRB) optical flares from the Swift /UVOT catalog. We compile 119 optical flares, including 77 flares with redshift measurements. Some tight correlations among the timescales of optical flares are found. For example, the rise time is correlated with the decay time, and the duration time is correlated with the peak time of optical flares. These two tight correlations indicate that longer rise times are associated with longer decay times of optical flares and also suggest that broader optical flares peak at later times, which are consistent with the corresponding correlations of X-ray flares. We also study the frequency distributions of optical flare parameters, including the duration time, rise time, decay time, peak time, and waiting time. Similar power-law distributions for optical and X-ray flares are found. Our statistic results imply that GRB optical flares and X-ray flares may share the similar physical origin, and both of them are possibly related to central engine activities.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2010

Fiber Breakage Model for Carbon Composite Stress Rupture Phenomenon: Theoretical Development and Applications

Stress rupture failure of Carbon Composite Overwrapped Pressure Vessels (COPVs) is of serious concern to Science Mission and Constellation programs since there are a number of COPVs on board space vehicles with stored gases under high pressure for long durations of time. It has become customary to establish the reliability of these vessels using the so called classic models. The classical models are based on Weibull statistics fitted to observed stress rupture data. These stochastic models cannot account for any additional damage due to the complex pressure-time histories characteristic of COPVs being supplied for NASA missions. In particular, it is suspected that the effects of proof test could significantly reduce the stress rupture lifetime of COPVs. The focus of this paper is to present an analytical appraisal of a model that incorporates damage due to proof test. The model examined in the current paper is based on physical mechanisms such as micromechanics based load sharing concepts coupled with creep rupture and Weibull statistics. For example, the classic model cannot accommodate for damage due to proof testing which every flight vessel undergoes. The paper compares current model to the classic model with a number of examples. In addition, several applications of the model to current ISS and Constellation program issues are also examined.

Murthy, Pappu L. N.↗
DOE OSTI2020

Explainable and trustworthy artificial intelligence for correctable modeling in chemical sciences

Data science has primarily focused on big data, but for many physics, chemistry, and engineering applications, data are often small, correlated and, thus, low dimensional, and sourced from both computations and experiments with various levels of noise. Typical statistics and machine learning methods do not work for these cases. Expert knowledge is essential, but a systematic framework for incorporating it into physics-based models under uncertainty is lacking. Here, we develop a mathematical and computational framework for probabilistic artificial intelligence (AI)–based predictive modeling combining data, expert knowledge, multiscale models, and information theory through uncertainty quantification and probabilistic graphical models (PGMs). We apply PGMs to chemistry specifically and develop predictive guarantees for PGMs generally. Our proposed framework, combining AI and uncertainty quantification, provides explainable results leading to correctable and, eventually, trustworthy models. The proposed framework is demonstrated on a microkinetic model of the oxygen reduction reaction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Are There Enough Decoy States to Ensure Key Secrecy in Quantum Cryptography?

The decoy state method has been proposed to detect a photon number splitting (PNS) attack. Today, the decoy state method is considered to be an almost universal method for proving the secrecy of quantum cryptography protocols and calculating the length of a secret key. In this paper, it is shown that there exist attacks, for example, a beam splitting (BS) attack, to which the decoy state method turns out to be insensitive. The decoy state method is oriented to the detection of changes in the photocount statistics of information states and decoy states under a PNS attack. Under a BS attack, the photocount statistics is not changed. As a result, the decoy state method significantly overestimates the length of the key. Thus, the decoy state method is not a universal method that allows the detection of various attacks. Apparently, due to a large number of publications on the decoy state method, a widespread opinion has been formed that this method is universal. This fact has led to attempts to adopt this method as an international standard in quantum cryptography, which is clearly premature.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

The meson spectroscopy program with CLAS12 at Jefferson Laboratory

The study of the hadronic spectrum is one of the most powerful tools to investigate the mechanism at the basis of quark confinement within hadrons. A precise determination of the spectrum allows not only to assess the properties of the hadrons in their fundamental and excited states, but also to investigate the existence of states resulting from alternative configurations of quarks and gluons, such as the glue-balls, hybrid hadrons and many-quarks configurations. The study of the mesonic part of the spectrum can play a central role in this investigation thanks to the strong signature that the hybrid mesons are expected to have: the presence of explicit gluonic degrees of freedom in such states may result in JPC configurations not allowed for the standard q ¯ q states. From the experimental side the expected high-multiplicity decays of the hybrid mesons require an apparatus with high performances in terms of rate-capability, resolution and acceptance. The CLAS12 experiment (formally MesonEx) is one of new-generation experiments at Thomas Jefferson National Laboratory (JLAB) for which an unprecedented statistics of events, with fully reconstructed kinematics for large particle multiplicity decays, will be available. A wide scientific program that will start in 2016 has been deployed for meson spectrum investigation with the CLAS12 apparatus in Hall B at energies up to 11 GeV. One of the main parts of the program is based on the use of the Forward Tagger apparatus, which will allow CLAS12 experiment to extend the study of meson electro-production to the quasi-real photo-production kinematical region (very low Q2), where the production of hybrid mesons is expected to be favoured. The data analysis which is required to extract the signal from hybrid states should go beyond the standard partial wave analysis techniques and a new analysis framework is being set up through the international network Haspect. The Haspect Network gathers people involved into theoretical and experimental hadronic physics all over the world, to investigate and propose new analysis models and new statistical techniques to unfold signal and background. The new analysis framework is being developed and tested using the existing CLAS data and results are projected to the CLAS12 performances, showing that the quest for hybrid exotic mesons is at reach.

Rizzo, Alessandro↗
DOE OSTI2016

The large deviation principle and steady-state fluctuation theorem for the entropy production rate of a stochastic process in magnetic fields

Fluctuation theorem is one of the major achievements in the field of nonequilibrium statistical mechanics during the past two decades. There exist very few results for steady-state fluctuation theorem of sample entropy production rate in terms of large deviation principle for diffusion processes due to the technical difficulties. Here we give a proof for the steady-state fluctuation theorem of a diffusion process in magnetic fields, with explicit expressions of the free energy function and rate function. The proof is based on the Karhunen-Loève expansion of complex-valued Ornstein-Uhlenbeck process.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Investigation of the Self-Assembly Behavior of Statistical Bottlebrush Copolymers via Self-Consistent Field Theory Simulations

Bottlebrush copolymers, a class of high-density side-chain-grafted copolymers with high molecular weights, have recently attracted extensive research attention due to the dramatic reduction of chain entanglement in these copolymers, unique self-assembly behaviors, and distinctive physical properties. In this study, we employ self-consistent field theory simulations to systemically investigate the phase behavior of statistical bottlebrush copolymers A-stat-B, an intriguing subclass of bottlebrush copolymers with random or alternating sequences of A and B side chains. We find that a broader variety of ordered mesophases can be stabilized in the melts of these copolymers relative to the more extensively studied bottlebrush block copolymers. In particular, we observe that these statistical copolymers can stabilize sphere phases over a larger range of species volume fractions than their diblock and tetrablock counterparts but require much higher segregation strength to achieve this. The sphere and hexagonally packed cylinder phases formed by conformationally symmetric statistical bottlebrush copolymers possess interesting discrete core–shell structures with the backbone and lower volume fraction side-chain species forming shells and cores, respectively. Moreover, we uncover a new strategy of stabilizing the Frank–Kasper phase A15 by introducing conformational asymmetry through different side-chain lengths and also observe deflection of order–order phase boundaries and significant mixing between the backbones and short side chains as conformational asymmetry is introduced.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Structural crossover in a supercooled metallic liquid and the link to a liquid-to-liquid phase transition

Time-resolved synchrotron measurements were carried out to capture the structure evolution of an electrostatically levitated metallic-glass-forming liquid during free cooling. The experimental data shows a crossover in the liquid structure at ~1000 K, about 115 K below the melting temperature and 150 K above the crystallization temperature. The structure change is characterized by a dramatic growth in the extended-range order below the crossover temperature. Molecular dynamics simulations have identified that the growth of the extended-range order was due to an increased correlation between solute atoms. These results provide structural evidence for a liquid-to-liquid-phase-transition in the supercooled metallic liquid.

Glass transitions↗
DOE OSTI2016

Phase dependence of the unnormalized second-order photon correlation function

We investigate the resonant quantum dynamics of a multi-qubit ensemble in a microcavity. Both the quantum-dot subsystem and the microcavity mode are pumped coherently. We find that the microcavity photon statistics depends on the phase difference of the driving lasers, which is not the case for the photon intensity at resonant driving. This way, one can manipulate the two-photon correlations. In particular, higher degrees of photon correlations and, eventually, stronger intensities are obtained. Furthermore, the microcavity photon statistics exhibits steady-state oscillatory behaviors as well as asymmetries.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1992

Statistical mechanics of violent relaxation

We propose a functional that is extremized through violent relaxation. It is based on the Ansatz that the wave-particle scattering during violent dynamical processes can be approximated as a sequence of discrete scattering events that occur near a particle's perigalacticon. This functional has an extremum whose structure closely resembles that of spheroidal stellar systems such as elliptical galaxies. The results described here, therefore, provide a simple framework for understanding the physical nature of violent relaxation and support the view that galaxies are structured in accord with fundamental statistical principles.

Spergel, David N.↗
DOE OSTI2021

Understanding protein-complex assembly through grand canonical maximum entropy modeling

Inside a cell, heterotypic proteins assemble in inhomogeneous, crowded systems where the abundance of these proteins vary with cell types. While some protein complexes form putative structures that can be visualized with imaging, there are far more protein complexes that are yet to be solved because of their dynamic associations with one another. Nevertheless, it is possible to infer these protein complexes through a physical model. However, it is often not clear to physicists what kind of data from biology is necessary for such a modeling endeavor. Here, we aim to model these clusters of coarse-grained protein assemblies from multiple subunits through the constraints of interactions among the subunits and the chemical potential of each subunit. We obtained the constraints on the interactions among subunits from the known protein structures. We inferred the chemical potential that dictates the particle number distribution of each protein subunit from the knowledge of protein abundance from experimental data. Guided by the maximum entropy principle, we formulated an inverse statistical mechanical method to infer the distribution of particle numbers from the data of protein abundance as chemical potentials for a grand canonical multicomponent mixture. Using grand canonical Monte Carlo simulations, we captured a distribution of high-order clusters in a protein complex of succinate dehydrogenase with four known subunits. The complexity of hierarchical clusters varies with the relative protein abundance of each subunit in distinctive cell types such as lung, heart, and brain. When the crowding content increases, we observed that crowding stabilizes emergent clusters that do not exist in dilute conditions. We, therefore, proposed a testable hypothesis that the hierarchical complexity of protein clusters on a molecular scale is a plausible biomarker of predicting the phenotypes of a cell.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

While legume-based rotation influences the chemical composition of mineral-associated organic matter, tillage has little effect on its persistence

Legume-based rotations are key to enhancing soil carbon (C) storage in stable forms such as mineral-associated organic matter (MAOM), improving soil health, and helping maintain long-term C stock. This study investigates how plant inputs and tillage practices influence the distribution, composition, and persistence of MAOM in wheat cropping systems when legumes are incorporated into the rotation. Soil samples were collected from Walla Walla silty loam of northeast Oregon, including conventional tillage (CT) and no-till (NT) treatments in a long-term dryland winter wheat (Triticum aestivum L.) and spring pea (Pisum sativum L.) rotation experiment, with an adjacent grass pasture (GP) plot serving as the baseline. Bulk soil, particulate organic matter (POM), and MAOM fractions were scanned using mid-infrared (Mid-IR) spectroscopy and analyzed for total C and nitrogen (N) as well as radiocarbon age, aggregate stability, and mineralizable C. In WP systems, NT promoted greater C accumulation and stabilization in MAOM than CT, reaching levels similar to those under native GP conditions. Furthermore, our findings indicate that decades of continuous legume-based rotation were insufficient to restore total bulk C content to levels observed in the undisturbed reference site (GP) in the 0–30 cm layer, with increases limited to the surface layer. The Mid-IR data provided insights into how cultivation may alter the chemical composition of MAOM. An increase in amide bands was observed in GP, while legume-based rotation systems favored an abundance of aliphatic (C-H) components in the MAOM fraction. In cultivated soils, C in MAOM exhibited longer residence times under arable conditions than in grassland, while POM remained an actively cycled pool across all treatments. Despite variations in the chemical composition of MAOM across treatments, mineralizable C emissions from MAOM fractions remained statistically unchanged during the 96-hour incubation period. Our findings suggest that soil physical protection, enhanced by reduced tillage, is the primary factor influencing MAOM cycling.

Carbon stabilization↗
DOE OSTI2022

Quantifying Errors in Effective Cluster Interactions of Lattice Gas Cluster Expansions

The promise of lattice gas (LG) cluster expansions (CEs) is that they can describe a given system property to any level of accuracy since the orthogonal “cluster basis functions” span the complete space of available configurations. Such an approach can be constructed to an arbitrarily large surface of a finite number of distinct adsorption sites. Unfortunately, this is only true for the case of an ideal, fixed lattice decorated with components at precise lattice points (the lattice “sites”) with no distortions or relaxations subsequently allowed. Since most systems, and surfaces specifically, do not conform to such an ideal set of constraints, errors in LG CEs must be expected or CE convergence severely hampered. Beyond this, numerical errors in the provided data can complicate the proper construction of a truly predictive and/or physically significant CE. In this work, we show here how reliance on typical statistical tools like confidence intervals cannot be expected to provide an accurate representation of the uncertainty of effective cluster interactions (ECIs) in the CE due to the nature of the target ab initio data and the nature of CEs themselves. We develop a method for estimating these errors that does not rely on statistical assumptions about the model or data. We then use these ECI errors to quantify fundamental consequences on the uncertainty of ECIs in CEs built from O/Fe(100) data whose surface and adsorbates have been allowed to relax in a typical manner and from O/Fe(100) data whose surface and adsorbates are fixed in ideal lattice positions. We also quantify the effect of using a different density functional theory exchange–correlation functional, using these ECI errors to assess the significance in any deviations. In both cases, our method is shown to have remarkable utility in the quantification of errors in the ECIs of CEs. While we stick to the lattice gas convention in this work, the method is equally applicable to the Ising convention or, in principle, any linear model of sufficient complexity.

08 HYDROGEN↗
DOE OSTI2018

Derivation of a realistic forcing term to reproduce the turbulent characteristics of round jets on the centerline

Turbulence forcing techniques are often required in the numerical simulation of statistically stationary turbulent flows. However, the existing forcing techniques are not based on physics, but rather arbitrary numerical methods that sustain the turbulent kinetic energy. In this work, a forcing technique is devised to reproduce the centerline turbulent characteristics of round jets in a triply periodic box. It is derived from the Navier-Stokes equations by applying aReynolds decompositionwith themean velocity of the axisymmetric jet. The result is an anisotropic linear forcing term, which is intended to be used in a three-dimensional box to create turbulence. Four direct numerical simulations with different Re λ have been performed with these forcing terms. We report the budget of the terms in the kinetic energy equation is very close to the experimental measurement on the centerline. The anisotropy, kinetic energy k, and dissipation rate ε of the simulations are also comparable to experimental values. Finally, the kinetic energy spectrum in the axial direction, $\phi(κ_1)$, is presented. With appropriate normalizations, the spectrum agrees well with the round jet spectrum on its centerline.

42 ENGINEERING↗
DOE OSTI2020

Joint Estimation of Behind-the-Meter Solar Generation in a Community

Distribution grid planning, control, and optimization require accurate estimation of solar photovoltaic (PV) generation and electric load in the system. Most of the small residential solar PV systems are installed behind-the-meter making only the net load readings available to the utilities. This paper presents an unsupervised framework for joint disaggregation of the net load readings of a group of customers into the solar PV generation and electric load. Our algorithm synergistically combines a physical PV system performance model for individual solar PV generation estimation with a statistical model for joint load estimation. The electric loads for a group of customers are estimated jointly by a mixed hidden Markov model (MHMM) which enables modeling the general load consumption behavior present in all customers while acknowledging the individual differences. At the same time, the model can capture the change in load patterns over a time period by the hidden Markov states. The proposed algorithm is also capable of estimating the key technical parameters of the solar PV systems. Our proposed method is evaluated using the net load, electric load, and solar PV generation data gathered from residential customers located in Austin, Texas. Testing results show that our proposed method reduces the mean squared error of state-of-the-art net-load disaggregation algorithms by 67%.

behind-the-meter solar generation↗
DOE OSTI2016

SU-F-T-156: Monte Carlo Simulation Using TOPAS for Synchrotron Based Proton Discrete Spot Scanning System

Purpose: This study provides an overview of the design and commissioning of the Monte Carlo (MC) model of the spot-scanning proton therapy nozzle and its implementation for the patient plan simulation. Methods: The Hitachi PROBEAT V scanning nozzle was simulated based on vendor specifications using the TOPAS extension of Geant4 code. FLUKA MC simulation was also utilized to provide supporting data for the main simulation. Validation of the MC model was performed using vendor provided data and measurements collected during acceptance/commissioning of the proton therapy machine. Actual patient plans using CT based treatment geometry were simulated and compared to the dose distributions produced by the treatment planning system (Varian Eclipse 13.6), and patient quality assurance measurements. In-house MATLAB scripts are used for converting DICOM data into TOPAS input files. Results: Comparison analysis of integrated depth doses (IDDs), therapeutic ranges (R90), and spot shape/sizes at different distances from the isocenter, indicate good agreement between MC and measurements. R90 agreement is within 0.15 mm across all energy tunes. IDDs and spot shapes/sizes differences are within statistical error of simulation (less than 1.5%). The MC simulated data, validated with physical measurements, were used for the commissioning of the treatment planning system. Patient geometry simulations were conducted based on the Eclipse produced DICOM plans. Conclusion: The treatment nozzle and standard option beam model were implemented in the TOPAS framework to simulate a highly conformal discrete spot-scanning proton beam system.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

NMR_DIFFSIM v. 1.0

The software generates and analyzes nuclear magnetic resonance spin diffusion simulations for the purpose of measuring domain sizes or spin diffusion constants in a heterogenous sample. Its primary capabilities are: 1. It solves the spin magnetization diffusion equation to simulate the results of a spin diffusion experiment using any structural input geometry, including structure models generated by molecular dynamics or other computational or experimental methods. 2. It compares the results of simulations on an input structure to experimental data, providing a data fitting algorithm to extract physical information from the experimental data via the simulation. 3. It can provide statistical measures to compare correlations between structural models, to determine domain clustering and connectedness, and to provide goodness-of-fit measures including chord length distributions, chi-squared values, Pearson coefficients and pair correlation functions. 4. It provides output reports, data files, and movies to visualize simulation results.

Sorte, Eric↗
DOE OSTI2022

Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation

Machine learning and artificial intelligence (AI/ML) methods are beginning to have significant impact in chemistry and condensed matter physics. For example, deep learning methods have demonstrated new capabilities for high-throughput virtual screening, and global optimization approaches for inverse design of materials. Recently, a relatively new branch of AI/ML, deep generative models (GMs), provide additional promise as they encode material structure and/or properties into a latent space, and through exploration and manipulation of the latent space can generate new materials. These approaches learn representations of a material structure and its corresponding chemistry or physics to accelerate materials discovery, which differs from traditional AI/ML methods that use statistical and combinatorial screening of existing materials via distinct structure-property relationships. However, application of GMs to inorganic materials has been notably harder than organic molecules because inorganic structure is often more complex to encode. In this work we review recent innovations that have enabled GMs to accelerate inorganic materials discovery. We focus on different representations of material structure, their impact on inverse design strategies using variational autoencoders or generative adversarial networks, and highlight the potential of these approaches for discovering materials with targeted properties needed for technological innovation.

36 MATERIALS SCIENCE↗