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DOE OSTI2015

determining the mass of kepler-78b with nonparametric Gaussian process estimation

Kepler-78b is a transiting planet that is 1.2 times the radius of Earth and orbits a young, active K dwarf every 8 hr. The mass of Kepler-78b has been independently reported by two teams based on radial velocity (RV) measurements using the HIRES and HARPS-N spectrographs. Due to the active nature of the host star, a stellar activity model is required to distinguish and isolate the planetary signal in RV data. Whereas previous studies tested parametric stellar activity models, we modeled this system using nonparametric Gaussian process (GP) regression. We produced a GP regression of relevant Kepler photometry. We then use the posterior parameter distribution for our photometric fit as a prior for our simultaneous GP + Keplerian orbit models of the RV data sets. We tested three simple kernel functions for our GP regressions. Based on a Bayesian likelihood analysis, we selected a quasi-periodic kernel model with GP hyperparameters coupled between the two RV data sets, giving a Doppler amplitude of 1.86 ± 0.25 m s{sup −1} and supporting our belief that the correlated noise we are modeling is astrophysical. The corresponding mass of 1.87{sub −0.26}{sup +0.27} M{sub ⊕} is consistent with that measured in previous studies, and more robust due to our nonparametric signal estimation. Based on our mass and the radius measurement from transit photometry, Kepler-78b has a bulk density of 6.0{sub −1.4}{sup +1.9} g cm{sup −3}. We estimate that Kepler-78b is 32% ± 26% iron using a two-component rock-iron model. This is consistent with an Earth-like composition, with uncertainty spanning Moon-like to Mercury-like compositions.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2013

Electron nongyrotropy in the context of collisionless magnetic reconnection

Collisionless magnetized plasmas have the tendency to isotropize their velocity distribution function around the local magnetic field direction, i.e., to be gyrotropic, unless some spatial and/or temporal fluctuations develop at the particle gyroscales. Electron gyroscale inhomogeneities are well known to develop during the magnetic reconnection process. Nongyrotropic electron velocity distribution functions have been observed to play a key role in the dissipative process breaking the field line connectivity. In this paper, we present a new method to quantify the deviation of a particle population from gyrotropy. The method accounts for the full 3D shape of the distribution and its analytical formulation allows fast numerical computation. Regions associated with a significant degree of nongyrotropy are shown, as well as the kinetic origin of the nongyrotropy and the fluid signature it is associated with. Using the result of 2.5D Particle-In-Cell simulations of magnetic reconnection in symmetric and asymmetric configurations, it is found that neither the reconnection site nor the topological boundaries are generally associated with a maximized degree of nongyrotropy. Nongyrotropic regions do not correspond to a specific fluid behavior as equivalent nongyrotropy is found to extend over the electron dissipation region as well as in non-dissipative diamagnetic drift layers. The localization of highly nongyrotropic regions in numerical models and their correlation with other observable quantities can, however, improve the characterization of spatial structures explored by spacecraft missions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2019

Big Data Framework with Machine Learning for D and D Applications - 19108

The nuclear industry is experiencing a steady increase in maintenance costs even though plants are maintained under high levels of safety, capability and reliability. Nuclear power plants are expected to run every unit at maximum capacity at all times, efficiently utilizing assets with minimal downtime. Surveillance and maintenance of nuclear decommissioning infrastructure provides lot challenges with respect to maintenance or decommissioning of the buildings. There is a need for a framework to analyze the huge amount of data generated by the sensors on the nuclear reactor components as well as structures, to monitor the conditions of these building over a period of time. Emerging technologies such as big data analytics have become a requirement in the nuclear industry to improve structural health monitoring and diagnostics. FIU will make use of existing mature technologies in the areas of imaging, robotics, big data, and machine learning/deep learning to assess the structural integrity of aging facilities at DOE sites. As these facilities await decommissioning, there is a need to understand the structural health of these structures. Many of these facilities were built over 50 years ago and in some cases these facilities have gone beyond the operational life expectancy. In other cases, the facilities have been placed in a state of 'cold and dark' and they are sitting unused, awaiting decommissioning. Finally, some older facilities are one-of-a-kind operational/production facilities supporting critical DOE missions and cannot be shut down. In any of these scenarios, the structural integrity of these facilities may be compromised, so it is imperative that adequate inspections and data collection/analysis be performed on a continuous and ongoing basis. The primary goals of the research include collecting various formats of data such as structured, unstructured and streaming data from the various sensors deployed in buildings, collect videos/pictures from various imaging sources, ingest them using a Hadoop distributed file system and process the data using Spark to perform batch processing and real time analytics. Research and development on various machine learning/deep learning algorithms will also be performed to analyze the heterogeneous data collected from nuclear decommission infrastructure. FIU will design the big data framework to ingest, store and process huge amounts of heterogeneous data collected from many sources and optimize the algorithms to provide insights into the data and predict anomalies observed when compared against baseline conditions. Various modules of this framework will include heterogeneous data sources, message broker, Hadoop distributed file system, Spark for stream and batch processing, machine learning/ deep learning, Cassandra - persistent data store and visualization. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2012

NEWS ON THE s PROCESS FROM YOUNG OPEN CLUSTERS

Recent spectroscopic measurements in open clusters younger than the Sun with [Fe/H] {approx}> 0 showed that the abundances of neutron-rich elements have continued to increase in the Galaxy after the formation of the Sun, roughly maintaining a solar-like distribution. This growth requires neutron fluences larger than those so far assumed, as these would have too few neutrons per iron seed. We suggest that the observed enhancements can be produced by nucleosynthesis in asymptotic giant branch (AGB) stars of low mass (M < 1.5 M{sub Sun }) if they release neutrons from the {sup 13}C({alpha},n){sup 16}O reaction in reservoirs larger by a factor of four than assumed in more massive AGB stars (M > 1.5 M{sub Sun }). Adopting such a stronger neutron source as a contributor to the abundances at the time of formation of the Sun, we show that this also affects the solar s-process distribution, so that its main component is well reproduced, without the need to assume ad hoc primary sources for the synthesis of s elements up to A {approx} 130, contrary to suggestions from other works. The changes in the expected abundances that we find are primarily due to the following reasons. (1) Enhancing the neutron source increases the efficiency of the s process, so that the ensuing stellar yields now mimic the solar distribution at a metallicity higher than before ([Fe/H ] {approx}> -0.1). (2) The age-metallicity relation is rather flat for several Gyr in that metallicity regime, so that those conditions remain stable and the enhanced nuclear yields, which are necessary to maintain a solar-like abundance pattern, can dominate the composition of the interstellar medium from which subsequent stars are formed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2019

Impact of Anthropogenic Emission Injection Height Uncertainty on Global Sulfur Dioxide and Aerosol Distribution

Anthropogenic sulfur compounds play an important role in acid deposition, aerosol particle formation, and subsequent radiative forcing and human fine particulate exposure. There are substantial uncertainties in processes influencing sulfate and precursor distributions, however, that have not yet been resolved through comparisons with observations. We find here an underappreciated factor that has a large impact on model results: uncertain emission height. Global aerosol-climate model simulations indicate that the assumed effective anthropogenic emission height is very important to SO 2 near-surface concentrations and vertical profile. The global range of near-surface SO 2 concentration over land (ocean) due to uncertainty in industrial (international shipping) emission injection height is 81% (76%), relative to the average concentration. This sensitivity is much larger than the uncertainty of SO 2 emission rates. Black carbon and primary organic matter concentration and profiles are also sensitive to emission heights (53% over land and 28% over oceans). The impact of emission height uncertainty is larger in winter for land-based emissions, but larger in summer over the Northern Hemisphere ocean for shipping emissions. The variation in aerosol optical depth related to shipping emission injection heights is 11% over oceans, revealing the potential importance of injection height on aerosol forcing and climatic effects. The large impact on SO 2 concentrations can confound attempts to use surface, aircraft, and satellite observations to constrain the importance of other processes that govern sulfur compound distributions in the atmosphere. In conclusion, the influence of emission height on vertical SO 2 column also will impact the accuracy of satellite retrievals.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Modulating the Mixing Gibbs Free Energy to Enhance Solid–Liquid Phase Separation for High–Performance Organic Solar Cells

Organic solar cells (OSC) feature a hierarchical structure with the electron donor/acceptor layer sandwiched by anode and cathode, which raises the importance of controlling the molecular crystal orientation, domain size, and vertical distribution to facilitate the charge collection at electrodes. However, the similar conjugated backbone of donor/acceptor material and fast film–formation kinetics have led to spinodal–decomposition–orientated phase separation that result in the film presenting an intimately mixed morphology and random molecular orientation. To solve the issue, the mixing Gibbs free energy–triggered solid–liquid phase separation during the film formation process is enhanced by solidifying one component and solvating the other based on a liquid additive. Following the liquid evaporation process, a favorable vertical distribution is obtained. Meanwhile, the prolonged solvation process enlarges the domain size and assists the molecules to diffuse and orient properly, enabling better exciton/charge dynamics during the power conversion processes. As a result, the fabricated devices exhibit a fill factor over 80% and an efficiency of 18.72%, which is one of the top efficiencies for binary OSCs. In conclusion, insights and a methodology is provided here to manipulate the organic donor/acceptor phase separation in terms of mixing Gibbs free energy.

14 SOLAR ENERGY↗

DOE OSTI2013

RELATIVITY AND THE EVOLUTION OF THE GALACTIC CENTER S-STAR ORBITS

We consider the orbital evolution of the S-stars, the young main-sequence stars near the supermassive black hole (SBH) at the Galactic center, and put constraints on competing models for their origin. Our analysis includes for the first time the joint effects of Newtonian and relativistic perturbations to the motion, including the dragging of inertial frames by a spinning SBH as well as torques due to finite-N asymmetries in the field-star distribution (resonant relaxation, RR). The evolution of the S-star orbits is strongly influenced by the Schwarzschild barrier (SB), the locus in the (E, L) plane where RR is ineffective at driving orbits to higher eccentricities. Formation models that invoke tidal disruption of binary stars by the SBH tend to place stars below (i.e., at higher eccentricities than) the SB; some stars remain below the barrier, but most stars are able to penetrate it, after which they are subject to RR and achieve a nearly thermal distribution of eccentricities. This process requires roughly 50 Myr in nuclear models with relaxed stellar cusps, or {approx}> 10 Myr, regardless of the initial distribution of eccentricities, in nuclear models that include a dense cluster of 10 M{sub Sun} black holes. We find a probability of {approx}< 1% for any S-star to be tidally disrupted by the SBH over its lifetime.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Spatio-Temporal Data Analysis at Scale Using Models Based on Gaussian Processes

Gaussian processes are the most commonly used statistical model for spatial and spatio-temporal processes that vary continuously. They are broadly applicable in the physical sciences and engineering and are also frequently used to approximate the output of complex computer models, deterministic or stochastic. We undertook research related to theory, computation, and applications of Gaussian processes as well as some work on estimating extremes of distributions for which a Gaussian process assumption might be inappropriate. Our theoretical contributions include the development of new classes of spatial-temporal covariance functions with desirable properties and new results showing that certain covariance models lead to predictions with undesirable properties. To understand how Gaussian process models behave when applied to deterministic computer models, we derived what we believe to be the first significant results on the large sample properties of estimators of parameters of Gaussian processes when the actual process is a simple deterministic function. Finally, we investigated some theoretical issues related to maxima of observations with varying upper bounds and found that, depending on the circumstances, standard large sample results for maxima may or may not hold. Our computational innovations include methods for analyzing large spatial datasets when observations fall on a partially observed grid and methods for estimating parameters of a Gaussian process model from observations taken by a polar-orbiting satellite. In our application of Gaussian process models to deterministic computer experiments, we carried out some matrix computations that would have been infeasible using even extended precision arithmetic by focusing on special cases in which all elements of the matrices under study are rational and using exact arithmetic. The applications we studied include total column ozone as measured from a polar-orbiting satellite, sea surface temperatures over the Pacific Ocean, and annual temperature extremes at a site in New York City. In each of these applications, our theoretical and computational innovations were directly motivated by the challenges posed by analyzing these and similar types of data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

EV Charging Infrastructure Energization: An Overview of Approaches for Simplifying Processes and Accelerating Timelines to Processing EV Charging Load Service Requests

The United States has seen significant growth in electric vehicle (EV) adoption, leading to increased demand for EV charging infrastructure and electricity. Growth in electricity demand from EV charging, coupled with growth in other sectors like data centers and electrification of other sectors, is impacting electricity infrastructure and load service request processes after two decades of relatively flat electricity demand. While most electric vehicle charging occurs at home, enroute and depot charging for medium and heavy-duty vehicles, both using high-powered EV charging, are critical to meet electric vehicle operational needs. Over the past decade, EV charging infrastructure site developers, site hosts, and electric distribution utilities have navigated the process to integrate chargers onto the electric grid. Site developers and site hosts have expressed distress that the integration process for high-powered EV charging projects does not meet the needs of the EV market for timeliness or cost. High-powered charging stations typically require a load service request or an agreement with the local utility to connect to the grid. The process of energizing a new high-powered charging site can be complex and time consuming, often taking up to 2 years. This timeline is the result of current utility energization processes having been designed for construction projects that take longer to build (i.e., buildings). The specific challenges stem from various factors, including compartmentalization in application processes, the integration of EV charging process approvals with other distributed energy resources (DERs), and the need to ensure grid reliability. The energization process needs to evolve to meet the growing demand for high-powered EV charging. This white paper compiles information gathered through various conversations with key stakeholders, including utilities, utility regulators, EV charging operators, site developers, and authorities having jurisdiction (AHJ) as well as through an extensive literature review. This document identifies the challenges and provides potential solutions to streamline the process of connecting EV charging infrastructure to the power grid in the United States, serving as a starting point for future conversations around these solutions.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Deep Reinforcement Learning for Distribution System Operations: A Tutorial and Survey

Here, the rapid evolution of modern electric power distribution systems into complex networks of interconnected active devices, distributed generation (DG), and storage poses increasing difficulties for system operators. The large-scale integration of distributed energy resources (DERs) and the rapid exchange of measurement data via communication networks present major opportunities for advancing grid operations but also introduce greater uncertainty, higher data dimensionality, more complex network and device models, and challenging control and optimization problems. Deep reinforcement learning (DRL) algorithms are promising in addressing these challenges. However, they have not been effectively adapted for power systems applications, requiring extensive customization for implementation and evaluation. This has resulted in reproducibility challenges and a steep learning curve for researchers new to applying DRL algorithms to the power systems domain. To bridge these gaps, this tutorial aims to serve as a valuable resource for researchers interested in exploring learning-based algorithms to operate active power distribution networks. Specifically, this work presents a generalized process for translating sequential decision-making problems in power distribution systems into Markov decision process (MDP) formulations, illustrated through concrete grid service examples. Additionally, we introduce a simple environment design strategy to develop and evaluate example DRL algorithms for distribution system applications, complete with an included code repository to guide users through environment construction.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Self-Stabilized LiNi 0.8 Mn 0.1 Co 0.1 O 2 in thiophosphate-based all-solid-state batteries through extra LiOH

Nickle-rich LiNi 0.8 Co 0.1 Mn 0.1 O 2 (NMC 811) cathode material exhibits engaging properties in high energy density and low cost, making it great potential for the next generation high-energy all-solid-state lithium batteries (ASSLBs). However, NMC 811 suffers from severe surface electrochemical, chemical, and voltage incompatibility towards solid-state electrolytes (SSE), especially thiophosphate-based electrolytes like Li 6 PS 5 Cl. Although diverse coating methods have been made to overcome this issue, they are typically cumbersome and expensive. A coating strategy that satisfied all the requirements of cost-efficiency, stability, uniformity, scalability, and easy-achieving is still challenging. Here, we developed a LiOH-based surface stabilization strategy that provides a ~10 nm stable permeable layer on NMC 811. After one-step sintering of NMC 811 precursor mixed with LiOH, which is commonly used for NMC 811 lithiation process, excessive LiOH simultaneously distributes on NMC 811 particles. Unlike other reported methods, this coating method can be easily controlled and fabricated without additional complicated processes. By simply controlling the thickness of LiOH layer, which protects the Li 6 PS 5 Cl solid electrolyte materials from being oxidized, optimized cycling stability can be obtained for 600 cycles with capacity of 130 mAh g – 1 on average at a wide electrochemical window of 2.50–4.20 V (vs. Li-In).

25 ENERGY STORAGE↗

DOE OSTI2014

Effects of prior deformation and annealing process on microstructure and annealing twin density in a nickel based alloy

The nickel based alloys with different Σ3 boundary density were achieved by cold-rolling and subsequent annealing treatment. Electron backscattered diffraction analysis showed that the grain size distribution changed with the processing parameters, and the discontinuous Σ3 boundary became continuous with the increase of prior deformation level. Furthermore, the Σ3 boundary density was found to be manipulated by both grain size distribution and Σ3 boundary density per grain which showed an increasing trend with prior deformation level and annealing temperature. - Highlights: • The prior deformation amount influenced the morphology of Σ3 boundary. • The grain size was not the only factor influencing Σ3 boundary density. • The fact that grain size distribution had an important effect on Σ3 boundary density was confirmed. • The nature of grain size distribution on Σ3 boundary density was revealed. • There was a great deviation in Σ3 boundary density between experimental results and predictions.

36 MATERIALS SCIENCE↗

DOE OSTI2013

Nonperturbative short-range dynamics in TMDs

This presentation covers: deep inelastic processes and transverse momentum distributions; chiral symmetry breaking, including the physical picture, the dynamical model, and parton distributions; partonic structures, including transverse momentum distributions, coordinate space correlator, and short range correlations; and measurements of semi-inclusive deep inelastic scattering, correlations, and multi-parton processes in pp interactions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2016

Effect of process parameters on hardness, temperature profile and solidification of different layers processed by direct metal laser sintering (DMLS)

In DMLS process objects are fabricated layer by layer from powdered material by melting induced by a controlled laser beam. Metallic powder melts and solidifies to form a single layer. Solidification map during layer formation is an important route to characterize micro-structure and grain morphology of sintered layer. Generally, solidification leads to columnar, equiaxed or mixture of these two types grain morphology depending on solidification rate and thermal gradient. Eutectic or dendritic structure can be formed in fully equiaxed zone. This dendritic growth has a large effect on material properties. Smaller dendrites generally increase ductility of the layer. Thus, materials can be designed by creating desired grain morphology in certain regions using DMLS process. To accomplish this, hardness, temperature distribution, thermal gradient and solidification cooling rate in processed layers will be studied under change of process variables by using finite element analysis, with specific application to Ti-6Al-4V.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Effects of Particle Size Distribution with Efficient Packing on Powder Flowability and Selective Laser Melting Process

The powder bed-based additive manufacturing (AM) process contains uncertainties in the powder spreading process and powder bed quality, leading to problems in repeatability and quality of the additively manufactured parts. This work focuses on identifying the uncertainty induced by particle size distribution (PSD) on powder flowability and the laser melting process, using Ti6Al4V as a model material. The flowability test results show that the effect of PSDs on flowability is not linear, rather the PSDs near dense packing ratios cause significant reductions in flowability (indicated by the increase in the avalanche angle and break energy of the powders measured by a revolution powder analyzer). The effects of PSDs on the selective laser melting (SLM) process are identified by using in-situ high-speed X-ray imaging to observe the melt pool dynamics during the melting process. The results show that the powder beds made of powders with dense packing ratios exhibit larger build height during laser melting. The effects of PSD with efficient packing on powder flowability and selective laser melting process revealed in this work are important for understanding process uncertainties induced by feedstock powders and for designing mitigation approaches.

36 MATERIALS SCIENCE↗

DOE OSTI2015

Rapid Analysis of Microalgal Triacylglycerols with Direct-Infusion Mass Spectrometry

Cultivation of microalgae has the potential to provide lipid-derived feedstocks for conversion to liquid transportation fuels. Lipid extracts from microalgae are significantly more complex than those of traditional seed oils, and their composition changes significantly throughout the microalgal growth period. With three acyl side chains per molecule, triglycerides (TAGs) are an important fuel precursor, and the distribution of acyl chain composition for TAGs has a significant impact on fuel properties and processing. Therefore, determination of the distribution of microalgal TAG production is needed to assess the value of algal extracts designed for fuel production and to optimize strain, cultivation, and harvesting practices. Methods utilized for TAG speciation commonly involve complicated and time-consuming chromatographic techniques. Here we present a method for TAG speciation and quantification based on direct-infusion mass spectrometry, which provides rapid characterization of TAG profiles without chromatographic separation. Specifically, we utilize Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) to provide a reference library of TAGs for the microalgae Nannochloropsis sp. that provides the basis for high-throughput TAG quantitation by time-of-flight mass spectrometry (TOF MS). In conclusion, we demonstrate the application of this novel approach for lipid characterization with respect to TAG compound distribution, which informs both immediate and future strain and process optimization strategies.

09 BIOMASS FUELS↗

DOE OSTI2021

Big PanDa Workflow Management on Titan for High Energy and Nuclear Physics and for Future Extreme Scale Scientific Application

Over a three year period, from 2016-2019, this project demonstrated the scientific benefits of integrating the Titan supercomputer at Oak Ridge Leadership Computing Facility into traditional high throughput grid based distributed computing systems managed by PanDA, the workflow management system used for the execution of all distributed computing applications by the ATLAS experiment at the Large Hadron Collider. PanDA manages millions of batch jobs daily at hundreds of clusters worldwide on request by thousands of physicist users, and processes more than an exabyte of data annually using grid middleware. High levels of operational use of Titan was sustained by PanDA in order to meet the physics goals of ATLAS. The success of this project led to the use of other supercomputers worldwide by ATLAS, and to the adoption of PanDA by other experiments and other scientists. Multiple innovative operational and computer science research goals were achieved supporting the use of supercomputers for scientific domains with large scale distributed data and distributed processing needs.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Study of PBAT Photostabilization with Ultraviolet Absorber in Combination with Hindered Amine Light Stabilizer and Vitamin E, Aiming Mulching Film Application

This work evaluated two combinations of additives, i.e. “carbon black and hindered amine light stabilizer (HALS)” and “carbon black and vitamin E” to prevent photodegradation of poly(butylene adipate-co-terephthalate) (PBAT) aiming its application as mulch films. A 2{sup 2} factorial rotatable central composite design (α = 1.414) with three central points was conducted. Five different concentrations for each additive were used (0.1, 0.5, 1.0, 1.5 and 2.0% in mass). The formulations were exposed in an environmental simulator to radiation and condensation cycles. The both combination did not avoid the gel formation but its action reduced PBAT chain scission process during photodegradation. Nevertheless, comparing the two different combinations with carbon black, HALS additive was more efficient than vitamin E to minimize the reduction on tensile strength and elongation. The chain scission and crosslinking processes were evaluated by chain scission distribution function. The chain scission was the predominant process during photodegradation of pure PBAT and it was possibly favored by hydrolysis during condensation cycles in the accelerated aging test. It was verified that higher concentrations of carbon black in the formulations led to a reduction on light transmission in both experimental designs. Considering the responses of studied factors on mechanical properties and light transmission it was possible to identify the most promising formulations for application in mulch films, which correspond to 1.5% (weight) for each additive.

36 MATERIALS SCIENCE↗