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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

TaZr_BCC_SolidSolution_128atoms_VASP6

We performed density functional theory (DFT) calculations for body-centered-cubic (BCC) structures with 128 lattices sites of solid solution binary alloys tantalum-zirconium (Ta-Zr). The electronic structures of alloys have been calculated using Vienna Ab initio Simulation Package (VASP). Within this package the DFT approach is used to reduce many-body Schrodinger equation to set of single particle Kohn-Sham (KS) equations. The generalized electronic exchange-correlation functional is described by generalized gradient approximation with the Perdew-Burke-Ernzerhof parametrization. The electron-ion interactions is described by pseudopotentials developed within the plane-wave basis projector augmented-wave (PAW) approach \cite{PAW}. These pseudopotentials are available at the VASP portal (http://cms.mpi.univie.ac.at/vasp/). Our calculations have been run with the pseudopotentials treating s and p semi-core states as valence in case for the elements Ta and Zr. The electronic densities and potentials are expanded over plane-waves with energy cutoff of 350 eV. 2x2x2 k-mesh and normal precision were used. The alloys were modeled by supercell containing 128 randomly distributed atoms. At initial step the atoms occupy perfect bcc lattice cites. This initial structure was optimized until energy changes less than 1e-6 eV, while forces acting on atoms don't exceed 1e-2 eV/angstrom. The electron-ion interaction is described by PAW pseudopotentials. The calculations have been collected by sampling chemical compositions across the entire compositional range. The chemical compositions have been sampled by progressively changing the number of atoms per constituent by 4. For each chemical composition of binaries and ternaries, the first-principle calculations have been run for 100 randomized arrangements of the constituents on the BCC lattice sites. We collected data for a total of 3,100 randomized atomic structures over 31 chemical compositions. The calculations have been collected on NERSC-Perlmutter and OLCF-Summit using the VASP 6.3.2. The VASP calculations for every atomic structure have been performed in 2 main steps: 1. Starting from an ideal body-centered-cubic (BCC) structure, geometry optimization with low precision has been executed to perform a preliminary optimization of the atomic structure. The output for this calculations is available in the files 0.CONTCAR, 0.OUTCAR, rlx1.out. 2. Using the atomic structure resulting from the preliminary geometry optimization, a second geometry optimization has been performed using normal precision. The output for this calculations is available in the files CONTCAR, OUTCAR, rlx2.out, vaspout.h5, and vasprun.xml. Cases 1-10 have been run without generating the file 'vaspout.h5'. Every chemical composition sampled across the composition range in the dataset has its own directory. The convention used to name the directories for binary alloys is AXBY, where A and B refer to the constituents, whereas X and Y are positive integers that represent the number of atoms for each constituent and their values still sum up to 128. Each atomic structure associated with a specific chemical composition has its own sub-directory within the directory of the corresponding chemical composition. The sub-directories for each atomic structure for each chemical composition are named 'case-*', where * is a positive integer that spans all the values from 1 through 100, extremes included. The files contained in each sub-directory 'case-*' for each atomic structure are as follows: FILES contained in each subdirectory with name "case-N" where N ranges between 11 and 80, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. 0.POSCAR: input file that defines the atomic structure of a system 4. 0.CONTCAR: output file that provides the atomic positions and cell parameters after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 5. 0.OUTCAR: output file that contains detailed information about the progress of a calculation after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.out: file with diagnostic information about the execution of the first geometry optimization with precision variable set to PREC=Low in the INCAR file 7. POSCAR: input file that defines the atomic structure of a system after the first geometry optimization has been run at low precision. This represents the input for the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. CONTCAR: output file that provides the atomic positions and cell parameters after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 9. OUTCAR: output file that contains detailed information about the progress of a calculation after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.out: file with diagnostic information about the execution of the second geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. 13. CHGCAR: contains the charge density data of a system. This data is crucial for analyzing electronic structures, calculating electrostatic potential, and studying the distribution of charge in a crystal or molecular system FILES contained in each subdirectory with name "case-N" where N ranges between 1 and 10 and between 81 and 100, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. {ID}.POSCAR: input file that defines the atomic structure of a system at the beginning of ID execution of geometry optimization with PREC=LOW 4. {ID}.CONTCAR: output file that provides the atomic positions and cell parameters at the end of ID execution of geometry optimization with PREC=LOW in the INCAR file 5. {ID}.OUTCAR: output file that contains detailed information about the progress of a calculation after the ID execution of geometry optimization that has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.{ID}.out: file with diagnostic information about the execution of the ID execution of the geometry optimization with precision variable set to PREC=Low in the INCAR file 7. N{ID}.POSCAR: input file that defines the atomic structure of a system after the geometry optimization run at low precision. This represents the input for the ID execution of the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. N{ID}.CONTCAR: output file that provides the atomic positions and cell parameters after the ID execution of the geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 9. N{ID}.OUTCAR: output file that contains detailed information about the progress of a calculation after the ID execution of the geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.{ID}.out: file with diagnostic information about the ID execution of geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. 13. CHGCAR: contains the charge density data of a system. This data is crucial for analyzing electronic structures, calculating electrostatic potential, and studying the distribution of charge in a crystal or molecular system This research is sponsored by the Artificial Intelligence Initiative as part of the Laboratory Directed Research and Development (LDRD) Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. This work used resources of the Oak Ridge Leadership Computing Facility, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725, under Directorate Discretionary awards MAT025 (Materials Science) and LRN026 (Machine Learning), and INCITE award MAT201. This work also used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, under award ERCAP0025216. REFERENCES (1) Kresse, G. & Hafner, J. Ab initio molecular dynamics for liquid metals. Phys. review B 47, 558 (1993). (2) Kresse, G. & Hafner, J. Ab initio molecular-dynamics simulation of the liquid-metal–amorphous-semiconductor transition in germanium. Phys. Rev. B 49, 14251 (1994) (3) Kresse, G. & Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. materials science 6, 15–50 (1996) (4) Kresse, G. & Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. review B 54, 11169 (1996) (5) Kresse, G. & Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. review b 59, 1758 (1999)

36 MATERIALS SCIENCE↗
DOE OSTI2024

Exploring interlayer coupling in the twisted bilayer PtTe 2

We have investigated interlayer interactions in the bilayer PtTe 2 system, which influence the electronic energy bands near the Fermi level. Our diffusion Monte Carlo (DMC) calculations for the high-symmetry bilayer stackings (AA, AB, AC) manifest distinct interlayer binding characteristics among the stacking modes by revealing significantly different interlayer separations depending on the stacking, which is critical to understanding the interlayer coupling of the twisted bilayers consisting of various local stacking arrangements. Furthermore, a comparison between the interlayer separations obtained from DMC and density functional theory (DFT) shows that meta-generalized gradient approximation (GGA)-based van der Waals–DFT results agree with DMC for different layer stackings, including twisted bilayers, but only the ground-state AA stacking matches well with GGA-based DFT predictions. This underscores the importance of accurate exchange-correlation potentials even for capturing the stacking-dependent interlayer binding properties. We further show that the variability in DFT-predicted interlayer separations is responsible for the large discrepancy of band structures in the 21 . 79 ∘ twisted bilayer PtTe 2 , affecting its classification as metallic or insulating. These results demonstrate the importance of obtaining a correct description of stacking-dependent interlayer coupling in modeling delicate bilayer systems at finite twists. Published by the American Physical Society 2024

36 MATERIALS SCIENCE↗
DOE OSTI2019

Efficient Multiscale Optoelectronic Prediction for Conjugated Polymers

Conjugated polymers represent a high-potential material class due to the tunability of their optoelectronic properties via straightforward processing protocols. However, to in silico tailor these optoelectronic properties, one must compute the conformationally dependent electronic structure over mesoscopic length and time scales. This task represents a challenging multiscale computational problem in which quantum chemistry and atomistic or coarse-grained molecular dynamics must be integrated. Recently, we introduced a methodology, called electronic coarse graining (ECG), that utilizes artificial neural networks to compute ab initio quality electronic structures using only the reduced degrees of freedom of coarse-grained models. Here, we adapt ECG to variable-molecular weight conjugated oligomers by casting the problem in the framework of sequence detection. A machine-learning (ML) architecture employing one-dimensional convolutional neural networks and long short-term memory networks is utilized to compute ground-state orbital energies, charge density distributions, and optical spectra solely from the coarse-grained model's configurational degrees of freedom. Robust molecular weight transferability for ECG is established via a A-ML approach that leverages model electronic Hamiltonians for ground and excited-state property determination. The accuracy and transferability of the ECG methodology opens the door for scalable optoelectronic property prediction in conjugated polymers directly from coarse-grained degrees of freedom.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Diagnosing the origin and impact of low-mode asymmetries in ignition experiments at the National Ignition Facility

Inertial confinement fusion ignition requires high inflight shell velocity, good energy coupling between the hotspot and shell, and high areal density at peak compression. Three-dimensional asymmetries caused by imperfections in the drive symmetry or target can grow and damage the coupling and confinement. Recent high-yield experiments have shown that low-mode asymmetries are a key degradation mechanism and contribute to variability. Here we show the experimental signatures and impacts of asymmetry change with increasing implosion yield given the same initial cause. This letter has implications for improving robustness to a key degradation in ignition experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Identifying Entangled Physics Relationships Through Sparse Matrix Decomposition to Inform Plasma Fusion Design

We report a sustainable burn platform through inertial confinement fusion (ICF) has been an ongoing challenge for over 50 years. Mitigating engineering limitations and improving the current design involves an understanding of the complex coupling of physical processes. While sophisticated simulation codes are used to model ICF implosions, these tools contain necessary numerical approximation but miss physical processes that limit predictive capability. Identification of relationships between controllable design inputs to ICF experiments and measurable outcomes (e.g., neutron yield, neutron velocity, areal density) from performed experiments can help guide the future design of experiments and development of simulation codes, to potentially improve the accuracy of the computational models used to simulate ICF experiments. We use sparse matrix decomposition methods to identify clusters of a few related design variables. Sparse principal component analysis (SPCA) identifies groupings that are related to the physical origin of the variables (laser, hohlraum, and capsule). A variable importance analysis finds that in addition to variables highly correlated with neutron yield, such as picket power and laser energy, variables that represent a dramatic change of the ICF design, such as number of pulse steps, are also very important. The obtained sparse components are then used to train a random forest (RF) regression surrogate for predicting total yield. The RF performance on the training and testing data compares with the performance of the RF trained using all the design variables considered. This work is intended to inform design changes in future ICF experiments by augmenting the expert intuition and simulation results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

V-HAMSTeR v1.0.0

V-HAMSTeR is a bioinformatics software tool designed to predict the hosts of viruses directly from genomic sequences. It can be used by researchers to predict animal, prokaryotic, plant, protist or fungal viral hosts including viruses that may be fragmented or discovered in environmental metagenomic datasets. Features & Uses: The software employs a novel dual-stream deep learning architecture that dynamically fuses implicit sequence embeddings from a genomic foundation model with 13 explicit, handcrafted biological features (e.g., coding density and strand switch rates). To ensure maximum reliability, V=HAMSTeR deploys a 5-fold deep ensemble calibrated via Joint Temperature Scaling, providing users with statistically rigorous confidence probabilities. It also features an automated sequence chunking and mean-pooling module to seamlessly process variable-length contigs. Advantages Over Similar Technologies: Existing tools (e.g., IPEV, RNAVirHost) typically rely on either basic k-mers or isolated neural networks. V-HAMSTeR's hybrid architecture captures both broad genomic context and specific biological motifs that standalone foundation models often miss. Furthermore, unlike competitor tools that struggle with incomplete data or exhibit extreme overconfidence, V-HAMSTeR is explicitly benchmarked and mathematically calibrated for fragmented assemblies (1kb–10kb). This makes it uniquely robust, accurate, and trustworthy for the messy reality of real-world environmental viromics.

Grigson, Susie [Lawrence Berkeley National Laborat↗
DOE OSTI2016

The North American Monsoon GPS Transect Experiment 2013

Northwestern Mexico experiences large variations in water vapor on seasonal time scales in association with the North American monsoon, as well as during the monsoon associated with upper-tropospheric troughs, mesoscale convective systems, tropical easterly waves, and tropical cyclones. Together these events provide more than half of the annual rainfall to the region. A sufficient density of meteorological observations is required to properly observe, understand, and forecast the important processes contributing to the development of organized convection over northwestern Mexico. The stability of observations over long time periods is also of interest to monitor seasonal and longer-time-scale variability in the water cycle. For more than a decade, the U.S. Global Positioning System (GPS) has been used to obtain tropospheric precipitable water vapor (PWV) for applications in the atmospheric sciences. There is particular interest in establishing these systems where conventional operational meteorological networks are not possible due to the lack of financial or human resources to support the network. We provide an overview of the North American Monsoon GPS Transect Experiment 2013 in northwestern Mexico for the study of mesoscale processes and the impact of PWV observations on high-resolution model forecasts of organized convective events during the 2013 monsoon. Some highlights are presented, as well as a look forward at GPS networks with surface meteorology (GPS-Met) planned for the region that will be capable of capturing a wider range of water vapor variability in both space and time across Mexico and into the southwestern United States.

58 GEOSCIENCES↗
DOE OSTI2020

Herbaceous Feedstock 2018 State of Technology Report

The U.S. Department of Energy (DOE) promotes the production of advanced liquid transportation fuels from lignocellulosic biomass by funding fundamental and applied research that advances the state of technology (SOT). As part of its involvement with this mission, Idaho National Laboratory (INL) completes an annual SOT report for biomass feedstock logistics. This report summarizes supply system impacts of Bioenergy Technologies Office (BETO)-funded research and development efforts at INL and elsewhere (such as the High-Tonnage Feedstock Logistics projects (Webb et al. 2013a, Webb et al. 2013b, Webb et al. 2013c, Webb and Sokhansanj 2014, Sokhansanj et al. 2014) that lead to improvements in feedstock supply systems. These include improvements to and observed performance of innovative harvest and collection methods, storage technologies, transportation and handling approaches, and advanced preprocessing technologies. Biomass quality and variability, and the interface between feedstock quality and conversion performance are key drivers in addition to delivered feedstock cost. In this report, we estimate the benefits of R&D improvements to individual supply system unit operations, and present the status of feedstock logistics technology development for converting biomass into biofuels. These analyses are supported by experimental data where possible, and help to align the SOT relative to the cost goals defined in the Multi-Year Program Plan. The 2018 Herbaceous SOT aligned feedstock logistic design with current biorefinery’s design capacity utilized by biochemical conversion platform. Currently biochemical conversion platform utilizes a 725,000 dry ton/year biorefiney design for the techno economic analysis. Hence, feedstock delivered cost in the 2018 Herbaceous SOT is calculated based on biorefinery’s 725,000 dry ton design capacity instead of 800, 000 dry ton capacity utilized in the 2017 Herbaceous SOT. Biomass availabilities in this SOT were updated to year 2018 data from the 2016 Billion-Ton Report (BT16) (DOE 2016a), with the exception of switchgrass, for which the 2018 Herbaceous SOT utilized the 2019 switchgrass availability data from BT16. The BT16 report (DOE 2016a) does not project switchgrass availability in 2018; the soonest switchgrass is available in the BT16 report is 2019. Therefore, availability of switchgrass for this analysis was that projected for 2019. The 2018 Herbaceous SOT incorporates same technologies utilized in the 2017 Herbaceous SOT. However, a sensitivity analysis is performed to understand the impact of variation of process parameters on those technologies on feedstock logistic cost. New R&D data that shows the variations of process parameters affecting process performance is incorporated in the 2018 SOT to measure the variations in delivered feedstock cost. The 2018 Herbaceous SOT has also provided projected delivered feedstock of 2022 design case based on near term technical target under BETO funded R&D project. Finally, updated biorefinery size of 725,000 dry ton/year was incorporated within least-cost formulation model to select optimal siting and depot scales during optimization of the least cost blend. This modification to the optimization algorithm allows the trade-off between the cost of increased supply radius and the savings from selecting biomass from higher producing counties to be assessed. Such optimization has also showed the economic benefit of decentralized depots in comparison to centralized preprocessing co-located with the biorefinery by decoupling the biorefinery and feedstock locations. The 2018 Herbaceous SOT report documents the current modeled cost of a herbaceous feedstock supply system (from harvest to the pretreatment reactor throat, including grower payment) for hydrocarbon fuel production via biochemical conversion, based on equipment and processes now available or potentially available in the near term. The modeled cost also considers both the required quality and the availability of the biomass resources. The 2018 Herbaceous SOT predicts a modeled delivered feedstock cost of $83.67/dry ton (2016$); this is a $0.23/dry ton (2016$) decrease from the 2017 Herbaceous SOT. The modification of biorefinery’s designed capacity and increased projected biomass availability in the same supply shed contributed to this modeled cost reduction. The least-cost formulation model to optimally site and scale local distributed preprocessing depots also contributed to the cost reduction by considering county-level grower payment and distance from the biorefinery as variables in the optimization algorithm. Sensitivity analysis on various process parameters that affect delivered feedstock cost in the 2018 Herbaceous SOT shows that the delivered cost could varies from $80.45-$88.83/dry ton. The top factors that causes such variations are: effective baling rate, bale density, hammer mill throughput, interest rate and storage dry matter loss.

09 BIOMASS FUELS↗
DOE OSTI2018

Combined Effects of Carbon and Nitrogen Source to Optimize Growth of Proteobacterial Methanotrophs

Methane, a potent greenhouse gas, and methanol, commonly called wood alcohol, are common by-products of modern industrial processes. They can, however, be consumed as a feedstock by bacteria known as methanotrophs, which can serve as useful vectors for biotransformation and bioproduction. Successful implementation in industrial settings relies upon efficient growth and bioconversion, and the optimization of culturing conditions for these bacteria remains an ongoing effort, complicated by the wide variety of characteristics present in the methanotroph culture collection. Here, we demonstrate the variable growth outcomes of five diverse methanotrophic strains - Methylocystis sp. Rockwell, Methylocystis sp. WRRC1, Methylosinus trichosporium OB3b, Methylomicrobium album BG8, and Methylomonas denitrificans FJG1 - grown on either methane or methanol, at three different concentrations, with either ammonium or nitrate provided as nitrogen source. Maximum optical density (OD), growth rate, and biomass yield were assessed for each condition. Further metabolite and fatty acid methyl ester (FAME) analyses were completed for Methylocystis sp. Rockwell and M. album BG8. The results indicate differential response to these growth conditions, with a general preference for ammonium-based growth over nitrate, except for M. denitrificans FJG1. Methane is also preferred by most strains, with methanol resulting in unreliable or inhibited growth in all but M. album BG8. Metabolite analysis points to monitoring of excreted formic acid as a potential indicator of adverse growth conditions, while the magnitude of FAME variation between conditions may point to strains with broader substrate tolerance. These findings suggest that methanotroph strains must be carefully evaluated before use in industry, both to identify optimal conditions and to ensure the strain selected is appropriate for the process of interest. As a result, much work remains in addressing the optimization of growth strategies for these promising microorganisms since disregarding these important steps in process development could ultimately lead to inefficient or failed bioprocesses.

09 BIOMASS FUELS↗
DOE OSTI2017

JVLA Observations of Young Brown Dwarfs

We present sensitive 3.0 cm JVLA radio continuum observations of six regions of low-mass star formation that include twelve young brown dwarfs (BDs) and four young BD candidates. We detect a total of 49 compact radio sources in the fields observed, of which 24 have no reported counterparts and are considered new detections. Twelve of the radio sources show variability in timescales of weeks to months, suggesting gyrosynchrotron emission produced in active magnetospheres. Only one of the target BDs, FU Tau A, was detected. However, we detected radio emission associated with two of the BD candidates, WL 20S and CHLT 2. The radio flux densities of the sources associated with these BD candidates are more than an order of magnitude larger than expected for a BD and suggest a revision of their classification. In contrast, FU Tau A falls on the well-known correlation between radio luminosity and bolometric luminosity, suggesting that the emission comes from a thermal jet and that this BD seems to be forming as a scaled-down version of low-mass stars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

A White Dwarf with Transiting Circumstellar Material Far outside the Roche Limit

We report the discovery of a white dwarf exhibiting deep, irregularly shaped transits, indicative of circumstellar planetary debris. Using Zwicky Transient Facility DR2 photometry of ZTF J013906.17+524536.89 and follow-up observations from the Las Cumbres Observatory, we identify multiple transit events that recur every ≈107.2 days, much longer than the 4.5–4.9 hr orbital periods observed in WD 1145+017, the only other white dwarf known with transiting planetary debris. The transits vary in both depth and duration, lasting 15–25 days and reaching 20%–45% dips in flux. Optical spectra reveal strong Balmer lines, identifying the white dwarf as a DA with ${T}_{\mathrm{eff}}=10,530\pm 140\,{\rm{K}}$ and $\mathrm{log}(g)=7.86\pm 0.06$. A Ca ii K absorption feature is present in all spectra both in and out of transit. Spectra obtained during one night at roughly 15% transit depth show increased Ca ii K absorption with a model atmospheric fit suggesting [Ca/H] = –4.6 ± 0.3, whereas spectra taken on three nights out of transit have [Ca/H] of –5.5, –5.3, and –4.9 with similar uncertainties. While the Ca ii K line strength varies by only 2σ, we consider a predominantly interstellar origin for Ca absorption unlikely. Here, we suggest a larger column density of circumstellar metallic gas along the line of site or increased accretion of material onto the white dwarf's surface are responsible for the Ca absorption, but further spectroscopic studies are required. In addition, high-speed time series photometry out of transit reveals variability with periods of 900 and 1030 s, consistent with ZZ Ceti pulsations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

SIMP J013656.5+093347 is likely a planetary-mass object in the carina-near moving group

We report on the discovery that the nearby (∼6 pc) photometrically variable T2.5 dwarf SIMP J013656.5+093347 is a likely member of the ∼200 Myr old Carina-Near moving group with a probability of >99.9% based on its full kinematics. Our vsin i measurement of 50.9 ± 0.8 km s{sup −1} combined with the known rotation period inferred from variability measurements provide a lower limit of 1.01 ± 0.02 R{sub Jup} on the radius of SIMP 0136+0933, an independent verification that it must be younger than ∼950 Myr, according to evolution models. We estimate a field interloper probability of 0.2% based on the density of field T0–T5 dwarfs. At the age of Carina-Near, SIMP 0136+0933 has an estimated mass of 12.7 ± 1.0 M{sub Jup} and is predicted to have burned roughly half of its original deuterium. SIMP 0136+0933 is the closest known young moving group member to the Sun and is one of only a few known young T dwarfs, making it an important benchmark for understanding the atmospheres of young planetary-mass objects.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Using Willingness to Pay to Forecast the Adoption of Solar Photovoltaics: A ''Parameterization + Calibration'' Approach

Distributed energy resources, such as rooftop solar photovoltaics (PV), are likely to comprise a substantial fraction of new generation capacity in the United States. However, forecasting technology adoption based on people's willingness to pay (WTP) faces two major challenges: the stated-intention and omitted-variable biases. Previous solar adoption literature has neglected to address these two biases altogether. Here, we adopt a 'parameterization + calibration' approach to address both biases and estimate customers' WTP for PV. After collecting survey data on respondents' WTP for adopting PV, we characterize its empirical cumulative density function using a gamma distribution. We further calibrate the gamma distribution parameters using a national distributed PV adoption simulation model, finding the parameters that produce the best fit between simulated and historic solar adoption. We then show that the calibrated gamma distribution improves the raw WTP data after correcting for the two biases. Finally, we use our optimally-calibrated WTP to forecast market demand for residential PV at the county-level of the United States in 2020. Improving estimates of customer willingness to pay has significant implications for policy directly, e.g. estimating the effect of a proposed policy on technology adoption, and other regulatory processes that use forecasting, e.g. integrated resource planning.

14 SOLAR ENERGY↗
DOE OSTI2017

Evaluation of performance indicators applied to a material recovery facility fed by mixed packaging waste

Highlights: • Material Flows assessment of a Material Recovery Facility has been realized. • Performance Indicators for a generic MRF have been proposed. • A Dashboard has been utilized to measure the facility performance. • Experimental values of incoming waste and output products composition are reported. - Abstract: Most of the integrated systems for municipal solid waste management aim to increase the recycling of secondary materials by means of physical processes including sorting, shredding and reprocessing. Several restrictions prevent from reaching a very high material recycling efficiency: the variability of the composition of new-marketed materials used for packaging production and its shape and complexity are critical issues. The packaging goods are in fact made of different materials (aluminium, polymers, paper, etc.), possibly assembled, having different shape (flat, cylindrical, one-dimensional, etc.), density, colours, optical properties and so on. These aspects limit the effectiveness and efficiency of the sorting and reprocessing plants. The scope of this study was to evaluate the performance of a large scale Material Recovery Facility (MRF) by utilizing data collected during a long period of monitoring. The database resulted from the measured data has been organized in four sections: (1) data related to the amount and type of inlet waste; (2) amount and composition of output products and waste; (3) operating data (such as worked hours for shift, planned and unscheduled maintenance time, setting parameters of the equipment, and energy consumption for shift); (4) economic data (value of each product, disposal price for the produced waste, penalty for non-compliance of products and waste, etc.). A part of this database has been utilized to build an executive dashboard composed by a set of performance indicators suitable to measure the effectiveness and the efficiency of the MRF operations. The dashboard revealed itself as a powerful tool to support managers and engineers in their decisions in respect to the market demand or compliance regulation variation as well as in the designing of the lay-out improvements. The results indicated that the 40% of the input waste was recovered as valuable products and that a large part of these (88%) complied with the standards of the recycling companies. The evaluation of the indicators led to the decision to modify the layout to improve the interception of some polymers for which the performance indicators were poor. In particular, two additional optical sorters have to be inserted to increase the yield indicator and to the overall performance of the facility. Definitely, the results of the work allowed to: increase the yield and purity of products’ flows; ensure the compliance of waste flows; increase the workability.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Forward Modeling of 3-D Ion Properties in Jupiter’s Magnetosphere Using Juno/JADE-I Data

The Jovian Auroral Distributions Experiment Ion sensor (JADE-I) on NASA’s Juno mission provides in-situ measurements of ions from 0.1 to 46.2 keV/q inside Jupiter’s magnetosphere. JADE-I is used to study the plasma with two types of datasets from the same measurement: Time-of-flight (TOF) and SPECIES. The TOF dataset provides mass-per-charge measurements with a range of 1–64 amu/q but oversamples particles over 6π steradian viewing per spacecraft spin and has little directional information. On the other hand, the SPECIES dataset can provide a good measurement of the flow direction but does not provide mass-per-charge information due to the telemetry limit. In this study, we developed a 2-step forward modeling method that combines the advantages and avoids the disadvantages of TOF and SPECIES data to derive the 3-D properties of heavy ions. Assuming that the ion velocity distribution can be described with the kappa distribution, we first perform the forward model fit of the TOF data to calculate the relative abundance of heavy ion species. Then we fix the relative abundance and perform the second forward model fit on the SPECIES data. Here, using this method, we obtain the densities of different heavy ions, the shared temperature and kappa value, and the 3-D flow velocity vector. Some data examples of the equatorial plasma disk before Perijove 24 are included to demonstrate the method. Plasma properties can then be mapped to explore spatial and temporal variabilities in Jupiter’s magnetosphere.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Climate-Related Trends of Within-Storm Intensities Using Dimensionless Temporal-Storm Distributions

Huff curves are probabilistic time distributions of rainfall expressed as dimensionless cumulative percentages of storm depth and duration. Previous studies have documented development factors, spatial robustness, and the utility of Huff curves in practical applications. However, the effects of trending rainfall on Huff curve intensity patterns have not yet been studied. As such, the goal of this paper is to fill this gap by studying Huff curve patterns in a watershed with demonstrated increasing trends of temperature and precipitation, with the intention that it can be generalized to other areas in the US and the world. To achieve this goal, the high temporal resolution precipitation data collected from a high spatial density, 72-year precipitation-gauge network on the 4.25-km 2 North Appalachian Experimental Watershed in east-central Ohio were used. Seasonal storm pattern trends from 1939 to 2010 were investigated using dimensionless depth (with the frequency of 50%, $d_{50}$) and the curve variability $(V = d_{80} - d_{20})$ at three dimensionless within-storms time periods (three verticals). The Spearman rank correlation procedure (correlation coefficient, ρ and significance probability, p) was used to statistically determine trends over time using 8 periods of 4-season sets of Huff curves over the 72 years. Two averages of ρ and p were computed: (1) by averaging the individual ρ and p obtained from 10 gauges (AvgI); and (2) by grouped averaging of all individual gauge values of $d_{50}$ and V and then computing ρ and p (AvgG). The test results of individual gauges showed that 4 cases for $d_{50}$ and 23 cases for V were significant for all seasons and verticals (total of 120 cases for each variable). The test results of AvgI for $d_{50}$ and V and AvgG for $d_{50}$ showed no significant trends in all seasons and verticals. Only the AvgG for V led to a significant trend for V in spring and fall at different times within storm patterns. The data do not provide sufficient evidence at the p=0.05 significance level to reject the null hypothesis of unchanging position of the dimensionless depth of the 50% Huff curves for individual or averages for all seasons and verticals. Also, there is insufficient evidence to reject the null hypothesis of unchanging variability, V, using the average ρ of individual gauges (AvgI for V) for all seasons and verticals. AvgG results showed a significant trend in V; however, this analysis may be affected by the nonindependence of storm data. The results suggest that it is likely that there is little if any effect of trending climate over approximately 70 years on Huff curve patterns. The results of this study add to the robustness characteristics of Huff curves and to their potential use in hydrological practice as design storms, as the foundation of stochastic storm generation, and for storm disaggregation, and they deserve further investigation. These different forms of inputs to watershed models have the potential to improve runoff estimation. The results suggest that, if verified in other studies, they have applicability to provide useful stationary precipitation patterns across the US and other areas of the world in areas of nonstationary climate. Also, individual rain gauge data may not be representative of trends even over small areas, and seasonal differences were noticeable as found in other studies. Recommendations are provided.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A copula-based rank histogram ensemble filter

Serial ensemble filters implement triangular probability transport maps to reduce high-dimensional inference problems to sequences of state-by-state univariate inference problems. The univariate inference problems are solved by sampling posterior probability densities obtained by combining constructed prior densities with observational likelihoods according to Bayes' rule. Many serial filters in the literature focus on representing the marginal posterior densities of each state. However, rigorously capturing the conditional dependencies between the different univariate inferences is crucial to correctly sampling multidimensional posteriors. This work proposes a new serial ensemble filter, called the copula rank histogram filter (CoRHF), that seeks to capture the conditional dependency structure between variables via empirical copula estimates; these estimates are used to rigorously implement the triangular (state-by-state univariate) Bayesian inference. The success of the CoRHF is demonstrated on two-dimensional examples and the Lorenz'63 problem. A practical extension to the high-dimensional setting is developed by localizing the empirical copula estimation, and is demonstrated on the Lorenz'96 problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Anomaly Detection in Liquid Sodium Cold Trap Operation with Multisensory Data Fusion Using Long Short-Term Memory Autoencoder

Sodium-cooled fast reactors (SFR), which use high temperature fluid near ambient pressure as coolant, are one of the most promising types of GEN IV reactors. One of the unique challenges of SFR operation is purification of high temperature liquid sodium with a cold trap to prevent corrosion and obstructing small orifices. We have developed a deep learning long short-term memory (LSTM) autoencoder for continuous monitoring of a cold trap and detection of operational anomaly. Transient data were obtained from the Mechanisms Engineering Test Loop (METL) liquid sodium facility at Argonne National Laboratory. The cold trap purification at METL is monitored with 31 variables, which are sensors measuring fluid temperatures, pressures and flow rates, and controller signals. Loss-of-coolant type anomaly in the cold trap operation was generated by temporarily choking one of the blowers, which resulted in temperature and flow rate spikes. The input layer of the autoencoder consisted of all the variables involved in monitoring the cold trap. The LSTM autoencoder was trained on the data corresponding to cold trap startup and normal operation regime, with the loss function calculated as the mean absolute error (MAE). The loss during training was determined to follow log-normal density distribution. During monitoring, we investigated a performance of the LSTM autoencoder for different loss threshold values, set at a progressively increasing number of standard deviations from the mean. The anomaly signal in the data was gradually attenuated, while preserving the noise of the original time series, so that the signal-to-noise ratio (SNR) averaged across all sensors decreased below unity. Results demonstrate detection of anomalies with sensor-averaged SNR < 1.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗