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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Performance of the CMS high-level trigger during LHC Run 2

The CERN LHC provided proton and heavy ion collisions during its Run 2 operation period from 2015 to 2018. Proton-proton collisions reached a peak instantaneous luminosity of 2.1 $\times$ 10$^{34}$ cm$^{-2}$s$^{-1}$, twice the initial design value, at $\sqrt{s}$ = 13 TeV. The CMS experiment records a subset of the collisions for further processing as part of its online selection of data for physics analyses, using a two-level trigger system: the Level-1 trigger, implemented in custom-designed electronics, and the high-level trigger, a streamlined version of the offline reconstruction software running on a large computer farm. This paper presents the performance of the CMS high-level trigger system during LHC Run 2 for physics objects, such as leptons, jets, and missing transverse momentum, which meet the broad needs of the CMS physics program and the challenge of the evolving LHC and detector conditions. Sophisticated algorithms that were originally used in offline reconstruction were deployed online. Highlights include a machine-learning b tagging algorithm and a reconstruction algorithm for tau leptons that decay hadronically.

high energy physics↗
DOE OSTI2022

Modeling Vapor Transport Deposition of Metal-halide Perovskite Thin Films for Photovoltaic and Optoelectronic Devices

Over the past decade, metal halide perovskites (MHPs) have emerged as a promising materials platform for high-efficiency solar cells and low-cost optoelectronics. However, there are challenges that frustrate the large-scale manufacturing of MHP devices, including difficulty in controlling film composition, interface formation and their device instability under ambient conditions. Vapor processing offers an attractive path to manufacturability, while also opening the door to new opportunities in device design that could favorably impact ultrahigh efficiency tandem solar cells or overall stability. Emphasis here is on the use of an alternate processing methodology, vapor transport deposition (VTD), to deposit the MHP layer. In experimental work, we found that our deposition system had significant run-to-run variations in film thickness and composition. To understand and resolve these issues, we use COMSOL Multiphysics software to model the precursor deposition rate numerically, trying to guide the broad range of parameters in the system. The result can be matched with experimental data and provides insight into system hydrodynamics and molar transport effects. Finally, we show that chamber pressure is a key factor to scale up the VTD technique.

Hsu, Wan-Ju↗
DOE OSTI2024

HTESP (High-throughput electronic structure package): A package for high-throughput ab initio calculations

High-throughput ab initio calculations are the indispensable parts of data-driven discovery of new materials with desirable properties, as reflected in the establishment of several online material databases. The accumulation of extensive theoretical data through computations enables data-driven discovery by constructing machine learning and artificial intelligence models to predict novel compounds and forecast their properties. Efficient usage and extraction of data from these existing online material databases can accelerate the next stage materials discovery that targets different and more advanced properties, such as electron–phonon coupling for phonon-mediated superconductivity. However, extracting data from these databases, generating tailored input files for different ab initio calculations, performing such calculations, and analyzing new results can be demanding tasks. Here, in this work, we introduce a software package named “HTESP” (High-Throughput Electronic Structure Package) written in Python and Bash languages, which automates the entire workflow including data extraction, input file generation, calculation submission, result collection and plotting. Our HTESP will help speed up future computational materials discovery processes.

36 MATERIALS SCIENCE↗
DOE OSTI2025

On-lattice kinetic Monte Carlo approaches for modeling molecular anisotropy in resveratrol crystallization

Stilbenes are a class of organic compounds with broad-ranging pharmaceutical and agricultural applications, which are typically isolated and purified through recrystallization. We are motivated by reducing experimental waste and optimizing yield via developing predictive simulations for processing-dependent crystal morphologies. Using resveratrol as a model stilbene system, we have developed an approach for simulating crystallization with molecular resolution using on-lattice kinetic Monte Carlo. In this work, we highlight modifications to the Stochastic Parallel PARticle Kinetic Simulator (SPPARKS) software package, which were essential to this application. Key enhancements include the incorporation of non-orthogonal cell shapes and monomer anisotropy approximations using bound hard spheres. This new SPPARKS application has been applied to resveratrol with attachment energy libraries obtained from density functional theory, resulting in excellent agreement with experimental morphology prediction.

crystallization↗
DOE OSTI2022

Hybrid-RL-MPC4CLR (Hybird-Reinforcement-Learning-Model-Predictive-Control-for-Reserve-Policy-Assisted-Critical-Load-Restoration-in-Distribution-Grids)

Hybrid-RL-MPC4CLR was developed as a hybrid controller for active distribution grid critical load restoration, combining deep reinforcement learning (RL) and model predictive control (MPC) aiming at maximizing total restored load following an extreme event. The RL determines a policy for quantifying operating reserve requirements, thereby hedging against uncertainty, while the MPC models grid operations incorporating the RL policy actions (i.e., reserve requirements), renewable (wind and solar) power predictions, and load demand forecasts. The developers formulated the reserve requirement determination problem as a sequential decision-making problem based on the Markov Decision Process (MDP) and design an RL learning environment based on the OpenAI Gym framework and MPC simulation. The RL agent reward and MPC objective function aim to maximize and monotonically increase total restored load and minimize load shedding and renewable power curtailment. The software is developed using various software packages in Python. The MPC's optimal power flow (OPF) model is implemented using the Pyomo package, the RL simulation environment is implemented using the MPC simulation with various scenarios of renewable energy and load demand profiles and power outage beginning times, based on the OpenAI Gym framework. The RL agent training is performed using the RLlib Ray package. The RL algorithm is trained offline using historical forecasts of renewable generation and load demand profiles. Simulation analysis and performance tests are conducted using a modified IEEE 13-bus distribution test feeder containing wind turbine, photovoltaic, microturbine, and battery.

Eseye, Abinet Tesfaye↗
DOE OSTI2023

AmeriFlux FLUXNET-1F US-AR1 ARM USDA UNL OSU Woodward Switchgrass 1

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-AR1 ARM USDA UNL OSU Woodward Switchgrass 1. This is the FLUXNET version of the carbon flux data for the site US-AR1 ARM USDA UNL OSU Woodward Switchgrass 1 produced by applying the standard ONEFlux (1F) software. Site Description - The ARM USDA UNL OSU Woodward Switchgrass 1 tower is located on public land owned by the USDA-ARS Southern Plains Range Research Station in Woodward, Oklahoma. The site is on a former native prairie that is in the process of changing to switchgrass. A second companion site (ARM USDA UNL OSU Woodward Switchgrass 2) is on a former wheat field. In Spring 2009, the former native prairie site was burned, cattle were put on the pasture to graze down emergent grass, and broadleaf herbicide was sprayed. In Summer 2009, the cattle were removed from the pasture, and the site was sprayed with herbicide to kill all grass. In Spring 2010, prior to the planting of switchgrass, final herbicide was sprayed to kill cheat grass and to control broadleaf plants.

Billesbach, Dave↗
DOE OSTI2023

AmeriFlux FLUXNET-1F US-AR2 ARM USDA UNL OSU Woodward Switchgrass 2

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-AR2 ARM USDA UNL OSU Woodward Switchgrass 2. This is the FLUXNET version of the carbon flux data for the site US-AR2 ARM USDA UNL OSU Woodward Switchgrass 2 produced by applying the standard ONEFlux (1F) software. Site Description - The ARM USDA UNL OSU Woodward Switchgrass 2 tower is located on public land owned by the USDA-ARS Southern Plains Range Research Station in Woodward, Oklahoma. The site is on a former wheat field that is in the process of changing to switchgrass. A companion site (ARM USDA UNL OSU Woodward Switchgrass 1) is on a former native prairie. Previous wheat was planted in Fall 2008. In Spring 2009, herbicide was applied to kill the wheat prior to switchgrass planting. Later in the year, the site was sprayed with post-emergence herbicide. In 2010, fertilization occurred before herbicide was sprayed for broadleaf control.

Billesbach, Dave↗
DOE OSTI2025

MontePy: a Python library for reading, editing, and writing MCNP input files.

The Monte Carlo N-Particle (MCNP) radiation transport code is a highly capable and accurate code with a long legacy. MCNP uses the Monte Carlo simulation process to simulate the path of particles (e.g., neutrons, photons, charged particles, etc.), and their interaction with materials. It is widely used in nuclear engineering, high-energy physics, and other fields. Its origins in the mid-twentieth century predate many modern software conventions. MCNP users provide an input file to MCNP, which it then uses to create an internal representation of the simulation problem. These input files originally had to be stored as punchcard decks, and the user manual still uses the terminology of cards and decks, despite moving beyond punchcards. MCNP predates nearly all modern human readable markup or data serialization languages, such as the extensible Markup Language (XML), the Standard Generalized Markup Language (SGML), YAML (YAML Ain’t Markup Language), and Javascript Object Notation (JSON). Due to this, MCNP uses an entirely custom defined syntax language for its input, making off-the-shelf libraries for XML, YAML, and JSON impossible to use for scripting various operations on MCNP input files (Kulesza et al., 2022).

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A VXS [VITA41] Trigger Processor for the 12GEV Experimental Programs at Jefferson Lab

The VXS_Trigger_Processor [VTP] was developed and commissioned for CLAS12 in the fall of 2016. This board is a VITA41 switch card and it collects data from a variety of front-end TDC and Flash ADC modules. The VTP has since been used in several experiments at Jefferson Lab serving as the L1 trigger module for a variety of detector types, such as stacked calorimeters, strip calorimeters, time-of-flight, Cerenkov, hodoscopes, drift chambers, and silicon strips. Trigger algorithms implemented include cluster finding (1D, 2D), drift chamber segment and road finding, geometry matching between various detectors, particle counting, and general global trigger bit processing. The VTP is also capable of reading out each front-end crate with up to 40Gbps Ethernet which is an enormous increase compared to the currently used 200MB/s VME bus. Recent progress has been made to show that a firmware and software upgrade can enable existing Jefferson Lab front-end crates to operate in a streaming DAQ mode. In February 2020, tests will be performed on a full calorimeter and matched hodoscope which are components of the CLAS12 Forward Tagger detector system with beam in Hall B. This paper details the hardware performance, triggered, and streaming applications that have been implemented using the VTP for several experiments at Jefferson Lab.

ABBOTT, David↗
DOE OSTI2024

STAT7 v1.2 Verification and Validation Report

This report documents the software testing which has been performed for the STAT7 Version 1.2 (hereafter STAT7 v1.2) software, a code for the statistical propagation of uncertainties in steady-state thermal hydraulics (TH) analysis of plate-fueled research and test reactors. The key capabilities addressed in this testing were selected based on the use of the software for analysis work supporting the conversion to low-enriched uranium fuel of U.S. High-Performance Research Reactors (USHPRR) such as the Massachusetts Institute of Technology Research Reactor (MITR-II). This testing scope includes comparison of the code’s statistical processing capabilities using statistical tests, comparison of coolant property evaluations to NIST data tables, and systematic comparison of key TH solver capabilities to the results of hand calculations and to predictions from PLTEMP/ANL Version 4.4. The good agreement achieved in this testing campaign, within the acceptance criteria of a 1% relative difference, also demonstrates that these capabilities have been implemented in STAT7 v1.2 correctly.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

STAT7 v2.0 Verification and Validation Report

This report documents the software testing which has been performed for STAT7 Version 2.0 (hereafter STAT7 v2.0), a code for the statistical propagation of uncertainties in steady-state thermal hydraulics (TH) analysis of plate-fueled research and test reactors. The key capabilities addressed in this testing were selected based on the use of the software for analysis work supporting the conversion to low-enriched uranium fuel of U.S. High-Performance Research Reactors (USHPRR) such as the Massachusetts Institute of Technology Research Reactor (MITR-II). This testing scope includes comparison of the code’s statistical processing capabilities using statistical tests, comparison of coolant property evaluations to NIST data tables, and systematic comparison of key TH solver capabilities to the results of hand calculations and to predictions from PLTEMP/ANL Version 4.4. The

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Projector Selection for SOFAST Testing

In Concentrating Solar Power (CSP) there is a need for mirrors that are optically accurate, as they are used to focus light onto a receiver at high concentration. To test the accuracy of the mirrors’ optical slopes, we employ a technique called deflectometry. Deflectometry measures mirror slope directly. A projector displays an image onto a screen or wall, and a mirror is positioned to reflect the image to a camera. The camera sees the mirror’s reflection of the screen and sends imaging information to software which analyzes the reflected images from the mirror to determine the local slopes and curvature of the mirror. For this, projectors that can provide a crisp and clear image are imperative. Choosing the right projector(s) can be a long and tedious process; this paper will cover the process we used to select projectors for the SOFAST systems in the Sandia Concentrating Solar Optics Laboratory (CSOL).

14 SOLAR ENERGY↗
DOE OSTI2024

High resolution hydrologic routing with machine learning assisted waterbody classification

This software contains the code for a machine learning-based pipeline for creating persistent waterbody databases used in hydrologic routing. It consists of three components, 1) a PyTorch library (TorchWBType) for classifying/labeling arbitrary waterbodies into lakes and non-lakes, 2) a batch processing orchestrator (wbextractor) for delineating waterbodies from remote sensing imagery, and 3) graphing/analysis scripts for reproducing the plots in an associated journal article (LA-UR-24-22590).

Stachelek, Jemma↗
DOE OSTI2025

Optimizing Grain Boundary Structures with LAMMPS Using Evolutionary Algorithms

Grain boundary structure optimization is an important part of materials modeling. Current methods for grain boundary structure optimization involve inefficient, time-consuming processes that do not fully explore the interface parameter space. Evolutionary algorithms have recently been demonstrated to be effective at determining both stable and metastable grain boundary interface structures. In this work, we demonstrate the use of GBOpt, a grain boundary structure optimization software designed to use the Large-scale Atomic/Molecular Massively Parallel Simulation (LAMMPS) software to efficiently determine grain boundary structures. We demonstrate that a only a few manipulations, namely atom insertion, atom removal, and relative grain displacement, are sufficient to explore much of the grain boundary structure parameter space. The efficacy of this approach is demonstrated on an FCC Ni system, and a BCC Fe system. The computational cost is compared against the gamma-surface sampling approach to demonstrate performance improvement.

Evolutionary algorithms↗
DOE OSTI2024

In Search of Optimum Fresh-Cut Raw Material: Using Computer Vision Systems as a Sensory Screening Tool for Browning-Resistant Romaine Lettuce Accessions

The popularity of ready-to-eat (RTE) salads has prompted novel technology to prolong the shelf life of their ingredients. Fresh-cut romaine lettuce is widely used in RTE salads; however, its tendency to quickly discolor continues to be a challenge for the industry. Selecting the ideal lettuce accessions for use in RTE salads is essential to ensure maximum shelf life, and it is critical to have a practical way to assess and compare the quality of multiple lettuce accessions that are being considered for use in fresh-cut applications. Thus, in this work we aimed to determine whether a computer vision system (CVS) composed of image acquisition, processing, and analysis could be effective to detect visual quality differences among 16 accessions of fresh-cut romaine lettuce during postharvest storage. The CVS involved a post-capturing color correction, effective image segmentation, and calculation of a browning index, which was tested as a predictor of quality and shelf life of fresh-cut romaine lettuce. The results demonstrated that machine vision software can be implemented to replace or supplement the scoring of a trained panel and instrumental quality measurements. Overall visual quality, a key sensory parameter that determines food preferences and consumer behavior, was highly correlated with the browning index, with a Pearson correlation coefficient of −0.85. Other important sensory decision parameters were also strongly or moderately correlated with the browning index, with Pearson correlation coefficients of −0.84 for freshness, 0.79 for off odor, and 0.57 for browning. The ranking of the accessions according to quality acceptability from the sensory evaluation produced a similar pattern to those obtained with the CVS. This study revealed that multiple lettuce accessions can be effectively benchmarked for their performance as fresh-cut sources via a CVS-based method. Future opportunities and challenges in using machine vision image processing to predict consumer preferences for RTE salad greens is also discussed.

Agriculture↗
DOE OSTI2021

Processed Surface Imagery & Raster Imagery taken onboard TigerShark (U3) UAS

Land-surface orthomosaic imagery derived from the images collected using the Micasense Altum Imager on board the UAS TigerShark (U3) on March 11th, 2021 at an altitude of 2000 ft. The Altum takes images of the land surface across five visible bands and one long wave infrared thermal band. The photogrametry software Agisoft PhotoScan v 1.4 is used to align and stitch the images into a larger composet image using the technique of structure from motion image capture to construct a dense cloud and 3-D model of the surface, which is used to produce a digital elevation model of the terraine surveyed and orthomosaic imagery. This dataset contains the raster calculated orthomosaics of various vegetative indicies, which are used to indicate plant health and land surface characteristics, including Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Water Index (NDWI), surface temperature, and Enhanced_vegetation_index (EVI). These orthomosaics were collected west of the Starkville, MS, airport, and include natural, agricultural, and industrial land coverage. The images have a resolution of 0.2 m/pix.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Optical emissivity dataset of multi-material heterogeneous designs generated with automated figure extraction

Optical device design is typically an iterative optimization process based on a good initial guess from prior reports. Optical properties databases are useful in this process but difficult to compile because their parsing requires finding relevant papers and manually converting graphical emissivity curves to data tables. Here, we present two contributions: one is a dataset of thermal emissivity records with design-related parameters, and the other is a software tool for automated colored curve data extraction from scientific plots. We manually collected 64 papers with 176 figures reporting thermal emissivity and automatically retrieved 153 colored curve data records. The automated figure analysis software pipeline uses Faster R-CNN for axes and legend object detection, EasyOCR for axes numbering recognition, and k-means clustering for colored curve retrieval. Additionally, we manually extracted geometry, materials, and method information from the text to add necessary metadata to each emissivity curve. Finally, we analyzed the dataset to determine the dominant classes of emissivity curves and determine the underlying design parameters leading to a type of emissivity profile.

47 OTHER INSTRUMENTATION↗