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At least 451 records · Page 25

Lattice dynamics of alpha-uranium measured on ARCS.

The combined inelastic neutron scattering data from ARCS for alpha-Uranium single crystal measured at 300K, 200K, 70K and 20K. The incident neutron energy was 30 meV. To obtain a significant four-dimensional Q-E volume, the crystal was measured in two scattering geometries, with the [001] and [011] directions oriented along the vertical rotations axis. For each geometry, the crystal was rotated in 1 degree steps with respect to the incident beam. All the individual angles data files at each temperature were merged and analyzed using SHIVER software package (https://neutrons.github.io/Shiver/). The files labeled a_U_HKL_20K_new.nxs, a_U_HKL_70K.nxs, a_U_HKL_200K.nxs and a_U_HKL_300K.nxs are for [011] oriented scattering at 20K, 70K, 200K, and 300K respectively. The files labeled a_U_HK0_70K.nxs, a_U_HK0_200K.nxs and a_U_HK0_300K.nxs are for [001] oriented scattering at temperatures 70K, 200K and 300K respectively.

36 MATERIALS SCIENCE↗

Upper-air soundings collected during the CROCUS Urban Canyons 2024 campaign in Chicago, Illinois USA

Funded by the Department of Energy’s Office of Science, Biological and Environmental Research program, Community Research on Climate and Urban Science (CROCUS) studies urban climate change and the impact it has on communities, with particular focus on disinvested, under-resourced communities. This information leads to new insights on urban climate challenges and informs future actions for mitigating and adapting to climate change at the street, neighborhood and regional levels.As part of the CROCUS effort, the Urban Canyons 2024 project was undertaken to study conditions at unprecedented detail over various neighborhoods in Chicago, Illinois. This dataset consists of upper air soundings that were collected as part of this effort. Soundings were launched during two intensive observing periods, IOP1 occurred on 22-23 July 2024, while IOP2 occurred on 27-28 July 2024. For IOP1, soundings were launched at coordinated times from three sites, Shedd Aquarium in Downtown Chicago, Abizu Campus High School in Humboldt Park, and Gary Comer Youth Center in West Woodlawn. For IOP2, the Gary Comer site was replaced by a neighborhood site in West Woodlawn, Chicago. The Abizu Campos site was operated by Valparaiso University and used iMET-4 rawinsondes, the other sites were operated by the University of Illinois Urbana-Champaign and used GRAW DFM-19 sondes.This dataset contains netCDF files containing quality-controlled temperature, dewpoint, geopotential height, pressure, and vector wind measurements at 1 second intervals following launch. These files are readable by the open-source netCDF software libraries available in many software packages (i.e., python, R, fortran, C++, etc.). The dataset also contains quicklook plots of each launch on a skew-T log-p thermodynamic diagram. These are in png format viewable by most web browsers.

54 ENVIRONMENTAL SCIENCES↗

Small Angle Scattering Data Analysis Assisted by Machine Learning Methods

Small angle scattering (SAS) is a widely used technique for characterizing structures of wide ranges of materials. For such wide ranges of applications of SAS, there exist a large number of ways to model the scattering data. While such analysis models are often available from various suites of SAS data analysis software packages, selecting the right model to start with poses a big challenge for beginners to SAS data analysis. Here, we present machine learning (ML) methods that can assist users by suggesting scattering models for data analysis. A series of one-dimensional scattering curves have been generated by using different models to train the algorithms. The performance of the ML method is studied for various types of ML algorithms, resolution of the dataset, and the number of the dataset. The degree of similarities among selected scattering models is presented in terms of the confusion matrix. The scattering model suggestions with prediction scores provide a list of scattering models that are likely to succeed. Therefore, if implemented with extensive libraries of scattering models, this method can speed up the data analysis workflow by reducing search spaces for appropriate scattering models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

High-Quality Revision of the Israeli Seismic Bulletin

Seismic bulletins, with trustworthy phase picks, origin times, and source locations are key for regional seismic studies, such as travel-time (TT) tomography, attenuation tomography, and anisotropy studies. To lay the groundwork for such studies in Israel, we revised the seismic bulletin of Israel and the surrounding area and obtained a trustworthy TT data set. From the earthquake and explosion bulletins of the Geophysical Institute of Israel, we compiled a starting data set of about 123,000 earthquakes and explosions that occurred during the past 40 yr. After screening out the poorly recorded events, we were left with a data set of ~38,000 well-recorded events. We then revised the remaining data set in two consecutive steps. In the first, we reviewed and updated station metadata, including changes in station metadata parameters over time. In the second step, we jointly relocated a list of selected seismic events, using the Bayesian hierarchical location software package (BayesLoc) of Myers et al. (2007) that performs joint relocation of multiple events. We observed striking dissimilarities between the spatial distributions of the newly relocated catalog and the initial locations. Although the depth distribution of the starting catalog is trimodal with peaks at 0, 5, and 10 km, the distribution in this study is unimodal, with a broad peak between 7.5 and 12.5 km. By differencing the observed arrival times and the origin times obtained through relocation with BayesLoc, we obtained a revised TT database that consists of 261,336 Pg, 132,876 Pn, 114,816 Sg, and 60,394 Sn arrivals, from a set of 30,458 jointly relocated seismic sources. In this work, we compared prerevision and postrevision TTs as a function of epicentral distance and concluded that the revised data set contains far fewer outliers and inconsistencies than the original data set. The revised TT data set may be used for seismic studies, such as TT tomography, attenuation tomography, and anisotropy studies.

58 GEOSCIENCES↗

WaterTAP 0.3 Release

The Water treatment Technoeconomic Assessment Platform (WaterTAP) is an open-source Python-based software package that supports the simulation and optimization of process-scale water treatment trains. WaterTAP seeks to provide the broader water research community with an integrated modeling capability to evaluate cost, energy, and environmental tradeoffs across water treatment options and identify high impact opportunities for innovation including novel materials, processes, and systems. An updated version of WaterTAP is released quarterly and each includes documentation and release notes.

IDAES↗

WaterTAP 0.4 Release

The Water treatment Technoeconomic Assessment Platform (WaterTAP) is an open-source Python-based software package that supports the simulation and optimization of process-scale water treatment trains. WaterTAP seeks to provide the broader water research community with an integrated modeling capability to evaluate cost, energy, and environmental tradeoffs across water treatment options and identify high impact opportunities for innovation including novel materials, processes, and systems. An updated version of WaterTAP is released quarterly and each includes documentation and release notes.

IDAES↗

WaterTAP 0.5 Release

The Water treatment Technoeconomic Assessment Platform (WaterTAP) is an open-source Python-based software package that supports the simulation and optimization of process-scale water treatment trains. WaterTAP seeks to provide the broader water research community with an integrated modeling capability to evaluate cost, energy, and environmental tradeoffs across water treatment options and identify high impact opportunities for innovation including novel materials, processes, and systems. An updated version of WaterTAP is released quarterly and each includes documentation and release notes.

IDAES↗

WaterTAP 0.6 Release

The Water treatment Technoeconomic Assessment Platform (WaterTAP) is an open-source Python-based software package that supports the simulation and optimization of process-scale water treatment trains. WaterTAP seeks to provide the broader water research community with an integrated modeling capability to evaluate cost, energy, and environmental tradeoffs across water treatment options and identify high impact opportunities for innovation including novel materials, processes, and systems. An updated version of WaterTAP is released quarterly and each includes documentation and release notes.

IDAES↗

WaterTAP 0.7 Release

The Water treatment Technoeconomic Assessment Platform (WaterTAP) is an open-source Python-based software package that supports the simulation and optimization of process-scale water treatment trains. WaterTAP seeks to provide the broader water research community with an integrated modeling capability to evaluate cost, energy, and environmental tradeoffs across water treatment options and identify high impact opportunities for innovation including novel materials, processes, and systems. An updated version of WaterTAP is released quarterly and each includes documentation and release notes.

IDAES↗

WaterTAP 0.8 Release

The Water treatment Technoeconomic Assessment Platform (WaterTAP) is an open-source Python-based software package that supports the simulation and optimization of process-scale water treatment trains. WaterTAP seeks to provide the broader water research community with an integrated modeling capability to evaluate cost, energy, and environmental tradeoffs across water treatment options and identify high impact opportunities for innovation including novel materials, processes, and systems. An updated version of WaterTAP is released quarterly and each includes documentation and release notes.

IDAES↗

WaterTAP 0.10 Release

The Water treatment Technoeconomic Assessment Platform (WaterTAP) is an open-source Python-based software package that supports the simulation and optimization of process-scale water treatment trains. WaterTAP seeks to provide the broader water research community with an integrated modeling capability to evaluate cost, energy, and environmental tradeoffs across water treatment options and identify high impact opportunities for innovation including novel materials, processes, and systems. An updated version of WaterTAP is released quarterly and each includes documentation and release notes.

IDAES↗

WaterTAP 0.11 Release

The Water treatment Technoeconomic Assessment Platform (WaterTAP) is an open-source Python-based software package that supports the simulation and optimization of process-scale water treatment trains. WaterTAP seeks to provide the broader water research community with an integrated modeling capability to evaluate cost, energy, and environmental tradeoffs across water treatment options and identify high impact opportunities for innovation including novel materials, processes, and systems. An updated version of WaterTAP is released quarterly and each includes documentation and release notes.

IDAES↗

WaterTAP 0.12 Release

The Water treatment Technoeconomic Assessment Platform (WaterTAP) is an open-source Python-based software package that supports the simulation and optimization of process-scale water treatment trains. WaterTAP seeks to provide the broader water research community with an integrated modeling capability to evaluate cost, energy, and environmental tradeoffs across water treatment options and identify high impact opportunities for innovation including novel materials, processes, and systems. An updated version of WaterTAP is released quarterly and each includes documentation and release notes.

IDAES↗

WaterTAP 1.0 Release

The Water treatment Technoeconomic Assessment Platform (WaterTAP) is an open-source Python-based software package that supports the simulation and optimization of process-scale water treatment trains. WaterTAP seeks to provide the broader water research community with an integrated modeling capability to evaluate cost, energy, and environmental tradeoffs across water treatment options and identify high impact opportunities for innovation including novel materials, processes, and systems. An updated version of WaterTAP is released quarterly and each includes documentation and release notes.

AS↗

PARETO UI 1.1.0 Release

PARETO is an open-source Python-based software package for oilfield produced water management and beneficiary reuse optimization. PARETO supports produced water industry by providing cost-effective water management solutions. This version introduced an updated User Interface (UI) which makes it easier to navigate and understand the solution for industry users. New Features: - Map files are added for visualization - Added output export function button - Water residual view added - Workflow was streamlined - File extension was expanded - Minor bugfix

AS↗

TX$^2$: Transformer eXplainability and eXploration

The Transformer eXplainability and eXploration (Martindale & Stewart, 2021), or TX 2 software package, is a library designed for artificial intelligence researchers to better understand the performance of transformer models (Vaswani et al., 2017) used for sequence classification. The tool is capable of integrating with a trained transformer model and a dataset split into training and testing populations to produce an ipywidget (Project Jupyter Contributors, 2021) dashboard with a number of visualizations to understand model performance with an emphasis on explainability and interpretability. The TX 2 package is primarily intended to integrate into a workflow centered around Jupyter Notebooks (Kluyver et al., 2016), and currently assumes the use of PyTorch (Paszke et al., 2019) and Hugging Face transformers library (Wolf et al., 2020). The dashboard includes visualization and data exploration features to aid researchers, including an interactive UMAP embedding graph (McInnes et al., 2018) to understand classification clusters, a word salience map that can be updated as researchers alter textual entries in near real time, a set of tools to understand word frequency and importance based on the clusters in the UMAP embedding graph, and a set of traditional confusion matrix analysis tools.

97 MATHEMATICS AND COMPUTING↗

MLMOD: Machine Learning Methods for Data-Driven Modeling in LAMMPS

MLMOD is a software package for incorporating machine learning approaches and models into simulations of microscale mechanics and molecular dynamics in LAMMPS. Recent machine learning approaches provide promising data-driven approaches for learning representations for system behaviors from experimental data and high fidelity simulations. The package facilitates learning and using data-driven models for (i) dynamics of the system at larger spatial-temporal scales (ii) interactions between system components, (iii) features yielding coarser degrees of freedom, and (iv) features for new quantities of interest characterizing system behaviors. MLMOD provides hooks in LAMMPS for (i) modeling dynamics and time-step integration, (ii) modeling interactions, and (iii) computing quantities of interest characterizing system states. The package allows for use of machine learning methods with general model classes including Neural Networks, Gaussian Process Regression, Kernel Models, and other approaches. Here we discuss our prototype C++/Python package, aims, and example usage. For related papers, examples, updates, and additional information see https://github.com/atzberg/mlmod and http://atzberger.org/.

97 MATHEMATICS AND COMPUTING↗

AdditiveFOAM: A Continuum Multiphysics Code for Additive Manufacturing

AdditiveFOAM is a computational framework that simulates transport phenomena in Additive Manufacturing (AM) processes. It is built on OpenFOAM (Weller et al., 1998), the leading free, open-source software package for computational fluid dynamics (CFD). OpenFOAM offers an extensible platform for solving complex multiphysics problems using state-of-the-art finite volume methods. AdditiveFOAM leverages these capabilities to develop specialized tools aimed at addressing challenges in AM processing. Metal additive manufacturing, also known as metal 3D printing, is an advanced manufacturing technique that creates physical parts from a three-dimensional (3D) digital model by melting metal powder or wire feedstock. A significant area of research in metal AM focuses on process planning to mitigate anomalous features during printing that are deleterious to part performance (e.g., porosity and cracking), as well as controlling localized microstructure and material properties. Given the high costs and substantial time requirements associated with experimental methods for qualifying new materials and processes, there is a compelling incentive for researchers to utilize advanced computational simulations. In this context, AdditiveFOAM offers a simulation framework to better understand undesirable features in printing, thereby enhancing process planning and reducing the reliance on labor-intensive experimental campaigns.

Coleman, John [Oak Ridge National Laboratory (ORNL↗