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DOE OSTI2021

Data Product: Ka ARM Zenith Radar (KAZR), in CfRadial format: precipitation mode, corrected

The KAZRCFRCOR Value Added Product (VAP) performs several corrections to the ingested KAZR data and also creates a significant detection mask for each radar mode. The VAP computes gaseous attenuation as a function of time and radial distance from the radar antenna, based on ambient relative humidity, and corrects observed reflectivities for that effect. KAZR reflectivities are also corrected in the antenna ‘nearfield’ range to compensate for the near-field antenna pattern and gain. KAZRCFRCOR also dealiases mean Doppler velocities, to correct velocities whose magnitudes exceed the radar’s Nyquist velocity. This dataset contains the 'kazrpr' (precipitation) mode output from this VAP.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Data Product: Ka ARM Zenith Radar (KAZR), in CfRadial format: general mode, corrected

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Acceleration of the Parameterization of Unified Microphysics Across Scales (PUMAS) on the Graphics Processing Unit (GPU) With Directive-Based Methods

Cloud microphysics is one of the most time-consuming components in a climate model. In this study, we port the cloud microphysics parameterization in the Community Atmosphere Model (CAM), known as Parameterization of Unified Microphysics Across Scales (PUMAS), from CPU to GPU to seek a computational speedup. The directive-based methods (OpenACC and OpenMP target offload) are determined as the best fit specifically for our development practices, which enable a single version of source code to run either on the CPU or GPU, and yield a better portability and maintainability. Their performance is first examined in a PUMAS stand-alone kernel and the directive-based methods can outperform a CPU node as long as there is enough computational burden on the GPU. A consistent behavior is observed when we run PUMAS on the GPU in a practical CAM simulation. A 3.6× speedup of the PUMAS execution time, including data movement between CPU and GPU, is achieved at a coarse horizontal resolution (8 NVIDIA V100 GPUs against 36 Intel Skylake CPU cores). This speedup further increases up to 5.4× at a high resolution (24 NVIDIA V100 GPUs against 108 Intel Skylake CPU cores), which highlights the fact that GPU favors larger problem size. This study demonstrates that using GPU in a CAM simulation can save noticeable computational costs even with a small portion of code being GPU-enabled. Therefore, we are encouraged to port more parameterizations to GPU to take advantage of its computational benefit.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Ice Nucleating Particle Sources at Oliktok Point Field Campaign Report

The Ice Nucleating Particle Sources at Oliktok Point U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) facility field campaign focused on chemically characterizing springtime aerosol sources at Oliktok Point, Alaska. This work was completed to improve our understanding of air masses influencing the site and altering ice nucleating particle (INP) concentrations measured by Dr. Jessie Creamean of the National Oceanic and Atmospheric Administration (NOAA) during her coordinated, simultaneous ARM field campaign “Exploratory Ice Nucleation Measurements at Oliktok”. Together these ARM field campaigns comprised INPOP (Ice Nucleating Particles at Oliktok Point). The field campaign took place at the Oliktok Point Third ARM Mobile Facility (AMF3) site from March 1 to May 31, 2017. A Davis rotating uniform motion (DRUM) impactor was deployed to collect particles for offline analysis for single-particle chemical characterization (aerosol mixing state). Barring a few interruptions due to power outages at the site, daily samples (24-h duration) were collected for analysis by computer-controlled scanning electron microscopy with energy dispersive X-ray spectroscopy (CCSEM-EDX). Unfortunately our user facility grant at the Pacific Northwest National Laboratory (PNNL) Environmental Molecular Sciences Laboratory (EMSL) was placed on hold from October 2017 to February 2018, limiting our ability to do CCSEM-EDX, as our access is also limited at the Michigan Center for Materials Characterization. Therefore, only a subset of these samples were chosen in consultation with J. Creamean and analyzed by CCSEM-EDX. We have proposed to analyze additional samples, targeting samples collected during marine air masses, as part of our DOE Atmospheric System Research (ASR) proposal “Investigating marine and terrestrial aerosol processes and cloud ice nucleation during the Alaskan Arctic melt season”. The size-resolved aerosol mixing state data produced from the ARM field campaign represent the first of such observations in the springtime at Oliktok Point. Since this AMF3 site is located within the North Slope of Alaska (NSA) oil fields near Prudhoe Bay, the site is continually influenced by combustion emissions, with natural sources also contributing to the background aerosol population.

North Slope of Alaska, Oliktok Point, aerosols, or↗

DOE OSTI2021

Data Product: Ka ARM Zenith Radar (KAZR), in CfRadial format: moderate sensitivity mode, corrected

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Validation of HyRAM+ Version 5.1 Physics Models

The Hydrogen Plus Other Alternative Fuels Risk Assessment Models (HyRAM+) software has seen various improvements and additional physics capabilities since validation against experimental data was last published for HyRAM v3.1. Notably, HyRAM+ now includes four models allowing for the calculation of overpressure resulting from vapor cloud explosions from unconfined jet releases. As with the previous HyRAM validation report, validation data was gathered from available published literature and tested against HyRAM+ capabilities. The validation comparisons include tank blowdown, unignited dispersion jet plume, ignited jet flame, and enclosed accumulation and overpressure. The unconfined overpressure calculations in HyRAM+ v5.1.1 generally show good agreement with many of the experimental data sets for all four unconfined overpressure models, though HyRAM+ overpredicts the experimental data for small and cryogenic hydrogen releases. The comparisons for the other HyRAM+ physics models are largely unchanged from the previously published validation report.

08 HYDROGEN↗

DOE OSTI2019

Distributed Resources for the Earth System Grid Advanced Management (DREAM)

The DREAM project was funded more than 3 years ago to design and implement a next-generation ESGF (Earth System Grid Federation) architecture which would be suitable for managing and accessing data and services resources on a distributed and scalable environment. In particular, the project intended to focus on the computing and visualization capabilities of the stack, which at the time were rather primitive. At the beginning, the team had the general notion that a better ESGF architecture could be built by modularizing each component, and redefining its interaction with other components by defining and exposing a well defined API. Although this was still the high level principle that guided the work, the DREAM project was able to accomplish its goals by leveraging new practices in IT that started just about 3 or 4 years ago: the advent of containerization technologies (specifically, Docker), the development of frameworks to manage containers at scale (Docker Swarm and Kubernetes), and their application to the commercial Cloud. Thanks to these new technologies, DREAM was able to improve the ESGF architecture (including its computing and visualization services) to a level of deployability and scalability beyond the original expectations. Overall, during its three-year lifespan, DREAM has undertaken innovative research work and prototyping: many of its solutions (the new containerized architecture, the Visus visualization engine, the Solr Cloud based search service) were handed over to the ESGF collaboration, which will make them operational and use them as their future software basis.

42 ENGINEERING↗

DOE OSTI2020

Distributed Resources for the Earth System Grid Advanced Management (DREAM). Final Report

The DREAM project was funded more than 3 years ago to design and implement a next generation ESGF (Earth System Grid Federation) architecture which would be suitable for managing and accessing data and services resources on a distributed and scalable environment. In particular, the project intended to focus on the computing and visualization capabilities of the stack, which at the time were rather primitive. At the beginning, the team had the general notion that a better ESGF architecture could be built by modularizing each component, and redefining its interaction with other components by defining and exposing a well defined API. Although this was still the high level principle that guided the work, the DREAM project was able to accomplish its goals by leveraging new practices in IT that started just about 3 or 4 years ago: the advent of containerization technologies (specifically, Docker), the development of frameworks to manage containers at scale (Docker Swarm and Kubernetes), and their application to the commercial Cloud. Thanks to these new technologies, DREAM was able to improve the ESGF architecture (including its computing and visualization services) to a level of deployability and scalability beyond the original expectations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

LIVVkit 2.1: automated and extensible ice sheet model validation

A collection of scientific analyses, metrics, and visualizations for robustvalidation of ice sheet models is presented using the Land Ice Verificationand Validation toolkit (LIVVkit), version 2.1, and the LIVVkit Extensionsrepository (LEX), version 0.1. This software collection targets stand-aloneice sheet or coupled Earth system models, and handles datasets and analysesthat require high-performance computing and storage. LIVVkit aims to enableefficient and fully reproducible workflows for postprocessing, analysis, andvisualization of observational and model-derived datasets in a shareableformat, whereby all data, methodologies, and output are distributed to usersfor evaluation. Extending from the initial LIVVkit software framework, wedemonstrate Greenland ice sheet simulation validation metrics using thecoupled Community Earth System Model (CESM) as well as an idealizedstand-alone high-resolution Community Ice Sheet Model, version 2 (CISM2),coupled to the Albany/FELIX velocity solver (CISM-Albany or CISM-A). As oneexample of the capability, LIVVkit analyzes the degree to which modelscapture the surface mass balance (SMB) and identifies potential sources ofbias, using recently available in situ and remotely sensed data ascomparison. Related fields within atmosphere and land surface models, e.g.,surface temperature, radiation, and cloud cover, are also diagnosed. Appliedto the CESM1.0, LIVVkit identifies a positive SMB bias that is focusedlargely around Greenland's southwest region that is due to insufficientablation.

58 GEOSCIENCES↗

DOE OSTI2023

Three-dimensional phenotyping of peach tree-crown architecture utilizing terrestrial laser scanning

Tree training systems for temperate fruit have been developed throughout history by pomologists to improve light interception, fruit yield, and fruit quality. These training systems direct crown and branch growth to specific configurations. Quantifying crown architecture could aid the selection of trees that require less pruning or that naturally excel in specific growing/training system conditions. Regarding peaches [Prunus persica (L.) Batsch], access tools such as branching indices have been developed to characterize tree-crown architecture. However, the required branching data (BD) to develop these indices are difficult to collect. Traditionally, BD have been collected manually, but this process is tedious, time-consuming, and prone to human error. These barriers can be circumnavigated by utilizing terrestrial laser scanning (TLS) to obtain a digital twin of the real tree. TLS generates three-dimensional (3D) point clouds of the tree crown, wherein every point contains 3D coordinates (x, y, z). To facilitate the use of these tools for peach, we selected 16 young peach trees scanned in 2021 and 2022. These 16 trees were then modeled and quantified using the open-source software TreeQSM. As a result, “in silico” branching and biometric data for the young peach trees were calculated to demonstrate the capabilities of TLS phenotyping of peach tree-crown architecture. The comparison and analysis of field measurements (in situ) and in silico BD, biometric data, and quantitative structural model branch uncertainty data were utilized to determine the reconstructive model’s reliability as a source substitute for field measurements. Mean average deviation when comparing young tree (YT) height was approx. 5.93%, with crown volume was approx. 13.26% across both 2021 and 2022. All point clouds of the YTs in 2022 showed residuals lower than 12 mm to cylinders fitted to all branches, and mean surface coverage greater than 40% for both the trunk and primary branching orders.

09 BIOMASS FUELS↗

DOE OSTI2020

CYPminer: an automated cytochrome P450 identification, classification, and data analysis tool for genome data sets across kingdoms

Background: Cytochrome P450 monooxygenases (termed CYPs or P450s) are hemoproteins ubiquitously found across all kingdoms, playing a central role in intracellular metabolism, especially in metabolism of drugs and xenobiotics. The explosive growth of genome sequencing brings a new set of challenges and issues for researchers, such as a systematic investigation of CYPs across all kingdoms in terms of identification, classification, and pan-CYPome analyses. Such investigation requires an automated tool that can handle an enormous amount of sequencing data in a timely manner. Results: CYPminer was developed in the Python language to facilitate rapid, comprehensive analysis of CYPs from genomes of all kingdoms. CYPminer consists of two procedures i) to generate the Genome-CYP Matrix (GCM) that lists all occurrences of CYPs across the genomes, and ii) to perform analyses and visualization of the GCM, including pan-CYPomes (pan- and core-CYPome), CYP co-occurrence networks, CYP clouds, and genome clustering data. The performance of CYPminer was evaluated with three datasets from fungal and bacterial genome sequences. Conclusions: CYPminer completes CYP analyses for large-scale genomes from all kingdoms, which allows systematic genome annotation and comparative insights for CYPs. CYPminer also can be extended and adapted easily for broader usage.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

Entrainment rate diurnal cycle in marine stratiform clouds estimated from geostationary satellite retrievals and a meteorological forecast model: Entrainment Rate in Stratiform Clouds

The mean diurnal cycle of cloud entrainment rate (w e ) over the northeast Pacific region is for the first time computed by combining, in a mixed-layer model framework, the hourly composited GOES-15 satellite-based cloud top height (H T ) tendency, advection, and large-scale vertical velocity (w) during May to September 2013, with horizontal winds and w taken from the European Centre for Medium-Range Weather Forecasts (ECMWF) model. The tendency term dominates the magnitude and phase of the w e diurnal cycle, with a secondary role of w, and a modest advective contribution. The peak and minimum in w e occur between 20:00–22:00 LT and 9:00–11:00 LT, respectively, in close agreement with the diurnal cycle of turbulence driven by cloud top longwave cooling. Uncertainties in H T and ECMWF fields are assessed with in situ observations and three meteorological reanalysis data sets. This study provides the basis for constructing nearly global climatologies of w e by combining a suite of well-calibrated geostationary satellites.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

State-to-state rate coefficients for HCS+ in rotationally inelastic collisions with H2 at low temperatures

ABSTRACT HCS+ ions have been detected in several regions of the interstellar medium (ISM), but an accurate determination of the chemical-physical conditions in the molecular clouds where this molecule is observed requires detailed knowledge of the collisional rate coefficients with the most common colliders in those environments. In this work, we study the dynamics of rotationally inelastic collisions of HCS+ + H2 at low temperature, and report, for the first time, a set of rate coefficients for this system. We used a recently developed potential energy surface for the HCS+–H2 van der Waals complex and computed state-to-state rotational rate coefficients for the lower rotational states of HCS+ in collision with both para- and ortho-H2, analysing the influence of the computed rate coefficients on the determination of critical densities. Additionally, the computed rate coefficients are compared with those obtained by scaling the ones from HCS+ in collision with He (an approximation that is sometimes used when data is lacking), and large differences are found. Furthermore, the approximation of using the rates for the HCO+ + H2 collision as a rough approximation for those of the HCS+ + H2 system is also evaluated. Finally, the complete set of de-excitation rate coefficients for the lowest 30 rotational states of HCS+ by collision with H2 is reported from 5 to 100 K.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Pose Classification Using Three-Dimensional Atomic Structure-Based Neural Networks Applied to Ion Channel–Ligand Docking

The identification of promising lead compounds showing pharmacological activities toward a biological target is essential in early stage drug discovery. With the recent increase in available small-molecule databases, virtual high-throughput screening using physics-based molecular docking has emerged as an essential tool in assisting fast and cost-efficient lead discovery and optimization. However, the best scored docking poses are often suboptimal, resulting in incorrect screening and chemical property calculation. We address the pose classification problem by leveraging data-driven machine learning approaches to identify correct docking poses from AutoDock Vina and Glide screens. To enable effective classification of docking poses, we present two convolutional neural network approaches: a three-dimensional convolutional neural network (3D-CNN) and an attention-based point cloud network (PCN) trained on the PDBbind refined set. We demonstrate the effectiveness of our proposed classifiers on multiple evaluation data sets including the standard PDBbind CASF-2016 benchmark data set and various compound libraries with structurally different protein targets including an ion channel data set extracted from Protein Data Bank (PDB) and an in-house KCa3.1 inhibitor data set. Our experiments show that excluding false positive docking poses using the proposed classifiers improves virtual high-throughput screening to identify novel molecules against each target protein compared to the initial screen based on the docking scores.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2011

GALACTIC H{sub 2}CO DENSITOMETRY. I. PILOT SURVEY OF ULTRACOMPACT H II REGIONS AND METHODOLOGY

We present a pilot survey of 21 lines of sight toward ultracompact H II (UCH II) regions and three toward continuum-free lines of sight in the formaldehyde (H{sub 2}CO) 1{sub 10}-1{sub 11} (6 cm) and 2{sub 11}-2{sub 12} (2 cm) transitions, using the H{sub 2}CO centimeter lines as a molecular gas densitometer. Using Arecibo and Green Bank beam-matched observations, we measure the density of 51 detected H{sub 2}CO line pairs and present upper limits on density for an additional 24 detected 1{sub 10}-1{sub 11} lines. We analyze the systematic uncertainties in the H{sub 2}CO densitometer, achieving H{sub 2} density measurements with accuracies {approx}0.1-0.3 dex. The densities measured are not correlated with distance, implying that it is possible to make accurate density measurements throughout the galaxy without a distance bias. We confirm that UCH II regions are associated with, and possibly embedded in, gas at densities n(H{sub 2}) {approx}> 10{sup 5} cm{sup -3}. The densities measured in line-of-sight molecular clouds suggest that they consist of low volume filling factor (f {approx} 10{sup -2}) gas at high (n(H{sub 2}) > 10{sup 4} cm{sup -3}) density, which is inconsistent with purely supersonic turbulence and requires high-density clumping greater than typically observed in gravoturbulent simulations. We observe complex line morphologies that indicate density variations with velocity around UCH II regions, and we classify a subset of the UCH II molecular envelopes as collapsing or expanding. We compare these measurements to Bolocam Galactic Plane Survey 1.1 mm observations, and note that most UCH II regions have 1.1 mm emission consisting of significant (5%-70%) free-free emission and are therefore not necessarily dominated by optically thin dust emission as is often assumed when computing clump masses. A comparison of our data with the Mangum et al. starburst sample shows that the area filling factor of dense (n(H{sub 2}) {approx} 10{sup 5} cm{sup -3}) molecular gas in typical starburst galaxies is {approx}< 0.01, but in extreme starburst galaxies like Arp 220, is {approx}0.1, suggesting that Arp 220 is physically similar to an oversized UCH II region.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Enhancing Cloud Cybersecurity: Prescriptive Controls for Operational Technology

This whitepaper provides strategic insights and recommendations into security cloud-based solutions for electric utilities, encompassing operational technology (OT), virtual power plants (VPP), distributed energy resources (DERs), applications, networks, and data storage as they transition to and leverage cloud infrastructure through managed service providers (MSPs) and cloud service providers (CSPs). Principles derived from established frameworks serve as a foundation for best practices across cybersecurity projects and remove the constraints of settling on a single framework. For organizations that prefer not to integrate a specific framework altogether, elements of the proposed approach could be adopted or tailored to best fit defined requirements and expected functionalities. The Cirrus assessment, a utility cloud feasibility tool, and the roadmap it provides serve as a precursor to this paper, which seeks to be a valuable resource for defining next steps following cloud technology integration feasibility appraisal. With its comprehensive approach to adoption, the Cirrus framework offers strategic guidance on responsibly preparing for or deploying a utility cloud solution. The previously published whitepaper, “Use Case-Informed Framework for Utility Cloud Migration,” details the guiding strategy, research, and deployment of cloud solutions within electric and interconnected grid systems. Before implementing the controls suggested in this document, it is recommended that stakeholders complete Cirrus's cloud integration assessment and pair the results with their unique cybersecurity controls to form a comprehensive cloud-based utility cybersecurity plan. The Cirrus outcome will consider a series of future architectures for the grid before and after the energy transition and evaluate the arguments for and against cloud applications for each electric and interconnected grid layer. This document is a companion to the original whitepaper, "Use Case-Informed Framework for Utility Cloud Migration" to further identify and recommend security controls based on Cirrus’s cloud integration assessment output. The following whitepaper outlines the cybersecurity controls that secure cloud-service models pertinent to the electric sector using the predefined categories identify, protect, detect, and respond and recover. The objective is to outline prescriptive security controls based on the type of architecture and data stored in the cloud. The focus includes dissecting the shared responsibility model and elucidating what on-premises Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) entail. A pivotal consideration in this context is allocating responsibility for foundational cybersecurity aspects—having used Cirrus for the cloud integration assessment. The ensuing controls detailed herein also represent a checklist of controls necessary for a secure cloud transition, equipping utilities with the knowledge to navigate this digital transformation with confidence and strategic foresight in a safe and responsible manner.

42 ENGINEERING↗

DOE OSTI2026

Viskores: Integrating Parallel Scientific Visualization Research into Applications

Viskores is a scientific visualization library that is the primary deployment of such algorithms to the parallel accelerated processors of modern DOE supercomputers. In this paper, we review the capabilities provided by Viskores and how these capabilities are leveraged by other software in the high-performance computing ecosystem. We discuss the Viskores data representation and pay particular attention to array management. Through this array management we describe how data is adapted between Viskores and other software along with strategies for converting dynamic, polymorphic objects to static representations better suited to GPU processing. We conclude with several examples of Viskores integrating with high-performance software that is used in production today.

Moreland, Ken [ORNL] (ORCID:0000000270513288)↗

DOE OSTI2015

Multiresolution persistent homology for excessively large biomolecular datasets

Although persistent homology has emerged as a promising tool for the topological simplification of complex data, it is computationally intractable for large datasets. We introduce multiresolution persistent homology to handle excessively large datasets. We match the resolution with the scale of interest so as to represent large scale datasets with appropriate resolution. We utilize flexibility-rigidity index to access the topological connectivity of the data set and define a rigidity density for the filtration analysis. By appropriately tuning the resolution of the rigidity density, we are able to focus the topological lens on the scale of interest. The proposed multiresolution topological analysis is validated by a hexagonal fractal image which has three distinct scales. We further demonstrate the proposed method for extracting topological fingerprints from DNA molecules. In particular, the topological persistence of a virus capsid with 273 780 atoms is successfully analyzed which would otherwise be inaccessible to the normal point cloud method and unreliable by using coarse-grained multiscale persistent homology. The proposed method has also been successfully applied to the protein domain classification, which is the first time that persistent homology is used for practical protein domain analysis, to our knowledge. The proposed multiresolution topological method has potential applications in arbitrary data sets, such as social networks, biological networks, and graphs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗