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At least 613 records · Page 34

Redesign of the Timeline Generator at Fermilab using a web-based Flutter Application, GraphQL API and an IOC

Redesign of the Timeline Generator at Fermilab using a web-based Flutter application, GraphQL API and an IOC ABSTRACT = The control system at Fermilab is undergoing an evolution with a shift towards web-based applications with connections to the EPICS infrastructure. The Timeline Generator (TLG) is an application that serves to coordinate events across the lab using different timing links. These links include the Tevatron clock (TCLK), a 10 MHz serial link with events encoded at 20Hz and Ma-chine Data (MDAT), a communication link with states encoded at 720Hz. This paper covers the redesign of the major components of the TLG. This includes a web-based Flutter application for building timelines. A placement service is in use that has a GraphQL interface and uses a timeline input to compute a schedule of events and states. The Flutter application sends this computed schedule to the TLG IOC via a GraphQL interface to the Data Pool Manager (DPM). The TLG IOC runs on an Arria FPGA, the Accelerator Clock Generator (ACLK-GEN), which is responsible for writing the events and states on to the different timing links.

Carmichael, Linden [Fermilab]↗

Wind Energy: Bans or Moratoriums (2022)

This dataset identifies counties and municipalities that had a wind energy ban or moratorium as of April 2022. A TIF data file and a PNG map of the data are provided, showing areas where wind energy bans or moratoriums exist across the contiguous United States. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

Array↗

Wind Turbine Sound Setbacks and Supply Curves: Ordinances and Extrapolated Trends, 110 Hub Height, 130 Rotor Diameter

This dataset provides a comprehensive set of wind turbine sound setbacks from every residential structure in the contiguous United States (CONUS). A sound setback is defined as the minimum required distance between a residential structure and a hypothetical turbine installation site to ensure that modeled sound levels received at the residence do not exceed local sound ordinances, which are commonly expressed in A-weighted decibels (dBA). Therefore, sound setbacks are a local spatial assessment combining multiple factors, including the sound pressure curve as a function of the observer location (distance and direction) relative to the turbine, local sound regulations, and the geographical distribution of residential structures. The dataset is organized into multiple scenario-based products, detailed as follows: 1. Existing and extrapolated sound setbacks. An existing scenario characterizes sound setbacks only in states or counties that have implemented sound regulations as of 2022. The extrapolated scenarios extend a constant sound threshold to counties that lack explicit sound regulations, with thresholds ranging from 35 to 60 dBA, in 5-dBA increments reflecting the variation observed in current sound ordinances. 2. Sound setbacks in directional and worst scenarios. The directional scenario accounts for the distance and orientation of residential structures relative to a hypothetical turbine location, utilizing the turbine's sound emissions in that specific direction. In contrast, the worst scenario takes loudest sound level at each distance step from the turbine, irrespective of directional considerations, which aligns with current industry practice. 3. Supply curves for Open and Reference Access scenarios. This dataset includes supply curves generated by the reV model, which integrates each of the above sound setbacks into both Open and Reference siting scenarios. In addition, two Open and Reference baselines scenarios were included which do not consider sound setbacks for comparative analysis. All sound setback data are stored in TIF files, with partial maps of the data provided in PNG format. The values in the sound setback raster range from 0 to 1, representing the fraction of developable land within a 90 meter by 90 meter pixel due to sound ordinances. A value of 0 indicates areas where wind energy development is prohibited, while a value of 1 signifies areas fully permissible. The wind turbine parameters used in the sound modeling are based on the land-based turbine from International Energy Agency (IEA), featuring a rated electrical power of 3.4 MW, a rotor diameter of 130 meters, and a hub height of 110 meters. The atmospheric conditions, including wind speed/direction, turbulence, air temperature, relative humidity, and air pressure, that drive the sound generation are obtained from the WIND Toolkit dataset.

Array↗

Constrained or unconstrained? Neural-network-based equation discovery from data

Throughout many fields, practitioners often rely on differential equations to model systems. Yet, for many applications, the theoretical derivation of such equations and/or the accurate resolution of their solutions may be intractable. Instead, recently developed methods, including those based on parameter estimation, operator subset selection, and neural networks, allow for the data-driven discovery of both ordinary and partial differential equations (PDEs), on a spectrum of interpretability. The success of these strategies is often contingent upon the correct identification of representative equations from noisy observations of state variables and, as importantly and intertwined with that, the mathematical strategies utilized to enforce those equations. Specifically, the latter has been commonly addressed via unconstrained optimization strategies. Representing the PDE as a neural network, we propose to discover the PDE (or the associated operator) by solving a constrained optimization problem and using an intermediate state representation similar to a physics-informed neural network (PINN). The objective function of this constrained optimization problem promotes matching the data, while the constraints require that the discovered PDE is satisfied at a number of spatial collocation points. We present a penalty method and a widely used trust-region barrier method to solve this constrained optimization problem, and we compare these methods on numerical examples. Our results on several example problems demonstrate that the latter constrained method outperforms the penalty method, particularly for higher noise levels or fewer collocation points. This work motivates further exploration into using sophisticated constrained optimization methods in scientific machine learning, as opposed to their commonly used, penalty-method or unconstrained counterparts. For both of these methods, we solve these discovered neural network PDEs with classical methods, such as finite difference methods, as opposed to PINNs-type methods relying on automatic differentiation. Here, we briefly highlight how simultaneously fitting the data while discovering the PDE improves the robustness to noise and other small, yet crucial, implementation details.

Data-driven discovery↗

Ecosystems and Networks Integrated with Genes and Molecular Assemblies (ENIGMA) (Final Scientific/Technical Report, Subcontract Award No. 6953691)

The overarching objective the Baliga lab is to develop a mechanistic understanding of the field relevant metabolic processes underlying community partitioning of respiration pathways and physiological state shifts. In order to dissect how environmental perturbations influence microbial regulatory networks, we have constructed data-driven models that capture the dynamic interplay between abiotic and biotic factors during laboratory simulations. Specifically, we have developed multiple computational models (i.e., cMonkey2, EGRIN 2.0, and multiple genome-scale metabolic models) connecting environmental influence to genome-encoded regulatory programs. In addition, we have generated new tools such as Live Anaerobic Cell Sorting (LAnCS) and Fluidized Bed Reactors (FBRs) to investigate the dynamics of community partitioning of metabolic processes supporting sulfate or nitrate respiration, which are two critical activities observed from the field site at Oak Ridge National Labs (ORNL). This framework of connecting field phenomena with laboratory simulations and computational modeling has created the Environmental Simulations and Modelling (EnvSim) campaign within ENIGMA. The campaign, which spans multiple laboratories across ENIGMA and is led by the Baliga group, has established synthetic communities (SynComs) to discover, characterize, and dissect key microbial processes relevant to field observations, such as emissions of the greenhouse gas nitrous oxide (N 2 O). For example, the EnvSim campaign so far has deduced four potential mechanisms that may account for the N 2 O emissions at the FRC and are currently being investigated by labs across ENIGMA. Denitrification may be driven by complete denitrifiers, however, their NosZ enzymes, which catalyze the final step in denitrification by converting N 2 O to N 2 , may be sensitive at a lower pH. Additionally, the denitrification process could be partitioned among organisms and some may have pH-sensitive NosZ genes. Another factor contributing to variable N 2 O emissions relates to the metal co-factors involved in the denitrification pathway. For instance, excess Cu, Al, Mn, U, Ni, Co, Cu, and/or Cd may have inhibitory effects on multiple enzymatic steps during denitrification, while the enzymatic production of nitrite, the precursor of N 2 O, can be limited by the essential metal Mo. Lastly, abiotic production of N 2 O may occur as a result of chemodenitrification, in which metals like Fe, Mn, and some organic compounds can drive redox reactions that convert nitrogen cycle intermediates to N 2 O under the right conditions. Current investigations and analyses have generated multiple transcriptomic profiles aiding the refinement of new metabolic and gene regulatory network model for our denitrifying SynCom. In sum, the previous funding cycle has generated 33 peer-reviewed publications spanning predictive network biology to co-operativity of mutualistic interspecies interactions of evolved communities. Our integration of multiple omics datasets and the development of new network modelling tools has revealed important insights that can explain field observed phenomenon and generated new hypotheses to be investigated in the field.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Agilent AgileBioFoundry CRADA (Final Report)

The mission of this CRADA with Agilent was to couple powerful MS platforms (QQQ, IM-QTOF-MS) with Agilent’s novel Ultra-High-Performance Liquid Chromatography (UHPLC) fast metabolomic workflows and perform ABF Machine Learning (ML) to generated datasets. Agilent transferred UHPLC methods to PNNL and LBNL and methods were implemented and demonstrated in both labs, achieving total acquisition times of < 10 min. Metabolites analyzed using Agilent’s shared methods included metabolites from central carbon metabolism, common across hosts, and metabolites unique to engineered strains. Standards were acquired in an UHPLC-Drift Tube Ion Mobility Mass Spectrometer (DTIMS) system for the first time within the context of ABF and methods were optimized based on Agilent’s protocols. Samples from ABF hosts Pseudomonas putida, Aspergillus pseudoterreus, Aspergillus niger and Rhodosporidium toruloides were analyzed using the UHPLC-DTIMS platform for a total of 276 runs. A data analysis workflow compatible with the Experimental Data Depot (EDD) and completely shareable was developed for the acquired UHPLC-DTIMS data. Samples were analyzed using a Data Independent Acquisition Approach (DIA), which for most of the standards provided more transitions therefore increasing detection confidence. Using the data acquired by PNNL, LBNL, and Agilent’s specifications from previous ML projects, SNL applied an ensemble ML strategy to pick the best performing model for automated LC-method selection. Finally, with the contribution of the participant labs and Agilent, SNL developed an Automated Method Selection (AMS) software tool to predict the best liquid chromatography method for analysis of any new molecules of interest. Samples with novel pathways and new metabolite targets of interest are generated at a high pace in the ABF. Therefore, our accomplishment in this CRADA improved the efficiency and accuracy of strain testing by developing and implementing fast analytical methods, robust processing tools, and software for predicting the best methods for UHPLC analysis.

97 MATHEMATICS AND COMPUTING↗

Further Development of the Tamped Richtmyer-Meshkov Instability Method and Application to Molybdenum Dynamic Strength Calibration and Tabulation

The high pressure and high strain rate dynamic strength of Mo is experimentally and computationally investigated in the 3–20 GPa stress, 50–600 C temperature, and 10 5 –10 6 /s strain rate regimes using a modified tamped Richtmyer-Meshkov instability (RMI) method. Modifications to the tamped RMI method include a method to determine loading states during strain, a new strength calibration function based on interface shape, and a robust uncertainty quantification method. These modifications improve fidelity of the tamped RMI method, allowing evaluation of the compensating effects of pressure hardening, strain rate hardening, strain hardening, and thermal softening. The new calibration function based on interface shape is not limited to sinusoidal corrugations and could be applied to additional interface shapes. Plate impact experiments are performed at Argonne National Laboratory’s Advanced Photon Source’s Dynamic Compression Sector operated by Washington State University (DCS), driving a planar shock front through a corrugated Mo-D 2 O or Mo-C 8 F 18 interface, forcing the corrugation to significantly deform. The extent of interfacial deformation, RMI growth, is experimentally observed using X-ray phase contrast imaging at the DCS. RMI jet lengths and jet shapes are extracted from the experimental radiographs, then used to calibrate numerical simulations performed with the Sandia National Laboratories (SNL) hydrocode CTH. Mo yield strength, Y, as a function of shock pressure, P, strain rate, $\dot{\varepsilon }$, accumulated strain, ϵ, relative volumetric compression, RD , and temperature, T , is determined for each impact experiment and presented. The calibrated Mo yield strength values range 1.2–1.8 GPa, with strength generally decreasing as the impact stress increases. This trend is likely caused by thermal softening or strain localization. The tabular yield strength versus loading condition data presented in this paper can be used to fit complex strength models.

Voorhees, T. J. [Sandia National Lab. (SNL-CA), Li↗

Autogenerating a Domain-Specific Question-Answering Data Set from a Thermoelectric Materials Database to Enable High-Performing BERT Models

We present a method for autogenerating a large domain-specific question-answering (QA) dataset from a thermoelectric materials database. We show that a small language model, BERT, once fine-tuned on this automatically generated dataset of 99,757 QA pairs about thermoelectric materials, affords better performance in the field of thermoelectric materials compared to a BERT model fine-tuned on the generic English-language QA data set, SQuAD-v2. We further show that mixing the two data sets (ours and SQuAD-v2), which have significantly different syntactic and semantic scopes, allows the BERT model to achieve even better performance. The best-performing BERT model fine-tuned on the mixed data set outperforms the models fine-tuned on the other two data sets by scoring an exact match of 67.93% and an F1 score of 72.29% when evaluated on our test data set. This has important implications as it demonstrates the ability to realize high-performing small language models, with modest computational resources, empowered by domain-specific materials data sets which can be generated according to our method.

biological databases↗

First Measurement of the Neutron-Emission Probability with a Surrogate Reaction in Inverse Kinematics at a Heavy-Ion Storage Ring

Neutron-induced reaction cross sections of short-lived nuclei are imperative to understand the origin of heavy elements in stellar nucleosynthesis and for societal applications, but their measurement is extremely complicated due to the radioactivity of the targets involved. One way of overcoming this issue is to combine surrogate reactions with the unique possibilities offered by heavy-ion storage rings. In this work, we describe the first surrogate-reaction experiment in inverse kinematics, which we successfully conducted at the Experimental Storage Ring (ESR) of the GSI/FAIR facility, using the 208 Pb⁢(p,p') reaction as a surrogate for neutron capture on 207 Pb. Thanks to the outstanding detection efficiencies possible at the ESR, we were able to measure for the first time the neutron-emission probability as a function of the excitation energy of 208 Pb. We have used this probability to select different descriptions of the γ -ray strength function and nuclear level density, and provide reliable results for the neutron-induced radiative capture cross section of 207 Pb at energies for which no experimental data exist.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Four-year Cosmology Large Angular Scale Surveyor (CLASS) Observations: On-sky Receiver Performance at 40, 90, 150, and 220 GHz Frequency Bands

The Cosmology Large Angular Scale Surveyor (CLASS) observes the polarized cosmic microwave background (CMB) over the angular scales of 1° ≲ θ ≤ 90° with the aim of characterizing primordial gravitational waves and cosmic reionization. We report on the on-sky performance of the CLASS Q-band (40 GHz), W-band (90 GHz), and dichroic G-band (150/220 GHz) receivers that have been operational at the CLASS site in the Atacama desert since 2016 June, 2018 May, and 2019 September, respectively. We show that the noise-equivalent power measured by the detectors matches the expected noise model based on on-sky optical loading and lab-measured detector parameters. Using Moon, Venus, and Jupiter observations, we obtain power to antenna temperature calibrations and optical efficiencies for the telescopes. From the CMB survey data, we compute instantaneous array noise-equivalent-temperature sensitivities of 22, 19, 23, and 71 $\mu {{\rm{K}}}_{\mathrm{cmb}}\sqrt{{\rm{s}}}$ for the 40, 90, 150, and 220 GHz frequency bands, respectively. These noise temperatures refer to white noise amplitudes, which contribute to sky maps at all angular scales. Future papers will assess additional noise sources impacting larger angular scales.

Observational cosmology↗

AOI-2, A Novel Access Control Blockchain Paradigm for Cybersecure Sensor Infrastructure in Fossil Power Generation Systems

Fossil power generation systems are increasingly vulnerable to attack from both cybercriminals as well as internal threats. These vulnerabilities demand that emerging technologies such as blockchains be utilized to secure the data involved in the information flows within the Supervisory Control and Data Acquisition (SCADA) systems of the fossil power generation plants. The publicly accessible blockchain protocols, although secure, are visible to everyone. Even private blockchains currently are unable to support different levels of access to different participants, which is a critical requirement for the existing SCADA systems running the power plants. In light of the above, novel blockchain protocols that are specifically adapted to fossil power generation environments need to be developed in order to achieve the goal of cybersecure sensor networks. In this work, we address this question by creating a novel blockchain technology, namely smart private ledger, for cybersecure communication within the fossil power generation systems. A lab-scale sensor network consisting of strain and temperature sensors is constructed to develop the ledger. The technology has hierarchical access control which is compatible with the existing SCADA systems in fossil power plants. The sensor data is used with cryptographic digital signatures and secret sharing protocols within the nodes of the blockchain technology. The research results will lead to cybersecurity for machine-to-machine interactions, infrastructure for secure data logging for sensors, decentralized data storage, and second-layer technologies for high volume machine-to-machine interactions in the power plants. The work aims to largely address the concerns for the security of distributed sensor networks in such systems that can be compromised by insider threats and by cybercriminals. The research has led to the training of the next generation of engineers and scientists in the important areas of sensor engineering and blockchain technology.

01 COAL, LIGNITE, AND PEAT↗

Water quality data collected from the Muskegon River (MI, USA) using AquaBOT June-July 2024.

This dataset contains processed output from AquaBOT, which combines 30-second interval measurements of GPS location and YSI water quality parameters (temperature (degrees Celsius), dissolved oxygen (mg/l), specific conductance(microSiemens/cm at 25 degrees Celsius), and turbidity (NTU)), for 3 sections of the Muskegon River in Michigan, USA. The file aquabot2024_MuskegonRiver.csv has information on locations, dates and times, and observations. The data were processed by removing observations recorded before and after AquaBOT was in the water and observations where no data values were recorded. No other QA/QC was done. This research was performed as part of the DOE Research Development and Partnership Pilot award “Expanding Collaborative Capacity to Address Climate Resiliency in the Great Lakes Region”, which aims to expand collaborations between researchers at Central Michigan University and U.S. Department of Energy labs and projects focused on enhancing climate resilience in Great Lakes communities and ecosystems.

54 ENVIRONMENTAL SCIENCES↗

Characterization of the scintillation time response of liquid argon detectors for dark matter search

The scintillation time response of liquid argon has a key role in the discrimination of electronic backgrounds in dark matter search experiments. However, its extraordinary rejection power can be affected by various detector effects such as the delayed light emission of TetraPhenyl Butadiene, the most commonly used wavelength shifter, and the electric drift field applied in Time Projection Chambers. Herein, we characterized the TetraPhenyl Butadiene delayed response and the dependence of the pulse shape discrimination on the electric field, exploiting the data acquired with the ARIS, a small-scale single-phase liquid argon detector exposed to monochromatic neutron and gamma sources at the ALTO facility of IJC Lab in Orsay.

47 OTHER INSTRUMENTATION↗

FY2021 Improvements to the New CTH Code Verification & Validation Test Suite

Over the past few years, the CTH multiphysics hydrocode has overhauled its software quality and testing processes, implementing current best practices in software quality and building a robust V&V test suite comprised of traditional hydrocode verification problems, including ASC Tri-Lab Test Suite and Enhanced Tri-Lab Test Suite problems, as well as validation problems for some of CTH’s most frequently used equations of state, materials models, and other key capabilities. Substantial progress towards building this new test suite was made in FY19 and FY20. In FY21, the test suite has been expanded to include verification and validation tests of the Steinberg-Guinan-Lund (ST) viscoplastic model and the Johnson Cook (JFRAC) fracture model. Additionally, two new verification tests were added, covering hydrodynamics and high explosive (HE) modeling capabilities: the Kidder Gaussian density problem and the Escape of HE Products (EHEP) problem from the Tri-Lab Test Suite. This report discusses each of these test problems in detail. Verification test results are compared to analytic solutions. Validation test results are compared to experimental data. Wherever possible, convergence or mesh refinement studies are included. Additionally, while implementing the Kidder verification problem, a bug was identified that affects the use of tables to initialize pressure or density in 1D or 2D calculations. A brief discussion of the bug and its fix is included. CTH demonstrates good performance overall on the new test suite problems. Simulation results showed good agreement with analytic solutions for the Kidder problem, with convergence rates ranging between 1.8 and sub-linear, and relatively good agreement for the EHEP problem, though convergence rates for pressure and density were nearly 0. The ST and JFRAC strain rate loading verification tests show good agreement with analytic solutions. Likewise, CTH simulation results show good agreement with experimental validation data, including Taylor rod impact testing, for the materials tested. Future V&V work will focus on adding 2D and 3D versions of existing verification tests as well as adding validation tests of other frequently used capabilities such as other fracture models.

42 ENGINEERING↗

Sierra/SD – User’s Guide for NasGen – 5.20

NasGen provides a path for migration of structural models from Nastran bulk data format (BDF) into both an Exodus mesh file and an ASCII input file for Sierra Structural Dynamics (Salinas) and Solid Mechanics (Adagio). Many tools at Sandia National Labs (SNL) use the Exodus format. This document describes capabilities and limitations of the NasGen translation software.

97 MATHEMATICS AND COMPUTING↗

Sierra/SD – User’s Guide for NasGen – 5.18

NasGen provides a path for migration of structural models from NASTRAN bulk data format (BDF) into both an Exodus mesh file and an ASCII input file for Sierra Structural Dynamics (Salinas) and Solid Mechanics (Presto). Many tools at Sandia National Labs (SNL) use the Exodus format. NasGen was written specifically for Salinas and Presto but should be usable with a number of these packages.

97 MATHEMATICS AND COMPUTING↗

Sierra/SD – User’s Guide for NasGen (V.5.16)

NasGen provides a path for migration of structural models from Nastran bulk data format (BDF) into both an Exodus mesh file and an ASCII input file for Sierra Structural Dynamics (Salinas) and Solid Mechanics (Adagio). Many tools at Sandia National Labs (SNL) use the Exodus format. This document describes capabilities and limitations of the NasGen translation software.

97 MATHEMATICS AND COMPUTING↗

Sierra/SD – User’s Guide for NasGen – 5.22

NasGen provides a path for migration of structural models from Nastran bulk data format (BDF) into both an Exodus mesh file and an ASCII input file for Sierra Structural Dynamics (Salinas) and Solid Mechanics (Adagio). Many tools at Sandia National Labs (SNL) use the Exodus format. This document describes capabilities and limitations of the NasGen translation software.

97 MATHEMATICS AND COMPUTING↗