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This is the public-facing repository for the LANL-developed and maintained HIGRAD/FIRETEC software, a CFD software designed to simulate the coupled fire-atmospheric processes at a landscape-scale of resolution. Distribution is possible under OSS license O5086.
Abstract Local environments have strict influence over (bio)mineralization in calcifying systems. This snapshot review discusses recent insights into the roles of Ca 2+ -macromolecule interactions on the nucleation of calcium carbonate and calcium phosphate minerals. Experimental findings combined with simulations/modeling are providing breakthrough information and raising important questions for future studies. The emerging picture is that both nucleation and growth are driven by local ordering of ions and water about the macromolecule interface, rather than broader properties or molecular class. Tuning macromolecular properties at the atomic scale thus provides opportunities for highly specific controls on mineralization; however, many limitations and challenges remain. We highlight studies employing in-situ atomic force microscopy (AFM) and transmission electron microscopy (TEM) to observe crystallization processes on or near macromolecular substrates. As the distribution and ability of these techniques increases, fundamental studies integrating experimental and computational methods will be crucial to inform a broad range of applications. Graphical abstract
Energy storage has a unique and pivotal role to play in the transition to a low-carbon economy because it can help the electric grid accommodate more renewable energy. However, a number of barriers currently impede the process of connecting energy storage systems to the distribution grid. The Toolkit and Guidance for the Interconnection of Energy Storage and Solar-Plus-Storage (the “BATRIES Toolkit”), which is the publication subject to this Final Technical / Scientific Report, provides vetted solutions to eight regulatory and technical barriers to the interconnection of standalone storage and solar-plus-storage systems (referred to as “energy storage”) to the distribution grid. These recommendations are based on over a year of research and analysis by utility and industry experts. The BATRIES Toolkit also includes model language that utilities and utility regulators can use to update state interconnection rules to reduce the costs and time to safely interconnect energy storage and solar-plus-storage systems. The solutions are nationally applicable and can be applied in diverse states and markets across the U.S.
The NUCLEI project, as defined by the scope of work, developed, implemented and run codes for large-scale computations of many topics in low-energy nuclear physics. Physics studied include the properties of nuclei and nuclear decays, nuclear structure and reactions, and the properties of nuclear matter. The computational techniques used include Quantum Monte Carlo, Configuration Interaction, Coupled Cluster, and Density Functional methods. The research program emphasized areas of high interest to current and possible future DOE nuclear physics facilities, including ATLAS and FRIB (nuclear structure and reactions, and nuclear astrophysics), TJNAF (neutron distributions in nuclei, few body systems, and electroweak processes), NIF (thermonuclear reactions), MAJORANA and FNPB (neutrinoless double-beta decay and physics beyond the Standard Model), and LANSCE (fission studies).
Natural gas in the United States offers substantial economic opportunities due to its abundance but its transportation is challenging because its primary component, methane (CH 4 ), does not liquefy at ambient temperature and typical pressures. As a result, a significant portion of natural gas is either used for heat, flared in remote locations, or remains unutilized, presenting a lost economic opportunity and an environmental harm. Converting natural gas to larger hydrocarbons in an economically competitive manner would enable transportation of products and further boost the economy and reduce environmental footprint. Our research goal is to enable efficient non-oxidative methane conversion (NMC) via catalyst innovation to convert CH 4 in one-step to olefins and aromatics and hydrogen (H 2 ) co-product. The catalysts are made of supported single metal atoms and operated at medium-high temperatures. The single metal atoms achieve methane activation by heterogeneous surface dehydrogenation to generate a hydrocarbon pool and importantly limit coke formation. The integration of novel single atom catalysts in a short contact time microreactor enable unprecedented NMC performance that could lead to economically-feasible, distributed natural gas upgrading by advanced manufacturing and process intensification. Our innovated catalyst and reactor technology promise to tap into previously uneconomic natural gas resources, such as stranded, vented, and flared methane.
Wildfires have shown increasing trends in both frequency and severity across the Contiguous United States (CONUS). However, process-based fire models have difficulties in accurately simulating the burned area over the CONUS due to a simplification of the physical process and cannot capture the interplay among fire, ignition, climate, and human activities. The deficiency of burned area simulation deteriorates the description of fire impact on energy balance, water budget, and carbon fluxes in the Earth System Models (ESMs). Alternatively, machine learning (ML) based fire models, which capture statistical relationships between the burned area and environmental factors, have shown promising burned area predictions and corresponding fire impact simulation. We develop a hybrid framework (ML4Fire-XGB) that integrates a pretrained eXtreme Gradient Boosting (XGBoost) wildfire model with the Energy Exascale Earth System Model (E3SM) land model (ELM) version 2.1. A Fortran-C-Python deep learning bridge is adapted to support online communication between ELM and the ML fire model. Specifically, the burned area predicted by the ML-based wildfire model is directly passed to ELM to adjust the carbon pool and vegetation dynamics after disturbance, which are then used as predictors in the ML-based fire model in the next time step. Evaluated against the historical burned area from Global Fire Emissions Database 5 from 2001-2020, the ML4Fire-XGB model outperforms process-based fire models in terms of spatial distribution and seasonal variations. Sensitivity analysis confirms that the ML4Fire-XGB well captures the responses of the burned area to rising temperatures. The ML4Fire-XGB model has proved to be a new tool for studying vegetation-fire interactions, and more importantly, enables seamless exploration of climate-fire feedback, working as an active component in E3SM.
Wildfires have shown increasing trends in both frequency and severity across the Contiguous United States (CONUS). However, process-based fire models have difficulties in accurately simulating the burned area over the CONUS due to a simplification of the physical process and cannot capture the interplay among fire, ignition, climate, and human activities. The deficiency of burned area simulation deteriorates the description of fire impact on energy balance, water budget, and carbon fluxes in the Earth System Models (ESMs). Alternatively, machine learning (ML) based fire models, which capture statistical relationships between the burned area and environmental factors, have shown promising burned area predictions and corresponding fire impact simulation. We develop a hybrid framework (ML4Fire-XGB) that integrates a pretrained eXtreme Gradient Boosting (XGBoost) wildfire model with the Energy Exascale Earth System Model (E3SM) land model (ELM). A Fortran-C-Python deep learning bridge is adapted to support online communication between ELM and the ML fire model. Specifically, the burned area predicted by the ML-based wildfire model is directly passed to ELM to adjust the carbon pool and vegetation dynamics after disturbance, which are then used as predictors in the ML-based fire model in the next time step. Evaluated against the historical burned area from Global Fire Emissions Database 5 from 2001-2020, the ML4Fire-XGB model outperforms process-based fire models in terms of spatial distribution and seasonal variations. Sensitivity analysis confirms that the ML4Fire-XGB well captures the responses of the burned area to rising temperatures. The ML4Fire-XGB model has proved to be a new tool for studying vegetation-fire interactions, and more importantly, enables seamless exploration of climate-fire feedback, working as an active component in E3SM.
Wildfires have shown increasing trends in both frequency and severity across the contiguous United States (CONUS). However, process-based fire models have difficulties in accurately simulating the burned area over the CONUS due to a simplification of the physical process and cannot capture the interplay among fire, ignition, climate, and human activities. The deficiency of burned area simulation deteriorates the description of fire impact on energy balance, water budget, and carbon fluxes in the Earth system models (ESMs). Alternatively, fire models based on machine learning (ML), which capture statistical relationships between the burned area and environmental factors, have shown promising burned area predictions and corresponding fire impact simulation. We develop a hybrid framework (ELM2.1-XGBFire1.0) that integrates an eXtreme Gradient Boosting (XGBoost) wildfire model with the Energy Exascale Earth System Model (E3SM) land model (ELM) version 2.1. A Fortran–C–Python deep learning bridge is adapted to support online communication between ELM and the ML fire model. Specifically, the burned area predicted by the ML-based wildfire model is directly passed to ELM to adjust the carbon pool and vegetation dynamics after disturbance, which are then used as predictors in the ML-based fire model in the next time step. Evaluated against the historical burned area from Global Fire Emissions Database 5 from 2001–2019, the ELM2.1-XGBFire1.0 outperforms process-based fire models in terms of spatial distribution and seasonal variations. The ELM2.1-XGBFire1.0 has proven to be a new tool for studying vegetation–fire interactions and, more importantly, enables seamless exploration of climate–fire feedback, working as an active component of E3SM.
rustpix is a high-performance, open-source Rust library with first-class Python bindings (via PyO3) for processing pixel-detector data in neutron imaging. It targets time-stamping detectors such as Timepix3 (TPX3) at ORNL's Spallation Neutron Source (VENUS beamline), where each detected neutron deposits charge across a cluster of pixels within a very high-rate event stream (96M+ hits/sec). rustpix parses TPX3 event data in parallel using memory-mapped I/O, offers four interchangeable clustering algorithms (ABS adjacency-based search, DBSCAN, graph/union-find connected components, and a parallel grid method), and extracts weighted, super-resolved centroids to produce neutron-event lists. A streaming architecture lets it process files larger than available memory. rustpix is distributed as a pip-installable Python package (with NumPy integration), Rust crates, a command-line tool, and an interactive GUI; it writes HDF5, Apache Arrow, and CSV; and it is designed to extend to TPX4 and other detector types. Released as open-source under the MIT License.
These data were collected during the Aerosol and Cloud Experiments in the Eastern North Atlantic field campaign as part of ARM Aerial Facility deployment (ACE-ENA, https://www.arm.gov/research/campaigns/aaf2017ace-ena). The ARM Aerial Facility Gulfstream-1 was deployed at Lajes Air Base (IATA: TER, ICAO: LPLA), on Terceira Island in the Azores, Portugal, for the two Intensive Observation Periods from June 20 through July 22, 2017 (IOP#1) and from January 11 through February 22, 2018 (IOP#2). The G-1 aircraft performed 20+19 research flights over the ARM Eastern North Atlantic (ENA) site and Atlantic Ocean to measure atmospheric turbulence, cloud water content and drop size distributions, aerosol precursor gases, aerosol chemical composition and size distributions. The current data set presents re-processed Particle Volume Monitor PVM-100A (aka Gerber probe) data: Liquid Water Content (LWC), Particle Surface Area (PSA), and the effective droplet radius (re) averaged to 50 Hz, 10 Hz, and 1 Hz.
These data were collected during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI; https://www.arm.gov/research/campaigns/amf2018cacti ) field campaign in the Sierras de Córdoba mountain range of north-central Argentina as part of the ARM Aerial Facility (AAF) deployment. The ARM Aerial Facility Gulfstream-1 was operated from Las Higueras Airport (IATA: RCU, ICAO: SAOC), Río Cuarto, Córdoba, Argentina, for the Intensive Observation Period (IOP) from Nov. 1 through Dec. 15, 2018. The G-1 aircraft performed 22 research flights over the first ARM Mobile Facility (AMF1) location in the Sierras de Córdoba mountain range to measure atmospheric state and turbulence, cloud water content and droplet size distributions, aerosol precursor gases, and aerosol chemical composition and size distributions. The current data set presents re-processed Particle Volume Monitor PVM-100A (aka Gerber probe) data: Liquid Water Content (LWC), Particle Surface Area (PSA), and the effective droplet radius (re) averaged to 50Hz, 10Hz, and 1Hz.
A temperature sensor qualification device is undergoing development and testing in order to qualify temperature sensors for advanced reactor metrology applications. This device will provide National Institute of Standards and Technology (NIST) traceable performance testing of temperature sensors while under the high radiation environments experienced while in-core. This is completed by utilizing retractable sensors which transfer the measurements from ex-core to in-core sensors. A detailed discussion of the uncertainty propagation associated with this comparative process is provided. Experimental testing of the temperature distribution occurring in the device has begun and is reported on. It is pertinent to minimize this temperature distribution to prevent variation in sensor measurements. This variation will be indistinguishable from sensor drift for in-pile applications and would result in unnecessarily conservative uncertainty bounds.
Power grids face challenges in their infrastructure related to the integration of electric vehicles (EV). In particular, EV charging stations may induce instability in key system parameters such as substantial voltage drops, active power losses, and transformer overload due to high demand during charging periods. This article presents a seasonal reconfiguration strategy based on the differential evolution (DE) algorithm, aimed at enhancing system performance under highly variable and stochastic load profiles, particularly those driven by EV charging. The DEA algorithm is hybridized with the find-union (FU) algorithm to efficiently ensure network radiality throughout the optimization process. The proposed methodology is validated on a hybrid distribution system composed of the IEEE 33-bus network, a modified IEEE 13-bus system, and a specific 13-bus microgrid. Results have demonstrated that seasonal reconfiguration significantly reduces active power losses and mitigates transformer loading during critical demand hours, thereby quantifiably increasing the system’s performance. As an integral component of the proposed approach, an analysis of CO2 emissions associated with energy losses is included, allowing a contextualized assessment of the environmental benefits of seasonal reconfiguration in various geographical areas.
The unpredictability of random numbers is fundamental to both digital security and applications that fairly distribute resources. However, existing random number generators have limitations—the generation processes cannot be fully traced, audited and certified to be unpredictable. The algorithmic steps used in pseudorandom number generators are auditable, but they cannot guarantee that their outputs were a priori unpredictable given knowledge of the initial seed. Device-independent quantum random number generators can ensure that the source of randomness was unknown beforehand, but the steps used to extract the randomness are vulnerable to tampering. Here we demonstrate a fully traceable random number generation protocol based on device-independent techniques. Our protocol extracts randomness from unpredictable non-local quantum correlations, and uses distributed intertwined hash chains to cryptographically trace and verify the extraction process. This protocol forms the basis for a public traceable and certifiable quantum randomness beacon that we have launched. Over the first 40 days of operation, we completed the protocol 7,434 out of 7,454 attempts—a success rate of 99.7%. Each time the protocol succeeded, the beacon emitted a pulse of 512 bits of traceable randomness. The bits are certified to be uniform with error multiplied by actual success probability bounded by 2−64. Further, the generation of certifiable and traceable randomness represents a public service that operates with an entanglement-derived advantage over comparable classical approaches.
The application of N,N,N',N'-tetraoctyl diglycolamide (TODGA) in solvent extraction systems for lanthanide (Ln) separations is well understood. In these systems, the formation of a third phase has motivated the use of phase modifiers to enable higher concentrations of H + and Ln common to industrial processes. Several different phase modifiers with applications to diglycolamide (DGA) systems have previously been reported, with a focus on tri-n-butyl phosphate (TBP), N,N'-dihexylactanamide (DHOA), N,N-dioctyl-2-hydroxyacetamide (DOHyA), N,N'-dimethyl-N,N'-dioctylhexylethoxy malonamide (DMDOHEMA), and octanol. While the primary utility of phase modifiers is the increased metal loading, they can have significant effects on the metal distribution ratios, which are well described by the energetics of the extraction process itself. However, the mechanisms by which phase modifiers impact distribution ratios are not generally understood. This work considers the ability of phase modifiers to affect Ln distribution ratios by using phase modifiers with two different functional groups (–Cl and –C≡N) and an octyl alkyl chain in a TODGA and n-dodecane system. Determining the effect of chlorooctane and octane nitrile is important for understanding how phase modifier functional groups and their hydrogen bonding interactions affect Ln extraction. Through combining distribution ratio measurements with organic phase spectroscopic investigations, the impact of chlorooctane and octane nitrile on Ln extraction and their inner-sphere complexes is reported. The addition of either chlorooctane or octane nitrile to TODGA in n-dodecane decreases Ln extraction while maintaining the same inner-sphere Ln complex. The lack of change in inner-sphere Ln-TODGA coordination upon incorporation of phase modifiers and the significant impact of these phase modifiers on distribution ratios suggest the importance of a supramolecular structure. Understanding the role of chlorooctane and octane nitrile on the organic phase structure at longer length scales has been identified as an avenue for future investigations.
We study Collins azimuthal asymmetries for pion-in-jet production in polarized lepton-proton collisions, extending previous analyses of polarized pp scattering to a complementary and theoretically simpler process. We keep adopting a simplified transverse momentum dependent (TMD) approach, with a collinear configuration for the initial state, and employ the transversity and Collins fragmentation functions as extracted from semi-inclusive deep inelastic scattering and $e^{+}e^{-}$ annihilation processes. We then compute azimuthal asymmetries for the Electron-Ion Collider (EIC) kinematics, both within a leading order (LO) approach and by including quasireal photon exchange in the Weizsäcker-Williams approximation. Although this contribution is relevant in the whole kinematical range explored, it does not spoil the dominance of quark-initiated channels, leaving only a marginal role to their gluon counterparts. In this respect, Collins asymmetries in lepton-proton processes allow for a much clearer access to the transversity distribution, including its sea-quark component. As we will argue, a comparison with future EIC data could represent a further step in testing the hypothesis of the universality of the Collins function as well as of the TMD factorization for this class of processes.
Angular correlations between heavy quarks provide a unique probe of the quark-gluon plasma created in ultrarelativistic heavy-ion collisions. Results are presented of a measurement of the azimuthal angle correlations between muons originating from semileptonic decays of heavy quarks produced in 5.02 TeV Pb+Pb and pp collisions at the LHC. The muons are measured with transverse momenta and pseudorapidities satisfying p$^μ_T$ > 4 GeV and |η μ | < 2.4, respectively. The distributions of azimuthal angle separation ΔΦ for muon pairs having pseudorapidity separation |Δη| > 0.8, are measured in different Pb +Pb centrality intervals and compared to the same distribution measured in pp collisions at the same center-of-mass energy. Results are presented separately for muon pairs with opposite-sign charges, same-sign charges, and all pairs. A clear peak is observed in all ΔΦ distributions at ΔΦ ~ π, consistent with the parent heavy-quark pairs being produced via hard-scattering processes. The widths of that peak, characterized using Cauchy-Lorentz fits to the ΔΦ distributions, are found to not vary significantly as a function of Pb +Pb collision centrality and are similar for pp and Pb +Pb collisions. This observation will provide important constraints on theoretical descriptions of heavy-quark interactions with the quark-gluon plasma.
Interconnection studies for distributed energy resources (DERs) is a time-intensive process, primarily due to the necessity of solving large number of power flow scenarios. Hosting capacity analysis (HCA) is a time-consuming aspect of interconnection studies that is divided into single-site HCA (SHCA) and multi-site HCA (MHCA). From a computational and understandable standpoint, the industry seeks iteration-based solutions for SHCA, although it doesn't maximize the total DER hosting capacity (DERHC) of the grid, as MHCA does. While non-iterative solutions are available for MHCA, they involve a trade-off between the modeling accuracy of the distribution system, solution quality, and ease of understanding. In this work, we present a fast iterative solution for MHCA, reducing computational complexity by eliminating the need to solve power flows for a large amount of search space, thus making iterative solutions feasible. This iterative approach guarantees both a global optimal solution with sufficient time and a fast, close-to-optimal solution through efficient search space pruning. It also easily integrates with existing utility HCA tools. The results are demonstrated on select locations in the IEEE-123 bus system for community-scale interconnection studies. We highlight the benefits of skipping the need to solve millions of power flows, all while maximizing the grid's total DERHC.