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

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462 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Intelligent Experiments Through Real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and Future EIC Detectors (Final Report)

The overall vision of this project was to integrate real-time artificial intelligence (AI) directly into the data acquisition and detector-control systems of nuclear physics experiments, including both fast online event selection and an autonomous detector-control feedback loop. The work carried out under the award focused on the fast online event-selection half of that vision: the efficient recording of low-momentum heavy-flavor (HF) hadron decays in proton-proton collisions at the sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC)—an observable that requires fast tracking and topological trigger selection not previously demonstrated at RHIC, and that is essential for QCD studies at future facilities such as the Electron-Ion Collider (EIC). The autonomous detector-control (GPU-based feedback) component named in the project title remained a design concept and was not implemented under this award. The Massachusetts Institute of Technology (MIT) group led the offline simulation and data processing needed to train the machine-learning (ML) models, the translation of trained models to Field-Programmable Gate Array (FPGA) firmware using the hls4ml framework, and the physics validation of heavy-flavor reconstruction. Over the award period, the team developed and hardware-tested the principal components of an AI-based heavy-flavor trigger on simulated and recorded sPHENIX tracker data: a software Bipartite Graph Attention Network (BiGAT) trigger model reaching > 95% signal efficiency at 99% background rejection; an FPGA-native hit clusterizer matching the offline clustering; smaller networks synthesized to FPGA within the required sub-10 µs latency; and an assembled decoder–clusterizer–inference firmware chain exercised on the FELIX readout board. A complete, fully integrated hardware demonstrator was not finished within the award period. This report documents the project goals, the MIT group’s contributions, the technical accomplishments, and the outlook toward applications at the future EIC ePIC detector.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

WHONDRS laboratory time series moisture manipulative experiment from soil core layers across eastern contiguous US: time series aerobic respiration, geochemistry, and aggregates

This dataset supports a broader study examining the effects of wetting and drying on soil layers across the eastern contiguous United States (CONUS). The dataset provides data generated from a laboratory moisture manipulation experiment. The contents include time series aerobic respiration and moisture; dissolved oxygen; sediment geochemistry data; and field metadata. Samples were collected as part of a collaboration between WHONDRS (Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems; https://whondrs.pnnl.gov) and MONet (Molecular Observation Network; https://www.emsl.pnnl.gov/monet). The field samples (soil cores) were labeled as MEL_##_COR and subsequent subsamples begin with MEL_##. Additional subsamples were taken for the laboratory experiment and were labeled as EL_##. The labels from the MEL field samples and the EL subsamples can be mapped directly based on the digits following the prefix and underscore (i.e., EL_01 is a subsample from MEL_01). See the critical details section below for more details on sample naming and experimental design.For details on how to navigate this data package, see this infographic from the River Corridor SFA https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About.In addition to this readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions.This dataset is comprised of (1) a folder containing environmental context photos; (2) file-level metadata; (3) data dictionary; (4) field metadata; (5) readme; (6) international generic sample number (IGSN) mapping file; and (7) a subfolder with soil sample data from field samples and the incubation experiment. The sample data subfolder contains (1) effect size; (2) gravimetric moisture from field samples and incubation experiment; (3) respiration rates, raw dissolved oxygen values, and plots; (4) specific conductance, pH, and temperature from the incubation; (5) soil aggregates; (6) a summary containing median values of each data type for each treatment (wet and dry) in the incubation; (7) a summary containing averages for each data type of each soil layer; and (8) methods codes. All files are .csv, .pdf, .jpeg, or .jpg.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The Novel Charfuel® Coal Refining Process 18 TPD Pilot Plant Project for Co- Producing an Upgraded Coal Product, and Commercially Valuable Co- Products: Area of Interest #3 – Coal Beneficiation Pilot Plant Testing (Final Report)

Operation of Carbon Fuels, LLC’s (“CF”) existing, permitted 18 TPD pilot plant located in Golden, Colorado using two individually ranked (ASTM D 388) coal types (two campaigns), employing the novel Charfuel® coal refining process to produce an upgraded coal product and a number of high-valued organic and inorganic coproducts (for which there presently exists large commercial markets) in order to produce engineering and product data which will then be utilized toward the design of a commercial scale integrated facility (pre-feed document). Carbon Fuels, LLC has developed the Charfuel® Coal Refining Process which refines domestically abundant, raw coal (in the same manner as crude oil is refined) to produce the identical, high value co-products that are refined from crude oil. Thus, gasoline, jet fuel, “green diesel”, fuel oil, and marine fuels, as well as petrochemicals such as benzene, toluene, xylene, and methanol are refined from raw coal using this process. The Charfuel® Coal Refining Process is not a coal conversion process, like pyrolysis, or indirect liquefaction. Nor is it an alternative energy system. Rather it is a coal refining process that has the ability to economically produce products traditionally associated with the refining of crude oil but using only abundant, raw coal as the refinery feed stock. The Charfuel® Coal Refining Process is more economical than crude oil refining and is environmentally benign. Therefore, this value added process yields a return on investment well above 50% for a commercial facility. Furthermore, the Charfuel® process, unlike alternatives such as ethanol and hydrogen, can utilize the existing transportation, delivery, and other petroleum based systems. Hence, there is no need for new engines, pipelines, tankers, or product acceptance. As a result, the profitability of the process is increased. Objectives: (1) Operation of the integrated 18 tpd pilot plant, using two coal types (ranks); (2) Demonstration of process flexibility in being able to produce different products (gas, liquid, and char), as well as determination of operating parameters for identifying scale up criteria for two coal types (ranks); (3) Generation of engineering and design information (process specifications) for use in designing a commercial scale plant (scale-up); (4) Determination of important environmental issues surrounding the process and the products such as fate of trace elements (mercury and other heavy metals) and distributions of SO2, NOx, and CO 2 by analysis of effluent streams; (5) Production of sufficient product to allow reliable commercial economic evaluation of both the refined coal product and the coproducts; and, (6) Assessment of longer-term reliability of unit operations. Period 1: reconfiguration of the 18 TPD plant to meet specific FOA requirements and to qualify the facility for operation; and, Period 2: operation of the 18 TPD plant for two campaigns using two coals types (ranks) which are widely commercially used and abundant - the first being a subbituminous (Powder River Basin (“PRB”)) coal, and the second a bituminous (Illinois #6) coal.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Quantifying microbial roles in environmental iron oxidation via an integrated kinetics, `omics and metabolic modeling study (Final Report)

Iron oxyhydroxides are extremely reactive components of environmental systems, and therefore exert a strong influence on biogeochemical cycles. These oxyhydroxides strongly adsorb many biologically-relevant elements, including organic carbon and phosphate, as well as a wide range of metals including uranium and actinide species. Thus, the formation mechanism of iron oxyhydroxides are key to understanding both nutrient and contaminant cycling. Microorganisms can catalyze iron oxidation and promote the formation of Fe biominerals and thus are increasingly recognized as important players in biogeochemical cycling. However, it is completely unknown how much of environmental iron oxidation is biologically mediated versus abiotic, and various challenges in studying microbial iron oxidation have hindered accurate incorporation into hydrobiogeochemical models. The overarching goal of our work was to quantify and constrain microbial iron oxidation rates and use ‘omics to gain insight into the controls on this process, while developing tools to enable integration of biotic iron oxidation into hydrobiogeochemical models. Our work focused on the Savannah River Site (SRS) in South Carolina, where extensive microbial iron oxidation has been observed. At Tims Branch, part of the Argonne National Laboratory Wetland Hydrobiogeochemistry Science Focus Area (Argonne SFA), where groundwater discharges into a stream, iron-oxidizing microbial mats form and appear to be a major sink of uranium. In the wetlands that surround Tims Branch, there are wide swaths of iron microbial mats and flocs (mobilized mat). We measured biotic and abiotic iron oxidation rates using mats and water sampled from these sites and found that iron oxidation is primarily carried out by chemolithotrophic microorganisms. The resulting rate constants can be incorporated into models. These mats were characterized by metagenomics and metatranscriptomics, which showed that aerobic chemolithotrophs were the dominant iron-oxidizing bacteria (FeOB), and these included Gallionellaceae and Leptothrix, and possibly Rhodoferax, which is known as an Fe-reducer but may also oxidize Fe(II). This demonstrated that diverse FeOB can coexist and suggests that there are a range of niches and therefore drivers of chemolithotrophic iron oxidation. Analysis of reconstructed genomes strongly suggests that a major factor in diversity is carbon source, as genomes contained varied pathways for autotrophy and heterotrophy. We performed an in-depth analysis of Leptothrix ochracea genomes, since this sheath-former is one of the primary mat builders, yet its physiology remained unresolved. A combination of genomics, transcriptomics, and metabolic modeling suggest that L. ochracea grows mixotrophically using a combination of Fe(II) and organics for energy and both inorganic and organic carbon to create biomass. This contrasts with the largely autotrophic Gallionellaceae (Gallionella, Sideroxydans, and Ferriphaselus) also present in the mats and flocs. Remarkably, multiple FeOB, both Leptothrix and Gallionellaceae, showed activity in response to Fe(II) in live mat incubations. We tracked the gene expression of individual MAGs to Fe(II) and found that various autotrophic and heterotrophic FeOB responded to Fe(II), increasing expression of both carbon fixation and organic utilization genes. The results of the integrated field, kinetics, and omics studies give detailed insight into 1) the taxa that oxidize Fe, and 2) how they connect Fe, C, and N cycles. Towards the goal of connecting omics data to hydrobiogeochemical models, we worked with the KBase team to create a template metabolic model for chemolithotrophic iron oxidation. We initially modeled the well-characterized isolate Gallionellaceae Sideroxydans lithotrophicus, and also applied the model to the mixotroph L. ochracea. In all, we have characterized diverse FeOB in a representative wetland system and solved key problems that enable better incorporation of iron-oxidizing microbes into hydrobiogeochemical models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Post-fire time series photos from five sites across the Oak Creek watershed, Washington

This dataset supports a broader study examining wildfire impacts on hydrologic connectivity across 5 sites within the Oak Creek watershed and the resulting biogeochemical impacts. Sites were selected using the Advanced Terrestrial Simulator (ATS) hydrologic model to identify locations with varying groundwater contributions and hydrologic responses across different burn severity scenarios. The Retreat Fire burned from July 23 to August 2, 2024, affecting all five sites. This dataset provides time series game camera photos, while the broader study includes continuous water quality monitoring, biogeochemical sampling of water and soils, precipitation data, and organic matter analysis. The other data types and additional metadata (include site environmental information) can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3018020. Because this study is ongoing, this data package will be updated regularly to include newly collected photos. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) readme; (5) field protocol; and (6) folders of game camera photos. The game camera photos are organized by site with subfolders by month of collection. The field metadata contains a subset of the information collected that is most relevant to photo-processing. The full set of field metadata can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3018020. All files are .csv, .pdf, or .jpg.

Burn severity↗
DOE OSTI2024

Laboratory time series moisture manipulative experiment from sediment across the contiguous US: time series aerobic respiration and geochemistry (v2)

This dataset supports a broader study examining the effects of wetting and drying on hyporheic zone respiration across the contiguous United States (CONUS). The dataset provides data generated from a laboratory moisture manipulation experiment. The contents include time series aerobic respiration and moisture; dissolved oxygen; sediment geochemistry data; and field metadata (including qualitative information on instream and river corridor characteristics). Samples were collected as part of the WHONDRS CONUS-Scale Model-Sample Study (CM). This study was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the CONUS. The data package associated with the CM study is available at https://data.ess-dive.lbl.gov/view/doi:10.15485/1923689. CM sampling began in April 2022 and ended in October 2023. This study uses subsamples from a subset of CM samples collected between June 2022 and June 2023. The original field samples were labeled as CM_###. Subsequent subsamples for this study were labeled as EC_###. The labels from the field samples and the EC subsamples can be mapped directly based on the digits following the prefix and underscore (i.e., EC_001 is a subsample from CM_001). See the critical details section below for more details on sample naming. This data package was originally published in August 2024. It was updated in February 2026 (v2; new and modified files). See the change history section in the readme for more details. For details on how to navigate this data package, see this infographic from the River Corridor SFA https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset is comprised of one folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data and one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) readme; (5) field protocol; and a (6) a subfolder with sediment sample data from the incubation experiment. The sample data subfolder contains (1) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC); (2) total nitrogen (TN); (3) adenosine triphosphate (ATP); (4) percent carbon and nitrogen; (5) effect size; (6) iron (II); (7) gravimetric moisture; (8) respiration rates and raw dissolved oxygen values; (9) specific conductance; (10) pH; (11) temperature; (12) a summary containing median values of each data type for each treatment (wet and dry); (13) methods codes; (14) FTICR-MS methods; and (15) a subfolder of 9.4 Tesla FTICR-MS data. This folder contains three subfolders, one containing the sediment .xml data files, one containing the sediment CoreMS output files, the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS). All files are .csv, .pdf, .R, .ref, or .xml.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Degradation of Poly- and Perfluoroalkyl Substances (PFAS) in Water via High Power, Energy-Efficient Electron Beam Accelerator

The goal of the 2-year workplan was to see if electron beam (EB) could be used to break down a sub-set of the larger chemical family of per and polyfluoroalkylated substances (PFAS) in an energy efficient and economical manner when compared to conventional water treatment technologies. Year one (Y1) work focused on sample EB treatment work in the Fermi National Accelerator Laboratory’s (FNALs) Accelerator Applications Demonstration and Development (A2D2) EB accelerator. While there are reportedly thousands of types of PFAS, for the point of most of the work herein, a small subset was examined, typically perfluorooctane sulfonate (PFOS) and perfluorooctanoate (PFOA). PFOA and PFOS are two of the most well studied PFAS and are studied for baseline evaluations and are considered most useful. The work from Y1 provided information about the optimal operating parameters and additives to use when treating PFOS and PFOA via EB. The data were then used to see where in a water treatment system an EB accelerator would be best suited to treat PFAS. A conventional water treatment technology, GAC, was then compared to e-beam treatment technology with respect to energy and costs for treatment. In year two (Y2), several conventional e-beam accelerator designs, and FNAL’s developmental compact SRF accelerator design, were evaluated for their suitability in PFAS treatment, from an energy efficiency and cost standpoint. Several EB parameters were evaluated and optimized for the removal of PFOA and PFOS from water at normal pressure and temperature, measured as total PFAS removal. Under the optimized test conditions both PFOA showed complete destruction to inorganic fluoride, and PFOS to inorganic fluoride and sulfate, with mass balance. The effect on PFAS removal relative to solution pH, total EB dose, EB dose rate, dissolved oxygen concentration (DO), temperature, and initial PFAS concentration were evaluated. In general, PFOA was easier to destroy than PFOS. Degradation products, typically observed under less-than-optimal EB conditions, provided insight to degradation mechanisms. Products were identified to rule out possible deleterious biproduct formation. The water radiolysis radical reaction kinetics with PFOS and PFOA were not dependent on the initial concentration over 5-orders of magnitude from 2 μg/L to 20 mg/L. This is thought to be because there was an overabundance of the reactive water radiolysis radicals relative to PFAS molecules and largely attributed to aqueous electrons. The reaction rates appeared to be diffusion limited. Testing at higher concentrations (100-200 mg/L) showed a decrease in removal efficiency, suggesting alternative kinetics, possibly second order rates, at higher concentrations. In all, we successfully defined a set of optimal EB parameters to treat PFOA and PFOS at concentrations of 20 mg/L in water with destruction efficiencies near 100%. We further tested the optimized EB parameters with other types of PFAS, including shorter and longer fluorocarbon chain homologs of PFOA and PFOS, and PFAS with alternative functional groups such as sulfonamides. Based on our results EB can be optimized as an effective destructive technology for removing PFAS from water. The conditions optimized for PFOA and PFOS were less effective with ultra-short fluorocarbon compounds like TFMS, PFES, PFPS and PFBS, and likely require re-optimization of parameters to them. In all, it was determined that from a cost and energy efficiency standpoint, EB would be best applied to waste streams with relatively high concentrations of PFOS and PFOA and is not as cost effective as GAC treatment for removing low concentrations of PFAS from water. Higher concentrations of PFAS can be found in the wastewater of conventional treatment processes such as RO and IE and therefore EB may be used to supplement such treatment technologies. Some real-world IE regeneration wash water and RO reject water containing higher concentrations of PFAS and obtained from pilot scale industrial wastewater treatment system at a fluorochemical manufacturing facility, showed that EB could remove PFAS from such types of wastewaters. The IE regenerant wash water appeared to be the most efficient of the two types of wastewaters tested. However, some further optimization of the EB parameters for the specific PFAS types present in those wastewaters may be required. Also, the effects of co-present TOC and mineral salts should be considered during such optimization efforts. From the experimental Y1 results it was seen that the aqueous electron drives degradation of the PFAS. In a hypothetical water treatment skid using EB for PFAS destruction the parameters of the system should be optimized to promote aqueous electron production. Before EB treatment, the PFAS should be preconcentrated when possible, the pH should be raised to pH 10 or higher to enhance aqueous electron production, and the water should be nitrogen purged to remove dissolved oxygen to minimize aqueous electron scavenging. An excel spreadsheet was created that calculates optimal conditions based on inlet PFAS concentration and desired outlet concentration, by optimizing the accelerator power, dose rate, water treatment rate, pH and dissolved oxygen levels to reach the desired endpoint. Given this information on accelerator operating conditions five different EB accelerator systems were compared. One EB system was a continuous-wave, linear superconducting accelerator being designed at Fermilab. Three other EB systems (IMPELA at 5% and 25% duty factor and the ILU-14) were normal conducting pulsed linear accelerators. The fifth system was an IBA Rhodotron which is a normal conducting, circular, continuous-wave accelerator. The accelerator efficiency (% of the incoming power that is used in water treatment) was the dominating factor in accelerator choice. The radio frequency (RF) power supply and the accelerator design (superconducting versus warm technology) drive the accelerator efficiency. The IBA Rhodotron was seen to be the most energy efficient commercially available technology with a wall-plug (total) power efficiency of 43% at 400 kW. The Fermilab design, with a prototype for a different application currently being fabricated, was the most energy efficient at 55% when driven by a Klystron RF power supply and as high as 77% when powered by a magnetron. As the Fermilab design was the most energy efficient by approximately 10-30%, further design work was done on the accelerator and beam delivery system specific to the destruction of PFAS in water. The Fermilab design is unique from industrial accelerators in that is superconducting. Superconducting technology allows for the acceleration of electrons without losses. The accelerator must be cooled to below the point where it is superconducting and is operated around 4 degrees Kelvin. The bulk of the design work for the accelerator is on making the accelerator as energy efficient as possible so that it does not require liquid helium and can be cooled with conduction cooling via cryocoolers. Final design work resulted in an EB accelerator that would operate at minimally 200 kW and 10 MeV. Prototype construction would cost $\$ $7.8 million dollars when driven by a Klystron power supply. A second version of the same accelerator would cost $\$ $5.5 million dollars when driven by a magnetron that is still under development. The commercially available 300 kW IBA Rhodotron cost was estimated at approximately $\$ $9 million. While it is hard to directly compare, an operational GAC system used by 3M for groundwater treatment capital cost (2022 dollars) was estimated to cost $\$ $3.3 million. While the capital expense of the EB accelerator systems was higher than GAC, the accelerator EB treatment would result in destruction of the PFAS and not just sequestration of PFAS to form a new waste stream that requires further treatment or disposal. The operating cost to destroy the PFAS via 400 kw EB system was less than $\$ $1000/kg of PFAS destroyed when treating at a 20 mg/L PFAS concentration, compared to GAC with operating costs that calculated at $\$ $27,530 per kg of PFAS sequestered when treating 100 μg/L PFOA and PFOS combined concentration.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Temporal Study 2022-2024: Sample-Based Surface Water Dissolved Inorganic Carbon, Dissolved Organic Carbon, Total Nitrogen, Stable Isotopes, and Total Suspended Solids from across Multiple Watersheds in the Yakima River Basin, Washington, USA

This dataset supports a broader study examining the drivers of temporal variability in sediment respiration rates in the Yakima River Basin. The dataset provides geochemistry data generated from samples collected at bi-weekly or monthly intervals at six sites across the Yakima River Basin in Washington, USA. Sample and sensor data from previous years (2021-2022) can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1898912 and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1892054, respectively. Related sensor data from 2022-2024 will be published separately. This dataset is comprised of one main data folder containing (1) file-level metadata; (2) data dictionary; (3) readme; (4) field metadata; (5) dissolved inorganic carbon (DIC) and averages; (6) dissolved organic carbon (DOC; reported as non-purgeable organic carbon; NPOC) and averages; (7) total dissolved nitrogen (TN) and averages; (8) total suspended solids (TSS); (9) stable isotopes; (10) surface water sampling protocol; (11) sensor protocol; (12) methods codes; and (13) international generic sample number (IGSN) mapping file. All files are .csv or .pdf. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. For data and scripts associated with "Shifts in rain-snow partitioning drive faster water transit times in the US Pacific Northwest" (Butler et al., 2026), go to https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3025481

18-O↗
DOE OSTI2025

Temporal Study 2022-2024: Sensor-Based Time Series of Surface Water Temperature, Specific Conductance, Total Dissolved Solids, Turbidity, Chlorophyll A, and Dissolved Oxygen from across Multiple Watersheds in the Yakima River Basin in Washington, USA

This dataset supports a broader study examining the drivers of temporal variability in sediment respiration rates in the Yakima River Basin. The dataset provides periodic (bi-weekly or monthly) in situ hydrological and water chemistry sensor data, handheld sensor water chemistry data, general environmental context photos, and field metadata collected at six sites across the Yakima River Basin in Washington, USA. Sample and sensor data from previous years (2021-2022) can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1898912 and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1892054, respectively. Related sample data from 2022-2024 are available at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2562910. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions This dataset contains a folder of environmental context photographs and videos and (1) file-level metadata; (2) data dictionary; (3) readme; (4) field metadata; (5) field protocols; (6) international generic sample number (IGSN) mapping file; (7) handheld sensor data; and (8) two sensor subfolders. Each sensor subfolder (BarotrollAtm and MantaRiverData) contains a subfolder containing sensor time series data and plots. The BarotrollAtm Data subfolder contains In Situ Rugged BaroTROLL sensor pressure and air temperature data. The MantaRiverData subfolder contains Eureka Manta+ 35B multisonde temperature, specific conductance, and chlorophyll A. All files are .csv, .pdf, .jpg, .jpeg, .mp4, .png, or .mov.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Woody Feedstock 2022 State of Technology Report

The U.S. Department of Energy promotes production of advanced liquid transportation fuels from lignocellulosic biomass by funding fundamental and applied research that advances the state of technology (SOT). As part of its involvement in this mission, Idaho National Laboratory completes an annual SOT report for n th -plant and 1 st -plant woody biomass feedstock logistics. The purpose of the SOT is to provide the status of feedstock supply system technology development for woody biomass to biofuels relative to technical targets and cost goals from specific design cases, based on data and experimental results. Conventional feedstock supply systems need to be modified to meet the demands of conversion pathways, specifically to have the ability to adjust the quality of the raw biomass materials. Advanced systems incorporate innovative methods of material handling, preprocessing and supply chain configuration. In advanced designs, variability of the raw biomass can be reduced to produce feedstocks of a uniform format, moving toward biomass commoditization. Against this backdrop, the 2022 Woody SOT for low-ash woody feedstocks utilizes feedstock fractionation by incorporating technologies that can separate the biomass into its anatomical fractions (wood, bark, needle, and extrinsic ash) to reduce impurities and attempt to maximize the retention of usable fractions that satisfy downstream quality considerations. By using a series of air classification steps, this strategy can reduce the extrinsic ash in forest residues, separate out a majority of the incoming needles (which can be supplied to alternate markets), and maximize the retention of whitewood in the usable fraction. The fractionated forest residues are then mixed with clean-pine chips in a 50-50 blend to prepare the feedstock for the desired conversion pathway. The n th -plant analysis estimated the delivered cost for the feedstock at $\$$69.23/dry ton (2016$\$$) which represents a $\$$6.64/dry ton decrease compared to the cost estimate of the 2021 Woody SOT supply system for low-ash woody feedstocks. The quality requirements in the 2022 Woody SOT were identical to those of the 2021 Woody SOT at = 1.00 wt % ash and = 50.51 wt% carbon. The cost savings derive primarily from reductions in dry matter losses during air classification. The GHG emissions for the n th -plant analysis were estimated at 178.39 kg CO2e/dry ton compared to 178.71 kg CO 2 e/dry ton in the 2021 Woody SOT, a decrease of 0.32 kg CO2e/dry ton. The small change stems from an increase in emissions attributed to preprocessing and slightly larger savings in emissions from transportation. In the 1 st -plant analysis of the 2022 Woody SOT system, the average throughput was estimated to be approximately 2,128 dry tons/day or 96.51% of the name plate capacity. During the simulation the daily throughput ranged from 1,090 dry tons/day to 2,200 dry tons/day, or 49.43% to 99.75% of the daily nameplate capacity. After the year of operation 722,403 tons of processed feedstock were produced in total without regard to quality considerations (99.64% of the annual nameplate capacity). The variability in throughput was primarily caused by equipment failures in the system. Regular failures, downtime caused by routine maintenance per manufacturer guidelines, contributed to a majority 62.50% of failures and 62.60% of downtime. Failures due to wear were the other cause of disruption within the system, impacting the rotary shear and orbital screen and accounting for 37.50% of the failures and 37.40% of the total downtime. Ultimately the system was on stream for 87.84% during the simulation period, which is only 2.16 percentage points below the nth-plant assumption for on-stream time. The production cost of the system averaged $\$$71.66/dry ton. The costs ranged from a minimum of $\$$71.23/dry ton to a maximum of $\$$2,115.30/dry ton. When dry matter losses (disposed low-quality fractions as well as other losses such as in grinders) were considered the costs increased to an average of $\$$75.11/dry ton with a minimum of $\$$74.69/dry ton and a maximum of $\$$2,136.86/dry ton...

09 BIOMASS FUELS↗
DOE OSTI2025

On Alfvénic turbulence of solar wind streams observed by Solar Orbiter during March 2022 perihelion and their source regions

It has been recently accepted that the standard classification of the solar wind solely according to flow speed is outdated, and particular interest has been devoted to the study of the origin and evolution of so-called Alfvénic slow solar wind streams and to what extent such streams resemble or differ from fast wind. In March 2022, Solar Orbiter completed its first nominal phase perihelion passage. During this interval, it observed several Alfvénic streams, allowing for characterization of fluctuations in three slow wind intervals (AS1-AS3) and comparison with a fast wind stream (F) at almost the same heliocentric distance. This work makes use of Solar Orbiter plasma parameters from the Solar Wind Analyzer (SWA) and magnetic field measurements from the magnetometer (MAG). The magnetic connectivity to the solar sources of selected solar wind intervals was reconstructed using a ballistic extrapolation based on measured solar wind speed down to the (spherical) source surface at 2.5 R s below which a potential field extrapolation was used to map back to the Sun. The source regions were identified using SDO/AIA observations. A spectral analysis of in situ measured magnetic field and velocity fluctuations was performed to characterize correlations, Alfvénicity, normalized cross-helicity, and residual energy in the frequency domain as well as intermittency of the fluctuations and spectral energy transfer rate estimated via mixed third-order moments. A machine learning technique was used to separate proton core, proton beam, and alpha particles and to study v − b correlations for the different ion populations in order to evaluate the role played by each population in determining the Alfvénic content of solar wind fluctuations. The comparison between fast wind and Alfvénic slow wind intervals highlights the differences between the two solar wind regimes: The fast wind is characterized by larger amplitude fluctuations, and magnetic and velocity fluctuations are closer to equipartition of energy. In fact the Alfvénic slow wind streams appear to be on a spectrum of wind types, with AS1, originating from open field lines neighboring active regions and displaying similarities with the fast wind in terms of fluctuation amplitude and turbulence characteristics, but not with respect to the alpha particles and proton beams. The other two slow streams differed both in their sources as well as plasma characteristics, with AS2 coming from the expansion of a narrow coronal hole corridor and AS3 from a region straddling a pseudostreamer. The latter displayed the coldest and highest density but the slowest stream with the smallest fluctuation amplitude and greatest magnetic energy excess. It also showed the largest scatter in proton beam speeds and the greatest difference in speed between proton beam and alpha particles. This study shows how the old fast–slow solar wind dichotomy, already called into question by the observations of slower Alfvénic solar wind streams, should further be refined, as the Alfvénic slow wind, originating in different solar wind regions, show significant differences in density, temperature, and proton and alpha-particle properties in the inner heliosphere. The observations presented here provide the starting point for a better understanding of the origin and evolution of different solar wind streams as well as the evolving turbulence contained within.

magnetohydrodynamics (MHD)↗
DOE OSTI2024

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗