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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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The poster presents the latest progress on utilizing a commercially scalable flat sheet sorbent for the effective enrichment of critical minerals from coal wastewater. It highlights the performance, scalability, and potential for industrial applications, addressing key challenges in critical recovery from complex wastewater streams.
Relativistic heavy ion collisions generate nuclear-sized droplets of quark-gluon plasma (QGP) that exhibit nearly inviscid hydrodynamic expansion. Smaller collision systems such as p + Au, d + Au, and 3 He + Au at the Relativistic Heavy Ion Collider, as well as p + Pb and high-multiplicity p + p at the Large Hadron Collider may create even smaller droplets of QGP. If so, the standard time evolution paradigm of heavy ion collisions may be extended to these smaller systems. These small systems present a unique opportunity to examine pre-hydrodynamic physics and extract properties of the QGP, such as the bulk viscosity, where the short lifetimes of the small droplets makes them more sensitive to these contributions. Here we focus on the influence of bulk viscosity, its temperature dependence, and cavitation effects on the dynamics in small and large systems using the publicly available hydrodynamic codes SONIC and MUSIC. In conclusion, we also compare pre-hydrodynamic physics in different frameworks including AdS/CFT strong coupling, IP-GLASMA weak coupling, and free streaming or no coupling.
Methods for catalytic fractionation of biomass provide a means to convert lignin directly into monomers while generating a high-quality cellulosic stream, contrasting conventional biomass pretreatment strategies that prioritize the cellulosic fraction. Here, a nonprecious-metal Co-N-P-C catalyst is identified for aerobic oxidative catalytic fractionation (OCF) of poplar feedstock in dimethyl carbonate that achieves significantly higher yields of aromatic monomers (24 wt %) relative to those obtained with a recently reported Co-N-C catalyst in acetone solvent (15 wt %). Mechanistic studies indicate that the acidic properties of the catalyst contribute to its improved performance by promoting extraction of lignin from insoluble polysaccharides. This OCF process is complemented by the development of a new centrifugal partition chromatography (CPC) method that supports isolation of all five major aromatic monomers (syringic acid, syringaldehyde, vanillic acid, vanillin, and para-hydroxybenzoic acid) in a single liquid–liquid extraction purification step. Furthermore, this OCF/CPC sequence has important implications for future lignin valorization efforts.
H-Canyon plans to discard Sodium Reactor Experiment (SRE) material that is currently stored in Tanks 16.3 and 16.4. Savannah River Remediation (SRR) determined that a portion of the SRE material can be added to Sludge Batch 9 (SB9) in Tank 40 and the remainder will be added to Sludge Batch 10 (SB10) that is currently being prepared in Tank 51. A processing flowsheet for both the nitric-formic acid flowsheet and the nitric-glycolic acid flowsheet were previously developed for SB9 sludge-only and coupled operation with the Actinide Removal Process – Modular Caustic Side Solvent Extraction Unit (ARP-MCU) and the Salt Waste Processing Facility (SWPF). Due to the compositional change in Tank 40 after the addition of SRE material, it is necessary to determine the influence on the acid stoichiometry operating window and hydrogen production for SB9 processing. An assessment was completed to address the influence of an addition of SRE and DR-3 Fuel from H-Canyon on the chemistry of Tank 40, the defined acid stoichiometry window (with and without SWPF streams) for the nitric-formic and nitric-glycolic acid flowsheets, and the resulting influence of the hydrogen production. Hydrogen production during the SRAT/SME cycles consists of thermolytic, radiolytic, and catalytic hydrogen. For the nitric-formic acid flowsheet, catalytic hydrogen dominates the hydrogen production during DWPF operation mode for the SRAT and SME vessels. This report was written to summarize that assessment. No SRNL testing is needed prior to adding up to 7,100 additional gallons of SRE and DR-3 Fuel from H-Canyon to Tank 40. Also, the addition of SRE will not exceed the Shielded Cells reported values for catalytic hydrogen for either the nitric-formic acid flowsheet (Technical Safety Requirement (TSR) limit of 0.15 lb/h) or the nitric-glycolic acid flowsheet (TSR limit 0.024 lb/h). The addition of the ARP stream from SWPF and the use of the nitric-glycolic acid flowsheet are both expected to decrease hydrogen generation, leading to a larger safety margin in the CPC.
In the contemporary era of high-precision spectroscopic surveys, led by projects like DESI, there is an increasing demand for optimizing the extraction of cosmological information from clustering data. This work conducts a thorough comparison of various methodologies for modeling the full shape of the two-point statistics in configuration space. We investigate the performance of both direct fits (Full Modeling) and the parameter compression approaches (ShapeFit and Standard). We utilize the ABACUS-SUMMIT simulations, tailored to exceed DESI's precision requirements. Particularly, we fit the two-point statistics of three distinct tracers (LRG, ELG, and QSO), by employing a Gaussian Streaming Model in tandem with Convolution Lagrangian Perturbation Theory and Effective Field Theory. We explore methodological setup variations, including the range of scales, the set of galaxy bias parameters, the inclusion of the hexadecapole, as well as model extensions encompassing varying ns and allowing for w 0 w a CDM dark energy model. Throughout these varied explorations, while precision levels fluctuate and certain configurations exhibit tighter parameter constraints, our pipeline consistently recovers the parameter values of the mocks within 1σ in all cases for a 1-year DESI volume. Additionally, we compare the performance of configuration space analysis with its Fourier space counterpart using three models: PyBird, FOLPS and velocileptors, presented in companion papers. We find good agreement with the results from all these models.
Temperature-based methods have been developed to infer 1D vertical exchange flux between a stream and the subsurface. Current analyses rely on fitting physically based analytical and numerical models to temperature time series measured at multiple depths to infer daily average flux. These methods have seen wide use in hydrologic science despite strong simplifying assumptions including a lack of consideration of model structural error or the impacts of multidimensional flow or the impacts of transient streambed hydraulic properties. We performed a “perfect-model experiment” investigation to examine whether regression trees, with and without gradient boosting, can extract sufficient information from model-generated subsurface temperature time series, with and without added measurement error, to infer the corresponding exchange flux time series at the streambed surface. Using model-generated, synthetic data allowed us to assess the basic limitations to the use of machine learning; further examination of real data is only warranted if the method can be shown to perform well under these ideal conditions. We also examined whether the inherent feature importance analyses of tree-based machine learning methods can be used to optimize monitoring networks for exchange flux inference.
A continuous wave (CW) electron accelerator for the treatment of industrial streams including an electron beam source, a modified high efficiency slot coupled cavity, at least one focusing magnet positioned surrounding the accelerator to contain the beam in the accelerator, an efficient radio frequency power supply means for supplying power of a radio frequency to the cavity to induce a TM01 accelerating mode in the cavity, an electron beam spreader or raster, a fixed magnet array or two-dimensional scanning magnet for deflecting the accelerated beam into a desired shape, and an exit window for extracting the deflected electron beam. The accelerator includes a graded-beta cavity to enable use with a low-power pulsed electron source. The accelerator benefits from a low wall-power loss accelerating cavity that is energized with efficient RF sources, enabling it to be operated in continuous wave mode.
As an abundant and cheap fossil energy source, natural gas has become a significant energy supply to support the United States’ economy. However, the large-scale extraction and utilization of natural gas also impose significant challenges on methane leakage. This problem is exacerbated by aging gas utility delivery systems, including interstate high-pressure pipelines, storage, and transmission facilities. This project aims to develop a cost-effective fiber optical sensing method that can perform multi-parameter real-time measurements of natural gas pipelines across long interrogation distances up to 100 km with 1-meter spatial resolution. This sensing tool can evaluate overall pipeline efficiency and reduce methane emissions for mid-stream methane infrastructures. To accomplish this objective, research and development efforts funded by this project have resulted in the following accomplishments: This project successfully has developed new functional sensory polymer materials using Metal-Organic Frameworks (MOFs) that can be coated on optical fiber through the reel-to-reel coating process. Functional polymer-coated optical fibers can perform sensitive methane detection through evanescence wave interaction and strain-based measurements to achieve 1% detection sensitivities. The new sensors fibers support both distributed measurements and multiplexed fiber sensors array for multi-point measurements. This project developed and optimized a new multi-core optical fiber that supports simultaneous and distributed measurements of strain and temperatures with 1-meter spatial resolutions across up to 100-km interrogation distance. This new fiber, combined with sensory polymercoated fiber, could perform both distributed temperature and methane detections. This project developed a new artificial intelligence big data algorithm approach that can effectively analyze high-resolution data harnessed by distributed fiber sensors to protect natural gas pipelines against external threats and detect internal defects induced by corrosion. Working with our industry partner, this project developed new optical fibers that support fiber sensor fabrications through polymer coating after the fibers are drawn. These new fibers eliminate the need for direct sensor fabrication when the fiber is fabricated on a fiber draw tower, which drastically expands fiber sensors' applicability. This research project has significantly advanced the distributed fiber sensing technology. It will dramatically increase the applicability and adaptability of distributed fiber sensors for a wide array of applications in energy, sustainability, and environmental science, including structural health monitoring of natural gas pipelines, oil infrastructures, hydrogen facilities, and environmental monitoring of carbon storage sites, water supply systems, and others.
Photovoltaic (PV) modules and batteries can either be recycled or disposed of in landfills at end-of-life (EoL). This work focuses on disposal since the benefit of recycling PV modules and batteries is well established. This study characterizes the potential toxicity due to metals leaching from selected PV modules and batteries through both the Toxicity Characteristic Leaching Procedure (TCLP) and in-house batch leaching protocols to probe the impact on metallic ion mobility as a function of the different types of e-waste entering the waste stream, the magnitude of device damage when placed into the waste stream, and the simulated municipal solid waste (SMSW) composition. Our results showed that one PV module and three battery types in this study should be classified as hazardous waste within the U.S. However, for some of the other e-wastes, metals of concern including Cr, Cu, Hg, Ni, Pb, and Zn leached during the batch tests but not in the TCLP regulatory method. For most of the waste types, the amounts of metals that leached in the TCLP test and batch tests were much lower than the total extractable amounts, demonstrating the potential for additional unaccounted for amounts of metals to leach. Our results demonstrate that the TCLP regulatory method might fail at predicting potential leaching and at capturing the complexity of e-waste leaching in landfill conditions. It confirms that additional work is needed urgently to develop appropriate EoL procedures for MSW with PV and battery e-waste.
Here, the Tritium Extraction eXperiment (TEX) is a forced-convection lead-lithium (PbLi) loop in the Safety and Tritium Applied Research (STAR) facility at Idaho National Laboratory (INL) with the purpose of providing validation data for the vacuum permeator tritium extraction concept. A vanadium tube of 1000 mm length, 12.7 mm outside diameter, and 0.50 mm wall thickness is installed in the test section of TEX. The installed vanadium tube is characterized to quantify impurity concentrations, surface chemistry, and microstructure to elucidate permeation phenomena observed in experimentation. Herein, the permeation properties of the vanadium tube are characterized by measuring deuterium permeation at 300 °C, 325 °C, and 350 °C at 100 kPa, 125 kPa, and 150 kPa total pressures with 5000 ppm deuterium in helium gas mixture in a once-through flow configuration. The hydrogen isotope permeation through the vanadium tube in the test section is measured with quadrupole mass spectrometers and the hydrogen isotope concentration in the feed and retentate gas stream is measured with gas chromatography. The transient permeation results are modeled with MELCOR-TMAP, a thermal-hydraulic tritium transport code, and compared well with literature data.
Increasing attention has recently focused on nontraditional dark-matter production mechanisms which result in primordial dark-matter velocity distributions with highly nonthermal shapes. Here, in this paper, we undertake an assessment of how the detailed shape of a general dark-matter velocity distribution impacts structure formation in the nonlinear regime. In particular, we investigate the impact on the halo-mass and subhalo-mass functions, as well as on astrophysical observables such as satellite and cluster-number counts. We find that many of the standard expectations no longer hold in situations in which this velocity distribution takes a highly nontrivial, even multimodal shape. For example, we find that the nominal free-streaming scale alone becomes insufficient to characterize the effect of free-streaming on structure formation. In addition, we propose a simple one-line conjecture which can be used to “reconstruct” the primordial dark-matter velocity distribution directly from the shape of the halo-mass function. Although our conjecture is completely heuristic, we show that it successfully reproduces the salient features of the underlying dark-matter velocity distribution even for nontrivial distributions which are highly nonthermal and/or multimodal, such as might occur for nonminimal dark sectors. Moreover, since our approach relies only on the halo-mass function, our conjecture provides a method of probing dark-matter properties even for scenarios in which the dark and visible sectors interact only gravitationally.
Hydropower is a reliable source of renewable energy, and its future expansion is likely to be in the form of either smaller new stream development (NSD) projects or powering existing non-powered dams. Thresholds for entrainment risk to fish and the requirements for fish exclusion at hydropower facilities often differ depending on the species involved, the characteristics of the facility, and the goals of stakeholders, but little quantitative information is present within the literature regarding the specific costs of fish exclusion measures. Cost data associated with protection, mitigation, and enhancement (PM&E) measures related to positive barrier screening were identified using keyword searches of an existing environmental mitigation cost data set and manual extraction from regulatory licensing documents available in the Federal Energy Regulatory Commission (FERC) eLibrary. This approach yielded a total of 50 p.m.&E mitigation measures with estimated capital construction costs pertaining to positive barrier screens and represented <10% of the 171 total FERC project dockets available in the data set. These data were highly skewed toward conventional relicensing projects, as <7% were associated with NSD projects. Results indicate highly variable costs are associated with fish screening, with flow-normalized costs one to two orders of magnitude higher for screening with the highest exclusion capability (≤0.09 in. spacing) compared with coarser screening (1–2 in.). These data provide an initial baseline for estimating exclusion costs for hydropower development and may help developers consider options for more fish-friendly generation technologies, though gaps remain relating to a lack of data, particularly for NSD projects.
Non-negative matrix factorization (NMF) is an appealing class of methods for performing unsupervised learning on streaming spectral data, particularly in time-sensitive applications such as in situ characterization of materials. These methods seek to decompose a dataset into a small number of components and weights that can compactly represent the underlying signal while effectively reconstructing the observations with minimal error. However, canonical NMF methods have no underlying requirement that the reconstruction uses components or weights that are representative of the true physical processes. In this work, we demonstrate how constraining a subset of the NMF weights or components as rigid priors, provided as known or assumed values, can provide significant improvement in revealing true underlying phenomena. We present a PyTorch-based method for efficiently applying constrained NMF and demonstrate its application to several synthetic examples. Our implementation allows an expert researcher-in-the-loop to provide and dynamically adjust the constraints during a live experiment involving streaming spectral data. Such interactive priors allow researchers to specify known or identified independent components, as well as functional expectations about the mixing or transitions between the components. We further demonstrate the application of this method to measured synchrotron x-ray total scattering data from in situ beamline experiments. In such a context, constrained NMF can result in a more interpretive and scientifically relevant decomposition than canonical NMF or other decomposition techniques. As a result, the details of the method are provided, along with general guidance for employing constrained NMF in the extraction of critical information and insights during time-sensitive experimental applications.
This data package provides data on the natural abundance isotopes of nitrate from various sources, including porewater, stream, and precipitation. The overall purpose of this data package is to complement additional data streams for nitrogen cycling metrics (e.g., riverine inorganic and organic nitrogen measurements) with specific data gleaned from the interpretation of terrestrial and riverine isotope measurements and collectively understand on how nitrogen is cycled around this system. The Rifle and East River are part of the Watershed Function Scientific Focus Area (WFSFA) located in the Upper Colorado River Basin, United States. Collection locations include: Pumphouse (multiple sampling dates), River (upstream/ downstream meander C), Shallow soil lysimeters (hillslope, toeslope, floodplain), Hillslope-floodplain borehole wells (i.e., Pumphouse Lower Montane (PLM) wells), and Potassium chloride(KCL)-extracted soil samples. Copper Creek sampling includes 8x river sample points and two tributaries sampled on Sept. Oct. 2017, July 2018. East River below the Rocky Mountain Biological Laboratory sampling includes 1 access point sampled on July, Sept. Oct. 2017. Rifle floodplain sampling includes well TT03 across multiple collection times over 2014. Data is provided in 5 individual files by location in csv format, or all compiled in Excel format.
As advances in digital engineering continue to push the technological boundaries, digital twin (DT) visualizations for diagnostics and safeguards advancement become much more feasible and practical. DTs generate large and complex data streams that require effective user interfaces to provide monitoring and diagnostic capabilities. Unfortunately, while these frameworks exist, there is not much research on the systematic documentation of human–computer interaction (HCI) for DT visualization. This work presents a dual-mode visualization methodology (two dimensional [2D] graphical user interface dashboard and 3D mixed reality) designed to support diagnostic tasks in DT systems and building on a validated framework and applying established HCI principles. The methodology is demonstrated through a case study of aqueous processing at Idaho National Laboratory, using experimental data from the chemical solvent extraction runs. Our interfaces display real-time alerts and monitoring to inform users of safeguards anomalies. The interfaces use immersive 3D mixed-reality visualization for further system and experiment investigation. This work demonstrates how the systematic application of HCI principles can inform DT visualization design for diagnostic and safeguards applications. While formal user evaluation studies remain as future work, this paper documents the systematic design methodology and demonstrates a proof-of-concept implementation.
We propose an efficient distributed out-of-memory implementation of the non-negative matrix factorization (NMF) algorithm for heterogeneous high-performance-computing systems. The proposed implementation is based on prior work on NMFk, which can perform automatic model selection and extract latent variables and patterns from data. In this work, we extend NMFk by adding support for dense and sparse matrix operation on multi-node, multi-GPU systems. The resulting algorithm is optimized for out-of-memory problems where the memory required to factorize a given matrix is greater than the available GPU memory. Memory complexity is reduced by batching/tiling strategies, and sparse and dense matrix operations are significantly accelerated with GPU cores (or tensor cores when available). Input/output latency associated with batch copies between host and device is hidden using CUDA streams to overlap data transfers and compute asynchronously, and latency associated with collective communications (both intra-node and inter-node) is reduced using optimized NVIDIA Collective Communication Library (NCCL) based communicators. Benchmark results show significant improvement, from 32X to 76x speedup, with the new implementation using GPUs over the CPU-based NMFk. Good weak scaling was demonstrated on up to 4096 multi-GPU cluster nodes with approximately 25,000 GPUs when decomposing a dense 340 Terabyte-size matrix and an 11 Exabyte-size sparse matrix of density 10 -6 .
Algal biorefining to fuels and chemicals frequently requires both fuel production and nutrient recycle pathways. Mild Oxidative Treatment (MOT) is a promising wet oxidation route that simultaneously converts nitrogen and carbon in algae hydrolysate and extracted residues into ammonia and carboxylic acids (up to C5), respectively. The latter can be converted to sustainable aviation fuel by ketonization, condensation, and hydrodeoxygenation. We show that for a Scenedesmus algae under certain oxidation conditions, the nitrogen in the residues can be converted quantitatively to ammonia and recovered by cation exchange, while almost half of the carbon can be converted to carboxylic acids, mainly acetic acid. At the same time, we also show that soluble phosphorus in the form of phosphate can be selectively recovered by anion exchange, leaving a clean aqueous carboxylic acid stream for upgrading to fuels.
Public transit agencies are focused on making their fixed-line bus systems more energy efficient by introducing electric (EV) and hybrid (HV) vehicles to their fleets. However, because of the high upfront cost of these vehicles, most agencies are tasked with managing a mixed-fleet of internal combustion vehicles (ICEVs), EVs, and HVs. In managing mixed-fleets, agencies require accurate predictions of energy use for optimizing the assignment of vehicles to transit routes, scheduling charging, and ensuring that emission standards are met. The current state-of-the-art is to develop separate neural network models to predict energy consumption for each vehicle class. Although different vehicle classes’ energy consumption depends on a varied set of covariates, we hypothesize that there are broader generalizable patterns that govern energy consumption and emissions. In this paper, we seek to extract these patterns to aid learning to address two problems faced by transit agencies. First, in the case of a transit agency which operates many ICEVs, HVs, and EVs, we use multi-task learning (MTL) to improve accuracy of forecasting energy consumption. Second, in the case where there is a significant variation in vehicles in each category, we use inductive transfer learning (ITL) to improve predictive accuracy for vehicle class models with insufficient data. As this work is to be deployed by our partner agency, we also provide an online pipeline for joining the various sensor streams for fixed-line transit energy prediction. Here, we find that our approach outperforms vehicle-specific baselines in both the MTL and ITL settings.