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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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Impact of resonance decays on critical point signals in net-proton fluctuations
Not Available
Erratum to: Measurements of higher-order cumulants of multiplicity and net-electric charge distributions in inelastic proton-proton interactions by NA61/SHINE
This Erratum replaces, due to a discovery of coding mistakes, the following quantities: κ 3 /κ 1 of the h + − h − distribution presented in Fig. 6 and Table 6, κ 4 listed in Table 4, and $\hat{C}$ 4 presented in Fig. 7 and Table 5. All mentioned figures and tables were updated.
BSTC: A Novel Binarized-Soft-Tensor-Core Design for Accelerating Bit-Based Approximated Neural Nets
Binarized neural network (or BNN) promises tremendous performance improvement over traditional DNNs through simplified bit-level computation and significantly reduced memory access/storage cost. In addition, it has other advantages of low-cost, low-energy and high-robustness, showing great utilization potential in resources-constrained, volatile and latency-critical applications, which are critical for future HPC execution. However, the promised significant performance gain of BNN inference has never been fully demonstrated on general-purpose processors, particularly on GPUs, due to: (i) the challenge to extract and leverage sufficient fine-grained bit-level-parallelism to saturate GPU cores when batch is small; (ii) the fundamental design conflict between bit-based BNN algorithm and underlying word-based architecture; (iii) architecture \& performance unfriendly BNN network design. To address (i) and (ii), we propose binarized-soft-tensor-core as a software-hardware codesign approach to construct bit-manipulation capability for modern GPUs to effectively harvest the emerging bit-level-parallelism. To tackle (iii), we propose intra- and inter-layer fusion techniques so that the entire BNN inference can be packed into a single GPU kernel, to avoid high-cost frequent launching. Experiments demonstrate that our design can achieve over 1000x speedup for raw inference latency and 10x for inference throughput over the state-of-the-art full-precision simulated BNN inference for AlexNet on ImageNet.
QUANT-NET: A testbed for quantum networking research over deployed fiber
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Network performance analysis for HPC datacenters (net_perf) v1.0
The software has two main features: (1) identify data movement trends in HPC data centers that use network flow monitoring (2) analyze the performance of individual data flows under the existing data movement management strategy and identify performance bottlenecks that impede timely data availability for science workflows. Its main advantage is that it is tailored for HPC network traffic by considering HPC data movement management intricacies.
Greenhouse gases, Regulated Emissions, and Energy use in Technologies Model ® (2020 .Net)
To fully evaluate energy and emission impacts of advanced vehicle technologies and new transportation fuels, the fuel cycle from wells to wheels and the vehicle cycle through material recovery and vehicle disposal need to be considered. Sponsored by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE), Argonne has developed a full life-cycle model called GREET (Greenhouse gases, Regulated Emissions, and Energy use in Technologies). It allows researchers and analysts to evaluate various vehicle and fuel combinations on a full fuel-cycle/vehicle-cycle basis. The first version of GREET was released in 1996. Since then, Argonne has continued to update and expand the model. GREET.Net provides the user with an easy to use and fully graphical toolbox to perform life cycle analysis simulations of alternative transportation fuels and vehicle technologies in a matter of a few clicks. It provides a comprehensive, life-cycle-based approach to compare the energy use and emissions of conventional and advanced vehicle technologies. The tool includes the data of both fuel-cycle (fuel production and vehicle operation) and vehicle-cycle (vehicle material recovery and production, vehicle component fabrication, vehicle assembly, and vehicle disposal/recycling). This public domain model is available free of charge for anyone to use.
Greenhouse gases, Regulated Emissions, and Energy use in Technologies Model ® (2021 .Net)
To fully evaluate energy and emission impacts of advanced vehicle technologies and new transportation fuels, the fuel cycle from wells to wheels and the vehicle cycle through material recovery and vehicle disposal need to be considered. Sponsored by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE), Argonne has developed a full life-cycle model called GREET (Greenhouse gases, Regulated Emissions, and Energy use in Technologies). It allows researchers and analysts to evaluate various vehicle and fuel combinations on a full fuel-cycle/vehicle-cycle basis. The first version of GREET was released in 1996. Since then, Argonne has continued to update and expand the model. GREET.Net provides the user with an easy to use and fully graphical toolbox to perform life cycle analysis simulations of alternative transportation fuels and vehicle technologies in a matter of a few clicks. It provides a comprehensive, life-cycle-based approach to compare the energy use and emissions of conventional and advanced vehicle technologies. The tool includes the data of both fuel-cycle (fuel production and vehicle operation) and vehicle-cycle (vehicle material recovery and production, vehicle component fabrication, vehicle assembly, and vehicle disposal/recycling). This public domain model is available free of charge for anyone to use.
Greenhouse gases, Regulated Emissions, and Energy use in Technologies Model ® (2022 .Net)
To fully evaluate energy and emission impacts of advanced vehicle technologies and new transportation fuels, the fuel cycle from wells to wheels and the vehicle cycle through material recovery and vehicle disposal need to be considered. Sponsored by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE), Argonne has developed a full life-cycle model called GREET (Greenhouse gases, Regulated Emissions, and Energy use in Technologies). It allows researchers and analysts to evaluate various vehicle and fuel combinations on a full fuel-cycle/vehicle-cycle basis. The first version of GREET was released in 1996. Since then, Argonne has continued to update and expand the model. GREET.Net provides the user with an easy to use and fully graphical toolbox to perform life cycle analysis simulations of alternative transportation fuels and vehicle technologies in a matter of a few clicks. It provides a comprehensive, life-cycle-based approach to compare the energy use and emissions of conventional and advanced vehicle technologies. The tool includes the data of both fuel-cycle (fuel production and vehicle operation) and vehicle-cycle (vehicle material recovery and production, vehicle component fabrication, vehicle assembly, and vehicle disposal/recycling). This public domain model is available free of charge for anyone to use.
Greenhouse gases, Regulated Emissions, and Energy use in Technologies Model ® (2023 .Net)
To fully evaluate energy and emission impacts of advanced vehicle technologies and new transportation fuels, the fuel cycle from wells to wheels and the vehicle cycle through material recovery and vehicle disposal need to be considered. Sponsored by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE), Argonne has developed a full life-cycle model called GREET (Greenhouse gases, Regulated Emissions, and Energy use in Technologies). It allows researchers and analysts to evaluate various vehicle and fuel combinations on a full fuel-cycle/vehicle-cycle basis. The first version of GREET was released in 1996. Since then, Argonne has continued to update and expand the model. GREET.Net provides the user with an easy to use and fully graphical toolbox to perform life cycle analysis simulations of alternative transportation fuels and vehicle technologies in a matter of a few clicks. It provides a comprehensive, life-cycle-based approach to compare the energy use and emissions of conventional and advanced vehicle technologies. The tool includes the data of both fuel-cycle (fuel production and vehicle operation) and vehicle-cycle (vehicle material recovery and production, vehicle component fabrication, vehicle assembly, and vehicle disposal/recycling). This public domain model is available free of charge for anyone to use.
Data for Robust Paths to Net Greenhouse Gas Mitigation and Negative Emissions via Advanced Biofuels
This zip file contains a UNIX-format DayCent model executable, input files, automation code, and associated directory structure necessary to re-produce the DayCent analysis underlying the manuscript. The main script “autodaycent.py” (written for Python 2.7) opens an interactive command line routine that facilitates: Calibrating the DayCent pine growth model; Initializing DayCent for a set of case studies sites; Executing an ensemble of model runs representing case study site reforestation, grassland restoration, or conversion to switchgrass cultivation; and Results analysis & generation of manuscript Fig. 3. Note that the interactive analysis code requires that all input files to be contained in the directory structure as uploaded, without modification. Executable versions of the DayCent model compatible with other operating systems are available upon request.
Gross and Net Soil Methane Flux and Ancillary Data, Edgewater, MD, USA, summer 2022
This data package contains measurements used to quantify methane cycling and environmental conditions in coastal forest soils during the 2022 growing season. It includes time‑series data of soil methane flux, soil respiration, soil temperature, and volumetric water content collected from soil monoliths transplanted along an inundation and salinity gradient. The package also provides one‑time measurements from a stable‑isotope pool‑dilution incubation, including gravimetric water content, methane headspace concentrations, and ¹³CH₄ enrichment over time. Data files are provided in comma‑separated values (CSV) format, with accompanying metadata and readme documentation in PDF and plain‑text formats. All files can be opened with standard software such as R, Python, or spreadsheet programs capable of handling CSV files. The metadata file describes variable definitions, units, processing steps, and the structure of each data table to support reuse and integration with other datasets.
Cover Crop Effects on Net Ecosystem Carbon Balance in Grain and Silage Maize
Not provided.
New machine learning techniques for simulation-based inference: InferoStatic nets, kernel score estimation, and kernel likelihood ratio estimation
We propose an intuitive, machine-learning approach to multiparameter inference, dubbed the InferoStatic Networks (ISN) method, to model the score and likelihood ratio estimators in cases when the probability density can be sampled but not computed directly. The ISN uses a backend neural network that models a scalar function called the inferostatic potential \varphi φ . In addition, we introduce new strategies, respectively called Kernel Score Estimation (KSE) and Kernel Likelihood Ratio Estimation (KLRE), to learn the score and the likelihood ratio functions from simulated data. We illustrate the new techniques with some toy examples and compare to existing approaches in the literature. We mention en passant some new loss functions that optimally incorporate latent information from simulations into the training procedure.
Reducing Data Center Loads for a Large-Scale, Net Zero Office Building (Brochure)
Case study highlighting the design, implementation strategies, and continuous performance monitoring of NREL's Research Support Facility data center.
Prototyping Energy Efficient Thermo-Magnetic and Induction Hardening for Heat Treat and Net Shape Forming Applications
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Machining Elimination through Application of Thread Forming Fasteners in Net-Shaped Cast Holes
The ultimate objective of this work was to eliminate approximately 30% of the machining performed in typical automotive engine and transmission plants by using thread forming fasteners in as-cast holes of aluminum and magnesium cast components. The primary issues at the source of engineers reluctance to implementing thread forming fasteners in lightweight castings are: • Little proof of consistency of clamp load vs. input torque in either aluminum or magnesium castings. • No known data to understand the effect on consistency of clamp load as casting dies wear. The clamp load consistency concern is founded in the fact that a portion of the input torque used to create clamp load is also used to create threads. The torque used for thread forming may not be consistent due to variations in casting material, hole size and shape due to tooling wear and process variation (thermal and mechanical). There is little data available to understand the magnitude of this concern or to form the basis of potential solutions if the range of clamp load variation is very high (> +/- 30%). The range of variation that can be expected in as-cast hole size and shape over the full life cycle of a high pressure die casting die was established in previous work completed by Pacific Northwest National Laboratory, (PNNL). This established range of variation was captured in a set of 12 cast bosses by designing core pins at the size and draft angles identified in the sited previous work. The cast bosses were cut into nuts that could be used in the Ford Fastener Laboratory test-cell to measure clamp load when a thread forming fastener was driven into a cast nut. There were two sets of experiments run. First, a series of cast aluminum nuts were made reflecting the range of shape and size variations to be expected over the life cycle of a die casting die. Taptite thread forming fasteners, (a widely used thread forming fastener suitable for aluminum applications), were driven into the various cored, as-cast nuts at a constant input torque and resulting clamp loads were recorded continuously. The clamp load data was used to determine the range of clamp loads to be expected. The bolts were driven to failure. The clamp load corresponding to the target input of 18.5 Nm was recorded for each fastener. In a like fashion, a second set of experiments were run with cast magnesium nuts and ALtracs thread forming fasteners, (a widely used thread forming fastener suitable for magnesium applications). Again all clamp loads were recorded and analyzed similarly to the Taptites in aluminum cast nuts. Results from previous work performed on the same test cell for a Battelle project using standard M8 bolts into standard M8 nuts were included as a comparator for a standard bolt and nut application. The results for the thread forming fasteners in aluminum cast holes were well within industry expectations of +/- 30% for out of the box and robustness range testing. The results for the dry and lubed extreme conditions were only slightly higher than industry expectations at +/- 35.6%. However, when compared to the actual Battelle results (+/- 40%) for a standard bolt and nut the tread forming fasteners performed slightly better. The results for the thread forming fasteners in magnesium cast holes were all well within industry expectations of +/- 30% for all three conditions. The robustness range (.05mm larger and smaller holes than the expected wear pattern of a die casting die at full life cycle) results also fell within the industry expectations for standard threaded fasteners. These results were very encouraging. It was concluded that this work showed that clamp load variation with thread forming fasteners is consistent with industry expectations for standard steel bolts and nuts at +/- 30%. There does not appear to be any significant increase in clamp load variation due to the application of thread forming fasteners in as-cast holes of aluminum or magnesium over the effective life of a die casting mold. The fully implemented potential benefit of thread forming fasteners in as-cast holes of aluminum and magnesium is estimated to be 6 trillion Btu per year for North America. Economic benefit is estimated to be nearly $800 million per year. Environmental benefits and quality improvements will also result from full implementation of this technology.