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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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Thermal Stability and Lattice Strain Evolution of High-Nb-Containing TiAl Alloy under Low-Cycle-Fatigue Loading
The micromechanical behavior and the effect of temperature on the micromechanical mechanism of high-Nb-containing TiAl alloy during low-cycle fatigue still remain uncertain. Herein, in situ and ex situ synchrotron-based high-energy X-ray (HEXRD) experiment results reveal that the gamma and omega(o) phases suffer compressive lattice strains but the lattice strain in the alpha(2) phase evolves from tensile to compressive during low-cycle fatigue at 900 degrees C. In addition, the three phases suffer compressive lattice strains during cooling to room temperature, which could result in larger compressive lattice strains in gamma and omega(o) phases and the change of the lattice strain state in the alpha(2) phase. The peak-broadening results show gamma recrystallization is dominant in the interrupted low-cycle-fatigue samples, whereas inhomogeneous deformation occurs in the failed low-cycle-fatigue samples. The performed synchrotron diffraction experiments offer a deeper insight into the phase transformations and micromechanism of TiAl alloy during low-cycle fatigue.
In-situ imaging of advanced materials subjected to in-plane biaxial loading using X-ray micro-computed tomography
Not provided.
Identifying properties of low-loaded CoO[subscript X]/CeO[subscript 2] via X-ray absorption spectroscopy for NO reduction by CO
Abstract not provided
Numerical study of damage in particulate composites during high-strain rate loading using novel damage model
Not provided.
Deformation mechanism and load transfer in an in-situ NiTi–Nb composite
Not Available
Optimizing Distributed Load Balancing for Workloads with Time-Varying Imbalance.
Abstract not provided.
Access Patterns and Performance Behaviors of Multi-layer Supercomputer I/O Subsystems under Production Load
Scientific computing workloads at HPC facilities have been shifting from traditional numerical simulations to AI/ML applications for training and inference while processing and producing ever-increasing amounts of scientific data. To address the growing need for increased storage capacity, lower access latency, and higher bandwidth, emerging technologies such as non-volatile memory are integrated into supercomputer I/O subsystems. With these emerging trends, we need a better understanding of the multilayer supercomputer I/O systems and ways to use these subsystems efficiently. In this work, we study the I/O access patterns and performance characteristics of two representative supercomputer I/O subsystems. Through an extensive analysis of year-long I/O logs on each system, we report new observations in I/O reads and writes, unbalanced use of storage system layers, and new trends in user behaviors at the HPC I/O middleware stack.
Design Load Case (DLC) Generator: Web-based tool to support IEC 62600-2 standard design load case analysis.
Abstract not provided.
A Fixed-Step Under Frequency Load Shedding Scheme based on Load Criticality
Presentation for NAPS paper
High-Fidelity Self-Learning Tool for Residential Load and Load Flexibility Forecasting
In this project, we developed a set of high-fidelity modeling and optimization algorithms to enable conversion of individual homes into grid-integrated buildings, and a community of homes into a virtual power plant (VPP).
Design Load Case (DLC) Generator: Web-based tool to determine IEC 62600-2 standard design load requirements.
Abstract not provided.
IM3 + EPRI Data Center Load Projections
This dataset contains scenarios of hourly total electricity demand with and without projected loads from data centers over the period 2022-2040. The root projections without data center demands are identical to those documented in Burleyson et al. 2024. In short, those projections encompass hourly electricity demands for 54 Balancing Authorities (BAs) in the United States across a range of eight of weather and socioeconomic scenarios. Refer to the root dataset and accompanying publication, Burleyson et al. 2025, for information about how those projections were generated. For this derivative dataset we used the base loads from the following scenarios: rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 The root load projections did not reflect the drastic expansion of data centers that has occurred in the last several years to support artificial intelligence and cloud computing. To reflect growth in data center demand, a second set of load projections were created in which we layered in additional data center load projections based on the data center load growth scenarios described in a 2024 report by the Electric Power Research Institute (EPRI): "Powering Intelligence: Analyzing Artificial Intelligence and Data Center Energy Consumption". The EPRI projections from the report are included in this dataset (EPRI_2024_Projections.xlsx). That report contained annual state-level data center load projections for four year-over-year growth rates for data center demands: Low (3.71% annual growth) Moderate (5% annual growth) High (10% annual growth) Higher (15% annual growth) To homogenize the load projections with and without data centers we had to get them to a common scale. The first step was to take the EPRI annual state-level data center energy consumption values and convert them to 8760-hr loads for each year. We did that by assuming a flat (e.g., not weather- or time-sensitive) load profile and distributing the data center loads in each state evenly across all hours in a year. From there the loads were downscaled from the state-level to the county-level using 2019 county-level populations as weights. Finally, the county-level hourly data center loads were summed to the BA-level using the county-to-BA mapping underpinning the root load projections. The net result is 16 (4 weather and socioeconomic scenarios crossed with 4 data center load growth scenarios) unique load projections for the period 2022-2040. The file format follows that of the root dataset with a single additional column "Scaled_TELL_BA_Load_with_DC_MWh" that contains the hourly loads with the added data center loads for a given BA-year-scenario combination. Please refer to the readme file in the root dataset for more information on the file format.
A Data-Driven Pivot-Point-Based Time-Series Feeder Load Disaggregation Method
The load profile at a feeder-head is usually known to utility engineers while the nodal load profiles are not. However, the nodal load profiles are increasingly important for conducting time-series analysis in distribution systems. Therefore, in this paper, we present a pivot-point based, two-stage feeder load disaggregation algorithm using smart meter data. The two stages are load profile selection (LPS) and load profile allocation (LPA). In the LPS stage, a random load profile selection process is first executed to meet the load diversity requirement. Then, a few pairs of pivot points are selected as the matching targets. After that, a matching algorithm will run repetitively to select one load profile at a time for matching the reference load profile at the pivot points. In the LPA stage, the LPS selected load profiles are allocated to each load node on the feeder considering distribution transformer loading limits, load composition, and square-footage. The proposed method is validated using actual data collected in a North Carolina service area. Finally, simulation results show that the proposed method can generate a unique load shape for each load node while match the shape of their aggregated profile with the actual feeder head load profile.