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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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At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Automated imaging of the annihilation of a transverse domain wall in patterned magnetic thin films

Imaging the magnetic domain wall behavior in patterned thin films under external stimuli can enable understanding the underlying energy landscape and the role of local microstructure and defects. We present an automated workflow for in situ Lorentz transmission electron microscopy to image magnetic domain walls at the nanometer length scale and at a time resolution in the sub-millisecond regime—the latter of which is limited by the speed of the available camera. Our workflow is modular and can be broadly applied to various types of in situ experiments, taking us a step closer to the future of autonomous imaging of nanomagnetic films with electron microscopy. Using our workflow, we show the transformation of a transverse domain wall with sub-millisecond time resolution under the application of an in situ transverse magnetic field, a study of whose dynamics are essential in the design of future domain wall mediated spintronic device applications.

Algebraic topology↗
DOE OSTI2024

HDX–MS finds that partial unfolding with sequential domain activation controls condensation of a cellular stress marker

Eukaryotic cells form condensates to sense and adapt to their environment [S. F. Banani, H. O. Lee, A. A. Hyman, M. K. Rosen,Nat. Rev. Mol. Cell Biol.18, 285–298 (2017), H. Yoo, C. Triandafillou, D. A. Drummond,J. Biol. Chem.294, 7151–7159 (2019)]. Poly(A)-binding protein (Pab1), a canonical stress granule marker, condenses upon heat shock or starvation, promoting adaptation [J. A. Ribacket al.,Cell168, 1028–1040.e19 (2017)]. The molecular basis of condensation has remained elusive due to a dearth of techniques to probe structure directly in condensates. We apply hydrogen–deuterium exchange/mass spectrometry to investigate the mechanism of Pab1’s condensation. Pab1’s four RNA recognition motifs (RRMs) undergo different levels of partial unfolding upon condensation, and the changes are similar for thermal and pH stresses. Although structural heterogeneity is observed, the ability of MS to describe populations allows us to identify which regions contribute to the condensate’s interaction network. Our data yield a picture of Pab1’s stress-triggered condensation, which we term sequential activation (Fig. 1A), wherein each RRM becomes activated at a temperature where it partially unfolds and associates with other likewise activated RRMs to form the condensate. Subsequent association is dictated more by the underlying free energy surface than specific interactions, an effect we refer to as thermodynamic specificity. Our study represents an advance for elucidating the interactions that drive condensation. Furthermore, our findings demonstrate how condensation can use thermodynamic specificity to perform an acute response to multiple stresses, a potentially general mechanism for stress-responsive proteins.

Science & Technology - Other Topics↗
DOE OSTI2023

Development of HELIOS/BIPR/PARCS/MCNP6 Computation Route for WWER RPV Neutron Fluence Analysis and Validation Against Ex-Vessel Detector Measurement Data

Here, a computational route was developed for precise calculation of fast neutron fluence on a WWER-type reactor pressure vessel (RPV). The method is based on the transfer of neutronics data from HELIOS-2 lattice calculations and nodal diffusion neutronics data (power, density, and temperature) from BIPR7.1 and PARCS 3.36/PATHS core calculations into a three-dimensional (pinwise axially distributed) fixed neutron source for modeling of transport of fast neutrons from the reactor core to the outer surface of the RPV using MCNP6.2. Validation of the proposed computational method was carried out based on comparative analysis of MCNP6.2-predicted and neutron dosimetry–measured reaction rates [ 54 Fe(n,p) 54 Мn, 93 Nb(n,n') 93 mNb, and 58 Ni(n,р) 58 Со] on the outer surface of the Armenian Nuclear Power Plant (ANPP) Unit 2 RPV. Validation revealed that the MCNP6.2-predicted fast neutron fluence results are very sensitive to the ENDF-B neutron data. Particularly, MCNP6.2 with ENDF/B-VIII.0 significantly underpredicts (20% to 30%) fast neutron fluence while using ENDF/B-VII.1 data overpredicts it. Adding revised beta-released evaluations of 54 Fe, 56 Fe, 57 Fe, and 16 O from the International Nuclear Data Evaluation Network (INDEN) to ENDF/B-VIII.0 allows one to obtain reasonable agreement with measurement results for all types of measured reaction rates.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Securing Smart Manufacturing: Detection of Cyber-Physical Attacks in CNC-Based Systems

As Industry 4.0 advances, the integration of computer numerical control (CNC) machines and advanced manufacturing technologies is transforming production into smart manufacturing systems that blend physical and digital processes as cyber-physical systems. However, this increased cyber-physical connectivity exposes manufacturing systems to cyber threats that can cause severe operational and financial disruptions. This paper presents a comparative study on cyber attacks and anomaly detection techniques in manufacturing, focusing on network traffic from CNC machines. The data extracted from network packets includes machine commands and control signals exchanged between the machine's interface and control system, crucial for maintaining operational integrity. We explore two types of cyber attacks, design modification and command injection, which pose substantial risks to CNC machine productivity and system integrity. Our investigation involves experiments on a real CNC system, highlighting the urgent need for effective detection mechanisms. To address these threats, we evaluate three anomaly detection methods: dynamic time warping (DTW), rolling average, and a deep learning, long short-term memory (LSTM) time-series-based autoencoder. Each is assessed for its effectiveness in identifying anomalous behaviors caused by the attacks. Our findings demonstrate the unique strengths and limitations of each detection technique, providing a deeper understanding of their applicability in realworld manufacturing environments. The comparative analysis indicates that while certain methods are highly effective against specific attack types, others offer broader applicability across different attacks. This study contributes to the accurate detection of anomalies in CNC machining processes, thereby enhancing the reliability and security of smart manufacturing systems against diverse cyber threats.

Williams, Bethanie [Tennessee Technological Univer↗
DOE OSTI2025

SymProp: Scaling Sparse Symmetric Tucker Decomposition via Symmetry Propagation

Sparse symmetric tensors are an important class of tensors, and their decompositions serve as powerful tools for revealing low-rank structures. This paper introduces SymProp, a novel approach for scaling sparse symmetric Tucker decomposition by propagating symmetry through intermediate computations. SymProp optimizes two key computational kernels: Sparse Symmetric Tensor Times Same Matrix chain (S3 TTMc) for Higher-Order Orthogonal Iteration (HOOI) and Sparse Symmetric Tensor Times Same Matrix chain Times Core (S3 TTMcTC) for Higher-Order QR Iteration (HOQRI). Our method employs a metaprogramming-based index iteration approach to efficiently handle the upper triangular parts of intermediate dense symmetric tensors. SymProp achieves up to 50.9× speedup over SPLATT and up to 360.8× over Compressed Sparse Symmetric (CSS) format on the S3 TTMc operation. Moreover, our S3 TTMc and S3 TTMcTC implementations support tensor orders four levels higher than state-of-the-art methods. Our HOQRI demonstrates superior scalability and up to a 33.6× speedup over optimized HOOI. By enabling more scalable Tucker decompositions for higher orders, decomposition ranks, and dimension sizes, SymProp opens new possibilities for analyzing complex hypergraph structures in fields such as network science, data mining, and machine learning.

Li, Zecheng [North Carolina State University]↗
DOE OSTI2020

A Sparse Tensor Benchmark Suite for CPUs and GPUs

Tensor computations present significant performance chal- lenges that impact a wide spectrum of applications ranging from machine learning, healthcare analytics, social network analysis, data mining to quantum chemistry and signal processing. Efforts to improve the perfor- mance of tensor computations include exploring data layout, execution scheduling, and parallelism in common tensor kernels. This work presents a benchmark suite for arbitrary-order sparse tensor kernels using state- of-the-art tensor formats: coordinate (COO) and hierarchical coordinate (HiCOO) on CPUs and GPUs. It presents a set of reference tensor kernel implementations that are compatible with real-world tensors and power law tensors extended from synthetic graph generation techniques. We also propose Roofline performance models for these kernels to provide insights of computer platforms from sparse tensor view. This benchmark suite along with the synthetic tensor generator is publicly available.

Li, Jiajia↗
DOE OSTI2022

Simulated 5g Network Traffic Dataset

This is a dataset of 5G network traffic for use with machine learning tools to benchmark attack detection capabilities for multiple different models. The dataset contains simulated normal and attack 5G network traffic. There is no software in this dataset, only simulated network traffic data.

Anderson, MatthewW↗
DOE OSTI2025

CRISPR-CARB/nocap

Network Optimization and Causal Analysis of Perturb-seq (NOCAP) is a software package for causal inference of gene regulation networks using data from perturb-seq.

George, August↗
DOE OSTI2021

Retrospective Analysis and Bayesian Model Averaging of CMIP6 Precipitation in the Nile River Basin

The Nile River basin is one of the global hotspots vulnerable to climate change impacts because of a fast-growing population and geopolitical tensions. Previous studies demonstrated that general circulation models (GCMs) frequently show disagreement in the sign of change in annual precipitation projections. Here, we first evaluate the performance of 20 GCMs from phase six of the Coupled Model Intercomparison Project (CMIP6) benchmarked against a high-spatial-resolution precipitation dataset dating back to 1983 from Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR). Next, a Bayesian model averaging (BMA) approach is adopted to derive probability distributions of precipitation projections in the Nile basin. Retrospective analysis reveals that most GCMs exhibit considerable (up to 64% of mean annual precipitation) and spatially heterogenous bias in simulating annual precipitation. Moreover, it is shown that all GCMs underestimate interannual variability; thus, the ensemble range is underdispersive and is a poor indicator of uncertainty. The projected changes from the BMA model show that the value and sign of change vary considerably across the Nile basin. Specifically, it is found that projected changes in the two headwaters basins, namely, the Blue Nile and Upper White Nile, are 0.03% and -1.65%, respectively; both are statistically insignificant at α = 0.05. The uncertainty range estimated from the BMA model shows that the probability of a precipitation decrease is much higher in the Upper White Nile basin whereas projected change in the Blue Nile is highly uncertain both in magnitude and sign of change.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Integrated SOAs enable energy-efficient intra-data center coherent links

Coherent optical links are becoming increasingly attractive for intra-data center applications as data rates scale. Realizing the era of high-volume short-reach coherent links will require substantial improvements in transceiver cost and power efficiency, necessitating a reassessment of conventional architectures best-suited for longer-reach links and a review of assumptions for shorter-reach implementations. In this work, we analyze the impact of integrated semiconductor optical amplifiers (SOAs) on link performance and power consumption, and describe the optimal design spaces for low-cost and energy-efficient coherent links. Placing SOAs after the modulator provide the most energy-efficient link budget improvement, up to 6 pJ/bit for large link budgets, despite any penalties from nonlinear impairments. Increased robustness to SOA nonlinearities makes QPSK-based coherent links especially attractive, and larger supported link budgets enable the inclusion of optical switches, which could revolutionize data center networks and improve overall energy efficiency.

Maharry, Aaron (ORCID:0000000273732697)↗
DOE OSTI2021

Estimating Surface Solar Irradiance Using Meteosat-8 Satellite for India and Surrounding Regions (2017-2019)

Using the Physical Solar Model, Version 3 (PSM V3), the National Renewable Energy Laboratory—in collaboration with the University of Wisconsin, the National Oceanic and Atmospheric Administration, and Solar Consulting Services—produced modeled surface solar irradiance based on Meteosat-8 satellite cloud information. PSM V3 implements a physics-based radiative transfer approach to model surface solar irradiance using cloud properties processed from raw satellite data and ancillary data such as aerosol optical depth and precipitable water vapor from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Further, the modeled surface solar irradiance was validated using 10 high-quality ground measurement data from the Baseline Surface Radiation Network. The modeled data showed good correlation with the ground measurement, and the overall uncertainty is less than 35% for all validation locations. Because of the satellite viewing geometry, the Indian and European locations demonstrated higher uncertainties than the African locations, where the Meteosat-8 satellite has a more nadir viewing geometry. Further, instruction on how to access the data is provided.

14 SOLAR ENERGY↗
DOE OSTI2021

Lessons from the COVID Era and Visions for the Future

In December 2020, the U.S. Department of Energy Office of Science convened a virtual Roundtable of its 27 operating scientific user facilities to discuss facility challenges and lessons learned during the COVID-19 pandemic as well as facility responses, best practices, and innovations that could be adopted going forward. Roundtable participants included facility staff, users, and user executive committee chairs. This report summarizes their discussions, which encompassed topics such as user research and facility operations in virtual and physically distanced contexts; user training and engagement; computation, data, and network resources; and crosscutting issues.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗