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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Path-Based Dictionary Augmentation: A Framework for Improving $k$ -Sparse Image Processing

In this study, we have previously shown that augmenting orthogonal matching pursuit (OMP) with an additional step in the identification stage of each pursuit iteration yields improved $k$ -sparse reconstruction and denoising performance relative to baseline OMP. At each iteration a “path” or geodesic, is generated between the two dictionary atoms that are most correlated with the residual and from this path a new atom that has a greater correlation to the residual than either of the two bracketing atoms is selected. Here, we provide new computational results illustrating improvements in sparse coding and denoising on canonical datasets using both learned and structured dictionaries. The two methods of constructing a path are investigated for each dictionary type: the Euclidean geodesic formed by a linear combination of the two atoms and the 2-Wasserstein geodesic corresponding to the optimal transport map between the atoms. We prove here the existence of a higher-correlation atom in the Euclidean case under assumptions on the two bracketing atoms and introduce algorithmic modifications to improve the likelihood that the bracketing atoms meet those conditions. Although, we demonstrate our augmentation on OMP alone, in general it may be applied to any reconstruction algorithm that relies on the selection and sorting of high-similarity atoms during an analysis or identification phase.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

$\mathrm {K_S}^{0}$- and (anti-)$\Lambda $-hadron correlations in $pp$ collisions at ${\sqrt{s}} = 13$ TeV

Two-particle Azimuthal correlations are measured with the ALICE apparatus in pp collisions at √s = 13 TeV to explore strangeness- and multiplicity-related effects in the fragmentation of jets and the transition regime between bulk and hard production, probed with the condition that a strange meson (KS o ) or baryon (Λ) with transverse momentum p T > 3 GeV/c is produced. Azimuthal correlations between kaons or Λ hyperons with other hadrons are presented at midrapidity for a broad range of the trigger (3 < $p$$^{trigg}_{T}$ < 20 GeV/c) and associated particle p T (1 GeV/c < $p$$^{assoc}_{T}$< $p$$^{trigg}_{T}$), for minimum-bias events and as a function of the event multiplicity. The near- and away-side peak yields are compared for the case of either K S o or Λ(Λ¯) being the trigger particle with that of inclusive hadrons (a sample dominated by pions). In addition, the measurements are compared with predictions from PYTHIA 8 and EPOS LHC event generators.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Evaluation of the Volatile Species Associated with the K-Area Complex Blend Cans (D5043)

In the K-Area Complex (KAC), blend cans are used to down blend plutonium oxide for future disposal at the Waste Isolation Pilot Plant (WIPP). The blend can is a thin walled screw lid container that is used to down blend a measured amount of plutonium oxide with adulterant to reduce the attractiveness of the plutonium. The oxide and adulterant are mixed within the blend can and the blend can is packaged into a thick walled outer container. The blend cans are procured from an offsite vendor that uses a lubricant in the can manufacturing process. A thin coating of the lubricant adheres to the outer surface of the blend can and both inner and outer surfaces of the blend can cap.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Intrahost SARS-CoV-2 k-mer Identification Method (iSKIM) for Rapid Detection of Mutations of Concern Reveals Emergence of Global Mutation Patterns

Despite unprecedented global sequencing and surveillance of SARS-CoV-2, timely identification of the emergence and spread of novel variants of concern (VoCs) remains a challenge. Several million raw genome sequencing runs are now publicly available. We sought to survey these datasets for intrahost variation to study emerging mutations of concern. We developed iSKIM (“intrahost SARS-CoV-2 k-mer identification method”) to relatively quickly and efficiently screen the many SARS-CoV-2 datasets to identify intrahost mutations belonging to lineages of concern. Certain mutations surged in frequency as intrahost minor variants just prior to, or while lineages of concern arose. The Spike N501Y change common to several VoCs was found as a minor variant in 834 samples as early as October 2020. This coincides with the timing of the first detected samples with this mutation in the Alpha/B.1.1.7 and Beta/B.1.351 lineages. Using iSKIM, we also found that Spike L452R was detected as an intrahost minor variant as early as September 2020, prior to the observed rise of the Epsilon/B.1.429/B.1.427 lineages in late 2020. iSKIM rapidly screens for mutations of interest in raw data, prior to genome assembly, and can be used to detect increases in intrahost variants, potentially providing an early indication of novel variant spread.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

MOA-2007-BLG-400 A Super-Jupiter-mass Planet Orbiting a Galactic Bulge K-dwarf Revealed by Keck Adaptive Optics Imaging

We present Keck/NIRC2 adaptive optics imaging of planetary microlensing event MOA-2007-BLG-400 that resolves the lens star system from the source. We find that the MOA-2007-BLG-400L planetary system consists of a 1.71 ± 0.27M {sub Jup} planet orbiting a 0.69 ± 0.04M {sub ⊙} K-dwarf host star at a distance of 6.89 ± 0.77 kpc from the Sun. So, this planetary system probably resides in the Galactic bulge. The planet–host star projected separation is only weakly constrained due to the close-wide light-curve degeneracy; the 2σ projected separation ranges are 0.6–1.0 au and 4.7–7.7 au for close and wide solutions, respectively. This host mass is at the top end of the range of masses predicted by a standard Bayesian analysis. Our Keck follow-up program has now measured lens-source separations for six planetary microlensing events, and five of these six events have host star masses above the median prediction under the assumption that assumes that all stars have an equal chance of hosting planets detectable by microlensing. This suggests that more massive stars may be more likely to host planets of a fixed mass ratio that orbit near or beyond the snow line. These results also indicate the importance of host star mass measurements for exoplanets found by microlensing. The microlensing survey imaging data from NASA’s Nancy Grace Roman Space Telescope (formerly WFIRST) mission will be doing mass measurements like this for a huge number of planetary events.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Plowing Through the Cost Barrier: Zero Energy K-12 Schools for Less: Preprint

There is a perception that zero energy K–12 schools cost more than conventional schools. Zero energy schools provide a number of unique benefits to school districts, students and staff, and communities. Among other things, a zero energy school requires far less energy to operate than a conventional school and uses on-site renewable energy systems to offset that reduced energy load. The money saved on energy can often be used to enhance educational programs. But does the extra value come at a premium? Research findings indicate that not only can zero energy schools be designed and built on conventional school budgets, they can cost less. In an integrated design and construction process, the cost of zero energy measures can be offset by, for example, downsizing heating, ventilating, and air-conditioning systems, reducing both life cycle and first costs. These findings are based on an examination of 88 zero energy or zero energy ready schools across the United States built during the last 15 years. The data collected on these schools include capital costs and the experiences of owners and design teams. The goal was to understand the perceived cost barrier, given that each building was built with a predetermined budget. The results will help future school stakeholders, program administrators, and design teams counter the perceived cost barriers. Successful strategies for achieving zero energy at no initial cost are presented. The lessons learned from existing zero energy schools can help transform the market such that all new schools can be zero energy.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2023

Sensor Anomaly Detection for Nuclear Reactor Systems Utilizing Linear Regression and K-Means Unsupervised Machine Learning

Nuclear reactors and related systems are becoming increasingly complex due to advancing technologies in next-generation power reactors. This increased complexity necessitates enhanced automation and data management capabilities. To successfully realize autonomous systems, methods must be developed to handle vast volumes of data and effectively distinguish anomalous data from noise and expected data. While impressive models utilizing digital twins and similar approaches are under development, here we propose a simplified model for analyzing fundamental methods and techniques. Initially, we created a general dataset by using initial data from PCTRAN in order to represent ideal steady-state conditions. We then inserted anomalies based on prevalent sensor anomaly types (e.g., point anomalies, linear drift, and downward deviations), along with unusual anomalies such as exponential drift and upward deviations. To detect anomalies, we developed a program that employs data partitioning and linear regression to preprocess and filter the anomalous data. A K-Means machine learning (ML) method was then applied to separate and count the data within the anomalous partition. The results from all datasets—apart from exponential growth—demonstrated positive outcomes, with each returning multiple instances of greaterthan-95% accuracy. We conducted further investigations using Idaho National Laboratory’s RAVEN software to perform a sensitivity analysis on the input variables (R 2 Tolerance, Slope Tolerance, and Window Size) and found that the output variables (Accuracy and Time) were most sensitive to the Window Size. Despite the promising results published, further development is required to effectively apply these methods to nuclear systems. Nevertheless, the strengths of this approach are evident and hold promise for future applications in the field.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Discovering the Most Severe K-Point Failure Based on Reinforcement Learning: Preprint

Smart devices are essential to ensure the stability of the power grid and resilience to intermittent energy production. However, smart devices can also be the target of cyber adversaries that may exploit false data injection attacks (FDIAs) to induce unstable grid conditions. A practical consideration of FDIA mitigation approaches is addressed here: given a finite available budget, for which smart device should cyber-threat mitigation be deployed first? In this work, this question is answered by identifying the so-called most-sensitive devices, i.e., the devices that, if compromised, can let an adversary induce the most serious grid instabilities. The method proposed utilizes an adversarial reinforcement learning (RL) framework to identify the k-mostsensitive smart devices (here, smart inverters). The adversarial agent can tamper with the compromised inverters' active and reactive operating power setup points, with the goal of maximizing voltage deviations. Numerical results show that the proposed RL method finds the optimal attack scenarios for 1-point failure and the near-optimal solution for the 2-point case. Additionally, the proposed RL method achieves an 8.8 speed-up ratio in running time compared to the brute force method for the 2-point case.

97 MATHEMATICS AND COMPUTING↗