Hydrogen Permeation and Absorption Tool (HyPAT v2.0): An update to facilitate analyses of data from hydrogen absorption experiments
Not Available
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
SEARCH · Engineering Papers
Results for “data fusion”
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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Search for the associated production of the Higgs boson and a vector boson in proton-proton collisions at $ \sqrt{s} $ = 13 TeV via Higgs boson decays to τ leptons
A search for the standard model Higgs boson, decaying to a pair of $\tau$ leptons and produced in association with a W or a Z boson is performed. A data sample of proton-proton collisions collected at $\sqrt{s} =$ 13 TeV by the CMS experiment at the CERN LHC is used, corresponding to an integrated luminosity of 35.9 fb$^{-1}$. The signal strength is measured relative to the expectation for the standard model Higgs boson, yielding $\mu =$ 2.5$^{+1.4} _{-1.3}$. These results are combined with earlier CMS measurements targeting Higgs boson decays to a pair of $\tau$ leptons, performed with the same data set in the gluon fusion and vector boson fusion production modes. The combined signal strength is $\mu =$ 1.24$ ^{+0.29} _{-0.27}$ (1.00$^{+0.24} _{-0.23}$ expected), and the observed significance is 5.5 standard deviations (4.8 expected) for a Higgs boson mass of 125 GeV.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Maternal exposure to ozone and PM{sub 2.5} and the prevalence of orofacial clefts in four U.S. states
Background: While there is some evidence that maternal exposure to ambient air pollution is associated with orofacial clefts in offspring, the epidemiologic studies have been largely equivocal. We evaluated whether maternal exposure to elevated county-level ambient fine particulate matter with aerodynamic diameter ≤2.5 µm (PM{sub 2.5}) and ozone during early gestation was associated with a higher prevalence of orofacial clefts. Methods: Birth data consisting of 4.7 million births from 2001 to 2007 were obtained from National Birth Defects Prevention Network for four states — Arizona, Florida, New York (excluding New York City), and Texas. The air pollution exposure assessment for gestational weeks 5–10 was based on county-level average concentrations of PM{sub 2.5} and ozone data generated using a Bayesian fusion model available through CDC's Environmental Public Health Tracking Network. Two outcomes were analyzed separately: cleft lip with or without cleft palate, cleft palate alone. In logistic regression analyses, we adjusted for factors that were suspected confounders or modifiers of the association between the prevalence of orofacial clefts and air pollution, i.e., infant sex, race-ethnicity, maternal education, smoking status during pregnancy, whether this was mother's first baby, maternal age. Results: Each 10 µg/m{sup 3} increase in PM{sub 2.5} concentration was significantly associated with cleft palate alone (OR =1.43, 95% CI: 1.11–1.86). There was no significant association between PM{sub 2.5} concentration and cleft lip with or without cleft palate. No associations were observed between ozone exposure and the two outcomes of orofacial clefts. Conclusions: Our study suggests that PM{sub 2.5} significantly increased the risk of cleft palate alone, but did not change the incidence of cleft lip with or without palate. Ozone levels did not correlate with incidence of orofacial clefts. - Highlights: • This is a large study with about 4.7 million births and 7000 orofacial cleft cases. • PM{sub 2.5} significantly increased the risk of cleft palate alone. • PM{sub 2.5} did not change the incidence of cleft lip with or without palate. • Ozone levels did not correlate with incidence of orofacial clefts.
54 ENVIRONMENTAL SCIENCES↗
Ab initio calculations of reactions of light nuclei
An ab initio (i.e., from first principles) theoretical framework capable of providing a unified description of the structure and low-energy reaction properties of light nuclei is desirable as a support tool for accurate evaluations of crucial reaction data for nuclear astrophysics, fusion-energy research, and other applications. We present an efficient many-body approach to nuclear bound and scattering states alike, known as the ab initio no-core shell model with continuum. In this approach, square-integrable energy eigenstates of the A-nucleon system are coupled to (A-A)+A target-plus-projectile wave functions in the spirit of the resonating group method to obtain an efficient description of the many-body nuclear dynamics both at short and medium distances and at long ranges. We show that predictive results for nucleon and deuterium scattering on 4 He nuclei can be obtained from the direct solution of the Schröedinger equation with modern nuclear potentials.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
High resolution >40 keV x-ray radiography using an edge-on micro-flag backlighter at NIF-ARC
Radiography of low-contrast features in high-density materials evolving on a nanosecond timescale requires a bright photon source in the tens of keV range with high temporal and spatial resolution. One application for sources in this category is the study of dynamic material strength in samples compressed to Mbar pressures at the National Ignition Facility, high-resolution measurements of plastic deformation under conditions relevant to meteor impacts, geophysics, armor development, and inertial confinement fusion. Here, we present radiographic data and the modulation transfer function (MTF) analysis of a multi-component test object probed at ~100 keV effective backlighter energy using a 5 μm-thin dysprosium foil driven by the NIF Advanced Radiographic Capability (ARC) short-pulse laser (~2 kJ, 10 ps). The thin edge of the foil acts as a bright line-projection source of hard x rays, which images the test object at 13.2× magnification into a filtered and shielded image plate detector stack. The system demonstrates a superior contrast of shallow (5 μm amplitude) sinusoidal ripples on gold samples up to 90 μm thick as well as enhanced spatial and temporal resolution using only a small fraction of the laser energy compared to an existing long-pulse-driven backlighter used routinely at the NIF for dynamic strength experiments.
47 OTHER INSTRUMENTATION↗
Printed Targets with Micron-Scale Feature Patterns for the Study of Ablator Defects on OMEGA
As per present models, laser imprint and implosion symmetry are insufficient to account for observed performance degradation of direct-drive cryogenic fusion implosions. More and better data are needed on ablator defects as a source of hydrodynamic instability and mix. To investigate this, a series of OMEGA experimental campaigns is underway to study isolated target defects. Key requirements are systematic variation of the laser intensity and pulse shape at shot, with highly controlled defect type, geometry, and location. Here, given the need for sub-micron resolution and precise registration of multiple features, two-photon polymerization (TPP) printing was identified as an ideal method to fabricate these targets. TPP printing has enabled controlled formation of designed domes, divots, and vacuoles for studying the combined effect of size and proximity of these features on the hydro performance.
Two-photon polymerization printing↗
Challenges and Opportunities in Deep Reinforcement Learning With Graph Neural Networks: A Comprehensive Review of Algorithms and Applications
Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields, including pattern recognition, robotics, recommendation-systems, and gaming. Similarly, graph neural networks (GNN) have also demonstrated their superior performance in supervised learning for graph-structured data. In recent times, the fusion of GNN with DRL for graph-structured environments has attracted a lot of attention. Here, this paper provides a comprehensive review of these hybrid works. These works can be classified into two categories: (1) algorithmic enhancement, where DRL and GNN complement each other for better utility; (2) application-specific enhancement, where DRL and GNN support each other. This fusion effectively addresses various complex problems in engineering and life sciences. Based on the review, we further analyze the applicability and benefits of fusing these two domains, especially in terms of increasing generalizability and reducing computational complexity. Finally, the key challenges in integrating DRL and GNN, and potential future research directions are highlighted, which will be of interest to the broader machine learning community.
97 MATHEMATICS AND COMPUTING↗
SU-E-T-589: Optimization of Patient Head Angle Position to Spare Hippocampus During the Brain Radiation Therapy
Purpose: Hippocampus is one of the important organs which controls emotions, behaviors, movements the memorizing and learning ability. In the conventional head & neck therapy position, it is difficult to perform the hippocampal-sparing brain radiation therapy. The purpose of this study is to investigate optimal head angle which can save the hippocampal-sparing and organ at risk (OAR) in conformal radiation therapy (CRT), Intensity modulation radiation therapy (IMRT) and helical tomotherapy (HT). Methods: Three types of radiation treatment plans, CRT, IMRT and Tomotherapy plans, were performed for 10 brain tumor patients. The image fusion between CT and MRI data were used in the contour due to the limited delineation of the target and OAR in the CT scan. The optimal condition plan was determined by comparing the dosimetric performance of the each plan with the use of various parameters which include three different techniques (CRT, IMRT, HT) and 4 angle (0, 15, 30, 40 degree). The each treatment plans of three different techniques were compared with the following parameters: conformity index (CI), homogeneity index (HI), target coverage, dose in the OARs, monitor units (MU), beam on time and the normal tissue complication probability (NTCP). Results: HI, CI and target coverage was most excellent in head angle 30 degree among all angle. When compared by modality, target coverage and CI showed good results in IMRT and TOMO than compared to the CRT. HI at the head angle 0 degrees is 1.137±0.17 (CRT), 1.085±0.09 (IMRT) and 1.077±0.06 (HT). HI at the head angle 30 degrees is 1.056±0.08 (CRT), 1.020±0.05 (IMRT) and 1.022±0.07 (HT). Conclusion: The results of our study show that when head angle tilted at 30 degree, target coverage, HI, CI were improved, and the dose delivered to OAR was reduced compared with conventional supine position in brain radiation therapy. This work was supported by the Radiation Technology R&D program (No. 2013M2A2A7043498) and the Mid-career Researcher Program (2014R1A2A1A10050270) through the National Research Foundation of Korea funded by the Ministry of Science, ICT&Future Planning.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Final Report for SERDP Project RC-1649: Advanced Chemical Measurements of Smoke from DoD-prescribed Burns
Objectives: Project RC-1649, “Advanced Chemical Measurement of Smoke from DoD-prescribed Burns” was undertaken to use advanced instrumental techniques to study in detail the particulate and vapor-phase chemical composition of the smoke that results from prescribed fires used as a land management tool on DoD bases, particularly bases in the southeastern U.S. The statement of need (SON) called for “(1) improving characterization of fuel consumption” and “(2) improving characterization of air emissions under both flaming and smoldering conditions with respect to volatile organic compounds, heavy metals, and reactive gases.” The measurements and fuels were from several bases throughout the southeast (Camp Lejeune, Ft. Benning, and Ft. Jackson) and were carried out in collaboration and conjunction with projects 1647 (models) and 1648 (particulates, SW bases). Technical Approach: We used an approach that featured developing techniques for measuring biomass burning emission species in both the laboratory and field and developing infrared (IR) spectroscopy in particular. Using IR spectroscopy and other methods, we developed emission factors (EF, g of effluent per kg of fuel burned) for dozens of chemical species for several common southeastern fuel types. The major measurement campaigns were laboratory studies at the Missoula Fire Sciences Laboratory (FSL) as well as field campaigns at Camp Lejeune, NC, Ft. Jackson, SC, and in conjunction with 1648 at Vandenberg AFB, and Ft. Huachuca. Comparisons and fusions of laboratory and field data were also carried out, using laboratory fuels from the same bases. Results: The project enabled new technologies and furthered basic science, mostly in the area of infrared spectroscopy, a broadband method well suited to biomass burn studies. Advances in hardware, software and supporting reference data realized a nearly 20x improvement in sensitivity and now provide quantitative IR spectra for potential detection of ~60 new species and actual field quantification of several new species such as nitrous acid, glycolaldehyde, α-/β-pinene and D-limonene. The new reference data also permit calculation of the global warming potential (GWP) of the greenhouse gases by enabling 1) detection of their ambient concentrations, and 2) quantifying their ability to absorb IR radiation.
47 OTHER INSTRUMENTATION↗
Requirements Document for the Redesign of the DIM-based Diagnostics Transport and Handling System at the National Ignition Facility (NIF)
The National Ignition Facility (NIF, Figure 1) is the world’s leading user research facility for inertial confinement fusion. Diagnostic instruments that collect data from the experiment shots are transported around the facility using a series of disjointed, suboptimal transport and handling (T&H) subsystems that compromise user efficiency and ergonomics. Significant time savings and improvements to the diagnostic exchange process could be realized by designing a new transport and handling system that minimizes the hand-offs between T&H subsystems, reduces operator hassle and ergonomic strain, and improves the long-term maintainability of the system. In contrast to the current system – which employs several manually-operated carts and containers to transport the diagnostic, dock it to its destination, and install it – the new diagnostic transporter integrates the contamination container with the transport cart and provides a dynamic range of adjustability to interface with its destination. This single transporter has the requisite maneuverability, height adjustability, and fine adjustment capabilities without any rigging transactions to support contaminated diagnostic transport and handling at the alignment station and Equatorial and Polar DIMs. A remote-controlled user interface device allows operators a more hands-off experience that reduces ergonomic strain and improves safety. Assuming the cost is not prohibitive, diagnostic storage will be accomplished by simply stowing the transporter in its lowest position in the designated area of NIF. This concept for a new diagnostic transport and handling system would be better aligned with the core values of NIF: safety, quality, and reliability.
42 ENGINEERING↗
FY24 Integrated Results for High-Temperature Mechanical Testing of LPBF 316H Stainless Steel
This report provides the mechanical test data of laser powder bed fusion (LPBF) 316H stainless steel (SS) collected during Fiscal Year 2024 (FY 24) under the US Department of Energy, Office of Nuclear Energy’s Advanced Materials and Manufacturing Technologies program, along with the current state of microstructural-based understandings of the behaviors. Materials with variabilities in manufacturing site, machine, laser parameters, powder chemistry, porosity, specimen geometry and heat treatment were tested in high-temperature tension, creep, fatigue and creep-fatigue. Electron microscopy and optical microscopy were performed on selected materials before and after the tests to provide microstructural-based understandings to the mechanical behavior. It was discovered that the as-built (AB) and stress-relieved (SR) materials exhibit similar behaviors in tension and creep, as do the solution-annealed (SA) and hot-isostatic pressed (HIP) materials. The AB and SR materials are softer but more ductile than the SA and HIP materials in tension. In creep, the LPBF materials have comparable rupture times but lower rupture strains compared to the wrought materials. The AB and SR materials have low creep rupture strains (<10%) when tested at 720°C and 800°C, while the SA and HIP materials are much more ductile. The LPBF materials exhibit large scatter in fatigue and creep-fatigue lives. The cyclic lives of creep-fatigue tests are lower than those of fatigue tests. In fatigue and creep-fatigue, SR materials outperform SA materials. A common observation from the cyclic tests is that, given the same post-build heat treatment, a lower initial peak stress generally results in a longer cyclic life regardless of hold time. It was also discovered that the batch variation can be more impactful than heat treatment, as demonstrated by the overall better performance of one batch of material than another, regardless of the heat treatment. The results provided insights into how different factors impact the behaviors of LPBF 316H SS. An outlook to FY 25 work scope is provided.
36 MATERIALS SCIENCE↗
FuSED Users Manual, 5.24
The Fusion of Simulation, Experiment, and Data (FuSED) team provides a set of tools for solving inverse problems in structural dynamics and thermal physics, and also sensor placement optimization via Optimal Experimental Design (OED). These methods are used for designing experiments, model calibration, and verification/validation analysis of systems. This document provides a user’s guide to the input for the three apps that are supported for these methods. Details of input specifications, output options, and optimization parameters are included.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
FuSED – Users Manual – (V.5.26)
The Fusion of Simulation, Experiment, and Data (FuSED) team provides a set of tools for solving inverse problems in structural dynamics (InverseSD) and thermal physics (InverseAria), a sensor placement optimization tool via Optimal Experimental Design (OED), and a decision boundary tool using SVMs (TRACE). These methods are used for designing experiments, model calibration, and verification/validation analysis of systems. This document provides a user’s guide.
97 MATHEMATICS AND COMPUTING↗
WHISPER: Wireless Home Identification and Sensing Platform for Energy Reduction
Many regions of the world benefit from heating, ventilating, and air-conditioning (HVAC) systems to provide productive, comfortable, and healthy indoor environments, which are enabled by automatic building controls. Due to climate change, population growth, and industrialization, HVAC use is globally on the rise. Unfortunately, these systems often operate in a continuous fashion without regard to actual human presence, leading to unnecessary energy consumption. As a result, the heating, ventilation, and cooling of unoccupied building spaces makes a substantial contribution to the harmful environmental impacts associated with carbon-based electric power generation, which is important to remedy. For our modern electric power system, transitioning to low-carbon renewable energy is facilitated by integration with distributed energy resources. Automatic engagement between the grid and consumers will be necessary to enable a clean yet stable electric grid, when integrating these variable and uncertain renewable energy sources. We present the WHISPER (Wireless Home Identification and Sensing Platform for Energy Reduction) system to address the energy and power demand triggered by human presence in homes. The presented system includes a maintenance-free and privacy-preserving human occupancy detection system wherein a local wireless network of battery-free environmental, acoustic energy, and image sensors are deployed to monitor homes, record empirical data for a range of monitored modalities, and transmit it to a base station. Several machine learning algorithms are implemented at the base station to infer human presence based on the received data, harnessing a hierarchical sensor fusion algorithm. Results from the prototype system demonstrate an accuracy in human presence detection in excess of 95%; ongoing commercialization efforts suggest approximately 99% accuracy. Using machine learning, WHISPER enables various applications based on its binary occupancy prediction, allowing situation-specific controls targeted at both personalized smart home and electric grid modernization opportunities.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Methods and decision making on a Mars rover for identification of fossils
A system for automated fusion and interpretation of image data from multiple sensors, including multispectral data from an imaging spectrometer is being developed. Classical artificial intelligence techniques and artificial neural networks are employed to make real time decision based on current input and known scientific goals. Emphasis is placed on identifying minerals which could indicate past life activity or an environment supportive of life. Multispectral data can be used for geological analysis because different minerals have characteristic spectral reflectance in the visible and near infrared range. Classification of each spectrum into a broad class, based on overall spectral shape and locations of absorption bands is possible in real time using artificial neural networks. The goal of the system is twofold: multisensor and multispectral data must be interpreted in real time so that potentially interesting sites can be flagged and investigated in more detail while the rover is near those sites; and the sensed data must be reduced to the most compact form possible without loss of crucial information. Autonomous decision making will allow a rover to achieve maximum scientific benefit from a mission. Both a classical rule based approach and a decision neural network for making real time choices are being considered. Neural nets may work well for adaptive decision making. A neural net can be trained to work in two steps. First, the actual input state is mapped to the closest of a number of memorized states. After weighing the importance of various input parameters, the net produces an output decision based on the matched memory state. Real time, autonomous image data analysis and decision making capabilities are required for achieving maximum scientific benefit from a rover mission. The system under development will enhance the chances of identifying fossils or environments capable of supporting life on Mars
Eberlein, Susan↗
DEB: A Diagnostic Experience Browser using similarity networks
Diagnostic Experience Browser (DEB): a fusion of knowledge base and data base that allows users to examine only the data which is most useful to them is described. The system combines a data base of historical cases of diagnostic trouble-shooting experience with similarity networks. A menu-driven natural language interface receives input about the user's current problem. Similarity networks provide the user with references to past cases that are most similar or most related to those they now face. The user can then choose the case that is most pertinent and browse its full textual description which, in turn, may include references to other related cases.
Casadaban, Cyprian E.↗
Learning and diagnosing faults using neural networks
Neural networks have been employed for learning fault behavior from rocket engine simulator parameters and for diagnosing faults on the basis of the learned behavior. Two problems in applying neural networks to learning and diagnosing faults are (1) the complexity of the sensor data to fault mapping to be modeled by the neural network, which implies difficult and lengthy training procedures; and (2) the lack of sufficient training data to adequately represent the very large number of different types of faults which might occur. Methods are derived and tested in an architecture which addresses these two problems. First, the sensor data to fault mapping is decomposed into three simpler mappings which perform sensor data compression, hypothesis generation, and sensor fusion. Efficient training is performed for each mapping separately. Secondly, the neural network which performs sensor fusion is structured to detect new unknown faults for which training examples were not presented during training. These methods were tested on a task of fault diagnosis by employing rocket engine simulator data. Results indicate that the decomposed neural network architecture can be trained efficiently, can identify faults for which it has been trained, and can detect the occurrence of faults for which it has not been trained.
Whitehead, Bruce A.↗
Beryllium–tungsten graded density inner shells in double shell capsules for improved hydrodynamic stability
The outer surface of the high-Z inner shell in the double shell configuration of inertial confinement fusion experiments experiences Rayleigh–Taylor instability growth during the implosion process due to inverted density and pressure gradients between a highly compressed foam interstitial layer and the accelerating dense inner shell. Graded density layers have long been known to reduce instability growth rates. In this study, we employ high-fidelity radiation hydrodynamic simulations to demonstrate this improved stability when grading beryllium into tungsten. We first characterize the response to L-band preheat of these layers using a newly calibrated radiation drive. While graded layer capsules suffer reduced performance (here, measured as DD neutron yield from a CD foam fuel) in 1D simulations due to reduced kinetic energy coupling and reduced fuel compression, they suffer less of a performance drop when 2D instabilities are accounted for. With the improved stability of graded layers, we explore the performance of capsules with larger fuel radii and thinner shells as a preliminary study to find new designs in which graded layers produce the highest yields.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗