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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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A low-energy spectrum of gamma-rays following muon capture in O-16 was obtained with a high-resolution 1 cu cm Ge(Li) detector. The graph of the spectrum shows the separation of contaminant peaks from the 120 keV N-16 line. It is pointed out that there is a large discrepancy between the capture rate obtained in the investigation and results reported by Deutsch et al. (1969).
The Flat Surface Damage Detection system (FSDDS} is a sensory system that is capable of detecting impact damages to surfaces utilizing a novel sensor system. This system will provide the ability to monitor the integrity of an inflatable habitat during in situ system health monitoring. The system consists of three main custom designed subsystems: the multi-layer sensing panel, the embedded monitoring system, and the graphical user interface (GUI). The GUI LABVIEW software uses a custom developed damage detection algorithm to determine the damage location based on the sequence of broken sensing lines. It estimates the damage size, the maximum depth, and plots the damage location on a graph. Successfully demonstrated as a stand alone technology during 2011 D-RATS. Software modification also allowed for communication with HDU avionics crew display which was demonstrated remotely (KSC to JSC} during 2012 integration testing. Integrated FSDDS system and stand alone multi-panel systems were demonstrated remotely and at JSC, Mission Operations Test using Space Network Research Federation (SNRF} network in 2012. FY13, FSDDS multi-panel integration with JSC and SNRF network Technology can allow for integration with other complementary damage detection systems.
The design concept of a control for rapid torque-limited slewing of a rigid-mast version of the NASA SCOLE configuration is presented and demonstrated by means of numerical simulation. The time-optimal control problem for the system is decomposed into separate single-axis problems, expanding analytically the implicit nonlinear transcendental expression for the SCOLE line-of-sight error, and the final Euler attitude angles and slew angles are determined. The simulation results are presented in tables and graphs, and it is found that bang-bang or bang-pause-bang slew maneuvers with control moment applied to the Shuttle and control force applied to the reflector, and with a 5-deg/s slew-rate limit, produce the best pointing accuracy and the shortest slew times, although the specified line-of-sight error of 0.02 deg cannot be achieved using such open-loop single-axis maneuvers.
The range of validity of the average absolute extinction law (AAEL) proposed by Cardelli et al. (1988 and 1989) is investigated, combining published visible and NIR data with IUE UV observations for three lines of sight through dense dark cloud environments with high values of total-to-selective extinction. The characteristics of the data sets and the reduction and parameterization methods applied are described in detail, and the results are presented in extensive tables and graphs. Good agreement with the AAEL is demonstrated for wavelengths from 3.4 microns to 250 nm, but significant deviations are found at shorter wavelengths (where previous studies of lines of sight through bright nebulosity found good agreement with the AAEL). These differences are attributed to the effects of coatings on small-bump and FUV grains.
Observations in lambda 5007 which reveal an ellipse of line emission around SN 1987A are reviewed. A simple expression for the rate at which the kinematically accessible paraboloid sweeps over the length of a thin uniform circular ring is derived, and a simple integral expression for the light curve F(t) of a fluorescent line in terms of epsilon(t), the local line emissivity, is presented. Many simple examples of the relationship between epsilon(t) and F(t) are graphed, and the results are used to deduce the characteristics of the local emissivity functions which produce the best fit to the observed semiforbidden N III 1750 and N V 1240 light curves. The N III and N V light curves are inverted to find the functions epsilon(t) which give best fits to the observations.
The chemical properties of oils are vital in the design of microemulsion systems. The hydrophilic–lipophilic difference equation used to predict microemulsions’ phase behavior expresses the oils’ physiochemical properties as the equivalent alkane carbon number (EACN). The experimental determination of EACN requires knowledge of the temperature dependence of the microemulsion system and the effects of different surfactant concentrations. Thus, the experimental determination is time-intensive and tedious, requiring days to months for proper separations. Furthermore, the experiments require high purity of chemicals because microemulsions are sensitive to impurities. Our work focuses on the quick and reliable predictions of the EACN with machine learning (ML) models. Due to the immaturity of ML chemical predictions, we compare three graph neural networks (GNNs) and a gradient-boosted tree algorithm, known as XGBoost. The GNNs use the molecular structures represented as simplified molecular-input line-entry system (SMILES) codes for the initial input, which allows us to assess whether geometry optimization is necessary for reliable results. The XGBoost model also begins with the SMILES representations of the molecules but uses molecular descriptors instead of geometry optimizations. As a result, the best model tested (crystal graph convolutional neural network with Merck molecular force field-94) has an error of 1.15 EACN units of the true EACN for unknown data with the errors skewed toward zero and an R² score of 0.9
Abstract The Stiff Stalk heterotic pool is a foundation of US maize seed parent germplasm and has been heavily utilized by both public and private maize breeders since its inception in the 1930s. Flowering time and plant height are critical characteristics for both inbred parents and their test crossed hybrid progeny. To study these traits, a 6-parent multiparent advanced generation intercross population was developed including maize inbred lines B73, B84, PHB47 (B37 type), LH145 (B14 type), PHJ40 (novel early Stiff Stalk), and NKH8431 (B73/B14 type). A set of 779 doubled haploid lines were evaluated for flowering time and plant height in 2 field replicates in 2016 and 2017, and a subset of 689 and 561 doubled haploid lines were crossed to 2 testers, respectively, and evaluated as hybrids in 2 locations in 2018 and 2019 using an incomplete block design. Markers were derived from a practical haplotype graph built from the founder whole genome assemblies and genotype-by-sequencing and exome capture-based sequencing of the population. Genetic mapping utilizing an update to R/qtl2 revealed differing profiles of significant loci for both traits between 635 of the DH lines and 2 sets of 570 and 471 derived hybrids. Genomic prediction was used to test the feasibility of predicting hybrid phenotypes based on the per se data. Predictive abilities were highest on direct models trained using the data they would predict (0.55–0.63), and indirect models trained using per se data to predict hybrid traits had slightly lower predictive abilities (0.49–0.55). Overall, this finding is consistent with the overlapping and nonoverlapping significant quantitative trait loci found within the per se and hybrid populations and suggests that selections for phenology traits can be made effectively on doubled haploid lines before hybrid data is available.
Dynamical Lie algebras (DLAs) are a versatile tool for various topics that span from the expressibility-trainability of variational quantum algorithms (VQAs), to simulation of many body Hamiltonians. Quantum gates and most of the Hamiltonians of interest consist of local interactions; therefore, the analysis of all possible DLAs generated by 1- and 2-local operators is crucial for quantum simulation and VQAs on current hardware. Previously in [R. Wiersema et al ., npj Quantum Inf. 10 , 110 (2024)], we analyzed the DLAs on linear, circular and all-to-all topologies, and obtained results about their dimensions and algebraic structure. Here, in this work, we extend our analysis into any possible hardware topology and provide a classification of all DLAs generated by Pauli strings on any undirected interaction graph. Our results indicate that the DLAs depend solely on whether the connectivity or interaction graph is bipartite or not. In addition, we find that the non-trivial polynomially scaling DLAs appear only on 1D line or circle topologies, and all other DLAs have dimensions scaling exponentially with the system size. Together with the current VQA literature, our results imply that either the majority of VQAs are non-trainable, or we are yet to understand the role of DLAs on the trainability of VQAs.
The dispersion calibration of spectroscopic velocity measurements made with the 150-ft tower telescope at Mt. Wilson Observatory is revised upward by 0.55 percent on the basis of observations of the six lines of comparable shape and equivalent width nearest the 5250.2-A line of Fe-I used in the solar Doppler rate measurements. The dispersion results are presented in a graph, and the superiority of the Kitt Peak wavelength tables (Pierce and Breckenridge, 1973) over those of Moore et al. (1966) is demonstrated. As a result of the recalibration, all recent spectroscopic velocities from this telescope must be revised downward by 0.55 percent.
Switches, electrical components that either permit or prevent the flow of electricity, are the most important and widely used electrical devices in integrated circuits. In microwave systems, switches are required for switching between the transmitter and receiver; in communication systems, they are needed for phase shifters in phased-array antennas, for radar and communication systems, and for the new class of digital or software definable radios. Ideally, switches would be lossless devices that did not depend on the electrical signal's frequency or power, and they would not consume electrical power to change from OFF to ON or to maintain one of these two states. Reality is quite different, especially at microwave frequencies. Typical switches in microwave integrated circuits are pin diodes or gallium arsenide (GaAs) field-effect transistors that are nonlinear, with characteristics that depend on the power of the signal. In addition, they are frequency-dependent, lossy, and require electrical power to maintain a certain state. A new type of component has been developed that overcomes most of these technical difficulties. Microelectromechanical (MEMS) switches rely on mechanical movement as a response to an applied electrical force to either transmit or reflect electrical signal power. The NASA Glenn Research Center has been actively developing MEMS for microwave applications for over the last 5 years. Complete fabrication procedures have been developed so that the moving parts of the switch can be released with near 100-percent yield. Moreover, the switches fabricated at Glenn have demonstrated state-of-the-art performance. A typical MEMS switch is shown. The switch extends over the signal and ground lines of a finite ground coplanar waveguide, a commonly used microwave transmission line. In the state shown, the switch is in the UP state and all the microwave power traveling along the transmission line proceeds unimpeded. When a potential difference is applied between the cantilever and the transmission line, the cantilever is pulled downward until it connects the signal line to the ground planes, creating a short circuit. In this state, all the microwave power is reflected. The graph shows the measured performance of the switch, which has less than 0.1 dB of insertion loss and greater than 30dB of isolation. These switches consume negligible electrical power and are extremely linear. Additional research is required to address reliability and to increase the switching speed.
A facility was established that uses collected data and feeds it into mathematical models that generate improved data arrays by correcting for various losses, base line drift, and conversion to unity scaling. These developed data arrays have headers and other identifying information affixed and are subsequently stored in a Laser Materials and Characteristics data base which is accessible to various users. The two part data base: absorption - emission spectra and tabulated data, is developed around twelve laser models. The tabulated section of the data base is divided into several parts: crystalline, optical, mechanical, and thermal properties; aborption and emission spectra information; chemical name and formulas; and miscellaneous. A menu-driven, language-free graphing program will reduce and/or remove the requirement that users become competent FORTRAN programmers and the concomitant requirement that they also spend several days to a few weeks becoming conversant with the GEOGRAF library and sequence of calls and the continual refreshers of both. The work included becoming thoroughly conversant with or at least very familiar with GEOGRAF by GEOCOMP Corp. The development of the graphing program involved trial runs of the various callable library routines on dummy data in order to become familiar with actual implementation and sequencing. This was followed by trial runs with actual data base files and some additional data from current research that was not in the data base but currently needed graphs. After successful runs, with dummy and real data, using actual FORTRAN instructions steps were undertaken to develop the menu-driven language-free implementation of a program which would require the user only know how to use microcomputers. The user would simply be responding to items displayed on the video screen. To assist the user in arriving at the optimum values needed for a specific graph, a paper, and pencil check list was made available to use on the trial runs.
Global connectivity enables effective coordination in Multi-Agent Systems (MAS). Solving these connection problems under hardware constraints is an NP-hard non-Euclidean Degree Constrained Minimum Spanning Tree (DCMST) problem. Prior MAS controllers coordinate team movement for task completion and collision avoidance; some considering Line-of-Sight (LOS) maintenance but prioritizing flexibility over guarantees. Evolutionary Algorithms (EA) have been shown to find good solutions for DCMST, but their performance degrades with larger populations required to support a large MAS. We present a method based on edge graph attention networks, trained offline to reduce online computation times. Empirical comparisons with greedy polynomial-time solvers and EA show that our method leverages latent graph information to consistently find constraint-satisfying solutions in less time.
Abstract We present a Bayesian graph neural network (BGNN) that can estimate the weak lensing convergence ( κ ) from photometric measurements of galaxies along a given line of sight (LOS). The method is of particular interest in strong gravitational time-delay cosmography (TDC), where characterizing the “external convergence” ( κ ext ) from the lens environment and LOS is necessary for precise Hubble constant ( H 0 ) inference. Starting from a large-scale simulation with a κ resolution of ∼1′, we introduce fluctuations on galaxy–galaxy lensing scales of ∼1″ and extract random sight lines to train our BGNN. We then evaluate the model on test sets with varying degrees of overlap with the training distribution. For each test set of 1000 sight lines, the BGNN infers the individual κ posteriors, which we combine in a hierarchical Bayesian model to yield constraints on the hyperparameters governing the population. For a test field well sampled by the training set, the BGNN recovers the population mean of κ precisely and without bias (within the 2 σ credible interval), resulting in a contribution to the H 0 error budget well under 1%. In the tails of the training set with sparse samples, the BGNN, which can ingest all available information about each sight line, extracts a stronger κ signal compared to a simplified version of the traditional method based on matching galaxy number counts, which is limited by sample variance. Our hierarchical inference pipeline using BGNNs promises to improve the κ ext characterization for precision TDC. The code is available as a public Python package, Node to Joy ⏬ .
A line plotting program has been developed using the DI-3000 graphics libraries and incorporates Grafmaker subroutines. The program allows multiple lines on a frame and multiple frames per run. Options such as automatic scaling, linear or single-cycle log graphs, and plot text such as titles, legends and axis labels are incorporated in the program. Greek and other fonts can be used in the plot text as well as upper and lower case text. Plot inputs are specified through a control file. The program also allows display of multiple independent data sets on a single graph.
Observations of premain-sequence (PMS) stars obtained with the IUE satellite since its launch in 1978 are reviewed. The major types of PMS stars are listed and briefly characterized: T Tau stars, Herbig Ae stars, FU Ori objects, weak-emission PMS stars, and Herbig-Haro objects. The IUE data for a catalog of PMS objects are then presented in a table, graphs, and sample spectra and described in detail. Topics addressed include general spectral profiles, the Mg II h and k lines, the FUV emission lines, the UV continuum, and the impact of the IUE on astrophysical models of PMS stars. The need for observations with higher spectral resolution and much greater sensitivity is indicated, and the ability of the Hubble Space Telescope High-Resolution Spectrograph and the proposed NASA/ESA Lyman FUV Spectroscopic Explorer to meet these requirements is considered.
The Hausdorff metric provides a way to measure the distance between nonempty compact sets in $\mathbb{R}^N$, from which we can build a geometry of sets. This geometry is very different than the standard Euclidean geometry and provides many interesting results. In this paper we focus on line segments in this geometry, where pairs of disjoint sets $A$ and $B$ satisfying certain distance conditions have the property that there are exactly $m$ different sets on the line segment $\overline{AB}$ at every distance from $A$, where $m$ can assume many values different than one. We provide new families of sets that generate previously unrecorded integer sequences via these values of $m$ by connecting the values of $m$ to the number of edge coverings of a graph corresponding to the sets $A$ and $B$.
This report presents a performance analysis of 75 photovoltaic systems based on PV system production data collected as part of a FEMP Federal PV Performance Assessment project combined with co-incident insolation, and ambient temperature to analyze how actual performance compares with a performance model. FEMP collaborated with 17 Federal agencies and sub-agencies to collect the information required to analyze the performance of each system. The systems represent a total capacity of 30,714 kW and range in size from 1 kW to 4,043 kW, with an average size of 410 kW, and were installed between 2011 and 2020. The data is analyzed for Key Performance Indicators, Availability, Performance Ratio and Energy Ratio by comparing the measured production data to model production data. The System Advisor Model (SAM) combines a description of the system (such as inverter capacity, de-rating for temperature, balance-of-system efficiency) with environmental parameters (coincident solar and temperature data) to calculate predicted performance. The performance metrics are calculated by lining up the measured production data with the model estimate on an hour-by-hour, day-by-day, or month-by-month basis (depending on the interval resolution of the production data). A report with system description, photo of the system, special assumptions made for the site, graph of measured production and model production, table of key performance indicators, and links to O&M resources that might improve performance was produced and delivered to site and agency staff with a short on-line briefing.
We study a novel geometric expansion for scattering amplitudes in the planar sector of $\mathcal{N}$ = 4 super Yang-Mills theory, in the context of the Amplituhedron which reproduces the all-loop integrand as a canonical differential form on the positive geometry. In a paper by Arkani-Hamed, Henn and one of the authors, it was shown that this result can be recast in terms of negative geometries with a certain hierarchy of loops (closed cycles) in the space of loop momenta, represented by lines in momentum twistor space. One can then calculate an all-loop order result in the approximation where only tree graphs in the space of all loops are considered. Furthermore, using differential equation methods, it is possible to calculate and resum integrated expressions and obtain strong coupling results. In this paper, we provide a more general framework for the ‘loops of loops’ expansion and outline a powerful method for the determination of differential forms for higher-order geometries. We solve the problem completely for graphs with one internal cycle, but the method can be used more generally for other geometries.