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NASA NTRS1986

Implications of the light curve of the A-type W UMa binary V566 Ophiuchi

Broadband (2585-3200-A) IUE photometric observations of V566 Oph obtained in three 8-h shifts on July 17-19, 1984 are reported and solved simultaneously with the ground-based optical observations of Bookmyer (1976). The observed light curves are fit to theoretical Roche-model light curves using the computer program described by Eaton (1986). The data and solutions are presented in tables and graphs, and the modifications required to match the solutions to data for 1965 (Bookmyer, 1969) and 1957 (Binnendjik, 1959) are considered. The lower-than-expected darkening observed is attributed to a reduced common-radiative-envelope temperature gradient, probably a result of circulation related to energy transfer between the components of V566 Oph.

Eaton, Joel A.↗

NASA NTRS1988

Browsing schematics: Query-filtered graphs with context nodes

The early results of a research project to create tools for building interfaces to intelligent systems on the NASA Space Station are reported. One such tool is the Schematic Browser which helps users engaged in engineering problem solving find and select schematics from among a large set. Users query for schematics with certain components, and the Schematic Browser presents a graph whose nodes represent the schematics with those components. The query greatly reduces the number of choices presented to the user, filtering the graph to a manageable size. Users can reformulate and refine the query serially until they locate the schematics of interest. To help users maintain orientation as they navigate a large body of data, the graph also includes nodes that are not matches but provide global and local context for the matching nodes. Context nodes include landmarks, ancestors, siblings, children and previous matches.

Ciccarelli, Eugene C.↗

NASA NTRS1987

Nature of the stratospheric haze on Uranus - Evidence for condensed hydrocarbons

The characteristics and origin of lower-stratosphere haze on Uranus are investigated on the basis of high-phase-angle images obtained at 430-600 nm with the wide-angle and narrow-angle cameras of Voyager 2 during its encounter with Uranus in January 1986. The data-reduction and model-fitting procedures are explained in detail, and the results are presented in extensive tables and graphs. The data are found to be best matched by a haze consisting of particles of modal radius 130 + or - 20 nm and number density 2 + or - 1 per cu cm at the 44-mbar level; such aerosols could be formed by the stratospheric condensation of photochemically produced hydrocarbon gases (locally formed diacetylene and ethane, acetylene, and diacetylene formed at higher altitudes). A total aerosol production rate of (2-15) x 10 to the -17th g/sq cm sec is estimated.

Pollack, James B.↗

DOE OSTI2026

Track Matching in the DUNE Near Detectors

The Deep Underground Neutrino Experiment (DUNE) is an international particle physics experiment looking answer some of the largest unanswered questions in neutrino physics. DUNE uses a high power neutrino beam produced at Fermi National Accelerator Laboratory (Fermilab), and consists of a near detector (ND) also located at Fermilab and a far detector (FD) 1300 km away at the Sanford Underground Research Facility (SURF) in South Dakota. In the first phase of the experiment, the ND complex will contain a modular liquid argon TPC (ND-LAr) and a solid scintillator-based muon spectrometer (TMS), in addition to a beam monitoring detector (SAND) and systems for moving ND-LAr and TMS away from the neutrino beam axis (PRISM). A prototype of ND-LAr, the 2x2 demonstrator, alongside a solid scintillator muon tagger provided by repurposed MINERvA planes, has been built and taken data at Fermilab. For analyses with the ND, connecting particle tracks (such as muons) that exit the liquid argon active volume into the solid scintillator muon detector can improve particle identification and energy reconstruction, and alleviate pileup due to the intense beam. To match tracks between detectors during reconstruction, we have explored using Graph Neural Networks (GNNs) to connect tracks segments between the liquid argon detector region and the solid scintillator detector planes. We have trained a GNN on reconstructed simulated data from the 2×2 demonstrator and repurposed MINERvA planes. We will evaluate its performance and then train a similar network on reconstructed ND-LAr and TMS simulations.

Xing, Daniel [U. Colorado, Boulder]↗

NASA NTRS1994

Backward-facing step measurements at low Reynolds number, Re(sub h)=5000

An experimental study of the flow over a backward-facing step at low Reynolds number was performed for the purpose of validating a direct numerical simulation (DNS) which was performed by the Stanford/NASA Center for Turbulence Research. Previous experimental data on back step flows were conducted at Reynolds numbers and/or expansion ratios which were significantly different from that of the DNS. The geometry of the experiment and the simulation were duplicated precisely, in an effort to perform a rigorous validation of the DNS. The Reynolds number used in the DNS was Re(sub h)=5100 based on step height, h. This was the maximum possible Reynolds number that could be economically simulated. The boundary layer thickness, d, was approximately 1.0 h in the simulation and the expansion ratio was 1.2. The Reynolds number based on the momentum thickness, Re(sub theta), upstream of the step was 610. All of these parameters were matched experimentally. Experimental results are presented in the form of tables, graphs and a floppy disk (for easy access to the data). An LDV instrument was used to measure mean velocity components and three Reynolds stresses components. In addition, surface pressure and skin friction coefficients were measured. LDV measurements were acquired in a measuring domain which included the recirculating flow region.

Jovic, Srba↗

NASA NTRS1990

Partitioning sparse matrices with eigenvectors of graphs

The problem of computing a small vertex separator in a graph arises in the context of computing a good ordering for the parallel factorization of sparse, symmetric matrices. An algebraic approach for computing vertex separators is considered in this paper. It is shown that lower bounds on separator sizes can be obtained in terms of the eigenvalues of the Laplacian matrix associated with a graph. The Laplacian eigenvectors of grid graphs can be computed from Kronecker products involving the eigenvectors of path graphs, and these eigenvectors can be used to compute good separators in grid graphs. A heuristic algorithm is designed to compute a vertex separator in a general graph by first computing an edge separator in the graph from an eigenvector of the Laplacian matrix, and then using a maximum matching in a subgraph to compute the vertex separator. Results on the quality of the separators computed by the spectral algorithm are presented, and these are compared with separators obtained from other algorithms for computing separators. Finally, the time required to compute the Laplacian eigenvector is reported, and the accuracy with which the eigenvector must be computed to obtain good separators is considered. The spectral algorithm has the advantage that it can be implemented on a medium-size multiprocessor in a straightforward manner.

Pothen, Alex↗

NASA NTRS1997

Wavelet Methods Developed to Detect and Control Compressor Stall

A "wavelet" is, by definition, an amplitude-varying, short waveform with a finite bandwidth (e.g., that shown in the first two graphs). Naturally, wavelets are more effective than the sinusoids of Fourier analysis for matching and reconstructing signal features. In wavelet transformation and inversion, all transient or periodic data features (as in compressor-inlet pressures) can be detected and reconstructed by stretching or contracting a single wavelet to generate the matching building blocks. Consequently, wavelet analysis provides many flexible and effective ways to reduce noise and extract signals which surpass classical techniques - making it very attractive for data analysis, modeling, and active control of stall and surge in high-speed turbojet compressors. Therefore, fast and practical wavelet methods are being developed in-house at the NASA Lewis Research Center to assist in these tasks. This includes establishing user-friendly links between some fundamental wavelet analysis ideas and the classical theories (or practices) of system identification, data analysis, and processing.

Le, Dzu K.↗

NASA NTRS1989

Diffraction of impulsive sounds by a curved obstruction - A time-domain study

The diffraction of spark-discharge-produced sound waves produced by a half circular cylinder covered with absorbing material is investigated theoretically and experimentally. The test bench setup and the measurement instrumentation and procedure are described; the derivation of an analytical model on the basis of the matched asymptotic expansion theory of Pierce (1981) is outlined; and the results are compared in graphs. Good general agreement is obtained, except in the deep shadow of the obstacle, where the theory underpredicts the magnitude of the received pulse. This discrepancy is tentatively attributed to the creeping-wave mechanism described by Pierce.

Berthelot, Yves H.↗

NASA NTRS1977

Synchronization of the ERDA-NASA 100 kW wind turbine generator with large utility networks

The synchronizing of a wind turbine generator against an infinite bus under random conditions is studied for the first time. With a digital computer, complete solutions for rotor speed, generator power angle, electromagnetic torque, wind turbine torque, wind turbine blade pitch angle, and armature current are obtained and presented by graphs. Experiments have been recently performed on the ERDA-NASA 100 kW wind turbine. Experimental results matched computer study results very closely and confirmed that the synchronization can be accomplished by means of the existing speed control system and an automatic synchronizer.

Hwang, H. H.↗

DOE OSTI2024

GraphAide: Advanced Graph-Assisted Query and Reasoning System

Curating knowledge from multiple siloed sources that contain both structured and unstructured data is a major challenge in many real-world applications. Pattern matching and querying represent fundamental tasks in modern data analytics that leverage this curated knowledge. The development of such applications necessitates overcoming several research challenges, including data extraction, named entity recognition, data modeling, and designing query interfaces. Moreover, the explainability of these functionalities is critical for their broader adoption. The emergence of Large Language Models (LLMs) has accelerated the development lifecycle of new capabilities. Nonetheless, there is an ongoing need for domain-specific tools tailored to user activities. The creation of digital assistants has gained considerable traction in recent years, with LLMs offering a promising avenue to develop such assistants utilizing domain-specific knowledge and assumptions. In this context, we introduce an advanced query and reasoning system, GraphAide, which constructs a knowledge graph (KG) from diverse sources and allows to query and reason over the resulting KG. GraphAide harnesses both the KG and LLMs to rapidly develop domain-specific digital assistants. It integrates design patterns from retrieval augmented generation (RAG) and the semantic web to create an agentic LLM application. GraphAide underscores the potential for streamlined and efficient development of specialized digital assistants, thereby enhancing their applicability across various domains.

Purohit, Sumit [BATTELLE (PACIFIC NW LAB)] (ORCID:↗

DOE OSTI2026

Intelligent Experiments Through Real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and Future EIC Detectors (Final Report)

The overall vision of this project was to integrate real-time artificial intelligence (AI) directly into the data acquisition and detector-control systems of nuclear physics experiments, including both fast online event selection and an autonomous detector-control feedback loop. The work carried out under the award focused on the fast online event-selection half of that vision: the efficient recording of low-momentum heavy-flavor (HF) hadron decays in proton-proton collisions at the sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC)—an observable that requires fast tracking and topological trigger selection not previously demonstrated at RHIC, and that is essential for QCD studies at future facilities such as the Electron-Ion Collider (EIC). The autonomous detector-control (GPU-based feedback) component named in the project title remained a design concept and was not implemented under this award. The Massachusetts Institute of Technology (MIT) group led the offline simulation and data processing needed to train the machine-learning (ML) models, the translation of trained models to Field-Programmable Gate Array (FPGA) firmware using the hls4ml framework, and the physics validation of heavy-flavor reconstruction. Over the award period, the team developed and hardware-tested the principal components of an AI-based heavy-flavor trigger on simulated and recorded sPHENIX tracker data: a software Bipartite Graph Attention Network (BiGAT) trigger model reaching > 95% signal efficiency at 99% background rejection; an FPGA-native hit clusterizer matching the offline clustering; smaller networks synthesized to FPGA within the required sub-10 µs latency; and an assembled decoder–clusterizer–inference firmware chain exercised on the FELIX readout board. A complete, fully integrated hardware demonstrator was not finished within the award period. This report documents the project goals, the MIT group’s contributions, the technical accomplishments, and the outlook toward applications at the future EIC ePIC detector.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS2004

Depth-Penetrating Measurements Developed for Thermal Barrier Coatings Incorporating Thermographic Phosphors

The insulating properties of thermal barrier coatings (TBCs) provide highly beneficial thermal protection to turbine engine components by reducing the temperature sustained by those components. Therefore, measuring the temperature beneath the TBC is critical for determining whether the TBC is performing its insulating function. Currently, noncontact temperature measurements are performed by infrared pyrometry, which unfortunately measures the TBC surface temperature rather than the temperature of the underlying component. To remedy this problem, the NASA Glenn Research Center, under the Information Rich Test Instrumentation Project, developed a technique to measure the temperature beneath the TBC by incorporating a thin phosphor layer beneath the TBC. By performing fluorescence decay-time measurements on light emission from this phosphor layer, Glenn successfully measured temperatures from the phosphor layer up to 1100 C. This is the first successful demonstration of temperature measurements that penetrate beneath the TBC. Thermographic phosphors have a history of providing noncontact surface temperature measurements. Conventionally, a thermographic phosphor is applied to the material surface and temperature measurements are performed by exciting the phosphor with ultraviolet light and then measuring the temperature-dependent decay time of the phosphor emission at a longer wavelength. The innovative feature of the new approach is to take advantage of the relative transparency of the TBC (composed of yttria-stabilized zirconia) in order to excite and measure the phosphor emission beneath the TBC. The primary obstacle to achieving depth-penetrating temperature measurements is that the TBCs are completely opaque to the ultraviolet light usually employed to excite the phosphor. The strategy that Glenn pursued was to select a thermographic phosphor that could be excited and emit at wavelengths that could be transmitted through the TBC. The phosphor that was selected was yttria doped with europia (Y2O3:Eu), which has a minor excitation peak at 532 nm (green) and an emission peak at 611 nm (red)--both are wavelengths that exhibit significant transmission through the TBC. The measurements were performed on specimens consisting of a 25- m-thick phosphor layer beneath a 100- m-thick TBC. The 532-nm (green) excitation light was provided by a frequency-doubled YAG:Nd (yttrium-aluminum-garnet:neodymium) laser, and the fluorescence decay time measurements were acquired with a modified Raman microscope. The preceding graph compares the intensity of the phosphor emission of the phosphor layer above the TBC versus that of the phosphor layer beneath the TBC. Although there was considerable attenuation of the phosphor signal (a factor of 30), the phosphor emission at the reduced intensity was more than sufficient to perform fluorescence decay time measurements. The following graph shows the fluorescence lifetime temperature dependency for the Y2O3:Eu phosphor layers both above and below the TBC. These curves show an excellent match and indicate that, despite the attenuation due to the overlying TBC, the phosphor layer beneath the TBC still functions as an effective temperature indicator.

Eldridge, Jeffrey I.↗

DOE OSTI2025

PySIDT: Subgraph Isomorphic Decision Trees for Molecular Property Prediction

Accurate molecular property prediction is important across all fields of chemistry. Deep neural networks (DNNs) have become increasingly popular due to their ability to train automatically, avoiding the incredibly tedious process of constructing and extending traditional property estimation schemes. However, DNNs require large amounts of training data, are challenging to interpret, require large amounts of memory to load even during inference, and have severe difficulties incorporating qualitative chemical knowledge, which are often desired for molecular property prediction tasks. Here, in this study, we present PySIDT (https://github.com/zadorlab/PySIDT), a software for training and running inference on Subgraph Isomorphic Decision Trees (SIDTs). SIDTs are graph-based decision trees made of nodes associated with molecular substructures. Inference is done by descending target molecular structures down the decision tree to nodes with matching subgraph isomorphic substructures and making predictions based on the final (most specific) nodes matched. SIDTs scale down well to dataset sizes much smaller than is feasible for DNNs. As trees of molecular substructures, SIDTs are inherently readable and easy to visualize, making them easy to analyze. They are also straightforward to extend and retrain, facilitate uncertainty estimation, and enable easy integration of expert knowledge. We demonstrate the SIDT approach discussing its application to a diverse range of molecular prediction tasks: rate coefficient estimation, diffusion coefficient estimation, thermochemistry estimation, transition state bond stretch prediction, p K a prediction, stability of molecular structures, stability of surface structures, and prediction of surface lateral interaction energetics. Additionally, we demonstrate the power of the SIDT algorithms in two direct learning curve vanilla comparisons with the popular DNN-based software Chemprop and the popular gradient boosted trees-based software XGBoost on enthalpy of formation and rate coefficient prediction tasks. In particular, in the enthalpy of formation case, vanilla PySIDT is able to outperform vanilla Chemprop and XGBoost across the full range of training/validation set sizes out to 11,560 data points.

Johnson, Matthew Sean [Sandia National Laboratorie↗

NASA NTRS1987

Solving very large, sparse linear systems on mesh-connected parallel computers

The implementation of Pan and Reif's Parallel Nested Dissection (PND) algorithm on mesh connected parallel computers is described. This is the first known algorithm that allows very large, sparse linear systems of equations to be solved efficiently in polylog time using a small number of processors. How the processor bound of PND can be matched to the number of processors available on a given parallel computer by slowing down the algorithm by constant factors is described. Also, for the important class of problems where G(A) is a grid graph, a unique memory mapping that reduces the inter-processor communication requirements of PND to those that can be executed on mesh connected parallel machines is detailed. A description of an implementation on the Goodyear Massively Parallel Processor (MPP), located at Goddard is given. Also, a detailed discussion of data mappings and performance issues is given.

Opsahl, Torstein↗

NASA NTRS2013

Bilayer Protograph Codes for Half-Duplex Relay Channels

Direct to Earth return links are limited by the size and power of lander devices. A standard alternative is provided by a two-hops return link: a proximity link (from lander to orbiter relay) and a deep-space link (from orbiter relay to Earth). Although direct to Earth return links are limited by the size and power of lander devices, using an additional link and a proposed coding for relay channels, one can obtain a more reliable signal. Although significant progress has been made in the relay coding problem, existing codes must be painstakingly optimized to match to a single set of channel conditions, many of them do not offer easy encoding, and most of them do not have structured design. A high-performing LDPC (low-density parity-check) code for the relay channel addresses simultaneously two important issues: a code structure that allows low encoding complexity, and a flexible rate-compatible code that allows matching to various channel conditions. Most of the previous high-performance LDPC codes for the relay channel are tightly optimized for a given channel quality, and are not easily adapted without extensive re-optimization for various channel conditions. This code for the relay channel combines structured design and easy encoding with rate compatibility to allow adaptation to the three links involved in the relay channel, and furthermore offers very good performance. The proposed code is constructed by synthesizing a bilayer structure with a pro to graph. In addition to the contribution to relay encoding, an improved family of protograph codes was produced for the point-to-point AWGN (additive white Gaussian noise) channel whose high-rate members enjoy thresholds that are within 0.07 dB of capacity. These LDPC relay codes address three important issues in an integrative manner: low encoding complexity, modular structure allowing for easy design, and rate compatibility so that the code can be easily matched to a variety of channel conditions without extensive re-optimization. The main problem of half-duplex relay coding can be reduced to the simultaneous design of two codes at two rates and two SNRs (signal-to-noise ratios), such that one is a subset of the other. This problem can be addressed by forceful optimization, but a clever method of addressing this problem is via the bilayer lengthened (BL) LDPC structure. This method uses a bilayer Tanner graph to make the two codes while using a concept of "parity forwarding" with subsequent successive decoding that removes the need to directly address the issue of uneven SNRs among the symbols of a given codeword. This method is attractive in that it addresses some of the main issues in the design of relay codes, but it does not by itself give rise to highly structured codes with simple encoding, nor does it give rate-compatible codes. The main contribution of this work is to construct a class of codes that simultaneously possess a bilayer parity- forwarding mechanism, while also benefiting from the properties of protograph codes having an easy encoding, a modular design, and being a rate-compatible code.

Divsalar, Dariush↗

NASA NTRS1991

A Large-Grain Mapping Approach for Multiprocessor Systems Through Data Flow Model Ph.D. Thesis

A large-grain level mapping method is presented of numerical oriented applications onto multiprocessor systems. The method is based on the large-grain data flow representation of the input application and it assumes a general interconnection topology of the multiprocessor system. The large-grain data flow model was used because such representation best exhibits inherited parallelism in many important applications, e.g., CFD models based on partial differential equations can be presented in large-grain data flow format, very effectively. A generalized interconnection topology of the multiprocessor architecture is considered, including such architectural issues as interprocessor communication cost, with the aim to identify the 'best matching' between the application and the multiprocessor structure. The objective is to minimize the total execution time of the input algorithm running on the target system. The mapping strategy consists of the following: (1) large-grain data flow graph generation from the input application using compilation techniques; (2) data flow graph partitioning into basic computation blocks; and (3) physical mapping onto the target multiprocessor using a priority allocation scheme for the computation blocks.

Kim, Hwa-Soo↗

NASA NTRS2017

Planetary Crater Detection and Registration Using Marked Point Processes, Graph Cut Algorithms, and Wavelet Transforms

This paper addresses the problem of semi-automatic image registration on planetary images. A joint feature-based and area-based approach is proposed. Firstly, the most relevant craters are extracted from the two images to register, and then, registration is performed in two steps. The first step matches the craters extracted from the images based on a generalized Hausdorff distance. In the second step, the mutual information between the two images is maximized to achieve high registration accuracy. Craters are detected by a stochastic-geometry approach based on a marked point process model and of a multiple-birth-and-cut energy minimization algorithm. The experimental validation is carried out with 13 images for the crater extraction stage, and with 20 semi-synthetic pairs of images with ground truth and several images extracted from actual multi-temporal lunar scenes for the registration phase.

Image Processing; Pattern Recognition↗

DOE OSTI2024

Verification, Validation, and Calibration Through a Causal Lens

While typical validation and verification approaches focus on identifying the associations between data elements using statistical and machine learning methods, the novel methods in this paper focus instead on identifying causal relationships between data elements. Statistical and machine-learning-based approaches are strictly data-driven, meaning that they provide quantitative comparison measures between data sets without explicitly considering the hypotheses behind them. This can lead to the erroneous conclusion that, if two data sets are close enough, the models that generated them are similar. In addition, when experimental and simulated data differ to an extent that fails to meet the acceptance criteria, calibration techniques are used to tweak simulation model parameters to reduce the gap between the two types of data. This produces the false expectation that a simulation model will match reality. The methods presented in this paper move away from these strictly data-driven methods for validation and calibration toward more robust, model-driven methods based on causal inference. Causal inference aims to identify the possible mechanisms that might have generated data. Thus, this analysis targets the prediction of the effects when one (or more) of the identified mechanisms are altered. There are many approaches to identify, quantify, and illustrate causal relationships. For the scope of this paper, directed graphs are employed as causal models. If the directed graph lacks cycles, it is known as a directed acyclic graph. A node in such a graph represents an observed data element while a directed edge connecting two nodes represents a causal relationship between two variables. The developed causal methods are designed to extract causal models from simulation models and experimental data. Causal models capture the causal relationships between data elements (e.g., simulated and experimental data). In this context, validation and verification are performed by comparing causal models. The proposed approach does not only inform system analysts on how a simulation model matches real-world data, but also identifies elements of the simulation model that should be revised when discrepancies between simulation and experimental data are observed. Through these causal methods, analysts can identify the portion of the model equation(s) that are behind an edge connecting two variables. Hence, once the structural differences between causal models have been determined, model calibration can occur by changing only those model parameters that impact the identified causal relationships.

97 MATHEMATICS AND COMPUTING↗