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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Graph Theory and IC Component Design Analysis

Graph analysis in large integrated circuit (IC) designs is an essential tool for verifying design logic and timing via dynamic timing analysis (DTA). IC designs resemble graphs with each logic gate as a vertex and the conductive connections between gates as edges. Using DTA digital statistical correlations, graph condensation, and graph partitioning, it is possible to identify high-entropy component centers and paths within an IC design. Identification of high-entropy component centers (HECC) enables focused DTA, effectively lowering the computational complexity of DTA on large integrated circuit graphs. In this paper, a devised methodology termed IC layout subgraph component center identification (CCI) is used to identify described. CCI lowers DTA computationally complexity by condensing IC graphs into reduced subgraphs in which dominant logic functions are verified.

42 ENGINEERING↗
DOE OSTI2025

CONCURRENT, CONDENSED STEIN VARIATIONAL GRADIENT DESCENT FOR UNCERTAINTY QUANTIFICATION OF NEURAL NETWORKS

In this work, we propose a Stein variational gradient descent (SVGD) method to concurrently sparsify, train, and provide uncertainty quantification (UQ) of a complexly parameterized model, such as a neural network (NN). It employs a graph reconciliation and condensation process to reduce complexity and increase similarity in the Stein ensemble of parameterizations. Therefore, the proposed concurrent, condensed SVGD (ccSVGD) method can provide UQ on parameters, not just outputs. Furthermore, the parameter reduction speeds up the convergence of the Stein gradient descent as it reduces the combinatorial complexity by aligning and differentiating the sensitivity to parameters. These properties are demonstrated with an illustrative example and an application to a mechanical response representation problem in solid mechanics.

42 ENGINEERING↗
NASA NTRS2004

Stirling Engine Dynamic System Modeling

The Thermo-Mechanical systems branch at the Glenn Research Center focuses a large amount time on Stirling engines. These engines will be used on missions where solar power is inefficient, especially in deep space. I work with Tim Regan and Ed Lewandowski who are currently developing and validating a mathematical model for the Stirling engines. This model incorporates all aspects of the system including, mechanical, electrical and thermodynamic components. Modeling is done through Simplorer, a program capable of running simulations of the model. Once created and then proven to be accurate, a model is used for developing new ideas for engine design. My largest specific project involves varying key parameters in the model and quantifying the results. This can all be done relatively trouble-free with the help of Simplorer. Once the model is complete, Simplorer will do all the necessary calculations. The more complicated part of this project is determining which parameters to vary. Finding key parameters depends on the potential for a value to be independently altered in the design. For example, a change in one dimension may lead to a proportional change to the rest of the model, and no real progress is made. Also, the ability for a changed value to have a substantial impact on the outputs of the system is important. Results will be condensed into graphs and tables with the purpose of better communication and understanding of the data. With the changing of these parameters, a more optimal design can be created without having to purchase or build any models. Also, hours and hours of results can be simulated in minutes. In the long run, using mathematical models can save time and money. Along with this project, I have many other smaller assignments throughout the summer. My main goal is to assist in the processes of model development, validation and testing.

Nakis, Christopher G.↗
DOE OSTI2023

Gene Expression Programming for Quantum Computing

Here, we introduce QuantumGEP, a scientific computer program that uses gene expression programming (GEP) to find a quantum circuit that either (1) maps a given set of input states to a given set of output states or (2) transforms a fixed initial state to minimize a given physical quantity of the output state. QuantumGEP is a driver program that uses evendim, a generic computational engine for GEP, both of which are free and open source. We apply QuantumGEP as a powerful solver for MaxCut in graphs and for condensed matter quantum many-body Hamiltonians.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1980

On deadlock detection in distributed systems

A hierarchically organized and a distributed protocol for deadlock detection in distributed databases are presented in a previous study Menasce and Muntz (1979). In this paper, it is shown that the distributed protocol is incorrect, and possible remedies are presented. However, the distributed protocol remains impractical because 'condensations' of 'transaction-wait-for' graphs make graph updates difficult to perform. Delayed graph updates cause the occurrence of false deadlocks in this as well as in some other deadlock detection protocols for distributed systems. The performance degradation that results from false deadlocks depends on the characteristics of each protocol.

Gligor, V. D.↗
DOE OSTI2023

Graph neural networks predict energetic and mechanical properties for models of solid solution metal alloy phases

Here, we developed a PyTorch-based architecture called HydraGNN that implements graph convolutional neural networks (GCNNs) to predict the formation energy and the bulk modulus for models of solid solution alloys for various atomic crystal structures and relaxed volumes. We trained the GCNN surrogate model on a dataset for nickel–niobium (NiNb) generated by the embedded atom model (EAM) empirical interatomic potential for demonstration purposes. The dataset was generated by calculating the formation energy and the bulk modulus as a prototypical elastic property for optimized geometries starting from initial body-centered cubic (BCC), face-centered cubic (FCC), and hexagonal compact packed (HCP) crystal structures, with configurations spanning the possible compositional range for each of the three types of initial crystal structures. Numerical results show that the GCNN model effectively predicts both the formation energy and the bulk modulus as function of the optimized crystal structure, relaxed volume, and configurational entropy of the model structures for solid solution alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems *

Abstract We introduce a multi-tasking graph convolutional neural network, HydraGNN, to simultaneously predict both global and atomic physical properties and demonstrate with ferromagnetic materials. We train HydraGNN on an open-source ab initio density functional theory (DFT) dataset for iron-platinum with a fixed body centered tetragonal lattice structure and fixed volume to simultaneously predict the mixing enthalpy (a global feature of the system), the atomic charge transfer, and the atomic magnetic moment across configurations that span the entire compositional range. By taking advantage of underlying physical correlations between material properties, multi-task learning (MTL) with HydraGNN provides effective training even with modest amounts of data. Moreover, this is achieved with just one architecture instead of three, as required by single-task learning (STL). The first convolutional layers of the HydraGNN architecture are shared by all learning tasks and extract features common to all material properties. The following layers discriminate the features of the different properties, the results of which are fed to the separate heads of the final layer to produce predictions. Numerical results show that HydraGNN effectively captures the relation between the configurational entropy and the material properties over the entire compositional range. Overall, the accuracy of simultaneous MTL predictions is comparable to the accuracy of the STL predictions. In addition, the computational cost of training HydraGNN for MTL is much lower than the original DFT calculations and also lower than training separate STL models for each property.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1976

Explanation for the very low Ga and Ge concentrations in some iron meteorite groups

Parallels between the abundance patterns of moderately volatile elements in iron meteorites and ordinary chondrites are pointed out and discussed in relation to condensation processes in the solar nebula. The discussion is centered around a graph in which As, Cu, Ga, Ge to Ni ratios, normalized to CI chondrites, are compared for IVB, IVA, IVB, and IIIAB irons and H-group ordinary chondrites. The patterns suggest that the same volatile loss mechanism was at work for both IVB irons and ordinary chondrites, but was more efficient in the IVB process. A picture for the process at the IVB location is proposed, according to which condensation occurred when temperature decreased rapidly and trace metals condensed as a fine aerosol that was later blown away by a T-Tauri solar storm. Condensation at the H-group location was more complete because of a less rapid temperature decrease, allowing trace metals to diffuse deeper into Fe-Ni grains. Possible ways in which IVA conditions may have differed from IIIAB or H conditions are also proposed and discussed.

Wasson, J. T.↗
DOE OSTI2025

Learning to Simulate Aerosol Dynamics with Graph Neural Networks

Aerosol effects on climate, weather, and air quality depend on characteristics of individual particles, which are tremendously diverse and change in time. Particle-resolved models are the only models able to capture this diversity in particle physiochemical properties, and these models are computationally expensive. As a strategy for accelerating particle-resolved microphysics models, we introduce Graph-based Learning of Aerosol Dynamics (GLAD) and use this model to train a surrogate of the particle-resolved model PartMC-MOSAIC. GLAD implements a Graph Network-based Simulator (GNS), a machine learning framework that has been used to simulate particle-based fluid dynamics models. In GLAD, each particle is represented as a node in a graph, and the evolution of the particle population over time is simulated through learned message passing. Here, we demonstrate our GNS approach on a simple aerosol system that includes condensation of sulfuric acid onto particles composed of sulfate, black carbon, organic carbon, and water. A graph with particles as nodes is constructed, and a graph neural network (GNN) is then trained using the model output from PartMC-MOSAIC. The trained GNN can then be used for simulating and predicting aerosol dynamics over time. Results demonstrate the framework's ability to accurately learn chemical dynamics and generalize across different scenarios, achieving efficient training and prediction times. We evaluate the performance across four scenarios, highlighting the framework's robustness and adaptability in modeling aerosol microphysics and chemistry.

aerosol chemistry dynamics↗
NASA NTRS1978

Computer Programs (Turbomachinery)

NASA computer programs are extensively used in design of industrial equipment. Available from the Computer Software Management and Information Center (COSMIC) at the University of Georgia, these programs are employed as analysis tools in design, test and development processes, providing savings in time and money. For example, two NASA computer programs are used daily in the design of turbomachinery by Delaval Turbine Division, Trenton, New Jersey. The company uses the NASA splint interpolation routine for analysis of turbine blade vibration and the performance of compressors and condensers. A second program, the NASA print plot routine, analyzes turbine rotor response and produces graphs for project reports. The photos show examples of Delaval test operations in which the computer programs play a part. In the large photo below, a 24-inch turbine blade is undergoing test; in the smaller photo, a steam turbine rotor is being prepared for stress measurements under actual operating conditions; the "spaghetti" is wiring for test instrumentation

Source record↗
NASA NTRS1988

Partitioning of rare earth elements between hibonite and melt and implications for nebular condensation of the rare earth elements

The effect of oxygen fugacity on the partitioning of REEs between hibonite and silicate melt is investigated in hibonite-growth experiments at 1470 C. The experimental procedures and apparatus are described, and the results are presented in extensive tables and graphs and characterized in detail. The absolute activity coefficients in hibonite are estimated as 330 for La, 1200 for Eu(3+), and 24,000 for Yb. It is inferred that ideal solution behavior cannot be assumed when calculating REE condensation temperatures for (Ca, Al)-rich inclusions in carbonaceous chondrites.

Drake, Michael J.↗
DOE OSTI2024

4-Clique network minor embedding for quantum annealers

Quantum annealing is a quantum algorithm for computing solutions to combinatorial optimization problems. This study proposes a method for minor embedding optimization problems onto sparse quantum annealing hardware graphs called 4-clique network minor embedding. This method is in contrast to the standard minor embedding technique of using a path of linearly connected qubits in order to represent a logical variable state. The 4-clique minor embedding is possible on Pegasus graph connectivity, which is the native hardware graph for some of the current D-Wave quantum annealers. The Pegasus hardware graph contains many cliques of size 4, making it possible to form a graph composed entirely of paths of connected 4-cliques on which a problem can be minor-embedded. The 4-clique chains come at the cost of additional qubit usage on the hardware graph, but they allow for stronger coupling within each chain, thereby increasing chain integrity, reducing chain breaks, and allow for greater usage of the available energy scale for programming logical problem coefficients on current quantum annealers. The 4-clique minor embedding technique is compared with the standard linear path minor embedding with experiments on two D-Wave quantum annealing processors with Pegasus hardware graphs. We show proof-of-concept experiments where the 4-clique minor embeddings can use weak chain strengths while successfully carrying out the computation of minimizing random all-to-all spin glass problem instances. Published by the American Physical Society 2024

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Orbital design of flat bands in non-line-graph lattices via line-graph wave functions

Line-graph (LG) lattices are known for having flat bands (FBs) from the destructive interference of Bloch wave functions encoded in only lattice symmetry. In this work, we develop a generic atomic/molecular orbital design principle for FBs in non-LG lattices. Based on linear combination of atomic orbital theory, we demonstrate that the underlying wave-function symmetry of FBs in a LG lattice can be transformed into the atomic/molecular orbital symmetry in a non-LG lattice. We illustrate such orbital-designed topological FBs in three 2D non-LG, square, trigonal, and hexagonal lattices, where the designed orbitals faithfully reproduce the corresponding lattice symmetries of checkerboard, kagome, and diatomic-kagome lattices, respectively. Interestingly, systematic design of FBs with a high Chern number is also achieved based on the same principle. Fundamentally our theory enriches the FB physics; practically, it significantly expands the scope of FB materials, since most materials have multiple atomic/molecular orbitals at each lattice site, rather than a single s orbital mandated in graph theory and generic lattice models.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Quantifying the atomistic free-volume morphology of materials with graph theory

Here, we introduce a new computational methodology for the identification and characterization of free volume within/around atomistic configurations. This scheme employs a three-stage workflow, by which spheres are iteratively grown inside of voxels, and ultimately converted to planar graphs, which are then characterized via a graph-based order parameter. Our approach is computationally efficient, physically intuitive, and universally transferable to any material system. Validation of our methodology is performed on several sets of materials problems: (1) classification of unique free volumes in various crystal phases, (2) autonomous detection and classification of complex surface defects during epitaxial growth simulations, (3) characterization of free volume defects in metals/alloys, and (4) quantification of the spatio-temporal behavior of nano-scale free volume morphologies as a function of both temperature and free-volume size. Our method accurately identifies and characterizes unique free volumes over a multitude of systems and length scales, indicating its potential for future use in understanding the relationship between free volume morphology and material properties under both static and dynamic conditions.

36 MATERIALS SCIENCE↗
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.↗
NASA NTRS1987

Solid hydrocarbon aerosols produced in simulated Uranian and Neptunian stratospheres

Solid hydrocarbon films were deposited in an RF plasma discharge of various CH4/H2/He mixtures under conditions simulating the formation of solid aerosols by magnetospheric charged particles in the stratospheres of Uranus and Neptune. The percentage of CH4 ranged from 0.0002 to 100 percent; the pressure in the discharge chamber was either 130 or 660 microbar; and the films produced were yellow to deep red-brown in color and 2-25 microns thick. The real and imaginary parts of the refractive index at wavelengths from 350 nm to 2.5 microns were determined for all the films, and the results are compared with observational data and the predictions of theoretical models in extensive tables and graphs. Good agreement is demonstrated, suggesting that aerosol production by this mechanism may be competitive with UV or charged-particle irradiation of hydrocarbon condensates.

Khare, B. N.↗