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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Scan2Sim: Software to Convert Network Scans to Emulations

Within operational technology (OT) systems design, the construction of testing environments for simulation is often a tedious, manual process that slows down safety and security evaluations. This document details the design and functionality of Scan2Sim, a program designed to construct high-fidelity topological schematics for OT systems without significant manual human input. Scan2Sim may take as input a detailed network scan of a system, and produces an instruction set to re-create the original scanned network within a virtualized simulation network. This construction is achieved via heuristic methods of machine template selection, which allows for a fast, performant approach to automated environment construction. The current tool is designed to produce topology schematics compatible with the Minimega, a tool designed by Sandia National Laboratories for repeatable experimentation management.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2013

Scheduling Operations for Massive Heterogeneous Clusters

High-performance computing (HPC) programming has become increasingly difficult with the advent of hybrid supercomputers consisting of multicore CPUs and accelerator boards such as the GPU. Manual tuning of software to achieve high performance on this type of machine has been performed by programmers. This is needlessly difficult and prone to being invalidated by new hardware, new software, or changes in the underlying code. A system was developed for task-based representation of programs, which when coupled with a scheduler and runtime system, allows for many benefits, including higher performance and utilization of computational resources, easier programming and porting, and adaptations of code during runtime. The system consists of a method of representing computer algorithms as a series of data-dependent tasks. The series forms a graph, which can be scheduled for execution on many nodes of a supercomputer efficiently by a computer algorithm. The schedule is executed by a dispatch component, which is tailored to understand all of the hardware types that may be available within the system. The scheduler is informed by a cluster mapping tool, which generates a topology of available resources and their strengths and communication costs. Software is decoupled from its hardware, which aids in porting to future architectures. A computer algorithm schedules all operations, which for systems of high complexity (i.e., most NASA codes), cannot be performed optimally by a human. The system aids in reducing repetitive code, such as communication code, and aids in the reduction of redundant code across projects. It adds new features to code automatically, such as recovering from a lost node or the ability to modify the code while running. In this project, the innovators at the time of this reporting intend to develop two distinct technologies that build upon each other and both of which serve as building blocks for more efficient HPC usage. First is the scheduling and dynamic execution framework, and the second is scalable linear algebra libraries that are built directly on the former.

Humphrey, John↗
DOE OSTI2021

Topological Machine Learning Methods for Power System Responses to Contingencies: Preprint

While deep learning tools, coupled with the emerging machinery of topological data analysis, are proven to deliver various performance gains in a broad range of applications, from image classification to biosurveillance to blockchain fraud detection, their utility in areas of high societal importance such as power system modeling and, particularly, resilience quantification in the energy sector yet remain untapped. To provide fast acting synthetic regulation and contingency reserve services to the grid while having minimal disruptions on customer quality of service, we propose a new topologybased system that depends on neural network architecture for impact metrics classification and prediction in power systems. This novel topology-based system allows one to evaluate the impact of three power system contingency types, namely, in conjunction with transmission lines, transformers, and transmission lines combined with transformers. We show that the proposed new neural network architecture equipped with local topological measures facilitates both more accurate classification of unserved load as well as the amount of unserved load. In addition, we are able to learn complex relationships between electrical properties and local topological measurements on the simulated response to contingencies for NREL-SIIP power system.

graph theory↗
DOE OSTI2020

Local Berry curvature signatures in dichroic angle-resolved photoelectron spectroscopy from two-dimensional materials

Topologically nontrivial two-dimensional materials hold great promise for next-generation optoelectronic applications. However, measuring the Hall or spin-Hall response is often a challenge and practically limited to the ground state. An experimental technique for tracing the topological character in a differential fashion would provide useful insights. In this work, we show that circular dichroism angle-resolved photoelectron spectroscopy provides a powerful tool that can resolve the topological and quantum-geometrical character in momentum space. In particular, we investigate how to map out the signatures of the momentum-resolved Berry curvature in two-dimensional materials by exploiting its intimate connection to the orbital polarization. A spin-resolved detection of the photoelectrons allows one to extend the approach to spin-Chern insulators. The present proposal can be extended to address topological properties in materials out of equilibrium in a time-resolved fashion.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Probing topological quantum matter with scanning tunnelling microscopy

The search for topological phases of matter is evolving towards strongly interacting systems, including magnets and superconductors, where exotic effects emerge from the quantum-level interplay between geometry, correlation and topology. Over the past decade or so, scanning tunnelling microscopy has become a powerful tool to probe and discover emergent topological matter, because of its unprecedented spatial resolution, high-precision electronic detection and magnetic tunability. Scanning tunnelling microscopy can be used to probe various topological phenomena, as well as complement results from other techniques. We discuss some of these proof-of-principle methodologies applied to probe topology, with particular attention to studies performed under a tunable vector magnetic field, which is a relatively new direction of recent focus. Finally, we then project the future possibilities for atomic-resolution tunnelling methods in providing new insights into topological matter.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Drastic enhancement of magnetic critical temperature and amorphization in topological magnet EuSn 2 P 2 under pressure

High pressure is an effective tool to induce exotic quantum phenomena in magnetic topological insulators by controlling the interplay of magnetic order and topological state. This work presents a comprehensive high-pressure study of the crystal structure and magnetic ground state up to 62 GPa in an intrinsic topological magnet EuSn 2 P 2 . With a combination of high resolution X-ray diffraction, 151 Eu synchrotron Mössbauer spectroscopy, X-ray absorption spectroscopy, molecular orbital calculations, and electronic band structure calculations, it has been revealed that pressure drives EuSn 2 P 2 from a rhombohedral crystal to an amorphous phase at 36 GPa accompanied by a fourfold enhancement of magnetic ordering temperature. In the pressure-induced amorphous phase, Eu ions take an intermediate valence state. The drastic enhancement of magnetic ordering temperature from 30 K at ambient pressure to 130 K at 41.2 GPa resulting from Ruderman–Kittel–Kasuya–Yosida (RKKY) interactions likely attributes to the stronger Eu–Sn interaction at high pressure. These rich results demonstrate that EuSn 2 P 2 is an ideal platform to study the correlation of the enhanced RKKY interactions, disordered lattice, intermediate valence, and topological state.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Topological data analysis of task-based fMRI data from experiments on schizophrenia

We use methods from computational algebraic topology to study functional brain networks, in which nodes represent brain regions and weighted edges represent similarity of fMRI time series from each region. With these tools, which allow one to characterize topological invariants such as loops in high-dimensional data, we are able to gain understanding into low-dimensional structures in networks in a way that complements traditional approaches based on pairwise interactions. In the present paper, we analyze networks constructed from task-based fMRI data from schizophrenia patients, healthy controls, and healthy siblings of schizophrenia patients using persistent homology, which allows us to explore the persistence of topological structures such as loops at different scales in the networks. We use persistence landscapes, persistence images, and Betti curves to create output summaries from our persistent-homology calculations, and we study the persistence landscapes and images using k-means clustering and community detection. Based on our analysis of persistence landscapes, we find that the members of the sibling cohort have topological features (specifically, their 1-dimensional loops) that are distinct from the other two cohorts. From the persistence images, we are able to distinguish all three subject groups and to determine the brain regions in the loops (with four or more edges) that allow us to make these distinctions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Electromagnetic energy calibration of the SoLid detector with horizontal muons

SoLid is a neutrino experiment at very-short baselinesearching for active-to-sterile oscillations of reactorantineutrinos. The detection principle is based on the pairing oftwo types of solid scintillators: polyvinyl toluene and $^{6}$Li:ZnS(Ag), which is a new technology used in this field ofPhysics. In addition to good neutron-gamma discrimination, thissetup allows the detector to be highly segmented; the basicdetection unit is a 5 cm cube. High segmentation provides numerousadvantages including precise localisation of the Inverse Beta Decay(IBD) products, the derivation of an antineutrino energy estimatorbased on the isolated positron energy, and a powerful backgroundreduction tool that relies on the topological signature of thesignal. Finally, the system is read out by a network ofwavelength-shifting fibres coupled to photosensors. A relativeelectromagnetic calibration is performed with horizontal cosmicmuons. This source poses the simplest calibration problem in which asingle detection unit is involved. In addition, large muon energydeposits allow us to perform a calibration at the most detailedlevel (i.e. per fibre) and to accurately define the fraction ofenergy escaping to neighbouring detection cells. A statisticalprecision at the sub-percent level is reached. The paper alsodiscusses two methods to calibrate the absolute energy scale andpresents their implementation and results. The first method relieson horizontal muons, though the precision is limited to around 10%because of the uncertainty in the energy distribution of suchmuons. A novel, alternative method based on the radioactiveamericium-beryllium source is proposed. It takes advantage of theelectron-positron pair-production process and provides a calibrationpoint at 3.4 MeV (i.e. in the core of the IBD positronspectrum). The paper is concluded with various cross-check includinga determination of the energy spectrum of the standard cosmogenicbackground candle: $^{12}$B.

Abreu, Y. [Antwerp U.]↗
DOE OSTI2025

Theory of fluctuating and critical quantum matter (Final technical report: DE-FG02-08ER46524)

The project developed the theory of electronic phenomena in quantum materials in which large fluctuations are present, driven by frustration, strong electronic interactions, topology, or other effects. Theoretical tools and results for specific materials were developed in parallel, allowing the results to be vetted and refined, and providing a resource for experimentalists. The results were reported in 53 publications over the period of the award. The sections below detail the most impactful scientific and technical results.

36 MATERIALS SCIENCE↗
NASA NTRS2003

A Micro-Ultrastable Oscillator (micro-US0) for Micro/Nano Sciencecraft

Syntonics LLC developed a prototype micro-Ultra Stable Oscillator (micro-USO) under a Space Base Technology Grant (NAGS-10395). Syntonics conducted the micro-USO Program in two phases. In Phase I, we developed a set of verified analytical models (including thermal, electrical, and control models) for a baseline USO, conducted a series of six technology studies, and built three approx.9OOg prototype units. These prototypes provided a tool for evaluating competing design topologies. In Phase II we prepared the conceptual design of a approx.100-15Og micro-USO.

Source record↗
DOE OSTI2022

Applications of persistent homology in nuclear collisions

Here, we introduce a novel set of observables associated to the rapidly developing field of persistent homology for the quantitative characterization of nuclear collisions and their evolution. Persistent homology allows for the identification of topological and homological characteristics of distributions in multidimensional spaces. We demonstrate here how to apply the tool kit of persistent homology to the extraction of novel clustering signatures and the identification of long-range flow correlations in the particle production process of nuclear collisions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Floquet-driven indirect exchange interaction mediated by topological insulator surface states

Light drives offer a potential tool for the dynamical control of magnetic interactions in matter. We theoretically investigate the indirect exchange coupling between two parallel chains of magnetic impurities on the surface of a topological insulator, driven by a time-periodic circularly polarized light field in the high-frequency, off-resonant regime. We derive a closed-form analytic expression for the spin susceptibility of the photon-dressed topological insulator surface states and obtain the irradiation dependence of the Ising, Heisenberg, and Dzyaloshinskii-Moriya exchange couplings between the impurity chains. Our results show a two-pronged modification of these exchange couplings by periodic drives. First, the Ruderman-Kittel-Kasuya-Yosida (RKKY) oscillation period of the exchange couplings can be extended by enhancing the driving strength. Second, increasing driving strength enhances the envelope of RKKY oscillations of the Ising type while suppressing those of the Heisenberg type and Dzyaloshinskii-Moriya type. Furthermore, our work provides useful insights for realizing Floquet engineering of collinear and noncollinear indirect exchange interactions in topological insulating systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

UMass 2-Body WEC Techno-Economic Assessment

The University of Massachusetts (UMass) is developing a 2-body wave energy converter (WEC) device that is converting mechanical power into electricity using a mechanical motion rectifier that allows the system to couple to a flywheel. UMass has completed numerical modeling, wave tank testing, and PTO sub-system testing and needed assistance in developing a techno-economic model to enable optimization of their topology, comparison to a generic heaving point absorber topology, and guide the next steps in their development efforts. The core objective was to develop a techno-economic approach and modeling tool that allows benchmarking of the two topologies across a wide range of scales to evaluate their respective competitiveness in different application spaces. This data includes the final report as well as a supporting spreadsheet containing the data produced for this report.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Quantum criticality and topology in non-equilibrium systems

A key goal of condensed matter physics research is to identify new phases of matter, and to understand the universal features of the phase transitions between them. In the past decade, physicists have uncovered a wealth of new phases with interesting surface properties, exemplified by the theoretical prediction and subsequent experimental discovery of topological insulators and superconductors. Traditional condensed matter systems are usually in a thermal equilibrium state and typically at very low temperature. Very recently, experimental advances have sparked interest in the non-equilibrium setting. Non-equilibrium systems can host new phases and phenomena with no equilibrium counterpart, and could also enable robust ways to build quantum memory devices to store and manipulate quantum information in a coherent manner. These phases and phenomena are inherently “dynamical”: they are described not by changes in the arrangement or structure of the constituent particles, but instead marked by sharp distinctions in how the particles move and exchange energy or quantum information. The discovery of robust non-equilibrium phases raises many fundamental questions: Can we develop a systematic theory of states of matter and of dynamical transitions between such states? How can such states be realized and probed experimentally? The main goal of this project was to explore the emergence of topological phases and quantum criticality (two cornerstones of modern condensed matter physics in equilibrium) in such non-equilibrium quantum systems. Specific goals included (1) using tensor networks to efficiently represent non-equilibrium states of matter and their phase transitions; (2) studying and designing new probes for periodically driven systems; and (3) developing analytic and numerical tools to analyze non-equilibrium topological phase transitions. Advances in these directions were achieved using novel techniques appropriate to study the non-equilibrium dynamics in many-body quantum systems combining strong interactions and randomness. Taken together, these results provide a new conceptual framework for understanding the emergence of quantum critical and topological properties in quantum systems far from thermal equilibrium.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Spin-resolved topology and partial axion angles in three-dimensional insulators

Abstract Symmetry-protected topological crystalline insulators (TCIs) have primarily been characterized by their gapless boundary states. However, in time-reversal- ( $${{{{{{{\mathcal{T}}}}}}}}$$ T -) invariant (helical) 3D TCIs—termed higher-order TCIs (HOTIs)—the boundary signatures can manifest as a sample-dependent network of 1D hinge states. We here introduce nested spin-resolved Wilson loops and layer constructions as tools to characterize the intrinsic bulk topological properties of spinful 3D insulators. We discover that helical HOTIs realize one of three spin-resolved phases with distinct responses that are quantitatively robust to large deformations of the bulk spin-orbital texture: 3D quantum spin Hall insulators (QSHIs), “spin-Weyl” semimetals, and $${{{{{{{\mathcal{T}}}}}}}}$$ T -doubled axion insulator (T-DAXI) states with nontrivial partial axion angles indicative of a 3D spin-magnetoelectric bulk response and half-quantized 2D TI surface states originating from a partial parity anomaly. Using ab-initio calculations, we demonstrate that β -MoTe 2 realizes a spin-Weyl state and that α -BiBr hosts both 3D QSHI and T-DAXI regimes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1993

Development of a large scale Chimera grid system for the Space Shuttle Launch Vehicle

The application of CFD techniques to large problems has dictated the need for large team efforts. This paper offers an opportunity to examine the motivations, goals, needs, problems, as well as the methods, tools, and constraints that defined NASA's development of a 111 grid/16 million point grid system model for the Space Shuttle Launch Vehicle. The Chimera approach used for domain decomposition encouraged separation of the complex geometry into several major components each of which was modeled by an autonomous team. ICEM-CFD, a CAD based grid generation package, simplified the geometry and grid topology definition by provoding mature CAD tools and patch independent meshing. The resulting grid system has, on average, a four inch resolution along the surface.

Pearce, Daniel G.↗
DOE OSTI2023

Topological structure of complex predictions

Abstract Current complex prediction models are the result of fitting deep neural networks, graph convolutional networks or transducers to a set of training data. A key challenge with these models is that they are highly parameterized, which makes describing and interpreting the prediction strategies difficult. We use topological data analysis to transform these complex prediction models into a simplified topological view of the prediction landscape. The result is a map of the predictions that enables inspection of the model results with more specificity than dimensionality-reduction methods such as tSNE and UMAP. The methods scale up to large datasets across different domains. We present a case study of a transformer-based model previously designed to predict expression levels of a piece of DNA in thousands of genomic tracks. When the model is used to study mutations in the BRCA1 gene, our topological analysis shows that it is sensitive to the location of a mutation and the exon structure of BRCA1 in ways that cannot be found with tools based on dimensionality reduction. Moreover, the topological framework offers multiple ways to inspect results, including an error estimate that is more accurate than model uncertainty. Further studies show how these ideas produce useful results in graph-based learning and image classification.

Computer Science↗
DOE OSTI2020

Topological Regularization via Persistence-Sensitive Optimization

Optimization, a key tool in machine learning and statistics, relies on regularization to reduce overfitting. Traditional regularization methods control a norm of the solution to ensure its smoothness. Recently, topological methods have emerged as a way to provide a more precise and expressive control over the solution, relying on persistent homology to quantify and reduce its roughness. All such existing techniques back-propagate gradients through the persistence diagram, which is a summary of the topological features of a function. Their downside is that they provide information only at the critical points of the function. We propose a method that instead builds on persistence-sensitive simplification and translates the required changes to the persistence diagram into changes on large subsets of the domain, including both critical and regular points. This approach enables a faster and more precise topological regularization, the benefits of which we illustrate with experimental evidence.

Nigmetov, Arnur↗