Search NASA⌕ Search

SEARCH · Search NASA

Results for “edge computing”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Observation of quantum Darwinism and the origin of classicality with superconducting circuits

The transition from quantum to classical behavior is a central question in modern physics. How can we rationalize everyday classical observations from an inherently quantum world? Quantum Darwinism offers a compelling framework to explain this by proposing that the environment redundantly encodes information about a quantum system, leading to the objective reality. Here, by leveraging cutting-edge superconducting quantum circuits, we observe the highly structured branching quantum states that support classicality and the saturation of quantum mutual information, establishing a robust verification of quantum Darwinism and the underlying geometric structure of quantum states. Additionally, we propose a particular class of observables that can be used as a computationally and experimentally inexpensive quantifier to probe quantum-to-classical transitions. Our investigation delves into how the quantum effects are inaccessible to observers, allowing only classical properties to be detected. It experimentally demonstrates the physical framework through which everyday classical observations emerge from underlying quantum principles and paves the way to settling the measurement problem.

Science & Technology - Other Topics↗
DOE OSTI2021

Scalable Approaches to Selecting Key Entities in Large Networked Infrastructure Systems

This work aims at bringing advances in discrete optimization algorithms to solving practical engineering problems at scale. Often times, in many engineering design problems, there is a need to select a small set of influential or representative elements from a large ground set of entities in an optimal fashion. Submodular optimization provides for a formal way to solve such problems. Common examples with infrastructure systems involve sensor placement and identification of key entities with certain objectives. However, scaling these approaches to large infrastructure systems can be challenging because of the high computational complexity of the overall framework that include the optimization algorithms as well as high-complexity compute-oracles that provide the necessary objective function values. In this work, we explore a well-studied and widely-applicable paradigm, namely leader-selection in a multi-agent networked setting in the context of scalable methodologies. We demonstrate novel frameworks that utilize variations of accelerated submodular optimization algorithms along with linear-algebraic methods that can help accelerate the oracle computations. We further explore this combination in conjunction with graph partitioning paradigms to take advantage of the accelerated algorithms in a distributed setting. Finally we demonstrate the key findings on a practical problem in an operational setting. For this, we leverage an example road network with approximately 18k nodes and 27k edges in a traffic control application, where we seek a limited number of k=200 key intersections. This problem can be solved in a serial setting in just under 5 hours providing more than 2 orders of magnitude speed-up over methods that do not consider acceleration techniques.

Visweswara Sathanur, Arun↗
DOE OSTI2025

From Edge to HPC: Investigating Cross-Facility Data Streaming Architectures

In this paper, we investigate three cross-facility data streaming architectures, Direct Streaming (DTS), Proxied Streaming (PRS), and Managed Service Streaming (MSS). We examine their architectural variations in data flow paths and deployment feasibility, and detail their implementation using the Data Streaming to HPC (DS2HPC) architectural framework and the SciStream memory-to-memory streaming toolkit on the production-grade Advanced Computing Ecosystem (ACE) infrastructure at Oak Ridge Leadership Computing Facility (OLCF). We present a workflow-specific evaluation of these architectures using three synthetic workloads derived from the streaming characteristics of scientific workflows. Through simulated experiments, we measure streaming throughput, round-trip time, and overhead under work sharing, work sharing with feedback, and broadcast and gather messaging patterns commonly found in AI-HPC communication motifs. Our study shows that DTS offers a minimal-hop path, resulting in higher throughput and lower latency, whereas MSS provides greater deployment feasibility and scalability across multiple users but incurs significant overhead. PRS lies in between, offering a scalable architecture whose performance matches DTS in most cases.

George, Anjus [ORNL] (ORCID:0000000179737061)↗
DOE OSTI2024

Simulation of gas mixture dynamics in a pipeline network using explicit staggered-grid discretization

Here we develop an explicit staggered finite difference discretization scheme for simulating the transport of highly heterogeneous gas mixtures through pipeline networks. This study is motivated by the proposed blending of hydrogen into natural gas pipelines to reduce end use carbon emissions while using existing pipeline systems throughout their planned lifetimes. Our computational method accommodates an arbitrary number of constituent gases with very different physical properties that may be injected into a network with significant spatiotemporal variation. In this setting, the gas flow physics are highly location- and time- dependent, so that local composition and nodal mixing must be accounted for. The resulting conservation laws are formulated in terms of pressure, partial densities and flows, and volumetric and mass fractions of the constituents. We include non-ideal equations of state that employ linear approximations of gas compressibility factors, so that the pressure dynamics propagate locally according to a variable wave speed that depends on mixture composition and density. We derive compatibility relationships for network edge boundary values that are more complex than for a homogeneous gas. The simulation method is evaluated on initial boundary value problems for a single pipe and a small network, is cross-validated with a lumped element simulation, and used to demonstrate a local monitoring and control policy for maintaining allowable concentration levels.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Qualitative and quantitative analysis of neutron irradiation effects in SiC/SiC composites using X-ray computed tomography

Silicon carbide fiber-reinforced silicon carbide matrix (SiC/SiC) composites are candidate materials for cladding of light water reactor (LWR) fuels. Loss of fission product gas retention due to the formation of microcrack networks is considered a potential failure mechanism for SiC/SiC-cladded fuels. In this work, a variety of SiC/SiC composite tubes were irradiated with and without an LWR-relevant radial heat flux in the High Flux Isotope Reactor, followed by detailed characterization with X-ray computed tomography (XCT). This first set of XCT data for neutron-irradiated samples confirmed that the internal stresses arising from a combination of temperature gradients and irradiation-induced swelling act as the primary driver for cracking. Consequently, while the observed cracking patterns varied depending on the tube architectures, the sharp edges of relatively large pores were found to be the common stress concentrator. These findings are useful to help improve the design and manufacturing of SiC/SiC fuel claddings for reduced failure probability.

42 ENGINEERING↗
DOE OSTI2022

Atomistic measurement and modeling of intrinsic fracture toughness of two-dimensional materials

Quantifying the intrinsic mechanical properties of two-dimensional (2D) materials is essential to predict the long-term reliability of materials and systems in emerging applications ranging from energy to health to next-generation sensors and electronics. Currently, measurements of fracture toughness and identification of associated atomistic mechanisms remain challenging. Herein, we report an integrated experimental–computational framework in which in-situ high-resolution transmission electron microscopy (HRTEM) measurements of the intrinsic fracture energy of monolayer MoS 2 and MoSe 2 are in good agreement with atomistic model predictions based on an accurately parameterized interatomic potential. Changes in crystalline structures at the crack tip and crack edges, as observed in in-situ HRTEM crack extension tests, are properly predicted. Such a good agreement is the result of including large deformation pathways and phase transitions in the parameterization of the inter-atomic potential. The established framework emerges as a robust approach to determine the predictive capabilities of molecular dynamics models employed in the screening of 2D materials, in the spirit of the materials genome initiative. Moreover, it enables device-level predictions with superior accuracy (e.g., fatigue lifetime predictions of electro- and opto-electronic nanodevices).

Zhang, Xu↗
DOE OSTI2016

Topological defects and topological materials

We report that defects in materials that interpolate between two orientation states such as domain walls and those which relate to some kind of material discontinuity, e.g. dislocations and disclinations, comprise topological defects. Noncoplanar vector arrangements, e.g. vortices, skyrmions, and possibly monopole-like excitations, also belong to this family of defects. Presence of such defects affects many of the macroscopic properties of materials, for example electronic transport and strength. Materials in which topological aspects, e.g. edge or surface states, affect the electronic, transport and other properties can be described as topological materials. Topological insulators, topological superconductors and certain Dirac materials, e.g. graphene and Weyl semimetals, belong to this class of materials. Lastly, these materials possess unusual functionalities potentially useful in a variety of information processing and quantum computation based applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machining of Thin-Walled Structures From Stiffness-Driven Additively Manufactured Preform Geometry

Additive manufacturing provides the means to build component preforms with reduced excess material to create functional parts. In the case of aero-structural and aero-engine components, additive manufacturing technologies offer the possibility to substantially reduce the volume of material to be removed by machining operations. To achieve this objective, the preform must be built with the minimum material necessary to contain the final geometry and simultaneously provide enough stiffness to withstand the magnitude of the machining forces. This work describes a computationally efficient method to calculate the geometry required from the preform to reliably manufacture typical thin-walled structures via finish machining processes. This is achieved by defining the preform with near constant static stiffness across the width of the preform, in combination with a prescribed magnitude of stiffness at the top edge of the preform. The prescribed static stiffness is the function of the machining force magnitude, a direct consequence of the preselected cutting conditions. In conclusion, this article illustrates the application of the method to a straight single boundary thin-walled structure as an introduction case and for ease of description.

Additive manufacturing↗
DOE OSTI2016

SU-F-T-557: Evaluation of Detector Response in Rectangular Small Field Dosimetry

Purpose: As stereotactic treatment modalities grow towards becoming the standard of care, the need for accurate dose computation in small fields is becoming increasingly essential. The purpose of this study is to evaluate the response of different detectors, intended for small field dosimetry, in jaw defined small rectangular fields by analyzing output factors from a stereotactic clinical accelerator. Methods: Two Dosimeters, the Exradin A26 Microionization Chamber (Standard Imaging) and Edge Diode Detector (Sun Nuclear) were used to measure output factors taken on the Varian Edge Stereotactic Linear accelerator. Measurements were taken at 6MV and 6FFF at 10cm depth, 100cm SSD in a 48×48×40cm3 Welhoffer BluePhantom2 (IBA) with X and Y jaws set from 0.6 to 2.0cm. Output factors were normalized to a 5×5cm2 machine-specific reference field. Measurements were made in the vertical orientation for the A26 and horizontal orientation for both the A26 and Edge. Output factors were measured as: OF{sub FS} = M{sub FS}/M{sub ref} where M{sub FS} and M{sub ref} are the measured signals for the clinical field and the reference field, respectively. Measured output factors were then analyzed to establish relative responses of the detectors in small fields. Results: At 6MV the Edge detector exhibited a variation in output factors dependent on jaw positioning (X-by-Y vs Y-by-X) of 5.7% of the 5×5cm reference output and a variation of 3.33% at 6FFF. The A26 exhibited variation of output factor dependent on jaw positioning of upto 7.7% of the 5×5cm reference field at 6MV and upto 5.33% at 6FFF. Conclusion: Both the Edge detector and A26 responded as expected at small fields however a dependence on the jaw positioning was noted. At 6MV and 6FFF the detector response showed an increased dependence on the positioning of the X jaws as compared to the positioning of the Y jaws.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Tunable plasmon-enhanced second-order optical nonlinearity in transition-metal dichalcogenide nanotriangles (Final Report for SC0008712)

The control of light-matter interaction at the nanoscale is a grand challenge that cuts across modern photonics, plasmonics, and optoelectronics. The development of simulation tools for predictive modeling of nanostructured light emitters and absorbers is critical to successfully tackling this grand challenge. Existing state-of-the-art tools are not suitable for the following reasons: a) Phenomenological models of materials, commonly employed in the full-wave solutions to Maxwell’s equations, are fit to experimental data for bulk materials and fall short when attempting to analyze nanostructures. b) Nonlinearities in optical response can arise solely from nanostructuring, and require time-dependent quantum electronic transport techniques to capture. c) Emergent materials with exotic and tunable physical properties are increasingly used, but their properties are very sensitive to the substrate, impurities, and edge termination, and require an accurate description of interband and intraband processes, as well as dissipation. d) Microscopic modeling of electronic transport usually employs simplified electrodynamics, and realistic models that couple full electronic transport with full-wave electrodynamics are needed for accurate photonics simulations. The objective of this project was to computational tools that will overcome the limitations faced by current modeling tools and provide an unprecedented level of accuracy for predictive modeling of light-matter interaction at the nanoscale.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Model Advancements to Enable Impact Analysis of Climate Change on Streamflow Temperature

With support from the Department of Energy’s Water Power Technologies Office, Pacific Northwest National Laboratory (PNNL) has developed new tools that incorporate cutting-edge climate and hydrological science capabilities to assess the potential long-term impacts of future climate conditions on unregulated streamflow and water temperature within watershed-river-reservoir systems. The objectives of this project were achieved by enhancing key hydrologic and hydrodynamic models and transferring them to a high-performance computing environment to provide a high-spatiotemporal resolution, multi-scale modeling framework. The new modeling framework has the potential to quantify risks related climate change impacts on runoff, unregulated streamflow, and water temperature. Initial development and demonstration of the modeling framework was conducted under historical and future climate conditions in the Columbia River Basin in the Pacific Northwest and the Connecticut River Basin in New England.

13 HYDRO ENERGY↗
DOE OSTI2019

Identification of RR Lyrae stars in multiband, sparsely-sampled data from the Dark Energy Survey using template fitting and Random Forest classification

Many studies have shown that RR Lyrae variable stars (RRL) are powerful stellar tracers of Galactic halo structure and satellite galaxies. The Dark Energy Survey (DES), with its deep and wide coverage (g ~ 23.5 mag in a single exposure; over 5000 deg2) provides a rich opportunity to search for substructures out to the edge of the Milky Way halo. However, the sparse and unevenly sampled multiband light curves from the DES wide-field survey (a median of four observations in each of grizY over the first three years) pose a challenge for traditional techniques used to detect RRL. We present an empirically motivated and computationally efficient template-fitting method to identify these variable stars using three years of DES data. When tested on DES light curves of previously classified objects in SDSS stripe 82, our algorithm recovers 89% of RRL periods to within 1% of their true value with 85% purity and 76% completeness. Using this method, we identify 5783 RRL candidates, ~28% of which are previously undiscovered. This method will be useful for identifying RRL in other sparse multiband data sets.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Compiling Quantum Circuits for Dynamically Field-Programmable Neutral Atoms Array Processors

Dynamically field-programmable qubit arrays (DPQA) have recently emerged as a promising platform for quantum information processing. In DPQA, atomic qubits are selectively loaded into arrays of optical traps that can be reconfigured during the computation itself. Leveraging qubit transport and parallel, entangling quantum operations, different pairs of qubits, even those initially far away, can be entangled at different stages of the quantum program execution. Such reconfigurability and non-local connectivity present new challenges for compilation, especially in the layout synthesis step which places and routes the qubits and schedules the gates. In this paper, we consider a DPQA architecture that contains multiple arrays and supports 2D array movements, representing cutting-edge experimental platforms. Within this architecture, we discretize the state space and formulate layout synthesis as a satisfiability modulo theories problem, which can be solved by existing solvers optimally in terms of circuit depth. For a set of benchmark circuits generated by random graphs with complex connectivities, our compiler OLSQ-DPQA reduces the number of two-qubit entangling gates on small problem instances by 1.7x compared to optimal compilation results on a fixed planar architecture. To further improve scalability and practicality of the method, we introduce a greedy heuristic inspired by the iterative peeling approach in classical integrated circuit routing. Using a hybrid approach that combined the greedy and optimal methods, we demonstrate that our DPQA-based compiled circuits feature reduced scaling overhead compared to a grid fixed architecture, resulting in 5.1X less two-qubit gates for 90 qubit quantum circuits. These methods enable programmable, complex quantum circuits with neutral atom quantum computers, as well as informing both future compilers and future hardware choices.

Physics↗
DOE OSTI2024

Multi-Level Structural Damage Characterization Using Sparse Acoustic Sensor Networks and Knowledge Transferred Deep Learning

Standard structural health monitoring techniques face well-known difficulties for comprehensive defect diagnosis in real-world structures that have structural, material, or geometric complexity. This motivates the exploration of machine-learning-based structural health monitoring methods in complex structures. However, creating sufficient training data sets with various defects is an ongoing challenge for data-driven machine (deep) learning algorithms. The ability to transfer the knowledge of a trained neural network from one component to another or to other sections of the same component would drastically reduce the required training data set. Also, it would facilitate computationally inexpensive machine learning based inspection systems. In this work, a machine-learning-based multi-level damage characterization is demonstrated with the ability to transfer trained knowledge within the sparse sensor network. A novel network spatial assistance and an adaptive convolution technique are proposed for efficient knowledge transfer within the deep learning algorithm. Proposed structural health monitoring method is experimentally evaluated on an aluminum plate with artificially induced defects. It was observed that the method improves the performance of knowledge transferred damage characterization by 50% during localization and 24% during severity assessment. Further, experiments using time windows with and without multiple edge reflections are studied. Results reveal that multiply scattered waves contain rich and deterministic defect signatures that can be mined using deep learning neural networks, improving the accuracy of both identification and quantification. In the case of a fixed sensor network, using multiply scattered waves shows 100% prediction accuracy at all levels of damage characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Energy release rate approximation for edge cracks using higher-order topological derivatives

Topological derivatives provide the variation of a functional when an infinitesimal hole is introduced into the domain. In Silva et al. (J Mech Phys Solids 59(5):925–939, 2011), the authors developed a first-order approximation of the energy release rate associated with a small crack at any boundary location and at any orientation using a first-order topological derivative. This approach offers significant computational advantages over other methods because (i) it requires only a single analysis while other methods require an analysis for each crack size-location-direction combination, and (ii) it is performed on the non-cracked domain, removing the need to create very fine meshes in the vicinity of the crack. In the present study, higher-precision approximations of the energy release rate are developed using higher-order topological derivatives which allow the analyst to accurately treat longer cracks and determine the crack lengths for which the first-order approximation is accurate.

42 ENGINEERING↗
DOE OSTI2021

Equation-of-Motion Coupled-Cluster Cumulant Green’s Function for Excited States and X-ray Spectra

Green’s function methods within many-body perturbation theory provide a general framework for treating electronic correlations in excited states and spectra. Conventional methods using the Dyson equation or the cumulant expansion are typically based on the GW self-energy approximation. In order to extend this approximation in molecular systems, a non-perturbative real-time coupled-cluster cumulant Green’s function approach has been introduced, where the cumulant is obtained as the solution to a set of coupled first order, non-linear differential equations. This approach naturally includes non-linear corrections to conventional cumulant Green’s function techniques where the cumulant is linear in the GW self-energy. The method yields the spectral function associated with the core Green’s function, which is directly related to the x-ray photoemission spectra (XPS) of molecular systems. The approach also yields very good results for binding energies and satellite excitations. The x-ray absorption spectrum (XAS) is then obtained as a convolution of the core spectral function and an effective one-body absorption spectrum. Here this approach is extended to include the full coupled-cluster-singles (CCS) core Green’s function by including the complete form of the non-linear contributions to the cumulant as well as all single, double, and triple cluster excitations in the CC amplitude equations. This approach naturally builds in orthogonality and shake-up effects analogous to those in the Mahan-Noizeres-de Dominicis edge singularity corrections that enhance the XAS near the edge. Themethod is illustrated for the XPS and XAS of NH 3 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

A Numerically Exact Approach to Quantum Impurity Problems in Realistic Lattice Geometries

We review a controlled numerical approach to quantum impurity problems in realistic geometries, consisting of exactly mapping the complete lattice Hamiltonian onto an equivalent one dimensional system through a unitary transformation. The resulting dimensional and entanglement reduction allows one to study the quantum many-body problem on arbitrary d-dimensional lattices using the density matrix renormalization group (DMRG) method. The real-space resolution allows one to position the impurities at the boundary or bulk of the sample and to study screening effects due to edge or surface modes. We describe how to generalize this approach to multi-impurity problems, discuss applications and possible extensions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗