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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 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Quantum critical behavior of the hyperkagome magnet Mn 3 CoSi

β -Mn-type family alloys Mn 3 T X ( T = Co , Rh, and Ir; X = Si and Ge) have a three-dimensional antiferromagnetic (AF) corner-shared triangular network, i.e., the hyperkagome lattice. The antiferromagnet Mn 3 RhSi shows magnetic short-range order over a wide temperature range of approximately 500 K above the Néel temperature T N of 190 K. In this family of compounds, as the lattice parameter decreases, the long-range magnetic ordering temperature decreases. Mn 3 CoSi has the smallest lattice parameter and the lowest T N in the family. The quantum critical point (QCP) from AF to the quantum paramagnetic state is expected near a cubic lattice parameter of 6.15 Å . Although the Néel temperature of Mn 3 CoSi is only 140 K, the emergence of the quantum critical behavior in Mn 3 CoSi is discussed. We study how the magnetic short-range order appears in Mn 3 CoSi by using neutron scattering, μ SR , and bulk characterization such as specific heat capacity. According to the results, the neutron scattering intensity of the magnetic short-range order in Mn 3 CoSi does not change much at low temperatures from that of Mn 3 RhSi , although the μ SR short-range order temperature of Mn 3 CoSi is largely suppressed to 240 K from that of Mn 3 RhSi . Correspondingly, the volume fraction of the magnetic short-range order regions, as shown by the initial asymmetry drop ratio of μ SR above T N , also becomes small. Instead, the electronic-specific heat coefficient γ of Mn 3 CoSi is the largest in this Mn 3 T Si system, possibly due to the low-energy spin fluctuation near the quantum critical point. Published by the American Physical Society 2024

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Close Proximity to a Quantum Phase Transition in TmZn2⁢GaO5

TmZn2⁢GaO5 is a newly synthesized triangular lattice magnet that exhibits a unique quantum phase characterized by strong Ising anisotropy, a pseudodoublet crystal electric field ground state, and a low-energy gapped excitation at the 𝐾 point. Unlike its well-known counterparts, TmMgGa⁢O4 and YbMgGa⁢O4, this material crystallizes in a distinct hexagonal structure, leading to a platform for investigating frustrated magnetism. Magnetic susceptibility, heat capacity, and inelastic neutron scattering measurements confirm the absence of long-range magnetic order down to 50 mK, placing TmZn2⁢GaO5 in a distinct region of the transverse-field Ising model phase diagram. Theoretical calculations based on spin-wave theory and mean-field modeling reproduce key experimental observations, reinforcing the material’s placement in a quantum disordered or multipolar state near a quantum critical point that separates it from the low-energy phase of TmMgGa⁢O4. These results highlight its potential for exploring quantum disordered states, anisotropic excitations, and exotic quantum phases in frustrated spin systems.

Ennis, Matthew [Department of Physics, Duke Univer↗
DOE OSTI2026

Spontaneous Twirls and Structural Frustration in Moiré Materials

Structural twirls form spontaneously in the domain wall networks of some moiré materials. We show that in heterobilayers, neighboring twirl chiralities tend to antialign, forming staggered patterns that are well described by antiferromagnetic lattice 𝜙 4 theories. In moiré systems with triangular domains, this leads to frustration in the chirality configuration of the twirls and to hysteresis with respect to variation of the average twist angle and possibly other control parameters. As a result, we expect that in typical materials, the ordering temperature of twirls is about 10 3 K, and that thermal fluctuations in individual twirl chiralities freeze below room temperature.

Elastic deformation↗
DOE OSTI2025

Engineering 2D Square Lattice Hubbard Models in 90° Twisted GeX/SnX (X =S, Se) Moiré Superlattices

Because of the large-period superlattices emerging in moiré two-dimensional (2D) materials, electronic states in such systems exhibit low energy flat bands that can be used to simulate strongly correlated physics in a highly tunable setup. While many investigations have thus far focused on moiré flat bands and emergent correlated electron physics in triangular, honeycomb, and quasi-one-dimensional lattices, tunable moiré realizations of square lattices subject to strong correlations remain elusive. Here, in this work, we propose a feasible scheme to construct moiré square lattice systems by twisting two or more layers of 2D materials in a rectangular lattice by 90°. We demonstrate the concept with twisted GeX/SnX (X =S, Se) moiré superlattices and calculate their electronic structures from first principles. We show that the lowest conduction flat band in these systems can be described by a square lattice Hubbard model with parameters which can be controlled by varying the choice of host materials, number of layers, and external electric fields. In particular, twisted double bilayer GeSe realizes a square lattice Hubbard model with strong frustration due to the next-nearest-neighbor hopping that could host unconventional superconductivity, in close analogy to the Hubbard model for copper-oxygen planes of cuprate high-temperature superconductors. The presented scheme uses 90° twisted 2D materials with rectangular unit cells as a promising platform for realizing the physical phenomena of square lattice Hubbard models, establishing a new route for studying its rich phase diagram of magnetism, charge order, and unconventional superconductivity in a highly tunable setting.

2-dimensional systems↗
DOE OSTI2021

rmc-discord : reverse Monte Carlo refinement of diffuse scattering and correlated disorder from single crystals

A user-friendly program has been developed to analyze diffuse scattering from single crystals with the reverse Monte Carlo method. The approach allows for refinement of correlated disorder from atomistic supercells with magnetic or structural (occupational and/or displacive) disorder. The program is written in Python and optimized for performance and efficiency. Refinements of two user cases obtained with legacy neutron-scattering data demonstrate the effectiveness of the approach and the developed program. It is shown with bixbyite, a naturally occurring magnetic mineral, that the calculated three-dimensional spin-pair correlations are resolved with finer real-space resolution compared with the pair distribution function calculated directly from the reciprocal-space pattern. With the triangular lattice Ba 3 Co 2 O 6 (CO 3 ) 0.7 , refinements of occupational and displacive disorder are combined to extract the one-dimensional intra-chain correlations of carbonate molecules that move toward neighboring vacant sites to accommodate strain induced by electrostatic interactions. The program is packaged with a graphical user interface and extensible to serve the needs of single-crystal diffractometer instruments that collect diffuse-scattering data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

The structure of nano-twinned rhombohedral YCuO 2.66 solved by electron crystallography

In the search for frustrated spin interactions, a YCuO 2.66 phase has been synthesized by a treatment under oxygen pressure of YCuO 2.5 . X-ray powder diffraction and electron diffraction studies have been conducted. Electron diffraction shows that the sample is twinned on a 10 nm scale. Precession electron diffraction data obtained from a twinned crystal was treated in order to obtain intensities corresponding to only one of the orientations of the twins. From this data a structure solution was obtained where, as in YCuO 2.5 , the Cu atoms form triangular planes. The Cu atoms are linked in two dimensions by oxygen atoms in the present structure whereas in YCuO 2.5 they are only linked in one-dimensional chains.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Parallel DTFE Surface Density Field Reconstruction

We improve the interpolation accuracy and efficiency of the Delaunay tessellation field estimator (DTFE) for surface density field reconstruction by proposing an algorithm that takes advantage of the adaptive triangular mesh for line-of-sight integration. The costly computation of an intermediate 3D grid is completely avoided by our method and only optimally chosen interpolation points are computed, thus, the overall computational cost is significantly reduced. The algorithm is implemented as a parallel shared-memory kernel for large-scale grid rendered field reconstructions in our distributed-memory framework designed for N-body gravitational lensing simulations in large volumes. We also introduce a load balancing scheme to optimize the efficiency of processing a large number of field reconstructions. Our results show our kernel outperforms existing software packages for volume weighted density field reconstruction, achieving ~ 10x speedup, and our load balancing algorithm gains an additional ~ 3.6x speedup at scales with ~ 16k processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Canopus: A Paradigm Shift Towards Elastic Extreme-Scale Data Analytics on HPC

Scientific simulations on high performance computing (HPC) platforms generate large quantities of data. To bridge the widening gap between compute and I/O, and enable data to be more efficiently stored and analyzed, simulation outputs need to be refactored, reduced, and appropriately mapped to storage tiers. However, a systematic solution to support these steps has been lacking on the current HPC software ecosystem. To that end, this paper develops Canopus, a progressive JPEGlike data management scheme for storing and analyzing big scientific data. It co-designs the data decimation, compression and data storage, taking the hardware characteristics of each storage tier into considerations. With reasonably low overhead, our approach refactors simulation data into a much smaller, reduced-accuracy base dataset, and a series of deltas that is used to augment the accuracy if needed. The base dataset and deltas are compressed and written to multiple storage tiers. Data saved on different tiers can then be selectively retrieved to restore the level of accuracy that satisfies data analytics. Thus, Canopus provides a paradigm shift towards elastic data analytics and enables end users to make trade-offs between analysis speed and accuracy on-the-fly. We evaluate the impact of Canopus on unstructured triangular meshes, a pervasive data model used by scientific modeling and simulations. In particular, we demonstrate the progressive data exploration of Canopus using the “blob detection” use case on the fusion simulation data.

Lu, Tao↗
DOE OSTI2014

Design of vacuum vessel for Aditya Upgrade Tokamak

Aditya tokamak is a limiter tokamak device with a torus vacuum vessel having square cross section. The existing Aditya Tokamak is being upgraded into a machine with divertor operation and good plasma control to support the future Indian Fusion program in a big way. The up-gradation of Aditya Tokamak to diverter tokamak needs additional magnetic field coils for diverter operation, close to the vessel. To accommodate the coils, the existing vessel of square cross section will be replaced with a circular cross section. This vacuum vessel will be made up of stainless steel 304L. It is bakeable up to 150° C. The ultimate vacuum is ∼ 10{sup -9} torr. Two numbers of identical semi torus will be assembled to torus shaped vessel. The end flanges of the semi torus are connected with sealing double Viton 'O' rings to form a torus. The major radius of torus vessel is 750 mm and inner radius is 305 mm. Its wall thickness is ∼8 mm. The vacuum vessel will have 110 various sizes of ports/openings including 4 tangential ports. The nonstandard ports like rectangular and triangular ports are blanked with wire seals. In this paper, the design of vacuum vessel for Aditya Upgrade tokamak is presented. (author)

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

SparseLU, A Novel Algorithm and Math Library for Sparse LU Factorization

Decomposing sparse matrices into lower and upper triangular matrices (sparse LU factorization) is a key operation in many computational scientific applications. We developed SparseLU, a sparse linear algebra library that implements a new algorithm for LU factorization on general sparse matrices. The new algorithm divides the input matrix into tiles to which OpenMP tasks are created for factorization computation, where only tiles that contain nonzero elements are computed. For comparative performance analysis, we used the reference library SuperLU. Testing was performed on synthetically generated matrices which replicate the conditions of the real-world matrices. SparseLU is able to reach a mean speedup of ~29× compared to SuperLU.

Valero Lara, Pedro↗
DOE OSTI2025

SymProp: Scaling Sparse Symmetric Tucker Decomposition via Symmetry Propagation

Sparse symmetric tensors are an important class of tensors, and their decompositions serve as powerful tools for revealing low-rank structures. This paper introduces SymProp, a novel approach for scaling sparse symmetric Tucker decomposition by propagating symmetry through intermediate computations. SymProp optimizes two key computational kernels: Sparse Symmetric Tensor Times Same Matrix chain (S3 TTMc) for Higher-Order Orthogonal Iteration (HOOI) and Sparse Symmetric Tensor Times Same Matrix chain Times Core (S3 TTMcTC) for Higher-Order QR Iteration (HOQRI). Our method employs a metaprogramming-based index iteration approach to efficiently handle the upper triangular parts of intermediate dense symmetric tensors. SymProp achieves up to 50.9× speedup over SPLATT and up to 360.8× over Compressed Sparse Symmetric (CSS) format on the S3 TTMc operation. Moreover, our S3 TTMc and S3 TTMcTC implementations support tensor orders four levels higher than state-of-the-art methods. Our HOQRI demonstrates superior scalability and up to a 33.6× speedup over optimized HOOI. By enabling more scalable Tucker decompositions for higher orders, decomposition ranks, and dimension sizes, SymProp opens new possibilities for analyzing complex hypergraph structures in fields such as network science, data mining, and machine learning.

Li, Zecheng [North Carolina State University]↗
DOE OSTI2021

Impact of Thermal Stress on Attachment and Stability of High Temperature Strain Sensors

Static and dynamic strain sensing is required in high-temperature (HT), harsh-environments (HE) for industrial, aerospace, and energy sector applications to ensure equipment and process safety, reduce the cost of operation and maintenance, and increase process efficiency. Challenges that arise in HT HE sensing applications include device mounting, packaging, integrity, and stability to HT HE conditions. In previously reported work, static and dynamic strain surface acoustic wave resonator (SAWR) sensors were fabricated on langasite (LGS) and mounted on Inconel 625 strain beams for wireless testing up to 400°C. In this work, it has been identified that after subjecting the mounted SAWR strain sensor to thermal cycling between 100°C and 425°C, the measured sensitivity to dynamic strain decreased by 73% due to cracking at the adhesive/LGS interface, further deteriorating after additional thermal cycles. Strain modeling of the mounted LGS sensor chip up to 400°C revealed the existence of concentrated strain at the borders of the LGS chip. Microcracks caused by dicing make the chip boarders the most susceptible location for cracks to initiate when the sensor is subjected to thermal stress. In an attempt to mitigate the high strain at the LGS chip borders due to heating, adhesive shaping is proposed in this work. Simulations indicate a strain reduction of 50% at the border is achieved using both circular and triangular adhesive shapes, while also reducing the maximum strain over the entire adhesive/LGS interface by around 30%. Furthermore, the technique is thus promising for improving the integrity, reliability, and stability of static and dynamic strain sensors, particularly while operating under HT HE with several hundreddegree Celsius temperature excursions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Novel Technique for Developing Linearized Convex System Models from Experimentally Derived Data

This paper presents a novel technique for generating a convex system model from an experimentally derived data set which features variance among repeated measurements. The convex system model developed as a test case characterizes the dynamic system losses of a vanadium redox flow battery as a function of the active power output and the battery state of charge. The technique hinges on a pre-cleaning via clustering procedure which precedes the formation of a planar convex hull comprised of triangular simplices. The clustering procedure efficiently reduces the experimental data set while mitigating variance among repeated measurements and removes outliers. Ultimately, the lower evelope of the planar convex hull serves as the desired convex system model. The proposed technique reduces systematic model error which is otherwise present when directly developing a planar convex hull model based on an unreduced data set.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Dynamic Shaping of Grid Response of Multi-Machine Multi-Inverter Systems Through Grid-Forming IBRs

We consider the problem of controlling the frequency response of weakly-coupled multi-machine multi-inverter low-inertia power systems via grid-forming inverter-based resources (IBRs). In contrast to existing methods, our approach relies on dividing the larger system into multiple strongly-coupled subsystems, without ignoring either the underlying network or approximating the subsystem response as an aggregate harmonic mean model. Rather, through a structured clustering and recursive dynamic shaping approach, the frequency response of the overall system to load perturbations is shaped appropriately. We demonstrate the proposed approach for a three-node triangular configuration and a small-scale radial network. Furthermore, previous synchronization analysis for heterogeneous systems requires the machines to satisfy certain proportionality property. In our approach, the effective transfer functions for each cluster can be tuned by the IBRs to satisfy such property, enabling us to apply the shaping control to systems with a wider range of heterogeneous machines.

frequency-shaping control↗
DOE OSTI2024

GPU Accelerated Sparse Cholesky Factorization

The solution of sparse symmetric positive definite linear systems is an important computational kernel in large-scale scientific and engineering modeling and simulation. We will solve the linear systems using a direct method, in which a Cholesky factorization of the coefficient matrix is performed using a right-looking approach and the resulting triangular factors are used to compute the solution. Sparse Cholesky factorization is compute intensive. In this work we investigate techniques for reducing the factorization time in sparse Cholesky factorization by offloading some of the dense matrix operations on a GPU. We will describe the techniques we have considered. We achieved up to 4x speedup compared to the CPU-only version.

Karsavuran, M Ozan↗
DOE OSTI2022

A multi-modal scanning system to digitize CBRNE emergency response scenes

A handheld system developed to digitize a contextual understanding of the scene at a chemical, biological, radiological, nuclear and/or explosives (CBRNE) events is described. The system uses LiDAR and cameras to create a colorized 3D model of the environment, which helps domain experts that are supporting responders in the field. To generate the digitized model, a responder scans any suspicious objects and the surroundings by carrying the system through the scene. The scanning system provides a real-time user interface to inform the user about scanning progress and to indicate any areas that may have been missed either by the LiDAR sensors or the cameras. Currently, the collected data are post-processed on a different device, building a colorized triangular mesh of the encountered scene, with the intention of moving this pipeline to the scanner at a later point. The mesh is sufficiently compressed to be sent over a reduced bandwidth connection to a remote analyst. Furthermore, the system tracks fiducial markers attached to diagnostic equipment that is placed around the suspicious object. The resulting tracking information can be transmitted to remote analysts to further facilitate their supporting efforts. The paper will discuss the system's design, software components, the user interface used for scanning a scene, the necessary procedures for calibration of the sensors, and the processing steps of the resulting data. The discussion will close by evaluating the system's performance on 11 scenes.

Salathe, Marco↗
DOE OSTI2018

SIRIUS: Enabling Progressive Data Exploration for Extreme-Scale Scientific Data

Scientific simulations on high performance computing (HPC) platforms generate large quantities of data. To bridge the widening gap between compute and I/O, and enable data to be more efficiently stored and analyzed, simulation outputs need to be refactored, reduced, and appropriately mapped to storage tiers. However, a systematic solution to support these steps has been lacking in the current HPC software ecosystem. To that end, this paper develops SIRIUS, a progressive JPEG-like data management scheme for storing and analyzing big scientific data. It co-designs data decimation, compression, and data storage, taking the hardware characteristics of each storage tier into considerations. With reasonably low overhead, our approach refactors simulation data, using either topological or uniform decimation, into a much smaller, reduced-accuracy base dataset, and a series of deltas that is used to augment the accuracy if needed. The base dataset and deltas are compressed and written to multiple storage tiers. Data saved on different tiers can then be selectively retrieved to restore the level of accuracy that satisfies data analytics. Thus, SIRIUS provides a paradigm shift towards elastic data analytics and enables end users to make trade-offs between analysis speed and accuracy on-the-fly. This paper further develops algorithms to preserve statistics for data decimation, a common requirement for reducing data. Here, we assess the impact of SIRIUS on unstructured triangular meshes, a pervasive data model used in scientific simulations. In particular, we evaluate two realistic use cases: the blob detection in fusion and high-pressure area extraction in computational fluid dynamics.

97 MATHEMATICS AND COMPUTING↗