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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Accurate numerical simulations of open quantum systems using spectral tensor trains

Decoherence between qubits is a major bottleneck in quantum computations. Decoherence results from intrinsic quantum and thermal fluctuations as well as noise in the external fields that perform the measurement and preparation processes. With prescribed colored noise spectra for intrinsic and extrinsic noise, we present a numerical method, Quantum Accelerated Stochastic Propagator Evaluation (Q-ASPEN), to solve the time-dependent noise-averaged reduced density matrix in the presence of intrinsic and extrinsic noise. Q-ASPEN is arbitrarily accurate and can be applied to provide estimates for the resources needed to error-correct quantum computations. We employ spectral tensor trains, which combine the advantages of tensor networks and pseudospectral methods, as a variational ansatz to the quantum relaxation problem and optimize the ansatz using methods typically used to train neural networks. Here, the spectral tensor trains in Q-ASPEN make accurate calculations with tens of quantum levels feasible. We present benchmarks for Q-ASPEN on the spin-boson model in the presence of intrinsic noise and on a quantum chain of up to 32 sites in the presence of extrinsic noise. In our benchmark, the memory cost of Q-ASPEN scales as a low-order polynomial in the size of the system once the number of system states surpasses the number of basis functions used in the spectral expansion.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
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

Verification of Coupled Hydraulic Fracturing Simulators Using Laboratory-Scale Experiments

In this work, we aim to verify the predictions of the numerical simulators, which are used for designing field-scale hydraulic stimulation experiments. Although a strong theoretical understanding of this process has been gained over the past few decades, numerical predictions of fracture propagation in low-permeability rocks still remains a challenge. Against this background, we performed controlled laboratory-scale hydraulic fracturing experiments in granite samples, which not only provides high-quality experimental data but also a well-characterized experimental set-up. Using the experimental pressure responses and the final fracture sizes as benchmark, we compared the numerical predictions of two coupled hydraulic fracturing simulators—CSMP and GEOS. Both the simulators reproduced the experimental pressure behavior by implementing the physics of Linear Elastic Fracture Mechanics (LEFM) and lubrication theory within a reasonable degree of accuracy. The simulation results indicate that even in the very low-porosity (1–2 %) and low-permeability (10 -18 m 2 - 10 -19 m 2 ) crystalline rocks, which are usually the target of EGS, fluid-loss into the matrix and unsaturated flow impacts the formation breakdown pressure and the post-breakdown pressure trends. Therefore, underestimation of such parameters in numerical modeling can lead to significant underestimation of breakdown pressure. The simulation results also indicate the importance of implementing wellbore solvers for considering the effect of system compressibility and pressure drop due to friction in the injection line. The varying injection rate as a result of decompression at the instant of fracture initiation affects the fracture size, while the entry friction at the connection between the well and the initial notch may cause an increase in the measured breakdown pressure.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Generic, Sparse Tensor Core for Neural Networks

Sparse neural network attracts more attention for model compression, fast execution, and power reduction. The state-of-the-art designed sparse tensor core for structured and static sparsity, which did not support well for generic or dynamic sparsity. We design a sparse tensor core to support generic sparsity pruning with a novel hybrid and blocked sparse matrix storage format, HB-ELL, which saves computation and storage while keeping the most significant elements, as well as supporting dynamic sparsity for data flow in neural networks. We achieve better performance with preliminary results than the state-of- the-art on an NVIDIA GPU simulator.

Wu, Xiaolong↗
DOE OSTI2026

A GPU-based compressible combustion solver for applications exhibiting disparate space and time scales

High-speed chemically active flows pose significant computational challenges due to their disparate space and time scales, with stiff chemistry often dominating simulation time. While modern scientific computing programs achieve exascale performance by leveraging graphics processing units (GPUs), existing GPU-based compressible combustion solvers face critical limitations in memory management, load balancing, and handling the highly localized nature of chemical reactions. To this end, we present a high-performance compressible reacting flow solver built on the AMReX framework and optimized for multi-GPU settings. Here, our approach addresses three GPU performance bottlenecks: memory access patterns through column-major storage optimization, computational workload variability via a bulk-sparse integration strategy for chemical kinetics, and multi-GPU load distribution for adaptive mesh refinement applications. The solver adapts existing matrix-based chemical kinetics formulations to multi-grid contexts. Using representative combustion applications, including 2D and 3D detonations and a 3D jet-in-crossflow configuration, we demonstrate 1.4–5× performance improvements over initial implementations on an in-house cluster of NVIDIA H100 GPUs, and near-ideal weak scaling on the Frontier supercomputer (Oak Ridge Leadership Computing Facility) with up to 1024 AMD Instinct MI250X GPUs. Roofline analysis reveals substantial improvements in arithmetic intensity for both convection (∼ 10 ×) and chemistry (∼ 4 ×) routines, confirming efficient utilization of GPU memory bandwidth and computational resources.

42 ENGINEERING↗
DOE OSTI2021

Three-dimensional atomic scale characterization of {11$\overline{2}$2} twin boundaries in titanium

The {11$\overline{2}$2}<11$\overline{23}$> compression twin can accommodate a considerable amount of strain under c-axis compression in Ti. However, unlike the tensile twin, the structure of the {10$\overline{1}$2}$\langle$$\overline{1}$011$\rangle$ compressive twins has not been completely characterized. In this study, we apply a combined technique of HR-TEM characterization, topological analysis, and atomistic simulations to explore the facets that bound the {11$\overline{2}$2}<11$\overline{23}$> twin in Ti. In addition to the currently known facets (CTB and B-Py), six new facets are observed and categorized for the first time from atomic-scale TEM observations along five crystallographic directions. The six new facets are (11$\overline{2}$0)//(11$\overline{2}$6), PrPr1, PyPy1, (2$\overline{11}$1)//($\overline{1}$2$\overline{1}$2), (1$\overline{1}$04)//(01$\overline{11}$), and (01$\overline{1}$0)//(2$\overline{11}$4). Results from the topological and computational analysis are in reasonable agreement with and support the HRTEM observations. Specifically, (1) the observed facets align with low-index interfaces in both twin and matrix domains, (2) the facets with lower surface energies are found to form extended interfaces, and (3) high-surface-energy facets are found at the twin tip region and explained by the fact that the energy of the combined facet and facet junction configuration is energetically preferred in the twin tip region. These results not only provide a comprehensive understanding of the 3D structure of the {11$\overline{2}$2}<11$\overline{23}$> compressive twins in Ti, but also validate the MD procedure and the Ti interatomic potential employed. This is extremely important for future study of the {11$\overline{2}$2} twin mobility and interactions with other defects, both features that remain extremely challenging to capture in experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Understanding the High-Temperature Deformation Behaviors in Additively Manufactured Al6061+TiC Composites via In Situ Neutron Diffraction

Aluminum matrix composites (AMCs) are designed to enhance the performance of conventional aluminum alloys for engineering applications at both room and elevated temperatures. However, the dynamic phase-specific deformation behavior and load-sharing mechanisms of AMCs at elevated temperatures have not been extensively studied and remain unclear. Here, in situ neutron diffraction experiments are employed to reveal the phase-specific structure evolution of additively manufactured Al6061+TiC composites under compressive loading at 250 °C. It is found that the addition of a small amount of nano-size TiC significantly alters the deformation behavior and increases the strength at 250 °C in comparison to the as-printed Al6061. Unlike the two-stage behavior observed in Al6061, the Al6061+TiC composites exhibit three stages during compression triggered by changes in the interphase stress states. Further analysis of Bragg peak intensity and broadening reveals that the presence of TiC alters the dislocation activity during deformation at 250 °C by influencing dislocation slip planes and promoting dislocation accumulation. These findings provide direct experimental observations of the phase-specific dynamic process in AMCs under deformation at an elevated temperature. The revealed mechanisms provide insights for the future design and optimization of high-performance AMCs.

36 MATERIALS SCIENCE↗
DOE OSTI2026

BoBa

BoBa is a C++ software library for working with large matrices, tensors, and tensor decompositions. The library provides tools for dense matrix and tensor operations, tensor decompositions, and tensor decomposition methods that support modern CPU and GPU architectures. It includes portable abstractions for linear algebra, tensor algebra, and multidimensional computation. BoBa is intended for scientific computing applications that involve large multidimensional data sets or high dimensional mathematical models. Its capabilities support tasks such as data compression, linear algebra, efficient numerical computation, and the development of scalable algorithms for heterogeneous hardware. Tutorials, tests, and example applications are included to help users learn and apply the library.

Yao, Jin [Lawrence Livermore National Laboratory (↗
DOE OSTI2022

Pairwise connected tensor network representation of path integrals

It has been recently shown how the tensorial nature of real-time path integrals (PIs) involving the Feynman-Vernon influence functional can be utilized with matrix product states, taking advantage of the finite length of the bath-induced memory. Tensor networks (TNs) promise to provide a unified language to express the structure of a PI. A generalized TN specifically incorporating the pairwise interaction structure of the influence functional and its invariance with respect to the average forward-backward position or the sojourn value in the form of the blip representation is derived and implemented. This pairwise connected TNPI (PC-TNPI) is illustrated through applications to typical spin-boson problems and explorations of the differences caused by the exact form of the spectral density. The storage and performance scalings are reported, showing the compactness of the representation and the efficiency of the contraction process. Finally, taking advantage of the compressed representation, the viability of using PC-TNPI for simulating multistate problems is demonstrated. The PC-TNPI structure can be shown to yield other TN algorithms currently in use. Consequently, it should be possible to use it as a starting point for deriving other optimized procedures.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Design of a V–Ti–Ni alloy with superelastic nano-precipitates

Stress-induced martensitic transformations enable metastable alloys to exhibit enhanced strain hardening capacity, leading to improved formability and toughness. As is well-known from transformation-induced plasticity (TRIP) steels, however, the resulting martensite can limit ductility and fatigue life due to its intrinsic brittleness. In this work, we explore an alloy design strategy that utilizes stress-induced martensitic transformations but does not retain the martensite phase. This strategy is based on the introduction of superelastic nano-precipitates, which exhibit reverse transformation after initial stress-induced forward transformation. To this end, utilizing ab-initio simulations and thermodynamic calculations we designed and produced a V 45 Ti 30 Ni 25 (at%) alloy. In this alloy, TiNi is present as nano-precipitates uniformly distributed within a ductile V-rich base-centered cubic (bcc) β matrix, as well as being present as a larger matrix phase. We characterized the microstructure of the produced alloy using various scanning electron microscopy (SEM) and transmission electron microscopy (TEM) methods. Furthermore, the bulk mechanical properties of the alloy are demonstrated through tensile tests, and the reversible transformation in each of the TiNi morphologies were confirmed by in-situ TEM micro-pillar compression experiments, in-situ high-energy diffraction synchrotron cyclic tensile tests, indentation experiments, and differential scanning calorimetry experiments. The observed transformation pathways and variables impacting phase stability are critically discussed.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Strain hardening magnesium-silicate-hydrate composites (SHMSHC) reinforced with short and randomly oriented polyvinyl alcohol microfibers

Magnesium-silicate-hydrate (M-S-H) cement has emerged as an alternative binder with potentially lower energy requirements and emissions. Due to the intrinsically low pH values of its matrix, however, normal steel reinforcement is not appropriate for M-S-H system. To toughen the matrix and to overcome the brittle nature of the material, a new strain hardening magnesium-silicate-hydrate composite (SHMSHC) is developed for the first time by incorporating 2 vol% short and randomly oriented polyvinyl alcohol (PVA) microfibers. The resulting SHMSHC exhibits significant strain hardening with a tensile strain capacity of more than 3%, a compressive strength beyond 50 MPa and a tensile strength of around 3 MPa. Remarkably, saturated multiple cracking with a tight crack width less than 10 μm invisible to the human naked eyes was observed. The newly developed SHMSHC addresses the fundamental challenge of M-S-H binder and is expected to widen possible application areas of M-S-H system.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Recovery of MOF-5 from Extreme High-Pressure Conditions Facilitated by a Modern Pressure Transmitting Medium

Here mechanisms underlying the mechanically induced amorphization of metal-organic frameworks (MOFs) are of current interest, and both high-pressure experimentation and molecular dynamics simulations have been used to reveal the fundamentals of load bearing, deformation, and pressure-induced amorphization (PIA) in these highly porous materials. Unfortunately, MOFs are typically highly susceptibility to amorphization, which limits the conditions under which they can be processed and used. However, their flexible structures can be stabilized to high pressures by incorporating guest species into the framework matrix. In this study, a large molecule pressure transmitting medium (DAPHNE 7575 ) is used as a structure-fortifying guest species to stabilize the prototypical MOF -5 to ultra-high pressures ( >9 GPa) and enable the recovery of crystalline material upon decompression. Structural changes associated with the penetration of the pressure transmitting medium on compression are examined using a combination of high pressure synchrotron powder diffraction and molecular dynamics simulations. This work enhances the understanding of PIA in MOFs while showcasing a potential route for the stabilization of MOFs to surprisingly high-pressures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

3D Seismic Anatomy of a Watershed Reveals Climate–Topography Coupling That Drives Water Flowpaths and Bedrock Weathering

To investigate how bedrock transforms to soil, we mapped the topography of the interface demarcating onset of weathering under an east-west trending shale watershed in the Valley and Ridge province in the U.S.A. Using wave-equation travel-time tomography from a seismic array of >4000 geophones, we obtained a 3D P-wave velocity (Vp) model that resolves structures ~20 m below land surface (mbls). The depth of mobile soil and the onset of dissolution of chlorite roughly match Vp = 600 m/s and Vp = 2700 m/s, respectively. Chlorite dissolution initiates porosity growth in the shale matrix. Depth to the 2700 m/s contour is greater under the N- as compared to S-facing hillslopes and under sub-planar as compared to concave-up land surfaces. Broadly, the geometries of the ‘soil’ and ‘chlorite’ Vp contours are consistent with the calculated potential for shear fracture opening under weak regional compression. However, this calculated fracture potential does not consistently explain observations related to N- vs. S-facing aspect nor fracture density observed by borehole televiewer. Apparently, regional compression is only a secondary influence on Vp: the primary driver of P-wave slowing in the upper layers of this catchment is topographic control of reactive water flowpaths and their integrated effects on weathering. Here, the Vp result is best explained as the long-term integrated effect of groundwater flow-induced geochemical weathering of shale in response to climate-driven patterns of micro- and macro-topography.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A Linear-Complexity Tensor Butterfly Algorithm for Compressing High-Dimensional Oscillatory Integral Operators

This paper presents a multilevel tensor compression algorithm called tensor butterfly algorithm for efficiently representing large-scale and high-dimensional oscillatory integral operators, including Green's functions for wave equations and integral transforms such as Radon transforms and Fourier transforms. The proposed algorithm leverages a tensor extension of the so-called complementary low-rank property of existing matrix butterfly algorithms. The algorithm partitions the discretized integral operator tensor into subtensors of multiple levels and factorizes each subtensor at the middle level as a Tucker-type interpolative decomposition, whose factor matrices are formed in a multilevel fashion. For a d-dimensional (d > 1) integral operator discretized into a 2d-mode tensor with n2d entries, the overall CPU time and memory requirement scale as O(nd), in stark contrast to the O(nd log n) complexity of existing matrix algorithms such as matrix butterfly algorithms and fast Fourier transforms (FFTs), where n is the number of points per direction. When comparing with other tensor algorithms such as quantized tensor train (QTT), the proposed algorithm also shows superior CPU and memory performance for tensor contraction. Remarkably, the tensor butterfly algorithm can efficiently model high-frequency Green's function interactions between two unit cubes, each spanning 512 wavelengths per direction, which represents problems of scale over 512× larger than that existing butterfly algorithms can handle, with the same amount of computation resources. On the other hand, for a problem representing 64 wavelengths per direction, which is the largest size existing algebraic matrix algorithms can handle, our tensor butterfly algorithm exhibits 200x speedups and 30× memory reduction compared with existing ones. Moreover, the tensor butterfly algorithm also permits O(nd)-complexity FFTs and Radon transforms up to d = 6 dimensions.

Kielstra, P Michael↗
DOE OSTI2023

Uncovering acoustic signatures of pore formation in laser powder bed fusion

Abstract We present a machine learning workflow to discover signatures in acoustic measurements that can be utilized to create a low-dimensional model to accurately predict the location of keyhole pores formed during additive manufacturing processes. Acoustic measurements were sampled at 100 kHz during single-layer laser powder bed fusion (LPBF) experiments, and spatio-temporal registration of pore locations was obtained from post-build radiography. Power spectral density (PSD) estimates of the acoustic data were then decomposed using non-negative matrix factorization with custom $$\varvec{k}$$ k -means clustering (NMF $$\varvec{k}$$ k ) to learn the underlying spectral patterns associated with pore formation. NMF $$\varvec{k}$$ k returned a library of basis signals and matching coefficients to blindly construct a feature space based on the PSD estimates in an optimized fashion. Moreover, the NMF $$\varvec{k}$$ k decomposition led to the development of computationally inexpensive machine learning models which are capable of quickly and accurately identifying pore formation with classification accuracy of supervised and unsupervised label learning greater than 95% and 90%, respectively. The intrinsic data compression of NMF k , the relatively light computational cost of the machine learning workflow, and the high classification accuracy makes the proposed workflow an attractive candidate for edge computing toward in-situ keyhole pore prediction in LPBF.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Adhesion in thermomechanically processed seaweed-lignocellulosic composite materials

The increasing concerns associated with petroleum-derived polymers motivate the development of sustainable, renewably sourced alternatives. In ubiquitous applications such as structural materials for infrastructure, the built environment as well as packaging, where natural materials such as wood are used, we rely on nonrenewable and nondegradable polymers to serve as adhesives. In wood panels, such as medium density fiberboards (MDFs), formaldehyde-based resins are predominantly used to bond wood fibers and to provide strength to the materials. To further mitigate the environmental impact of construction materials, more sustainable adhesives need to be investigated. Here, in this article, we introduce Ulva seaweed as an adhesive to enable cohesion and strength in hot-pressed wood panels. Upon hot-pressing, powdered Ulva flows in between the wood particles, generating a matrix, which provides strong binding. We show that the flexural strength of Ulva-bonded wood biocomposites increases with increasing Ulva concentrations. At an Ulva concentration of 40 wt%, our composites reach an average elastic modulus of 6.1 GPa, and flexural strength of 38.2 MPa (compared to 4.7 GPa and 22.6 MPa, respectively, for pure wood compressed at the same pressing conditions). To highlight the bonding mechanisms, we performed infrared and x-ray photoelectron spectroscopy and identified indications of fatty acid mobility during hot-pressing. In addition, we demonstrate that the presence of Ulva improves other properties of the composites such as water resistance and fame retardancy. Ulva is also shown to behave as an excellent adhesive agent between two prepressed beams. Finally, we perform an in-depth analysis of the environmental impact of wood-Ulva biocomposites.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Structural Phase Separation and Enhanced Superconductivity in La 1.875 Ba 0.125 CuO 4 Under Uniaxial Strain

Strain engineering has attracted significant attention in recent years due to its capability in tuning lattice and electronic structures of quantum materials. Using moderate uniaxial compressive strain, structural phase separation is induced in the low-temperature phase of x = 1/8 La 2-x Ba x CuO 4 (LBCO) single crystals. These structures are low temperature tetragonal (LTT), low temperature less orthorhombic (LTLO), and a plastically deformed nano-domain structure (PDNS), comprised of few-nanometer-sized orthorhombic domains within an amorphous matrix. These three structures exhibit distinct superconducting behaviors. The volume fraction of the LTT structure is suppressed with increasing strain, while its superconducting transition temperature increases and broadens. The LTLO structure exhibits a sharp superconducting transition above 32 K, which increases up to ≈ 36 K at maximum strain. The PDNS phase exhibits a very broad superconducting transition and persists even after removing the strain. This study illustrates the sensitivity of superconductivity to the structure of the LBCO sample near its stripe instability.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

ElasTool v3.0: Efficient computational and visualization toolkit for elastic and mechanical properties of materials

Efficient computation and visualization of elastic and mechanical properties are crucial in the selection of materials and the design of new materials. Here, the ElasTool v3.0 toolkit marks a significant advancement in the computational analysis and visualization of elastic and mechanical properties of materials, essential in material selection and design. This enhanced version extends beyond standard calculations like elastic tensor, Young's modulus, bulk modulus, and Poisson's ratio. It introduces capabilities for computing minimum thermal conductivity, linear compressibility, rendering the Christoffel equation, and elastic energy density. Notably, it integrates advanced visualization tools, including compatibility with Plotly and Elate web platforms for interactive web-based property exploration. A key feature of ElasTool v3.0 is the implementation of second-order elastic constants (SOECs) for tubular 2D-based nanostructures and nanotubes. Leveraging high-efficiency strain-matrix sets (OHESS), the toolkit now facilitates efficient computation of elastic constants and mechanical properties at both zero and finite temperatures for 1D, 2D, and 3D dimensions. ElasTool is openly accessible on GitHub: https://github.com/gmp007/elastool.

1D, 2D, 3D, and tubular 2D nanostructure and nanot↗