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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

An Algebraic Sparsified Nested Dissection Algorithm Using Low-Rank Approximations

Here, we propose a new algorithm for the fast solution of large, sparse, symmetric positive-definite linear systems, spaND (sparsified Nested Dissection). It is based on nested dissection, sparsification, and low-rank compression. After eliminating all interiors at a given level of the elimination tree, the algorithm sparsifies all separators corresponding to the interiors. This operation reduces the size of the separators by eliminating some degrees of freedom but without introducing any fill-in. This is done at the expense of a small and controllable approximation error. The result is an approximate factorization that can be used as an efficient preconditioner. We then perform several numerical experiments to evaluate this algorithm. We demonstrate that a version using orthogonal factorization and block-diagonal scaling takes fewer CG iterations to converge than previous similar algorithms on various kinds of problems. Furthermore, this algorithm is provably guaranteed to never break down and the matrix stays symmetric positive-definite throughout the process. We evaluate the algorithm on some large problems show it exhibits near-linear scaling. The factorization time is roughly $\mathcal{O}$(N), and the number of iterations grows slowly with N.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Probing for the Trace Estimation of a Permuted Matrix Inverse Corresponding to a Lattice Displacement

We report thatpProbing is a general technique that is used to reduce the variance of the Hutchinson stochastic estimator for the trace of the inverse of a large, sparse matrix A. The variance of the estimator is the sum of the squares of the off-diagonal elements of A -1 . Therefore, this technique computes probing vectors that when used in the estimator annihilate the largest off-diagonal elements. For matrices that display decay of the magnitude of |A$^{-1}_{ij}$| with the graph distance between nodes i and j, this is achieved through graph coloring of increasing powers A k . Equivalently, when a matrix stems from a lattice discretization, it is computationally beneficial to find a distance-k coloring of the lattice. Previously, a hierarchical coloring was proposed so that k can be increased at runtime as needed without discarding previous work. In this work, we study probing for the more general problem of computing the trace of a permutation of A -1 , say PA -1 . The motivation comes from lattice quantum chromodynamics (QCD), where we need to construct “disconnected diagrams” to extract flavor-separated generalized parton functions. In lattice QCD, where the matrix has a four-dimensional toroidal lattice structure, these nonlocal operators correspond to a PA -1 , where P is the permutation relating to some displacement $\vec{p}$ in one or more dimensions. We focus on a single dimension displacement (p), but our methods are general. We show that probing on A k or (PA) k does not annihilate the largest magnitude elements. To resolve this issue, our displacement-based probing works on PA k using a new coloring scheme that works directly on appropriately displaced neighborhoods on the lattice. We prove lower bounds on the number of colors needed and study the effect of this scheme on variance reduction, both theoretically and experimentally on a real-world lattice QCD calculation. We achieve orders of magnitude speedup over the unprobed or the naively probed methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data

High-energy large-scale particle colliders produce data at high speed in the order of 1 terabytes per second in nuclear physics and petabytes per second in high energy physics. Developing real-time data compression algorithms to reduce such data at high throughput to fit permanent storage has drawn increasing attention. Specifically, at the newly constructed sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC), a time projection chamber is used as the main tracking detector, which records particle trajectories in a volume of three-dimensional (3D) cylinder. The resulting data are usually very sparse with occupancy around 10.8%. Such sparsity presents a challenge to conventional learning-free lossy compression algorithms, such as SZ, ZFP, and MGARD. The 3D convolutional neural network (CNN)-based approach, Bicephalous Convolutional Autoencoder (BCAE), outperforms traditional methods both in compression rate and reconstruction accuracy. BCAE can also utilize the computation power of graphical processing units suitable for deployment in a modern heterogeneous highperformance computing environment. This work introduces two BCAE variants: BCAE++ and BCAE-2D. BCAE++ achieves a 15% better compression ratio and a 77% better reconstruction accuracy measured in mean absolute error compared with BCAE. BCAE-2D treats the radial direction as the channel dimension of an image, resulting in a 3× speedup in compression throughput. In addition, we demonstrate an unbalanced autoencoder with a larger decoder can improve reconstruction accuracy without significantly sacrificing throughput. Lastly, we observe both the BCAE++ and BCAE-2D can benefit more from using half-precision mode in throughput (76 - 79% increase) without loss in reconstruction accuracy. The source code and links to data and pretrained models can be found at https://github.com/BNL-DAQ-LDRD/NeuralCompression_v2

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Accuracy optimized neural networks do not effectively model optic flow tuning in brain area MSTd

Accuracy-optimized convolutional neural networks (CNNs) have emerged as highly effective models at predicting neural responses in brain areas along the primate ventral stream, but it is largely unknown whether they effectively model neurons in the complementary primate dorsal stream. We explored how well CNNs model the optic flow tuning properties of neurons in dorsal area MSTd and we compared our results with the Non-Negative Matrix Factorization (NNMF) model, which successfully models many tuning properties of MSTd neurons. To better understand the role of computational properties in the NNMF model that give rise to optic flow tuning that resembles that of MSTd neurons, we created additional CNN model variants that implement key NNMF constraints – non-negative weights and sparse coding of optic flow. While the CNNs and NNMF models both accurately estimate the observer's self-motion from purely translational or rotational optic flow, NNMF and the CNNs with nonnegative weights yield substantially less accurate estimates than the other CNNs when tested on more complex optic flow that combines observer translation and rotation. Despite its poor accuracy, NNMF gives rise to tuning properties that align more closely with those observed in primate MSTd than any of the accuracy-optimized CNNs. This work offers a step toward a deeper understanding of the computational properties and constraints that describe the optic flow tuning of primate area MSTd.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Chemomechanical origin of morphological disparity in lithium metal electrodeposition

The morphology of electrochemically deposited lithium (Li) critically governs the cycling stability and safety of Li metal batteries, yet the underlying controlling factors remain poorly understood. Even within the same coin cell, Li deposits can exhibit strikingly different morphologies, for example, sparse whiskers coexisting with particle-like deposits, indicting strong local variations in growth conditions. By combining cryogenic transmission electron microscopy with phase-field modeling, we here identify the root cause of this disparity. We reveal that the morphological divergence originates from the variations in structure and chemical composition of the initial SEI layer formed on the copper (Cu) substrate. These variations arise from substrate defects that induce localized electrochemical fields, driving selective dissociation of either solvent or salt, producing SEI layers with distinct chemomechanical properties. Solvent-derived organic SEI favors whisker growth, whereas salt-derived SEI promotes particle formation. This study establishes a direct mechanistic link between SEI chemomechanical properties and Li morphology, providing design principles for tailoring SEI layers to control Li deposition

energy storage↗
DOE OSTI2023

Artificial Intelligence Guided Studies of van der Waals Magnets

A materials informatics framework to explore a large number of candidate van der Waals (vdW) materials is developed. In particular, in this study a large space of monolayer transition metal halides is investigated by combining high-throughput density functional theory calculations and artificial intelligence (AI) to accelerate the discovery of stable materials and the prediction of their magnetic properties. Here, the formation energy is used as a proxy for chemical stability. Semi-supervised learning is harnessed to mitigate the challenges of sparsely labeled materials data in order to improve the performance of AI models. This approach creates avenues for the rapid discovery of chemically stable vdW magnets by leveraging the ability of AI to recognize patterns in data, to learn mathematical representations of materials from data and to predict materials properties. Using this approach, previously unexplored vdW magnetic materials with potential applications in data storage and spintronics are identified.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A Global Methane Observation System to Reduce Uncertainty for Anthropogenic and Natural Sources and Sinks for Detecting and Attributing Climate Feedbacks

Atmospheric methane (CH4) concentrations are accelerating global warming as net emissions increase. Observing systems that quantify sources remain too sparse and fragmented to detect trends—especially in remote regions where climate‐driven natural emissions may be rising. We provide a framework for quantifying uncertainty reductions through the implementation of a global ecosystem‐methane observing system designed to: (i) substantially lower uncertainty in sectoral and regional emissions, (ii) separate co‐occurring anthropogenic and natural fluxes, and (iii) trend detection at regional scales to verify mitigation progress and provide early warning of natural feedbacks. Using bottom‐up inventories and process‐model ensembles for 2014–2023, we show that anthropogenic emissions remain uncertain by ∼32% globally, while natural sources—tropical and boreal‐arctic wetlands, fires, and inland waters—carry far larger uncertainties (+ 70%) and trend uncertainties reaching ∼200%. Additional observations must match spatial emission structure to increase observability of emissions: high‐resolution satellite constellations for point sources combined with expanded flux networks and wetland mapping for diffuse sources, and denser ground‐based atmospheric column measurements to restore observability in under‐sampled tropics and high latitudes. Notional analyses indicate that targeted additions of flux towers and ∼20 in situ atmospheric column concentration instruments per key tropical region could reduce continental‐scale uncertainties at modest cost. Conceptual illustration of a Global Ecosystem Methane Observing System (GEM‐OS) integrating satellites, aircraft, atmospheric networks, and ecosystem measurements to quantify methane emissions from anthropogenic and natural sources. The multi‐scale observing framework improves source attribution, reduces uncertainty in regional methane budgets, and enables early detection of climate‐driven feedbacks from wetlands, fires, permafrost, agriculture, and fossil‐fuel emissions.

Ciais, P↗
DOE OSTI2023

3D-Printed Graded Electrode with Ultrahigh MnO 2 Loading for Non-Aqueous Electrochemical Energy Storage

Electrolytic manganese dioxide is one of the promising cathode candidates for electrochemical energy storage devices due to its high redox capacity and ease of synthesis. Yet, high-loading MnO 2 often suffers from sluggish reaction kinetics, especially in non-aqueous electrolytes. The non-uniform deposition of MnO 2 on a porous current collectors also makes it difficult to fully utilize the active materials at high mass loading. Here, a 3D printed graded graphene aerogel (3D GA) that contains sparsely separated exterior ligaments is developed to create large open channels for mass transport as well as densely arranged interior ligaments providing large ion-accessible active surface. The unique structural design homogenizes the thickness of electro deposited MnO 2 even at an ultrahigh mass loading of ≈70 mg cm -2 . The electrode achieves a remarkable volumetric capacity of 29.1 mA h cm -3 in the non-aqueous electrolyte. A Li-ion hybrid capacitor device assembled with a graded 3D GA/MnO2 cathode and graded 3D GA/VO x anode exhibits a wide voltage window of 0–4 V and a superior volumetric energy density of 20.2 W h L -1 . The findings offer guidance on 3D printed electrode design for supporting ultrahigh loading of active materials and developments of high energy density energy storage devices.

electrochemical energy storage↗
DOE OSTI2026

Localized Heterogeneous Nucleation for Vapor‐Assisted Sequential Deposition of Metal Halide Perovskites

Vapor-assisted hybrid two-step deposition, which combines thermally evaporated inorganic layers with solution-processed organic halides to form halide perovskites, has emerged as a scalable and industry-compatible route for textured tandem photovoltaics. However, this process is often hindered by reaction-limited phase formation, particularly when compact, non-porous, and highly crystalline inorganic layers formed by thermal evaporation restrict subsequent conversion, resulting in incomplete reaction and pronounced depth-dependent heterogeneity. In this study, we introduce a strategy to regulate the inorganic precursor layer by incorporating localized heterogeneous nucleation sites. Sparsely distributed hydrophilic metal oxide species serve as effective nucleation centers during vapor deposition, enabling effective control over film morphology and crystal orientation from the early stages of growth. This tailored inorganic framework facilitates the subsequent incorporation of organic halides, alleviating reaction limitations and suppressing residual unreacted precursors. Consequently, the perovskite films exhibit improved stoichiometric uniformity and enhanced optoelectronic quality, enabling wide-bandgap perovskite solar cells with markedly improved performance and operational stability. This work provides important mechanistic insight into crystal growth engineering of vapor-deposited perovskite thin films.

nucleation↗
DOE OSTI2020

Loamy sand soil approaches organic carbon saturation after 37 years of conservation tillage

Conservation tillage is reported to increase soil organic carbon (SOC) and total nitrogen (TN) contents, but long-term (>30 yr) field results quantifying the responses in Coastal Plain Ultisols are sparse. The distribution, accumulation, and topsoil storage of SOC and TN after 37 yr of crop production using conventional (CvT) or conservation tillage (CnT) on a Norfolk loamy sand (fine-loamy, kaolinitic, thermic, Typic Kandiudults) were quantified. Soil samples were collected annually from the 0-5-, 5-10-, and 10-15-cm depth increments beneath corn (Zea mays L.), soybean [Glycine max (L.) Merr.], and cotton (Gossypium hirsutum L.) crops. Overall, SOC and TN accumulation in the 0-5-cm depth were highest for Norfolk soil under CnT. Focusing on total long-term changes for the 0-15-cm sampling depth beneath various corn crops shows that CnT and CvT sequestered 24.7 and 21.4 Mg C ha -1 , respectively. Between 1978 and 2016, there was a highly significant (P < .0001) exponential increase in SOC within the top 5-cm soil depth. However, the exponential curves began to plateau suggesting the Norfolk topsoil was approaching its organic carbon (OC) storage capacity. These field measurements strongly indicate that additional topsoil SOC increases with current tillage and crop management practices are limited.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Estimating rates and patterns of diversification with incomplete sampling: a case study in the rosids

Premise Recent advances in generating large‐scale phylogenies enable broad‐scale estimation of species diversification. These now common approaches typically are characterized by (1) incomplete species coverage without explicit sampling methodologies and/or (2) sparse backbone representation, and usually rely on presumed phylogenetic placements to account for species without molecular data. We used empirical examples to examine the effects of incomplete sampling on diversification estimation and provide constructive suggestions to ecologists and evolutionary biologists based on those results. Methods We used a supermatrix for rosids and one well‐sampled subclade (Cucurbitaceae) as empirical case studies. We compared results using these large phylogenies with those based on a previously inferred, smaller supermatrix and on a synthetic tree resource with complete taxonomic coverage. Finally, we simulated random and representative taxon sampling and explored the impact of sampling on three commonly used methods, both parametric (RPANDA and BAMM) and semiparametric (DR). Results We found that the impact of sampling on diversification estimates was idiosyncratic and often strong. Compared to full empirical sampling, representative and random sampling schemes either depressed or inflated speciation rates, depending on methods and sampling schemes. No method was entirely robust to poor sampling, but BAMM was least sensitive to moderate levels of missing taxa. Conclusions We suggest caution against uncritical modeling of missing taxa using taxonomic data for poorly sampled trees and in the use of summary backbone trees and other data sets with high representative bias, and we stress the importance of explicit sampling methodologies in macroevolutionary studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

BRE‐X Emissions Database for End‐of‐Life Scenarios of Selective Building Construction Materials to Enable Circular Economy in Construction

In the United States, construction and demolition debris predominately end up in landfills with minimal end‐of‐life Re‐X (recover, recycle, reuse, etc.) scenarios, resulting in large environmental impacts and lost opportunities for material recovery. Except for concrete and metals, which seem to have a few well‐defined end‐of‐life pathways, there seems to be a lack of well‐documented end‐of‐life scenarios for other construction materials, let alone their emissions data. Hence, there is a need for documented end‐of‐life Re‐X scenarios and end‐of‐life data of more building materials to motivate widespread use of Re‐X strategies in building design. This paper outlines the efforts of the National Renewable Energy Laboratory, Carbon Leadership Forum, Building Transparency, and Skidmore, Owings & Merrill to (a) create an open‐access BRE‐X (Building Re‐X) end‐of‐life emissions database consisting of greenhouse gas emissions data associated with various end‐of‐life scenarios for a select list of high‐impact building construction materials, and (b) integrate the BRE‐X end‐of‐life emissions database with CAD/BIM/LCA tools for evaluating various end‐of‐life scenarios. The paper also presents a few existing life cycle inventory databases that contain sparse amounts of end‐of‐life data for a few construction materials and their limitations in terms of scaling and data consolidation. Finally, a sample of how the collected data can be ingested into whole‐building LCA tools using open data formats and a public access link to the BRE‐X end‐of‐life emissions database is also included.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Providing performance portable numerics for Intel GPUs

Summary With discrete Intel GPUs entering the high‐performance computing landscape, there is an urgent need for production‐ready software stacks for these platforms. In this article, we report how we enable the Ginkgo math library to execute on Intel GPUs by developing a kernel backed based on the DPC++ programming environment. We discuss conceptual differences between the CUDA and DPC++ programming models and describe workflows for simplified code conversion. We evaluate the performance of basic and advanced sparse linear algebra routines available in Ginkgo's DPC++ backend in the hardware‐specific performance bounds and compare against routines providing the same functionality that ship with Intel's oneMKL vendor library.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Population genomic response to geographic gradients by widespread and endemic fishes of the Arabian Peninsula

Genetic structure within marine species may be driven by local adaptation to their environment, or alternatively by historical processes, such as geographic isolation. The gulfs and seas bordering the Arabian Peninsula offer an ideal setting to examine connectivity patterns in coral reef fishes with respect to environmental gradients and vicariance. The Red Sea is characterized by a unique marine fauna, historical periods of desiccation and isolation, as well as environmental gradients in salinity, temperature, and primary productivity that vary both by latitude and by season. The adjacent Arabian Sea is characterized by a sharper environmental gradient, ranging from extensive coral cover and warm temperatures in the southwest, to sparse coral cover, cooler temperatures, and seasonal upwelling in the northeast. Reef fish, however, are not confined to these seas, with some Red Sea fishes extending varying distances into the northern Arabian Sea, while their pelagic larvae are presumably capable of much greater dispersal. These species must therefore cope with a diversity of conditions that invoke the possibility of steep clines in natural selection. Here, we test for genetic structure in two widespread reef fish species (a butterflyfish and surgeonfish) and eight range-restricted butterflyfishes across the Red Sea and Arabian Sea using genome-wide single nucleotide polymorphisms. We performed multiple matrix regression with randomization analyses on genetic distances for all species, as well as reconstructed scenarios for population subdivision in the species with signatures of isolation. We found that (a) widespread species displayed more genetic subdivision than regional endemics and (b) this genetic structure was not correlated with contemporary environmental parameters but instead may reflect historical events. We propose that the endemic species may be adapted to a diversity of local conditions, but the widespread species are instead subject to ecological filtering where different combinations of genotypes persist under divergent ecological regimes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Enhanced detection of paramagnetic fluorine-19 magnetic resonance imaging agents using zero echo time sequence and compressed sensing

Fluorine-19 ( 19 F) magnetic resonance imaging (MRI) is an emerging technique offering specific detection of labeled cells in vivo. Lengthy acquisition times and modest signal-to-noise ratio (SNR) makes three-dimensional spin-density–weighted 19 F imaging challenging. Recent advances in tracer paramagnetic metallo-perfluorocarbon (MPFC) nanoemulsion probes have shown multifold SNR improvements due to an accelerated 19 F T 1 relaxation rate and a commensurate gain in imaging speed and averages. However, 19 F T 2 -reduction and increased linewidth limit the amount of metal additive in MPFC probes, thus constraining the ultimate SNR. To overcome these barriers, we describe a compressed sampling (CS) scheme, implemented using a “zero” echo time (ZTE) sequence, with data reconstructed via a sparsity-promoting algorithm. Our CS-ZTE scheme acquires k-space data using an undersampled spherical radial pattern and signal averaging. Image reconstruction employs off-the-shelf sparse solvers to solve a joint total variation and -norm regularized least square problem. To evaluate CS-ZTE, we performed simulations and acquired 19 F MRI data at 11.7 T in phantoms and mice receiving MPFC-labeled dendritic cells. For MPFC-labeled cells in vivo, we show SNR gains of ~6.3 × with 8-fold undersampling. We show that this enhancement is due to three mechanisms including undersampling and commensurate increase in signal averaging in a fixed scan time, denoising attributes from the CS algorithm, and paramagnetic reduction of T 1 . Importantly, 19 F image intensity analyses yield accurate estimates of absolute quantification of 19 F spins. Overall, the CS-ZTE method using MPFC probes achieves ultrafast imaging, a substantial boost in detection sensitivity, accurate 19 F spin quantification, and minimal image artifacts.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Multilevel graph embedding

The goal of the present paper is the design of embeddings of a general sparse graph into a set of points in $\mathbb{R}^d$ for appropriate d ≥ 2. The embeddings that we are looking at here aim to keep vertices that are grouped in communities together and keep the rest apart. To achieve this property, we utilize coarsening that respects possible community structures of the given graph. We employ a hierarchical multilevel coarsening approach that identifies communities (strongly connected groups of vertices) at every level. The multilevel strategy allows any given (presumably expensive) graph embedding algorithm to be made into a more scalable (and faster) algorithm. We demonstrate the presented approach on a number of given embedding algorithms and large-scale graphs and achieve speed-up over the methods in a recent paper.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A greedy algorithm for computing eigenvalues of a symmetric matrix with localized eigenvectors

Here, we present a greedy algorithm for computing selected eigenpairs of a large sparse matrix $H$ that can exploit localization features of the eigenvector. When the eigenvector to be computed is localized, meaning only a small number of its components have large magnitudes, the proposed algorithm identifies the location of these components in a greedy manner, and obtains approximations to the desired eigenpairs of $H$ by computing eigenpairs of a submatrix extracted from the corresponding rows and columns of $H$. Even when the eigenvector is not completely localized, the approximate eigenvectors obtained by the greedy algorithm can be used as good starting guesses to accelerate the convergence of an iterative eigensolver applied to $H$. We discuss a few possibilities for selecting important rows and columns of $H$ and techniques for constructing good initial guesses for an iterative eigensolver using the approximate eigenvectors returned from the greedy algorithm. We demonstrate the effectiveness of this approach with examples from nuclear quantum many-body calculations and many-body localization studies of quantum spin chains.

97 MATHEMATICS AND COMPUTING↗