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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

GSoFa: Scalable Sparse Symbolic LU Factorization on GPUs

Decomposing a matrix $\mathbf {A}$ into a lower matrix $\mathbf {L}$ and an upper matrix $\mathbf {U}$, which is also known as LU decomposition, is an essential operation in numerical linear algebra. For a sparse matrix, LU decomposition often introduces more nonzero entries in the $\mathbf {L}$ and $\mathbf {U}$ factors than in the original matrix. A symbolic factorization step is needed to identify the nonzero structures of $\mathbf {L}$ and $\mathbf {U}$ matrices. Attracted by the enormous potentials of the Graphics Processing Units (GPUs), an array of efforts have surged to deploy various LU factorization steps except for the symbolic factorization, to the best of our knowledge, on GPUs. This article introduces gSoFa, the first GPU-based symbolic factorization design with the following three optimizations to enable scalable LU symbolic factorization for nonsymmetric pattern sparse matrices on GPUs. First, here we introduce a novel fine-grained parallel symbolic factorization algorithm that is well suited for the Single Instruction Multiple Thread (SIMT) architecture of GPUs. Second, we tailor supernode detection into a SIMT friendly process and strive to balance the workload, minimize the communication and saturate the GPU computing resources during supernode detection. Third, we introduce a three-pronged optimization to reduce the excessive space consumption problem faced by multi-source concurrent symbolic factorization. Taken together, gSoFa achieves up to 31× speedup from 1 to 44 Summit nodes (6 to 264 GPUs) and outperforms the state-of-the-art CPU project, on average, by 5×. Notably, gSoFa also achieves up to 47 percent of the peak memory throughput of a V100 GPU in the Summit Supercomputer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Predicting Flow in Fracture Networks With Quantum Algorithms

Uncertainty quantification plays a crucial role in the modeling of subsurface flow. For instance, uncertainties in the properties of geologic fracture networks significantly impact flow, requiring numerous simulations to accurately estimate quantities of interest. However, each simulation is computationally expensive because it requires solving a large linear system to capture features that involve both small and large fractures. An example is in percolation, where the interaction of many small fractures (which cumulatively can have a large surface area) with the rock matrix must be modeled precisely. Quantum computing is an emerging tool with the potential to address this issue. Quantum algorithms offer a significant speedup in solving linear systems, achieving efficiencies that are challenging to match with classical approaches. These classical approaches include direct solvers, such as LU decomposition, and iterative methods, notably preconditioned conjugate gradient, commonly used in subsurface modeling to solve large sparse systems. However, applying quantum algorithms to geologic fracture flow requires careful attention to algorithmic and problem-specific constraints to fully realize this quantum advantage. In this work we describe a quantum algorithm for generalized Monte Carlo applications with a quadratic speedup over the classical approaches which can be combined with the quantum speedup, currently under investigation, for solving quantum linear systems for subsurface flow. We show that for quantum algorithms the computational cost of estimating a quantity of interest for a statistical ensemble of networks is roughly the same as that of a single realization, essentially implying that one can get uncertainty quantification for free.

58 GEOSCIENCES↗
DOE OSTI2023

An efficient solution of low-frequency magnetic problems with voltage sources using all-frequency stable formulation

The modeling and simulation of magnetic problems at low frequencies are considered in this paper. When excited with a voltage source, conduction current is induced in the circuit of interest, which generates the magnetic field. This type of problems is modeled using the all-frequency stable formulation, which employs the potential description of fields with an inhomogeneous Coulomb gauge. With the aid of the stable formulation, the low-frequency breakdown problem is circumvented and both electric and magnetic fields can be solved in a single simulation. To solve large magnetic problems efficiently, it is necessary to employ an iterative solver with an efficient preconditioner. In this work, a preconditioner based on the incomplete LU decomposition is constructed and applied in a wide frequency range. Several numerical examples are given to demonstrate the performance of the proposed method.

Mekonnen, Minyechil↗
DOE OSTI2021

Linear solvers for power grid optimization problems: A review of GPU-accelerated linear solvers

The linear equations that arise in interior methods for constrained optimization are sparse symmetric indefinite, and they become extremely ill-conditioned as the interior method converges. These linear systems present a challenge for existing solver frameworks based on sparse LU or LDL T decompositions. Here, we benchmark five well known direct linear solver packages on CPU- and GPU-based hardware, using matrices extracted from power grid optimization problems. The achieved solution accuracy varies greatly among the packages. None of the tested packages delivers significant GPU acceleration for our test cases. For completeness of the comparison we include results for MA57, which is one of the most efficient and reliable CPU solvers for this class of problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A room temperature rechargeable Li 2 O-based lithium-air battery enabled by a solid electrolyte

Lithium-air batteries have scope to compete with gasoline in terms of energy density. However, in most systems, the reaction pathways either involve one- or two-electron transfer, leading to lithium peroxide (Li 2 O 2 ) or lithium superoxide (LiO 2 ), respectively. Kondori et al. investigated a lithium-air battery that uses a ceramic-polyethylene oxide–based composite solid electrolyte and found that it can undergo a four-electron redox reaction through lithium oxide (Li 2 O) formation and decomposition (see the Perspective by Dong and Lu). The composite electrolyte embedded with Li 10 GeP 2 S 12 nanoparticles shows high ionic conductivity and stability and high cycle stability through a four-electron transfer process.

25 ENERGY STORAGE↗
DOE OSTI2025

A two-level GPU-accelerated incomplete LU preconditioner for general sparse linear systems

This paper presents a parallel preconditioning approach based on incomplete LU (ILU) factorizations in the framework of Domain Decomposition (DD) for general sparse linear systems. We focus on distributed memory parallel architectures, specifically, those that are equipped with graphic processing units (GPUs). In addition to block-Jacobi, we present general purpose two-level ILU Schur complement-based approaches, where different strategies are presented to solve the coarse-level reduced system. These strategies are combined with modified ILU methods in the construction of the coarse-level operator, in order to effectively remove smooth errors by targeting an algebraically smooth vector. We leverage available GPU-based sparse matrix kernels to accelerate the setup and the solve phases of the proposed ILU preconditioner. We evaluate the efficiency of the proposed methods as a smoother for algebraic multigrid (AMG) and as a preconditioner for Krylov subspace methods on challenging anisotropic diffusion problems and a collection of general sparse matrices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Stabilization of reactive rare earth alkyl complexes through mechanistic studies

Rare earth tris(alkyl) complexes such as M(CH 2 SiMe 3 ) 3 (sol) n are widely used as precursors for many compounds and as homogeneous catalysts for alkene polymerization and alkane functionalization. However, the thermal instability of those most conveniently made from the commercially available lithium salt of the neosilyl anion, LiCH 2 SiMe 3 , Li(r), restricts their utility. We present a new range of synthetically useful, more kinetically stable rare earth neosilyl solvates, derived from a full kinetic study of the various possible decomposition mechanisms of 7 known and 12 new solvated rare earth neosilyl complexes M(CH 2 SiMe 3 ) 3 (sol) n M = Sc(III), Y(III), Lu(III), Sm(III), and sol = THF; TMEDA; DMPE; diglyme ((CH 3 ) 2 (OCH 2 CH 2 ) 2 O, G 2 ), triglyme ((CH 3 ) 2 (OCH 2 CH 2 ) 3 O, G 3 ). Surprisingly, simply using higher-denticity donors to sterically disfavor neosilyl γ-H elimination is not effective. While Sc(r) 3 ((CH 3 ) 2 (OCH 2 CH 2 ) 2 O) has a half-life, t 1/2 , of 258.1 h, six times longer than for Sc(r) 3 (C 4 H 8 O) 2 (t 1/2 = 43 h), Lu(r) 3 ((CH 3 ) 2 (OCH 2 CH 2 ) 2 O) and Y(r) 3 ((CH 3 ) 2 (OCH 2 CH 2 ) 2 O) do not show the expected, analogous increased t 1/2 . This is because new decomposition pathways appear for poorly fitting donors. Finally, kinetic studies demonstrate the impact of small, and increasing amounts of LiCl on the kinetics of the reactivity of the smaller alkyls Y(r) 3 (THF) 2 and Lu(r) 3 (THF) 2 ; molecules used in hydrocarbon chemistry and catalysis for fifty years. A new route to pure Y(r) 3 (THF) 2 , which avoids the traditional use of Li(r), is presented.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Molecular structures of residual solvent in polyacrylonitrile based electrolytes: Implications for conductivity and stability

Lithium-ion batteries increasingly play significant roles in modern technologies; however, increased energy density also raises concerns about electrolyte safety. Traditional electrolytes that use volatile organic solvents face risks of thermal runaways and fires from electrode shorting. In response, polymer-based solid electrolytes have been developed for replacement. Polyacrylonitrile (PAN) is a promising fire-resistant component for electrolyte fabrication, but its limited solubility necessitates using low-volatility solvents, which are notoriously difficult to remove in subsequent drying processes. Here, we use femtosecond two-dimensional infrared spectroscopy to provide an in-depth understanding of how residual solvent from processing affects the molecular structures and dynamics within a polymer electrolyte. To this end, linear and nonlinear infrared spectroscopies are employed to interrogate the molecular interactions in PAN-based electrolytes containing various contents of N,N-dimethylformamide (DMF). We show that the amount of DMF within the PAN electrolyte affects the Li+ structure. Further, the coordination can proceed through the carbonyl group and/or the amide nitrogen to form antiparallel structures with the nitrile groups of PAN through dipole–dipole interactions. The free motion of DMF is drastically inhibited upon interaction with Li+ and PAN, which decreases the ionic conductivity and potentially affects the stability (resistance toward removal and chemical decomposition). These findings have implications for the design and processing of solid polymer electrolytes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Effect of surface acidity modulation on Pt/Al 2 O 3 single atom catalyst for carbon monoxide oxidation and methanol decomposition

With ideal metal utilization efficiency and homogeneous active sites, single atom catalysts (SACs) have attracted extensive attention in heterogeneous catalysis field. The performance of SACs is highly dependent on the local coordination environment of metal single atoms. However, few works reported the effect of surface acidity modification on the performance of SACs such as Pt SACs which have been widely studied nowadays. In this work, a Pt/Al 2 O 3 SAC has been successfully prepared using a bayerite Al(OH) 3 as support through simple IWI method for Pt loading. Through post-modification of Pt/Al 2 O 3 with acidic WO 3 and basic MgO, the surface acidity of Pt/Al 2 O 3 was tuned. Here, activity of CO oxidation and methanol decomposition on the modified catalysts has been evaluated. It was found that the catalytic performance was highly related to the surface acidity of Pt SACs. Basic MgO modified Pt/Al 2 O 3 performed better in CO oxidation under the reaction condition with H 2 O. Acidic WO 3 modified Pt/Al 2 O 3 showed superior activity in methanol decomposition. Detailed characterization and in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) study suggested that the positive H 2 O effect on CO oxidation was due to the mitigated formation of bicarbonate species on Pt/Al 2 O 3 and WO 3 /Pt/Al 2 O 3 catalysts and the promoted desorption of carbonate species from MgO/Pt/Al 2 O 3 . The superior methanol decomposition performance on WO 3 /Pt/Al 2 O 3 was owing to its abundant oxygen vacancies and high resistance to carbonate accumulation. This study highlights the importance of surface acidity modulation of Pt SACs in heterogeneous catalysis and suggests that fine-tuning the surface acidity can be an effective strategy in new SACs development.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Privacy-Preserving Robust Consensus for Distributed Microgrid Control Applications

Consensus-based distributed control has been proposed for coordinating distributed energy resources (DERs) in microgrids (MGs). As one key component, distributed average observers are used to estimate the average of a group of reference signals (e.g., voltage, current, or power). State-of-the-art distributed average observers could lead to loss of privacy due to information exchange on the communication channels. The DERs' reference signals, which contain private information, could be inferred by an eavesdropper. In this article, a privacy-preserving distributed average observer is proposed that is based on robust consensus and uses the state decomposition method to preserve privacy. Compared to the existing methods, the proposed observer does not require the knowledge of the reference signal's derivative and gives accurate and smooth estimation, and is thus applicable for MG distributed control applications. A detailed analysis regarding the convergence and privacy properties of the proposed observer is presented. Here, the proposed observer is implemented on hardware controllers and validated in the context of distributed MG control applications through hardware-in-the-loop (HIL) tests.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Targeted synthesis of predicted metastable compounds using modulated elemental reactants

Three metastable compounds predicted to be kinetically stable using an “island” approach were successfully synthesized from designed modulated elemental reactants. Fe0.8V0.2Se2 was synthesized by depositing ultrathin elemental layers in a V|Fe|Se sequence to control the local composition. An alloyed rock salt structured Pb3Mn2Se5 constituent layer, which does not exist as a bulk compound, was synthesized in the heterostructure (Pb3Mn2Se5)0.6VSe2 by depositing a precursor with a V|Se|Pb|Se|Mn|Se|Pb|Se|Mn|Se|Pb|Se sequence of elemental layers that mimicked the compositional profile of the targeted heterostructure. The heterostructure (PbSe)1+δ(FeSe2)2 was prepared by depositing a precursor with a repeating layering sequence of Fe|Pb|Fe|Se, where each sequence contains the number of atoms required to form a single unit cell. In all three systems, the local compositions in the layer sequence kinetically favored the nucleation and growth of the targeted products during the deposition. The diffusion lengths to form the targeted compounds were short, and the diffusion was limited by postdeposition low temperature annealing to favor the growth of the targeted compounds and avoid the decomposition into a mixture of thermodynamically stable compounds.

Lemon, Mellie (ORCID:0000000328705881)↗
DOE OSTI2023

Metal alloy/oxide composite catalyst for ammonia decomposition

The present invention discloses a series of ammonia decomposition catalysts, the method of making such catalysts and the use of such catalysts. The said catalysts are made of composite metal or metal alloys supported on composite oxides or nitrides as the catalyst supports. The catalysts are useful in ammonia decomposition at various temperatures and pressures, including temperatures below 500° C. and pressures up to 30 atm.

Wu, Gang↗
DOE OSTI2023

Contrasting geochemical and fungal controls on decomposition of lignin and soil carbon at continental scale

Lignin is an abundant and complex plant polymer that may limit litter decomposition, yet lignin is sometimes a minor constituent of soil organic carbon (SOC). Accounting for diversity in soil characteristics might reconcile this apparent contradiction. Tracking decomposition of a lignin/litter mixture and SOC across different North American mineral soils using lab and field incubations, here we show that cumulative lignin decomposition varies 18-fold among soils and is strongly correlated with bulk litter decomposition, but not SOC decomposition. Climate legacy predicts decomposition in the lab, and impacts of nitrogen availability are minor compared with geochemical and microbial properties. Lignin decomposition increases with some metals and fungal taxa, whereas SOC decomposition decreases with metals and is weakly related with fungi. Decoupling of lignin and SOC decomposition and their contrasting biogeochemical drivers indicate that lignin is not necessarily a bottleneck for SOC decomposition and can explain variable contributions of lignin to SOC among ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

DRIPS: A framework for dimension reduction and interpolation in parameter space

Reduced-order models are often used to describe the behavior of complex systems, whose simulation with a full model is too expensive, or to extract salient features from the full model’s output. We introduce a new model-reduction framework DRIPS (dimension reduction and interpolation in parameter space) that combines the offline local model reduction with the online parameter interpolation of reduced-order bases (ROBs). The offline step of this framework relies on dynamic mode decomposition (DMD) to build a low-rank linear surrogate model, equipped with a local ROB, for quantities of interest derived from the training data generated by repeatedly solving the (nonlinear) high-fidelity model for multiple parameter points. The online step consists of the construction of a parametric reduced-order model for each target/test point in the parameter space, with the interpolation of ROBs done on a Grassman manifold and the interpolation of reduced-order operators done on a matrix manifold. The DMD component enables DRIPS to model (typically low-dimensional) quantities of interest directly, without having to access the (typically high-dimensional and possibly nonlinear) operators in a high-fidelity model that governs the dynamics of the underlying high-dimensional state variables, as required in projection-based reduced-order modeling. A series of numerical experiments suggests that DRIPS yields a model reduction, which is computationally more efficient than the commonly used projection-based proper orthogonal decomposition; it does so without requiring a prior knowledge of the governing equation for quantities of interest. Furthermore, for the nonlinear systems considered, DRIPS is more accurate than Gaussian-process interpolation (Kriging).

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Catalytic materials for lithium-sulfur batteries: mechanisms, design strategies and future perspective

Lithium-sulfur batteries (LSBs) are attractive candidates for post-lithium-ion battery technologies because of their ultrahigh theoretical energy density and low cost of active cathode materials. However, the commercialization of LSBs remains extremely challenging primarily due to poor cycling performance and safety concerns, which are inherently caused by low conductivity of S 8 and Li 2 S, severe polysulfide shuttling, and high polarization by solid Li 2 S 2 /Li 2 S deposition. Catalytic materials could facilitate the large-scale practical application of LSBs by overcoming all these challenges. In this review, we investigate the sulfur species evolution in LSBs and explore the roles of catalytic materials in charge/discharge processes, highlighting the catalysis of solid S 8 to liquid polysulfides and solid Li 2 S 2 to Li 2 S. Furthermore, we offer systematic strategies from atomic to macro levels, including defect engineering, morphology engineering and catalyst compositing, to enhance catalysis efficiency in terms of sulfur supercooling, fast charge transfer, thiosulfate generation, disulfide bond cleavage, tuneable Li 2 S growth and Li 2 S decomposition enhancement. Finally, the design and availability of the proposed catalytic materials will further advance LSB technology from coin cells and pouch cells to the subsequent commercialization scale.

25 ENERGY STORAGE↗
DOE OSTI2025

Closing the Loop between In Situ Stress Complexity and EGS Fracture Complexity

We present an agent-guided approach to CAD geometry decomposition that automates hex/hybrid meshing with graph neural networks (GNNs) to accelerate next-generation ModSim workflows. Our end-to-end pipeline (i) reduces 3D boundary-representation (B-Rep) models to a 2D chordal axis skeleton (CAT) and then to a 1D bipartite graph of surface and curve nodes, (ii) assigns per node labels as Cubit® WebCut actions, (iii) trains a multi-action GNN under supervised learning, and (iv) predicts five surface-node and three curve-node actions on out-of-distribution test geometries. Each graph node carries geometric, topological, and meshing attributes drawn from the B-Rep “skin” and CAT “skeleton,” with two-way mappings across 3D↔2D↔1D representations to maintain traceability back to 3D CAD. The supervised learning model exhibits stable convergence of the binary cross-entropy loss and achieves 98.7% accuracy on unseen lattice models. To operationalize decision-making, we rank predicted commands by geometric significance and prototyped the agent-guided workflow through the Cubit® Meshing PowerTool GUI. As a stretch goal, we explore reinforcement learning (RL) to reduce or remove label requirements and to learn policies for action sequences that maximize total reward (e.g., size of hex-meshable regions and resulting hex mesh quality). When all-hex meshing is not feasible, the agent assists in producing hybrid meshes—prioritizing hex in critical regions and transitioning to tetrahedral elements (tets) elsewhere—maintaining fidelity while ensuring robustness. The overarching objective is to replace manual, heuristics-based decomposition with data-driven, reproducible automation, cutting meshing turnaround time by orders of magnitude. We anticipate direct impact on simulation workflows through intelligent, scalable decomposition of complex CAD models into hex-meshable subdomains.

42 ENGINEERING↗