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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Electronic Layer Decoupling Driven by Density-Wave Order in La 4⁢ Ni 3 ⁢O 10

We probe the density-wave transition of the trilayer nickelate La 4⁢ Ni 3 ⁢O 10 with polarization-resolved infrared spectroscopy. The low-energy electrodynamics is strongly anisotropic, with metallic in-plane and insulating out-of-plane character. In the ordered phase, the anisotropy grows more than an order of magnitude as the out-of-plane conductivity is sharply suppressed. We interpret this enhancement as an effective electronic decoupling of the Ni-O layers driven by a spin-density-wave-induced redistribution of Ni−𝑑 𝑧 2 occupation within the trilayers. This electronic response is accompanied by clearly shifting and splitting out-of-plane phonons, compatible with a density-wave instability of electronic origin.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Review of honeycomb-based Kitaev materials with zigzag magnetic ordering

The search for a Kitaev quantum spin liquid in crystalline magnetic materials has fueled intense interest in the two-dimensional honeycomb systems. Many promising candidate Kitaev systems are characterized by a long-range-ordered magnetic structure with an antiferromagnetic zigzag-type order, where the static moments form alternating ferromagnetic chains. Recent experiments on high-quality single crystals uncovered the existence of intriguing multi-k magnetic structures, which evolved from zigzag structures. Those discoveries have sparked new theoretical developments and amplified interest in these materials. We present an overview of the honeycomb materials known to display this type of magnetic structure and provide detailed crystallographic information for the possible single- and multi-k variants.

36 MATERIALS SCIENCE↗
DOE OSTI2025

An Optimization-Based Coupling of Reduced Order Models with an Efficient Reduced Adjoint Basis Generation Approach

Optimization-based coupling (OBC) is an attractive alternative to traditional Lagrange multiplier approaches in multiple modeling and simulation contexts. However, application of OBC to time-dependent problems has been hindered by the computational cost of finding the stationary points of the associated Lagrangian, which requires primal and adjoint solves. This issue can be mitigated by using OBC in conjunction with computationally efficient reduced order models (ROMs). To demonstrate the potential of this combination, in this paper, we develop an optimization-based ROM-ROM coupling for a transient advection-diffusion transmission problem. We pursue the “optimize-then-reduce” path toward solving the minimization problem at each time step and solve reduced space adjoint system of equations, where the main challenge in this formulation is the generation of adjoint snapshots and reduced bases for the adjoint systems required by the optimizer. One of the main contributions of the paper is a new technique for an efficient adjoint snapshot collection for gradient-based optimizers in the context of optimization-based ROM-ROM couplings. In conclusion, we present numerical studies demonstrating the accuracy of the approach along with comparison between various approaches for selecting a reduced order basis for the adjoint systems, including decay of snapshot energy, average iteration counts, and timings.

coupled problems↗
DOE OSTI2019

Computation and consequences of high order amplitude- and parameter-dependent tune shifts in storage rings for high precision measurements

Nonlinear effects of the various electric field and magnetic field components of storage rings to confine the particles and bend their trajectory can cause substantial amplitude-dependent tune shifts within the beam. Furthermore, tune shifts are often sensitive to variations of system parameters, e.g. total particle momentum offsets $δp$. Such amplitude- and parameter-dependent tune shifts influence the dynamics and stability of a beam in particle storage rings. Thus, it is critical for high precision measurements to analyze and understand these influences. On this basis, we present in this paper normal form methods for the calculation of high order amplitude and system parameter dependencies of the horizontal and vertical tunes in storage rings using the differential algebra (DA) framework within COSY INFINITY. A storage ring is simulated using COSY INFINITY to generate a DA Poincaré return map describing the transverse phase space behavior after each revolution in the storage ring. The map is expanded around the parameter-dependent closed orbit of the system before transforming the resulting map into normal form coordinates to extract the high order tune dependencies on the phase space amplitude and variation in the system parameters. As a specific example, a storage ring similar to the Storage Ring of the Muon g-2 Experiment at Fermilab (E989) is investigated.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Efficient exascale discretizations: High-order finite element methods

Efficient exploitation of exascale architectures requires rethinking of the numerical algorithms used in many large-scale applications. These architectures favor algorithms that expose ultra fine-grain parallelism and maximize the ratio of floating point operations to energy intensive data movement. One of the few viable approaches to achieve high efficiency in the area of PDE discretizations on unstructured grids is to use matrix-free/partially assembled high-order finite element methods, since these methods can increase the accuracy and/or lower the computational time due to reduced data motion. In this paper we provide an overview of the research and development activities in the Center for Efficient Exascale Discretizations (CEED), a co-design center in the Exascale Computing Project that is focused on the development of next-generation discretization software and algorithms to enable a wide range of finite element applications to run efficiently on future hardware. CEED is a research partnership involving more than 30 computational scientists from two US national labs and five universities, including members of the Nek5000, MFEM, MAGMA and PETSc projects. We discuss the CEED co-design activities based on targeted benchmarks, miniapps and discretization libraries and our work on performance optimizations for large-scale GPU architectures. We also provide a broad overview of research and development activities in areas such as unstructured adaptive mesh refinement algorithms, matrix-free linear solvers, high-order data visualization, and list examples of collaborations with several ECP and external applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Data and scripts associated with “Non-random processes impacting organic matter chemistry are maximized in mid-order streams”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the publication “Non-random processes impacting organic matter chemistry are maximized in mid-order streams” submitted to Limnology and Oceanography (L&O) by Danczak et al. (in review). This package contains data and scripts used to investigate dissolved organic matter (DOM) molecular chemistry and diversification processes across 47 surface-water sampling sites in the Yakima River Basin, Washington, USA, during an August 2021 sampling campaign. The package contains analyses of ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), geochemical measurements, geospatial attributes, molecular diversity, and meta-metabolome ecological null models needed to reproduce the main manuscript results. The underlying field data were pulled from exising data packages at https://doi.org/10.15485/1892052 (Fulton et al., 2022) and https://doi.org/10.15485/1898914 (Grieger et al., 2022). For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. We thank the following organizations for providing access to field locations for sample collection: the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, the Confederated Tribes and Bands of the Yakama Nation, and the Cowiche Canyon Conservatory. Research was conducted under Washington State Parks and Recreation Commission Scientific Research Permit #210901. We are grateful to the Yakama Nation Tribal Council and Yakama Nation Fisheries for their collaboration in facilitating sample collection and ensuring data usage aligns with their values and worldview. This data package contains an R-Markdown file for analyses and five folders: (1) Data, (2) Geospatial Data, (3) Supplemental_Files, (5) Figures_pdf, (4) and src. The Data folder contains tabular inputs and derived files used in the manuscript analysis. The Geospatial Data folder contains climate and water-balance, hydrologic, land-cover, population/regional water-use, stream, topographic, and stream-order attribute CSV files. The src folder contains scripts used to process data, run analyses, and generate figures. The Figures_pdf folder contains manuscript figure outputs. The Supplemental_Files folder contains supplemental analysis products. All files are .csv, .pdf, .html, .png, .R, .Rmd, .svg, or .tre. This data package is associated with the rcfsa-RC2-SPS_Null_Modeling repository found at https://github.com/river-corridors-sfa/rcfsa-RC2-SPS_Null_Modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Non-Diffusive Volume Advection with A High Order Interface Reconstruction Method

We show that non-diffusive volume advection in two-dimensions is achieved with several benchmark problems using a newly developed high-order volume of fluids (VOF) interface reconstruction method. (1) A new VOF interface reconstruction method using circular/corner facets (linear facets are a degenerate case of arcs). We create a circular interface facet in each mixed zone by matching neighbor volume with a hybrid Newton’s-bisection method and the local solution is final. In the general case, the new VOF interface reconstruction has 3rd order accuracy and can be easily made seamless. The new method addresses intrinsic issues with Young’s method such as gaps between interface facets in the case of a curved interface, and inability to define curvature nor identify corners. (2) A non-diffusive volume advection scheme. In an ALE advection step, a well-defined interface can be carried over through a Lagrange step and used to compute volume distribution into a relaxed mesh. Then, an interface reconstruction step is performed to redefine the interface in the relaxed mesh. We must point out that the interface carried over is also a solution of interface reconstruction because all the volume fractions in the relaxed mesh are naturally matched. We provide an interface tracking method compatible with our reconstruction scheme, where it is granted to use the prior info as an initial guess to capture sub-mesh resolution features. As a result, we are able to treat multiple facets inside a single mixed cell and obtain highly accurate, non-diffusive solution for advection problems with rather coarse meshes. We show our solutions for two-dimensional incompressible flows with two materials with a) the X + O diagonal translation; b) the Zalesak rotational test; and c) the single vortex spiral test.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Prompt Phrase Ordering Using Large Language Models in HPC: Evaluating Prompt Sensitivity

Large language models (LLMs) have demonstrated effective performance in domain-specific tasks, often requiring a well-designed prompt to guide their responses. However, optimizing the right prompt is challenging due to prompt sensitivity—the phenomenon where small changes in the prompt can lead to significant variations in performance. In this study, we evaluate prompt performance by examining all permutations of independent phrases to investigate prompt sensitivity and robustness. We used two datasets: the GSM8k dataset, which assesses mathematical reasoning, and a custom template prompt for summarizing database metadata. Our goal was to evaluate the performance across all permutations of a sequence of prompt phrases. The study was conducted using the llama3-instruct- 7B model hosted on Ollama, with computations parallelized in a high-performance computing environment. By comparing the average index of phrases in the best and worst-performing prompts, we found that the order of independent phrases within a prompt significantly impacts LLM performance. Additionally, we used Hamming distance to assess changes between phrase orderings, concluding that prompt modifications can dramatically affect scores, often by almost random chance. These findings support existing research on prompt sensitivity. We discuss the challenges of prompt optimization, noting that altering phrases in a successful prompt does not always result in another successful prompt.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Medium-Range Order Resists Deformation in Metallic Liquids and Glasses

In crystals, lattice defects, such as dislocations, control mechanical deformation. Similarly, it is widely believed that even in glasses and liquids some kinds of defects, strongly disordered regions, play a major role in deformation. To identify defects researchers focused on the nature of the short-range order (SRO) in the nearest neighbor cage of atoms. However, recent results by experiment, simulation and theory raise serious questions about this assumption. They suggest that the atomic medium-range order (MRO) provides resistance against flow at the atomic level. Because the MRO is a bulk property, it implies that defects play only a limited role. This new insight is supported by the density wave theory which shows that the MRO is driven by a top-down global force, rather than being a consequence of the SRO in the bottom-up manner, and the MRO provides stiffness to resist deformation. We briefly summarize the density wave theory, show that the MRO is related to ductility of metallic glasses, and discuss the implications on the role of the MRO in the atomic-level mechanism of deformation.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural Networks

We present a novel method for graph partitioning, based on reinforcement learning and graph convolutional neural networks. Our approach is to recursively partition coarser representations of a given graph. The neural network is implemented using SAGE graph convolution layers, and trained using an advantage actor critic (A2C) agent. We present two variants, one for finding an edge separator that minimizes the normalized cut or quotient cut, and one that finds a small vertex separator. The vertex separators are then used to construct a nested dissection ordering to permute a sparse matrix so that its triangular factorization will incur less fill-in. The partitioning quality is compared with partitions obtained using METIS and SCOTCH, and the nested dissection ordering is evaluated in the sparse solver SuperLU. Our results show that the proposed method achieves similar partitioning quality as METIS and SCOTCH. Furthermore, the method generalizes across different classes of graphs, and works well on a variety of graphs from the SuiteSparse sparse matrix collection.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Generating An Advanced Cross-section Library For HTGR Pebble Bed Depletion Calculations Using Reduced-Order Model Generation Techniques

For code development, Advanced Reactor Technologies - Gas Cooled Reactors Program (ART-GCR) rely on a collaboration with the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, but the cross sections generation and the methodology definition is part of this program area goals. Based on previous studies in FY23, the size of microscopic cross section libraries increases rapidly with the number of tabulations, requiring significant amount of memory and drastically slowing down the Griffin calculations when evaluating cross sections via the multivariate linear interpolation approach. Rising to these challenges, this work investigates constructing Reduced-order Models (ROMs) for the multi-group microscopic cross sections to accelerate the cross section evaluation in Griffin. A database of multigroup cross sections is first collected considering all possible parameters that a designer could change for optimization. Down-selection of the ROM techniques afterward shows Deep Neural Network (DNN) as the best candidate when jointly consider memory efficiency, predictive accuracy, computational cost, scalability, flexibility and ease of implementation of the algorithms in comparison to the multidimensional interpolation. This work develops a specific interface that enables the cross section predictions using pre-trained DNN models into Griffin leveraging the existing ROM capabilities. DNNs have been trained for all isotopes for use in Griffin. Preliminary Griffin testing shows that DNNs exhibit exceptional predictive accuracy and the use of DNNs provides orders of magnitude improvement in memory efficiency compared to conventional interpolation techniques. With such ROM techniques, it holds great promise to further increase the fidelity of the Pebble Bed Reactor (PBR) simulation by increasing the number of tabulations/state variables during cross section evaluation, while maintaining the computational cost affordable in Griffin.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Reduce-Order Modeling of Multigroup Neutron Cross Sections for High-Temperature Gas-cooled Reactors

Deterministic neutronics calculations rely on multigroup neutron cross section libraries, which usually consists of a database of tabulated values, used to calculate the cross sections through multivariate linear interpolation. However, interpolation of the multidimensional cross section data becomes memory inefficient and time consuming as the number of tabulations increases, significantly slowing down the neutronics calculation, especially in the case of micro cross section libraries where every isotope (on the order of hundreds) has its own set of specific reactions and cross sections. To address this challenge, this work constructs efficient and robust reduced-order models (ROMs) of the multi-group cross sections to support the Griffin simulation of high-temperature gas-cooled reactors (HTGRs). The first part of the study investigates the linearity of the multi-group cross section data across isotopes, reaction types and energy groups on pre-generated datasets for the purpose of dimensionality reduction. Secondly, a down-selection of ROM techniques is presented on representative classical machine learning (ML) techniques, including variants of linear regression, kernel-based methods, tree-based algorithms, and artificial neural networks. The selection criteria jointly consider the memory efficiency, predictive accuracy, prediction speed, and scalability in comparison to the multidimensional interpolation. Among all the ML techniques, deep neural networks (DNNs) have proven to be the best selection with sufficient accuracy, high robustness, good memory efficiency, great scalability, and superior flexibility. DNNs for have been trained for all isotopes in this work and systematic Griffin testing is ongoing at this moment to ensure the feasibility of this ROM technique for cross section predictions.

42 - ENGINEERING↗
DOE OSTI2023

Sensitivity Analysis, Reduced-order Modeling, and Optimization of a Gas-Cooled Pebble Bed Reactor using Equilibrium-Core and DLOFC Performance

This work presents and applies a workflow for performing design optimization on gas-cooled pebble-bed reactors. Based on previous research, a representative equilibrium core of a pebble-bed reactor and a depressurized loss-of-forced-cooling model are created. These applications are built using the Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically utilizing Griffin, Pronghorn, and Bison. After defining design-related parameters and quantities of interest regarding reactor safety and efficiency, this multiphysics model is sampled using the MOOSE stochastic tools module. The result is a comprehensive dataset of configurations, enabling sensitivity analysis and the generation of reduced-order models. Subsequently, the dataset and reduced-order models are employed in an optimization study aimed at maximizing fuel utilization while adhering to safety and operational constraints. The optimization process leads to an improvement of fuel utilization by approximately 10\%, compared to engineering-judgment-based nominal conditions.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Orientational Ordering within Semiconducting Polymer Fibrils

Abstract Due to a general paucity of suitable characterization methods, the internal orientational ordering of polymer fibrils has rarely been measured despite its importance particularly for semi‐conducting polymers. An emerging tool with sensitivity to bond orientation is polarized resonant soft X‐ray scattering (P‐RSoXS). Here, P‐RSoXS reveals the molecular arrangement within fibrils (if type I or type II fibrils), the extent of orientation in the fibril crystal, and an explicit crystal‐amorphous interphase. Neat films as well as binary blends with a fullerene derivative are characterized for three different polymers, that are prototypical materials widely used in organic electronics applications. Anisotropic P‐RSoXS patterns reveal two different fibril types. Analysis of the q ‐dependence of the anisotropy from simulated and experimental scattering patterns reveal that neat polymer fibrillar systems likely comprise more than two phases, with the third phase in addition to crystal and amorphous likely being an interphase with distinct density and orientation. Intriguingly, the fibril type correlates to the H‐ or J‐aggregation signature in ultraviolet‐visible (UV–vis) spectroscopy, revealing insight into the fibril formation. Together, the results will open the door to develop more sophisticated structure‐function relationships between chemical design, fibril type, formation pathways and kinetics, interfacial ordering, and eventually device functions.

Mukherjee, Subhrangsu↗
DOE OSTI2021

Ionic Dopant-Induced Ordering Enhances the Thermoelectric Properties of a Polythiophene-Based Block Copolymer

Conjugated polymer-based block copolymers (CP-BCPs) are an unexplored class of materials for organic thermoelectrics. In this study, the authors report on the electronic conductivity (σ) and Seebeck coefficient (α) of a newly synthesized CP-BCP, poly(3-hexylthiophene)-block-poly (oligo-oxyethylene methacrylate) (P3HT-b-POEM), upon solution co-processing with lithium bis(trifluoromethanesulfonyl)imide (LiTFSI), and subsequently vapor-doping with a molecular dopant, 2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane (F4TCNQ). It is found that the addition of the hydrophilic block POEM greatly enhances the processability of P3HT, enabling homogeneous solution-mixing with LiTFSI. Notably, interactions between P3HT-b-POEM with ionic species significantly improve molecular order and unexpectedly cause electrical oxidizing doping of P3HT block both in solution and solid-states, a phenomenon that has not been previously observed in Li-salt containing P3HT. Vapor doping of P3HT-b-POEM-LiTFSI thin films with F4TCNQ further enhances σ and yields a thermoelectric power factor PF = α 2 σ of 13.0 μW m -1 K -2 , which is more than 20 times higher than salt-free P3HT-b-POEM sample. Through modeling thermoelectric behaviors of P3HT-b-POEM with the Kang-Snyder transport model, the improvement in PF is attributed to higher electronic charge mobility originating from the enhanced molecular ordering of P3HT. The results demonstrate that solution co-processing CP-BCPs with a salt is a powerful method to control structure and performance of organic thermoelectric materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Strain‐Driven Stabilization of a Room‐Temperature Chiral Multiferroic with Coupled Ferroaxial and Ferroelectric Order

Noncollinear ferroic materials are sought after as testbeds to explore the intimate connections between topology and symmetry, which result in electronic, optical, and magnetic functionalities not observed in collinear ferroic materials. For example, ferroaxial materials have rotational structural distortions that break mirror symmetry and induce chirality. When ferroaxial order is coupled with ferroelectricity arising from a broken inversion symmetry, it offers the prospect of electric-field-control of the ferroaxial distortions and opens up new tunable functionalities. However, chiral multiferroics, especially ones stable at room temperature, are rare. A strain-stabilized, room-temperature chiral multiferroic phase in single crystals of BaTiS 3 is reported here. Using first-principles calculations, the stabilization of this multiferroic phase having P6 3 space group for biaxial tensile strains exceeding 1.5% applied on the basal ab-plane of the room temperature P6 3 cm phase of BaTiS 3 is predicted. The chiral multiferroic phase is characterized by rotational distortions of TiS 6 octahedra around the long c-axis and polar displacement of Ti atoms along the c-axis. Furthermore, the ferroaxial and ferroelectric distortions and their domains in P6 3 -BaTiS 3 are directly resolved using atomic resolution scanning transmission electron microscopy. Landau-based phenomenological modeling predicts a strong coupling between the ferroelectric and the ferroaxial order making P6 3 -BaTiS 3 an attractive test bed for achieving electric-field-control of chirality.

Chalcogenide↗
DOE OSTI2025

Switching of Magnetic Order via Non‐Magnetic Al Addition in FeCoNiMnAl x Films

Magnetic high entropy alloys (HEAs) consisting of 3 d transition metals offer an exciting platform to explore novel magnetic phases as they often house competing exchange interactions in combination with random site disorders. In this work, a sensitive and tunable magnetic order is demonstrated in sputtered single-layer FeCoNiMnAl x films, as a function of non-magnetic Al addition, along with an unexpected exchange bias effect. Thin films of 50 nm FeCoNiMn exhibit a face-centered-cubic ( fcc ) phase, reentrant spin glass (SG) transition near 100 K, and a large exchange bias of over 500 Oe after field-cooling to 5 K. The exchange bias is increased to 930 Oe through a small addition of 5 at.% Al. Further Al addition to 12 at.% results in a body-centered-cubic ( bcc ) phase, coinciding with a large increase in the saturation magnetization, decrease of exchange bias to 50 Oe, and suppression of SG behavior. The change in magnetic order across the Al-induced structural transformation is mediated by the switching of Mn ground state from AF to FM, which is supported by first-principles calculations and experimentally confirmed via X-ray magnetic circular dichroism. These results open up new HEA strategies for explorations of novel magnetic phases.

competing exchange interactions↗
DOE OSTI2022

Magnetic Ordering in a High‐Spin Donor–Acceptor Conjugated Polymer

Abstract The development of open‐shell organic molecules that magnetically order at room temperature,which can be practically applied, remains a grand challenge in chemistry, physics, and materials science. Despite the exploration of vast chemical space, design paradigms for organic paramagnetic centers generally result in unpaired electron spins that are unstable or isotropic. Here, a high‐spin conjugated polymer is demonstrated, which is composed of alternating cyclopentadithiophene and benzo[1,2‐ c ;4,5‐ c ′]bis[1,2,5]thiadiazole heterocycles, in which macromolecular structure and topology coalesce to promote the spin center generation and intermolecular exchange coupling. Electron paramagnetic resonance (EPR) spectroscopy is consistent with spatially localized spins, while magnetic susceptibility measurements show clear anisotropic spin ordering and exchange interactions that persist at room temperature. The application of long‐range π‐correlations for spin center generation promotes remarkable stability. This work offers a fundamentally new approach to the implementation of this long‐sought‐after physical phenomenon within organic materials and the integration of manifold properties within emerging technologies.

Steelman, Michael E.↗