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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

ASCR Workshop Position Paper: Challenges and Opportunities in High Energy Physics

High energy particle physics and cosmology concern themselves with estimating fundamental parameters of nature, such as the masses and interactions of fundamental particles like the Higgs boson and the rate of expansion of the universe. In doing so, they analyze exabyte-scale datasets, some of the largest in all of science, and face many challenges in subsequent data analysis. These challenges are shared between the two disciplines, but we focus on particle physics to highlight one specific domain. In particle physics, the standard method for estimating parameters involves performing Monte Carlo (MC) integration as a function of both parameters of interest and nuisance parameters using an expensive simulator, counting the number of observed collision events (i.i.d. samples) from an experiment in the corresponding integration domains, and forming a Poisson likelihood function. This likelihood function is then used in a Frequentist manner to construct a maximum likelihood point estimate (MLE) and confidence set for the parameters. To sufficiently populate the high-dimensional integration domains, simulators consume billions of CPU-hours annually and produce hundreds of petabytes of intermediate output data. Several techniques have been developed to: optimize definitions of the integration domains so as to be maximally sensitive to a particular subset of parameters, efficiently estimate the integrals, and build robust surrogate models by interpolating between integral evaluations at different parameter points. One can view this whole endeavor as classical Simulation-Based Inference (SBI).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

A discontinuous piecewise polynomial generalized moving least squares scheme for robust finite element analysis on arbitrary grids

A variational approach is developed with a meshless discretization to enable accurate and robust numerical simulation of partial differential equations for meshes that are of poor quality. Traditional finite element methods use the mesh to both discretize the geometric domain and to define the finite element shape functions. The latter creates a dependence between the quality of the mesh and the properties of the finite element basis that may adversely affect the accuracy of the discretized problem. Here, we propose a new approach for defining finite element shape functions that breaks this dependence and separates mesh quality from the discretization quality, which we call discontinuous piecewise polynomial generalized moving least squares (DPP-GMLS). At the core of the approach is a meshless definition of the shape functions, which limits the purpose of the mesh to representing the geometric domain and integrating the basis functions without having any role in their approximation quality. The resulting non-conforming space can be utilized within a standard discontinuous Galerkin framework, providing a rigorous foundation for solving partial differential equations on low-quality meshes. We present a collection of numerical experiments demonstrating our approach in a wide range of settings: strongly coercive elliptic problems, linear elasticity in the compressible regime, and the stationary Stokes problem. We demonstrate convergence for all problems and stability for element pairs for problems which usually require inf-sup compatibility for conforming methods, also referring to a minor modification possible through the symmetric interior penalty Galerkin framework for stabilizing element pairs that would otherwise be traditionally unstable. Mesh robustness is particularly critical for elasticity, and we provide an example that our approach provides a greater than 5 x improvement in accuracy and allows for taking an 8 x larger stable timestep for a highly deformed mesh, compared to the continuous Galerkin finite element method.

97 MATHEMATICS AND COMPUTING↗
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 OSTI2024

Extending Rucio with modern cloud storage support

Rucio is a software framework designed to facilitate scientific collaborations in efficiently organising, managing, and accessing extensive volumes of data through customizable policies. The framework enables data distribution across globally distributed locations and heterogeneous data centres, integrating various storage and network technologies into a unified federated entity. Rucio offers advanced features like distributed data recovery and adaptive replication, and it exhibits high scalability, modularity, and extensibility. Originally developed to meet the requirements of the high-energy physics experiment ATLAS, Rucio has been continuously expanded to support LHC experiments and diverse scientific communities. Recent R&D projects within these communities have evaluated the integration of both private and commercially-provided cloud storage systems, leading to the development of additional functionalities for seamless integration within Rucio. Furthermore, the underlying systems, FTS and GFAL/Davix, have been extended to cater to specific use cases. This contribution focuses on the technical aspects of this work, particularly the challenges encountered in building a generic interface for self-hosted cloud storage, such as MinIO or CEPH S3 Gateway, and established providers like Google Cloud Storage and Amazon Simple Storage Service. Additionally, the integration of decentralised clouds like SEAL is explored. Key aspects, including authentication and authorisation, direct and remote access, throughput and cost estimation, are highlighted, along with shared experiences in daily operations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Support of domain decomposition-based solvers in Chroma

In multilevel integration, the correlation functions are decomposed into factors that depend only on fields localized into lattice subdomains so that they can be independently integrated. Although the standard formulation of the LQCD action is not local in the presence of fermions, past studies have shown approximations of the quark propagator and the fermionic determinant dependent on the gauge fields within specific subdomains can still be effective. We will present our current progress in supporting domain decomposition within Chroma, which is necessary for multilevel integration approaches. The efforts are focused on extending the code base to efficiently manipulate subdomains for lattice fields and operators and performing inversions and eigendecompositions within domains.

Alcalde, Eloy Romero↗
DOE OSTI2025

Optimization of Functionally Graded Materials Using Additive Manufacturing: An Integrated Experimental and Computational Approach (Abbreviated Final Report)

Many advanced technologies, such as next-generation energy systems, aerospace vehicles, and defense platforms, require materials that can withstand extreme environments, including high temperatures, corrosion, and radiation, while remaining strong and lightweight. Traditionally, joining different materials to achieve these properties introduces weak, failure-prone interfaces and defects that limit performance. Our research aimed to overcome this challenge by using additive manufacturing, specifically directed energy deposition, to create functionally graded materials—components with smooth transitions between different metals. This approach eliminates sharp boundaries and allows for tailored material properties throughout a part.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Controlling reversible phase transitions in rare-earth nickelates for novel memory devices

Resistive switching in correlated complex oxides is lucrative for emerging applications in neuromorphic computing, and densely scaled non-volatile memory. Electrical conductance of such complex oxides can be controllable switched across multiple orders of magnitude by either (a) electroforming a conduction channel (e.g., in tungsten oxide), or (b) inducing Mott-Hubbard transition (e.g., in rare-earth nickelates)– both via controlled migration of defects (such as oxygen vacancies) under applied bias. Nevertheless, the promise of such defect-driven electronic transitions are far from realized due to a lack of fundamental understanding of the atomic-scale processes that underlie migration and spatiotemporal evolution of oxygen vacancies over nano-to-mesoscopic length/timescales under applied electric field. In this project, we employ a synergistic integration of density functional theory (DFT) calculations, ab initio/classical molecular dynamics (AIMD/CMD) simulations, machine learning (ML), precision synthesis, and multi-modal X-ray imaging experiments to address this knowledge gap. Such an integrated approach offers to elucidate the correlations between subtle structural distortion and oxidation states; treat localized charge carriers; describe defect/ion transport in the presence of electric field; and, in turn, greatly advance the current understanding of microstructural evolution in complex oxides under applied bias. The fundamental knowledge gained from this work will enable precise control over hierarchical defect structures and unravel new routes to manipulate resistance states in complex oxides. This, in turn, will accelerate design of novel devices with desired set of neural functionalities, and high-speed densely-scaled resistive random access memory technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Gcn2 structurally mimics and functionally repurposes the HisRS enzyme for the integrated stress response

Protein kinase Gcn2 attenuates protein synthesis in response to amino acid starvation while stimulating translation of a transcriptional activator of amino acid biosynthesis. Gcn2 activation requires a domain related to histidyl-tRNA synthetase (HisRS), the enzyme that aminoacylates tRNA His . While evidence suggests that deacylated tRNA binds the HisRS domain for kinase activation, ribosomal P-stalk proteins have been implicated as alternative activating ligands on stalled ribosomes. We report crystal structures of the HisRS domain ofChaetomium thermophilumGcn2 that reveal structural mimicry of both catalytic (CD) and anticodon-binding (ABD) domains, which in authentic HisRS bind the acceptor stem and anticodon loop of tRNA His . Elements for forming histidyl adenylate and aminoacylation are lacking, suggesting that Gcn2 HisRS was repurposed for kinase activation, consistent with mutations in the CD that dysregulate yeast Gcn2 function. Substituting conserved ABD residues well positioned to contact the anticodon loop or that form a conserved ABD–CD interface impairs Gcn2 function in starved cells. Mimicry in Gcn2 HisRS of two highly conserved structural domains for binding both ends of tRNA—each crucial for Gcn2 function—supports that deacylated tRNAs activate Gcn2 and exemplifies how a metabolic enzyme is repurposed to host new local structures and sequences that confer a novel regulatory function.

Science & Technology - Other Topics↗
DOE OSTI2025

Measurements of radial neutral density profiles from Balmer- α emission in Wendelstein 7-X

Radial neutral density profiles are estimated from measurements of passive H α emission in the Wendelstein 7-X stellarator. To parametrize the generally three-dimensional distribution with a low number of degrees of freedom, the neutral density is reduced to a flux surface quantity. Accounting for emission from excitation and recombination processes, neutral density profiles are derived independently for each of the available lines of sight. Density profiles obtained from the different viewing geometries are found to vary within one order of magnitude. Toroidally oriented lines of sight predict systematically lower neutral densities when compared to poloidally oriented ones. This discrepancy is attributed to the simplifications inherent in the imposed model and significant differences in integration volumes across the viewing geometries. In line with expectations, obtained neutral densities are found to decrease with increasing plasma density. Key restrictions of the model include the reduction of the neutral density to a flux surface quantity, uncertainties in the plasma profiles and instrument function, and line integration effects outside the last closed flux surface.

Wendelstein 7-X↗
DOE OSTI2025

Functional Relevance of CASP16 Nucleic Acid Predictions as Evaluated by Structure Providers

ABSTRACT Accurate biomolecular structure prediction enables the prediction of mutational effects, the speculation of function based on predicted structural homology, the analysis of ligand binding modes, experimental model building, and many other applications. Such algorithms to predict essential functional and structural features remain out of reach for biomolecular complexes containing nucleic acids. Here, we report a quantitative and qualitative evaluation of nucleic acid structures for the CASP16 blind prediction challenge by 12 of the experimental groups who provided nucleic acid targets. Blind predictions accurately model secondary structure and some aspects of tertiary structure, including reasonable global folds for some complex RNAs; however, predictions often lack accuracy in the regions of highest functional importance. All models have inaccuracies in non‐canonical regions where, for example, the nucleic‐acid backbone bends, deviating from an A‐form helix geometry, or a base forms a non‐standard hydrogen bond (not a Watson‐Crick base pair). These bends and non‐canonical interactions are integral to forming functionally important regions such as RNA enzymatic active sites. Additionally, the modeling of conserved and functional interfaces between nucleic acids and ligands, proteins, or other nucleic acids remains poor. For some targets, the experimental structures may not represent the only structure the biomolecular complex occupies in solution or in its functional life cycle, posing a future challenge for the community.

Biochemistry & Molecular Biology↗
DOE OSTI2025

Shape Anisotropy-Dependent Leaking in Magnetic Neurons for Bio-Mimetic Neuromorphic Computing

Spiking neural networks seek to emulate biological computation through interconnected artificial neuron and synapse devices. Spintronic neurons can leverage magnetization physics to mimic biological neuron functions, such as integration tied to magnetic domain wall (DW) propagation in a patterned nanotrack and firing tied to the resistance change of a magnetic tunnel junction (MTJ), captured in the domain wall-magnetic tunnel junction (DW-MTJ) device. Leaking, relaxation of a neuron when it is not under stimulation, is also predicted to be implemented based on DW drift as a DW relaxes to a low energy position, but it has not been well explored or demonstrated in device prototypes. Here, in this work, we study DW-MTJ artificial neurons capable of leaky integrate-and-fire (LIF) behavior and demonstrate geometry-dependent leaking dynamics that results in repeatable, tunable LIF operation. Studying the behavior of five different device designs, we show tuning the geometry, stimulating fields and currents, and location of electrical contacts results in a wide range of neuron behavior. Additionally, implementation of an asymmetric notch allows for nonlinear pinning which increased expressivity without sacrificing leaking. The measured behavior is implemented in a simulated spiking neural network that outperforms a 1D model of continuous DW motion and approaches the performance of an ideal LIF activation function. The results show that the analog LIF capability of DW-MTJ neurons combines many desirable neuron functions into a single device, which can result in varied forms of multifunctional neuromorphic computing.

42 ENGINEERING↗
DOE OSTI2024

High-performance silicon photonic single-sideband modulators for cold-atom interferometry

The laser system is the most complex component of a light-pulse atom interferometer (LPAI), controlling frequencies and intensities of multiple laser beams to configure quantum gravity and inertial sensors. Its main functions include cold-atom generation, state preparation, state-selective detection, and generating a coherent two-photon process for the light-pulse sequence. To achieve substantial miniaturization and ruggedization, we integrate key laser system functions onto a photonic integrated circuit. Our study focuses on a high-performance silicon photonic suppressed-carrier single-sideband (SC-SSB) modulator at 1560 nanometers, capable of dynamic frequency shifting within the LPAI. By independently controlling radio frequency (RF) channels, we achieve 30-decibel carrier suppression and unprecedented 47.8-decibel sideband suppression at peak conversion efficiency of –6.846 decibels (20.7%). We investigate imbalances in both amplitudes and phases between the RF signals. Using this modulator, we demonstrate cold-atom generation, state-selective detection, and atom interferometer fringes to estimate gravitational acceleration, g ≈ 9.77 ± 0.01 meters per second squared, in a rubidium ( 87 Rb) atom system.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Integrated Hourly Meteorological Database of 20 Meteorological Stations (1981-2022) for Watershed Function SFA Hydrological Modeling

This dataset contains (a) a script “R_met_integrated_for_modeling.R”, and (b) associated input CSV files: 3 CSV files per location to create a 5-variable integrated meteorological dataset file (air temperature, precipitation, wind speed, relative humidity, and solar radiation) for 19 meteorological stations and 1 location within Trail Creek from the modeling team within the East River Community Observatory as part of the Watershed Function Scientific Focus Area (SFA). As meteorological forcings varied across the watershed, a high-frequency database is needed to ensure consistency in the data analysis and modeling. We evaluated several data sources, including gridded meteorological products and field data from meteorological stations. We determined that our modeling efforts required multiple data sources to meet all their needs. As output, this dataset contains (c) a single CSV data file (*_1981-2022.csv) for each location (20 CSV output files total) containing hourly time series data for 1981 to 2022 and (d) five PNG files of time series and density plots for each variable per location (100 PNG files). Detailed location metadata is contained within the Integrated_Met_Database_Locations.csv file for each point location included within this dataset, obtained from Varadharajan et al., 2023 doi:10.15485/1660962. This dataset also includes (e) a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and (f) a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type. Review the (g) ReadMe_Integrated_Met_Database.pdf file for additional details on the script, methods, and structure of the dataset.The script integrates Northwest Alliance for Computational Science and Engineering’s PRISM gridded data product, National Oceanic and Atmospheric Administration’s NCEP-NCAR Reanalysis 1 gridded data product (through the `RCNEP` R package, Kemp et al., doi:10.32614/CRAN.package.RNCEP), and analytical-based calculations. Further, this script downscales the input data into hourly frequency, which is necessary for the modeling efforts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Mechanical Toughening of Two-Dimensional Covalent Organic Frameworks Enabled by Graphene-Based Sandwich-Structured Nanocomposites

Low resistance to fracture in two-dimensional covalent organic frameworks (2D COFs) limits their practical applications, especially in mechanically demanding fields, such as flexible electronics and sensing devices. We address this critical limitation by fabricating a sandwich-structured nanocomposite consisting of graphene layers sandwiched between 2D COF layers (2D COFs/graphene/2D COFs) via chemical vapor deposition. Here, our sandwich-structured nanocomposites exhibit a remarkable improvement in modulus E, fracture toughness K IC , and critical energy release rate G C compared to pure 2D COFs. This enhancement is likely due to the graphene layers as the backbone of the sandwich structure effectively carrying and redistributing mechanical stress within the nanocomposite. Our findings demonstrate that a sandwich structure can improve the mechanical robustness of a 2D COF so that it can preserve the functionality and mechanical integrity for applications in stretchable electronics.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Revealing the hidden structure of disordered materials by parameterizing their local structural manifold

Abstract Durable interest in developing a framework for the detailed structure of glassy materials has produced numerous structural descriptors that trade off between general applicability and interpretability. However, none approach the combination of simplicity and wide-ranging predictive power of the lattice-grain-defect framework for crystalline materials. Working from the hypothesis that the local atomic environments of a glassy material are constrained by enthalpy minimization to a low-dimensional manifold in atomic coordinate space, we develop a generalized distance function, the Gaussian Integral Inner Product (GIIP) distance, in connection with agglomerative clustering and diffusion maps, to parameterize that manifold. Applying this approach to a two-dimensional model crystal and a three-dimensional binary model metallic glass results in parameters interpretable as coordination number, composition, volumetric strain, and local symmetry. In particular, we show that a more slowly quenched glass has a higher degree of local tetrahedral symmetry at the expense of cyclic symmetry. While these descriptors require post-hoc interpretation, they minimize bias rooted in crystalline materials science and illuminate a range of structural trends that might otherwise be missed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Designing workflows for materials characterization

Experimental science is enabled by the combination of synthesis, imaging, and functional characterization organized into evolving discovery loop. Synthesis of new material is typically followed by a set of characterization steps aiming to provide feedback for optimization or discover fundamental mechanisms. However, the sequence of synthesis and characterization methods and their interpretation, or research workflow, has traditionally been driven by human intuition and is highly domain specific. Here, we explore concepts of scientific workflows that emerge at the interface between theory, characterization, and imaging. In this study, we discuss the criteria by which these workflows can be constructed for special cases of multiresolution structural imaging and functional characterization, as a part of more general material synthesis workflows. Some considerations for theory–experiment workflows are provided. We further pose that the emergence of user facilities and cloud labs disrupts the classical progression from ideation, orchestration, and execution stages of workflow development. To accelerate this transition, we propose the framework for workflow design, including universal hyperlanguages describing laboratory operation, ontological domain matching, reward functions and their integration between domains, and policy development for workflow optimization. These tools will enable knowledge-based workflow optimization; enable lateral instrumental networks, sequential and parallel orchestration of characterization between dissimilar facilities; and empower distributed research.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Derivative-free stochastic optimization via adaptive sampling strategies

In this paper, we present a novel derivative-free framework for solving unconstrained stochastic optimization problems. Many problems in fields ranging from simulation optimization to reinforcement learning to quantum computing involve settings where only stochastic function values are obtained via a zeroth-order oracle, which has no available gradient information and necessitates the usage of derivative-free optimization methodologies. Our approach includes estimating gradients using stochastic function evaluations and integrating adaptive sampling techniques to control the accuracy in these stochastic approximations. Our framework encapsulates several gradient estimation techniques, including standard finite-difference, Gaussian smoothing, sphere smoothing, randomized coordinate finite-difference, and randomized subspace finite-difference methods. We provide theoretical convergence guarantees for our framework and analyze the worst-case iteration and sample complexities associated with each gradient estimation method. Finally, we demonstrate the empirical performance of the methods on logistic regression and nonlinear least squares problems.

Adaptive sampling↗
DOE OSTI2026

Supervised Learning-Based Spatial Position Estimation with Vertical Displacement for Hovering UAV Wireless Power Transfer

This study presents a supervised learning-based spatial position estimation approach for wireless power transfer (WPT) systems supporting hovering unmanned aerial vehicle (UAV) charging. Unlike stationary charging scenarios, hovering UAVs introduce continuous lateral misalignment and vertical displacement, leading to variations in magnetic coupling and reduced power transfer efficiency. To address this challenge, the proposed method estimates the relative spatial position of the receiver coil using only electrical measurements obtained at the secondary side. A supervised learning model is trained to map output voltage and current features to spatial coordinates, enabling position awareness without requiring external sensors, vision systems, or communication links. The sensing functionality is inherently integrated into the WPT system, allowing simultaneous power transfer and localization through the same magnetic interface. Experimental validation is conducted on a laboratory-scale prototype under varying lateral offsets and air-gap conditions. In addition, spline-based interpolation is employed to increase spatial data density for training. The results demonstrate that the proposed framework can capture spatial variations associated with both lateral and vertical displacement, providing reliable position estimation under hovering conditions. This work establishes a hardware-efficient, sensorless solution for UAV wireless charging and serves as a baseline for advanced data-driven position estimation methods in dynamic WPT systems.

Asa, Erdem [ORNL] (ORCID:0000000190884812)↗