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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 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Stochastic self-tuning hybrid algorithm for reaction-diffusion systems

Many biochemical phenomena involve reactants with vastly different concentrations, some of which are amenable to continuum-level descriptions, while the others are not. We present a hybrid self-tuning algorithm to model such systems. The method combines microscopic (Brownian) dynamics for diffusion with mesoscopic (Gillespie-type) methods for reactions and remains efficient in a wide range of regimes and scenarios with large variations of concentrations. Furthermore, its accuracy, robustness, and versatility are balanced by redefining propensities and optimizing the mesh size and time step. We use a bimolecular reaction to demonstrate the potential of our method in a broad spectrum of scenarios: from almost completely reaction-dominated systems to cases where reactions rarely occur or take place very slowly. The simulation results show that the number of particles present in the system does not degrade the performance of our method. This makes it an accurate and computationally efficient tool to model complex multireaction systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Simulating molecular polaritons in the collective regime using few-molecule models

The study of molecular polaritons beyond simple quantum emitter ensemble models (e.g., Tavis–Cummings) is challenging due to the large dimensionality of these systems and the complex interplay of molecular electronic and nuclear degrees of freedom. This complexity constrains existing models to either coarse-grain the rich physics and chemistry of the molecular degrees of freedom or artificially limit the description to a small number of molecules. In this work, we exploit permutational symmetries to drastically reduce the computational cost of ab initio quantum dynamics simulations for large N . Furthermore, we discover an emergent hierarchy of timescales present in these systems, that justifies the use of an effective single molecule to approximately capture the dynamics of the entire ensemble, an approximation that becomes exact as N → ∞. We also systematically derive finite N corrections to the dynamics and show that addition of k extra effective molecules is enough to account for phenomena whose rates scale as 𝒪( N − k ). Based on this result, we discuss how to seamlessly modify existing single-molecule strong coupling models to describe the dynamics of the corresponding ensemble. We call this approach collective dynamics using truncated equations (CUT-E), benchmark it against well-known results of polariton relaxation rates, and apply it to describe a universal cavity-assisted energy funneling mechanism between different molecular species. Beyond being a computationally efficient tool, this formalism provides an intuitive picture for understanding the role of bright and dark states in chemical reactivity, necessary to generate robust strategies for polariton chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Extending the Nuclide Inventory Validation Basis for High-Burnup Fuel with New Radiochemical Assay Data

Efforts are underway at Oak Ridge National Laboratory to improve the nuclide inventory validation basis for spent nuclear fuel at high burnups. Recently conducted radiochemical assay experiments provided new measurement data for nine samples of fuel irradiated in a pressurized water reactor, with estimated sample burnups in the 30 to 70 GWd/t range. This type of destructive assay data is essential for validating computational methods, tools, and nuclear data applied in nuclear safety analyses and for improving our understanding of the bias and uncertainty in code predictions. The measurement data include key actinides and fission products that span a gamut of needs and interests for nuclear science and engineering applications in criticality safety, reactor physics, nuclide inventory, decay heat, and radiation shielding. The SCALE 6.3 code system with ENDF/B-VII.1 cross-section libraries was used to simulate the irradiation histories of the measured fuel samples. The calculated nuclide concentrations are compared to corresponding measurement data. The significance of the comparisons is discussed, emphasizing how the addition of the new measurement data fills gaps in the validation basis at high burnups and contributes to the decrease in bias and uncertainty for predicted nuclide concentrations. The discussion addresses the effect of the sample burnup used in the simulation—which is based on reactor operator records or on calibration to measured data for burnup indicator fission products—on the validation results.

Nuclide inventory↗
DOE OSTI2019

Investigating Powder Spreading Dynamics in Additive Manufacturing Processes by In-situ High-speed X-ray Imaging

The quality of the powder bed is known to be one of the main factors that influence the quality of the part being manufactured by powderbed- based additive manufacturing processes. For example, in the laser powder bed fusion process, it has been demonstrated that improving the powder bed density and homogeneity improves the final quality of the part. Understanding powder spreading dynamics in the powder-bed-based additive manufacturing processes is critical for designing spreading structures and spreading parameters, identifying defect formation mechanisms in the powder bed, and guiding the design of optimized feedstock powders. Extensive efforts have been devoted to understand the fundamental mechanism of the powder flow and spreading process. Various powder flow testers were used to study the flow behavior of powders used in additive manufacturing. However, the powder flow conditions in flow meters are different from those in real additive manufacturing conditions. Models have also been developed to simulate and study the spreading process. The discrete element method (DEM) is currently the main tool for computationally simulating the spreading process. However, these models are based on many physical assumptions. Visible light cameras have been used recently to conduct in-situ study of powder spreading in additive manufacturing. Due to the limited resolution and penetration depth of visible light, it is extremely difficulty to study the particle-scale powder spreading dynamics. Here, to overcome the challenges of studying powder spreading dynamics under real additive manufacturing conditions, we developed an experimental approach to study the particle-scale powder spreading process with high spatial and temporal resolution using the high-speed high-energy X-ray imaging facility at beamline 32-ID-B of the APS. Figure 1 shows an overview of the research setup. It is composed of a vacuum chamber (building environment), an automatic powder spreading system, a laser system, an X-ray source (X-ray outlet), and an X-ray detection/imaging system.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Improving the stellarator through advances in plasma theory

Improvements to the stellarator concept can be realized through advancements in theoretical and computational plasma physics. Herein, recent advances are reported in the topical areas of: (1) improved energetic ion confinement, (2) the impact of three-dimensional (3D) shaping on turbulent transport, (3) reducing coil complexity, (4) novel optimization and design methods, and (5) computational magnetohydrodynamic tools. Furthermore, these advances enable the development of new stellarator configurations with improved confinement properties.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Low energy muon neutrino reconstruction in MicroBooNE

MicroBooNE is a Liquid Argon Time Projection Chamber (LArTPC) neutrino experiment on the Booster Neutrino Beamline at the Fermi National Accelerator Laboratory, with an 85-tonne active mass. One of MicroBooNE’s primary physics goals is to investigate the excess of electron neutrino events seen by MiniBooNE in the [200-600] MeV range. MicroBooNE will constrain the intrinsic electron neutrino component of the beam by measuring the muon neutrino spectrum. Several low-energy excess analyses are taking place in parallel, using independent reconstructions and selection schemes. This paper will focus on a low-energy excess analysis that makes use of deep learning algorithms applied to the high-resolution images provided by the MicroBooNE LArTPC. We present a novel 3D event reconstruction based on computer vision tools and a stochastic search algorithm, yielding a 2.2% energy resolution for 1μ1p muon neutrino interactions in the [200-1500] MeV range.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Performance portability for the CMS Reconstruction with Alpaka

For CMS, Heterogeneous Computing is a powerful tool to face the computational challenges posed by the upgrades of the LHC, and will be used in production at the High Level Trigger during Run 3. In principle, to offload the computational work on non-CPU resources, while retaining their performance, different implementations of the same code are required. This would introduce code-duplication which is not sustainable in terms of maintainability and testability of the software. Performance portability libraries allow to write code once and run it on different architectures with close-to-native performance. The CMS experiment is evaluating performance portability libraries for the near term future.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Graph-learning approach to combine multiresolution seismic velocity models

SUMMARY The resolution of velocity models obtained by tomography varies due to multiple factors and variables, such as the inversion approach, ray coverage, data quality, etc. Combining velocity models with different resolutions can enable more accurate ground motion simulations. Toward this goal, we present a novel methodology to fuse multiresolution seismic velocity maps with probabilistic graphical models (PGMs). The PGMs provide segmentation results, corresponding to various velocity intervals, in seismic velocity models with different resolutions. Further, by considering physical information (such as ray path density), we introduce physics-informed probabilistic graphical models (PIPGMs). These models provide data-driven relations between subdomains with low (LR) and high (HR) resolutions. Transferring (segmented) distribution information from the HR regions enhances the details in the LR regions by solving a maximum likelihood problem with prior knowledge from HR models. When updating areas bordering HR and LR regions, a patch-scanning policy is adopted to consider local patterns and avoid sharp boundaries. To evaluate the efficacy of the proposed PGM fusion method, we tested the fusion approach on both a synthetic checkerboard model and a fault zone structure imaged from the 2019 Ridgecrest, CA, earthquake sequence. The Ridgecrest fault zone image consists of a shallow (top 1 km) high-resolution shear-wave velocity model obtained from ambient noise tomography, which is embedded into the coarser Statewide California Earthquake Center Community Velocity Model version S4.26-M01. The model efficacy is underscored by the deviation between observed and calculated traveltimes along the boundaries between HR and LR regions, 38 per cent less than obtained by conventional Gaussian interpolation. The proposed PGM fusion method can merge any gridded multiresolution velocity model, a valuable tool for computational seismology and ground motion estimation.

Geochemistry & Geophysics↗
DOE OSTI2026

Generating entangled steady states in multistable open quantum systems via initial state control

Entanglement underpins the power of quantum technologies, yet it is fragile and typically destroyed by dissipation. Paradoxically, the same dissipation, when carefully engineered, can drive a system toward robust entangled steady states. However, this engineering task is nontrivial, as dissipative many-body systems are complex, particularly when they support multiple steady states. Here, we derive analytic expressions that predict how the steady state of a system evolving under a Lindblad equation depends on the initial state, without requiring integration of the dynamics. These results extend Refs. [V. V. Albert and L. Jiang, Phys. Rev. A 89, 022118 (2014); V. V. Albert et al., Phys. Rev. X 6, 041031 (2016)], showing that while the steady-state manifold is determined by the Liouvillian kernel, the weights within it depend on both the Liouvillian and the initial state. We identify a special class of Liouvillians for which the steady state depends only on the initial overlap with the kernel. Our framework provides analytical insight and a computationally efficient tool for predicting steady states in open quantum systems. As an application, we propose schemes to generate metrologically useful entangled steady states in spin ensembles via balanced collective decay.

Dissipative dynamics↗
DOE OSTI2024

A Novel Framework to Quantify Power Grid Resilience

The quantification of an operating power grid’s resilience is highly significant today, given its criticality as an enabler of other infrastructures, complexity, and the threat it faces due to a wide range of detrimental events, from extreme climate to cyber attacks. Currently, there exist no standardized definitions and metrics for measuring the resilience of an operating grid. In this paper, we introduce a novel resilience quantification framework and demonstrate a method to measure the flexibility towards topological/structural changes due to potential failures in the power grid to assess operational resilience. We start with the state estimation data from a large utility and use the graph analysis methods and power flow simulation tools to compute the identified resilience parameters.

Yoginath, Srikanth↗
DOE OSTI2025

Entanglement Benchmarking in Quantum Simulations of Spin Systems

We simulate quantum spin systems and measure entanglement using circuits tailored for near-term quantum computers. Traditional tools like entanglement entropy are limited to pure states and require full state tomography, making them impractical on current hardware. Instead, we employ the novel approach, Positive Partial Transpose (PPT) criterion to efficiently detect pairwise entanglement from two-spin reduced density matrices, applicable to both pure and mixed states. This method enables scalable entanglement detection, providing a practical route to study quantum correlations, phase transitions, and benchmark quantum devices.

Baul, Anshumitra [ORNL] (ORCID:0000000268947191)↗
DOE OSTI2023

A Prediction Method for Catchment Efficiency Loss due to Coaxial Nozzle Wear in Powder Fed Directed Energy Deposition Systems

Abstract Powder fed laser directed energy deposition systems show significant promise and their widespread adoption could greatly advance manufacturing technology; however, a significant amount of gas atomized metal powder is wasted during material deposition. As contaminated powder must be collected and disposed of as hazardous waste, this process is often criticized for its low catchment efficiency. This presents potential implementation and scaling issues, as widespread use of this technology would directly increase the amount of contaminated powder in need of safe disposal. A direct contribution to this waste issue was identified in past work as wearing exterior and interior surfaces on a replaceable nozzle component within the additive head of such a system integrated into a hybrid manufacturing machine tool. A computational fluid dynamics simulation is developed specifically for use within the manufacturing process by predicting catchment efficiency losses before a deposition operation is performed. The model is robust enough to calculate a catchment efficiency estimate within a reasonable transition time before deposition and is designed to replace visual estimation of nozzle deterioration by the machine operator. Both of two previously identified wear modes are represented in the simulation. Their effects on powder stream geometry are discussed in detail and the results are compared with in-situ measurements of powder stream geometry and concentrations. The resulting work contributes to the growing understanding of using prediction and prevention methods on catchment efficiency related part defects within these systems.

DeWitte, Lisa↗
DOE OSTI2021

Dynamic Interconversion of Metal Active Site Ensembles in Zeolite Catalysis

Catalysis science is founded on understanding the structure, number, and reactivity of active sites. Kinetic models that consider active sites to be static and noninteracting entities are routinely successful in describing the behavior of heterogeneous catalysts. Yet, active site ensembles often restructure in response to their external environment and even during steady-state catalytic turnover, sometimes requiring non-mean-field kinetic treatments to describe distance-dependent interactions among sites. Such behavior is being recognized more frequently in modern catalysis research, with the advent of experimental methods to quantify turnover rates with increasing precision, an expanding arsenal of operando characterization tools, and computational descriptions of atomic structure and motion at chemical potentials and timescales increasingly relevant to reaction conditions. This review focuses on dynamic changes to metal active site ensembles on zeolite supports, which are silica-based crystalline materials substituted with Al that generate binding sites for isolated and low-nuclearity metal site ensembles. Metal sites can become solvated and mobilized during reaction, facilitating interactions among sites that change their nuclearity and function. Such intersite communication can be regulated by the zeolite support, resulting in non-single-site and potentially non-mean-field kinetic behavior arising from mechanisms of catalytic action that combine elements of those canonically associated with homogeneous and heterogeneous catalysis. Here, we discuss recent literature examples that document dynamic active site behavior in metal-zeolites and outline methodologies to identify and interpret such behavior. We conclude with our outlook on future research directions to develop this evolving branch of catalysis science and harness it for practical applications.

operando characterization↗
DOE OSTI2018

Lagomorph

Existing tools for computational anatomy and image registration are not aimed at HPC environments. As such they are not easily adapted to multimode platforms and contain inefficient batch processing modes. Lagomorph addresses this by providing an easy to install and use pure python implementation of very efficient image registration and atlas building which is aimed at extreme-scale anatomical studies using modern HPC and GPU resources.

Hinkle, JacobD↗
DOE OSTI2020

Automatic Report Generator (ARG)

ARG is a computational simulation tool. It generates user-configurable reports, including graphs and images, from user data in various formats.SAND2019-9525 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Clay, Robert↗
DOE OSTI2024

UOWDetection

The code provides machine learning, data collection, and computer science tools used to detect undocumented orphaned wells in United States from aerial imagery.

Kim, Anastasiia↗
DOE OSTI2024

Anchoring

This software provides methods and functions for training deep image classification models based on the principle of anchoring. It features a user-friendly PyTorch wrapper that facilitates the easy conversion of any model into an anchored model. The software supports various standard datasets and includes scripts for conducting evaluations. Developed with PyTorch, it is compatible with common neural network architectures used for image data. Additionally, it offers tools for computing evaluation metrics to assess model performance.

Narayanaswamy, Vivek Sivaraman↗
DOE OSTI2025

MJO CP Tensor Decompositions

SAND2025-14273O MJO CP Tensor Decompositions is a Python tool for computing canonical polyadic (CP) decompositions of MERRA2 observational data to understand the onset and evolution of the Madden-Julian Oscillation (MJO). Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Bull, Diana [Sandia National Lab. (SNL-CA), Liverm↗