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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 1,675 records · Page 93
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Deep learning‑based metal artefact reduction in X-ray computed tomography of TRISO fuel compacts

TRISO (TRi-structural ISOtropic) – compact-type micro-particle fuels – are next generation nuclear fuel compacts designed with safety in mind. Structural integrity and characterisation before and after irradiation are important to determine the performance of the fuel compacts under real reactor conditions. X-ray computed tomography can be an important tool for non-destructive evaluation of these fuel compacts. The fuel particles are highly attenuating for X-rays which creates metal artefact, rendering the images unusable. Our artefact correction can mitigate these artefacts significantly. The proposed method works by first segmenting highly-attenuating structures (metal) and forward projecting to localise the source of artefacts in the projection domain, then, using a traditional or U-net-based deep learning architecture, contextually interpolating those regions to remove the artefacts. Finally the reconstruction from the modified projection is fused with the segmented metal. The proposed method shows a significant improvement in the image quality while significantly reducing the reconstruction time compared to the standard technique.

Rahman, Obaid [ORNL] (ORCID:0000000277810840)↗
DOE OSTI2017

The electromechanical behavior of piezoelectric thin film composite diaphragms possessing in-plane stresses

Many piezoelectric microelectromechanical systems (MEMS) measure or generate acoustic signals via the motion of radially non-uniform, thin film composite plates. The composite layers provide piezoelectric actuation, structural support, electrode metallization, passivation, etc. Often, the layers are non-uniform over the plate and contain residual stresses introduced during the fabrication process. Accurate models of non-uniform composite plate mechanics are crucial for predicting and optimizing device performance. In this paper, an analytical solution for a radially non-uniform, piezoelectric, circular composite plate incorporating residual stress is derived. The analytical solution is compared to experimental measurements of a MEMS piezoelectric diaphragm. The results show the improved accuracy of the analytical model when including film stress, the speed of the analytical solution as compared to finite element analysis, the sensitivity of device performance to residual stress and the importance of accurate film stresses as model inputs. Finally, the analytical model presented is useful as a design optimization tool given the efficiency of the computational time, shown to be 275 times less than a comparable finite element analysis.

42 ENGINEERING↗
DOE OSTI2020

Deep learning approaches for neural decoding across architectures and recording modalities

Decoding behavior, perception or cognitive state directly from neural signals is critical for brain–computer interface research and an important tool for systems neuroscience. In the last decade, deep learning has become the state-of-the-art method in many machine learning tasks ranging from speech recognition to image segmentation. The success of deep networks in other domains has led to a new wave of applications in neuroscience. In this article, we review deep learning approaches to neural decoding. Here, we describe the architectures used for extracting useful features from neural recording modalities ranging from spikes to functional magnetic resonance imaging. Furthermore, we explore how deep learning has been leveraged to predict common outputs including movement, speech and vision, with a focus on how pretrained deep networks can be incorporated as priors for complex decoding targets like acoustic speech or images. Deep learning has been shown to be a useful tool for improving the accuracy and flexibility of neural decoding across a wide range of tasks, and we point out areas for future scientific development.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Concurrent determination of nanocrystal shape and amorphous phases in complex materials by diffraction scattering computed tomography

Advanced functional materials often contain multiple phases which are (nano)crystalline and/or amorphous. The spatial distribution of these phases and their properties, including nanocrystallite size and shape, often drives material function yet is difficult to obtain with current experimental techniques. This article describes the use of diffraction scattering computed tomography, which maps wide-angle scattering information onto sample space, to address this challenge. The wide-angle scattering signal contains information on both (nano)crystalline and amorphous phases. Rietveld refinement of reconstructed diffraction patterns is employed to determine anisotropic nanocrystal shapes. The background signal from refinements is used to identify contributing amorphous phases through multivariate curve resolution. Thus it is demonstrated that reciprocal space analysis in combination with diffraction scattering computed tomography is a very powerful tool for the complete analysis of complex multiphase materials such as energy devices.

Birkbak, Mie Elholm↗
DOE OSTI2018

HPC Software Verification in Action: A Case Study with Tensor Transposition

As HPC platforms get increasingly complex, the complexity of software optimized for these platforms has also increased. There is a pressing need to ensure correctness of scientific applications to enhance our confidence in the results they produce. In this paper, we focus on checking the correctness of libraries providing a small but important functionality— tensor transposition—used in computational chemistry applications. While several correctness tools have been developed and deployed, there are several practical challenges in using them to verify production HPC software. We present our experiences using two tools—CIVL and CodeThorn—in checking the correctness of two index permutation libraries.

Model checking, equivalence checking, index permut↗
DOE OSTI2023

Deep Learning Based Workflow for Accelerated Industrial X-Ray Computed Tomography

X-ray computed tomography (XCT) is an important tool for high-resolution non-destructive characterization of additively-manufactured metal components. XCT reconstructions of metal components may have beam hardening artifacts such as cupping and streaking which makes reliable detection of flaws and defects challenging. Furthermore, traditional workflows based on using analytic reconstruction algorithms require a large number of projections for accurate characterization - leading to longer measurement times and hindering the adoption of XCT for in-line inspections. In this paper, we introduce a new workflow based on the use of two neural networks to obtain high-quality accelerated reconstructions from sparse-view XCT scans of single material metal parts. The first network, implemented using fully-connected layers, helps reduce the impact of BH in the projection data without the need of any calibration or knowledge of the component material. The second network, a convolutional neural network, maps a low-quality analytic 3D reconstruction to a high-quality reconstruction. Using experimental data, we demonstrate that our method robustly generalizes across several alloys, and for a range of sparsity levels without any need for retraining the networks thereby enabling accurate and fast industrial XCT inspections.

Rahman, Obaid↗
DOE OSTI2024

FunM2C: A Filter for Uncertainty Visualization of Multivariate Data on Multi-Core Devices

Uncertainty visualization is an emerging research topic in data visualization because neglecting uncertainty in visualization can lead to inaccurate assessments. In this paper, we study the propagation of multivariate data uncertainty in visualization. Although there have been a few advancements in probabilistic uncertainty visualization of multivariate data, three critical challenges remain to be addressed. First, the state-of-the-art probabilistic uncertainty visualization framework is limited to bivariate data (two variables). Second, existing uncertainty visualization algorithms use computationally intensive techniques and lack support for cross-platform portability. Third, as a consequence of the computational expense, integration into production visualization tools is impractical. In this work, we address all three issues and make a threefold contribution. First, we take a step to generalize the state-of-the-art probabilistic framework for bivariate data to multivariate data with an arbitrary number of variables. Second, through utilization of VTK-m’s shared-memory parallelism and cross-platform compatibility features, we demonstrate acceleration of multivariate uncertainty visualization on different many-core architectures, including OpenMP and AMD GPUs. Third, we demonstrate the integration of our algorithms with the ParaView software. We demonstrate the utility of our algorithms through experiments on multivariate simulation data with three and four variables.

Hari, Gautam↗
DOE OSTI2013

Prediction of radiation doses during the dismantling of the pressurized tank from emergency core cooling system of RBMK- 1500 reactor

Preparation for the decommissioning of the Ignalina Nuclear Power Plant involves multiple problems. Personnel radiation safety during the performance of dismantling activities is one of them. In order to assess the optimal personnel radiation safety, the modelling is performed for large components by the means of computer code 'VISIPLAN 3D ALARA Planning tool' developed by SCK CEN (Belgium). Modelling results of radiation doses during the dismantling of the pressurized tank from the emergency core cooling system (ECCS PT) of RBMK-1500 reactor are presented in this paper. The mass of one ECCS PT is approximately 47.6 tons. The radiological surveys indicate that the inner surface of the ECCS PT is contaminated with radioactive products of corrosion and sediments due to the radioactive water. The assessment of workers exposure was performed to comply with ALARA. The effective doses to the workers were modeled for different strategies of ECCS PT dismantling. The impact of dismantling tools and shielding types and extract ventilation flow rate during the dismantling of ECCS PT on effective doses were analyzed. The total effective personnel doses were obtained by summarizing the effective personnel doses from various sources of exposure, i. e., direct radiation from radioactive equipment, internal radiation due to inhalation of radioactive aerosols, and direct radiation from radioactive aerosols arising during hot cutting in premises. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

On the Strong Scaling of the Spectral Element Solver Nek5000 on Petascale Systems. In: EASC '16 Proceedings of the Exascale Applications and Software Conference 2016, Article No. 5

The present work is targeted at performing a strong scaling study of the high-order spectral element fluid dynamics solver Nek5000. Prior studies such as [5] indicated a recommendable metric for strong scalability from a theoretical viewpoint, which we test here extensively on three parallel machines with different performance characteristics and interconnect networks, namely Mira (IBM Blue Gene/Q), Beskow (Cray XC40) and Titan (Cray XK7). The test cases considered for the simulations correspond to a turbulent flow in a straight pipe at four different friction Reynolds numbers Reτ = 180, 360, 550 and 1000. Considering the linear model for parallel communication we quantify the machine characteristics in order to better assess the scaling behaviors of the code. Subsequently sampling and profiling tools are used to measure the computation and communication times over a large range of compute cores. We also study the effect of the two coarse grid solvers XXT and AMG on the computational time. Super-linear scaling due to a reduction in cache misses is observed on each computer. The strong scaling limit is attained for roughly 5000-10,000 degrees of freedom per core on Mira, 30,000 - 50,0000 on Beskow, with only a small impact of the problem size for both machines, and ranges between 10,000 and 220,000 depending on the problem size on Titan. This work aims at being a reference for Nek5000 users and also serves as a basis for potential issues to address as the community heads towards exascale supercomputers.

Offermans, Nicolas↗
DOE OSTI2020

High Performance Computing tutorials

This source code includes tutorials targeting a general audience that will help users to use high performance computing environments and systems at Livermore Computing and beyond. Examples of HPC tools covered include mpi and posix. Tutorials may include source code demonstrating usage.

Herriman, JaneE.↗
DOE OSTI2022

PeleLMeX [SWR-22-48]

PeleLMeX is a solver for high fidelity reactive flow simulations, namely direct numerical simulation (DNS) and large eddy simulation (LES). The solver combines a low Mach number approach, adaptive mesh refinement (AMR), embedded boundary (EB) geometry treatment and high performance computing (HPC) to provide a flexible tool to address research questions on platforms ranging from small workstations to the world's largest GPU-accelerated supercomputers. PeleLMeX has been used to study complex flame/turbulence interactions in RCCI engines and hydrogen combustion or the effect of sustainable aviation fuel on gas turbine combustion. PeleLMeX is part of the Pele combustion Suite (https://amrex-combustion.github.io/)

Day, Marcus↗
DOE OSTI2026

buhito

buhito is a Python library for graph analysis and machine learning. Graphs can represent networks with objects as nodes and their relationships as edges. buhito focuses on graphlet methods that study graphs through enumerating their component subgraphs to enable interpretable and fast models of complex systems. The package provides tools for different algorithmic designs for computing, analyzing, and applying graphlets to research problems such as machine learning, data compression, and anomaly detection in graph-structured data. A central feature is performing decomposition data analysis on graphs for machine learning models. Implemented in Python and built upon open-source scientific libraries such as NetworkX, NumPy, and SciPy, buhito provides high-performance methods for researchers exploring the mathematical and computational foundations of graphlet analysis applicable to systems of different sizes.

Pimonova, Yulia↗
DOE OSTI2021

Learning curves for drug response prediction in cancer cell lines

Motivated by the size and availability of cell line drug sensitivity data, researchers have been developing machine learning (ML) models for predicting drug response to advance cancer treatment. As drug sensitivity studies continue generating drug response data, a common question is whether the generalization performance of existing prediction models can be further improved with more training data. We utilize empirical learning curves for evaluating and comparing the data scaling properties of two neural networks (NNs) and two gradient boosting decision tree (GBDT) models trained on four cell line drug screening datasets. The learning curves are accurately fitted to a power law model, providing a framework for assessing the data scaling behavior of these models. The curves demonstrate that no single model dominates in terms of prediction performance across all datasets and training sizes, thus suggesting that the actual shape of these curves depends on the unique pair of an ML model and a dataset. The multi-input NN (mNN), in which gene expressions of cancer cells and molecular drug descriptors are input into separate subnetworks, outperforms a single-input NN (sNN), where the cell and drug features are concatenated for the input layer. In contrast, a GBDT with hyperparameter tuning exhibits superior performance as compared with both NNs at the lower range of training set sizes for two of the tested datasets, whereas the mNN consistently performs better at the higher range of training sizes. Moreover, the trajectory of the curves suggests that increasing the sample size is expected to further improve prediction scores of both NNs. These observations demonstrate the benefit of using learning curves to evaluate prediction models, providing a broader perspective on the overall data scaling characteristics. A fitted power law learning curve provides a forward-looking metric for analyzing prediction performance and can serve as a co-design tool to guide experimental biologists and computational scientists in the design of future experiments in prospective research studies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Knowledge-Informed Uncertainty-Aware Machine Learning for Time Series Forecasting of Dynamical Engineered Systems

The high complexity and multiscale nature of many engineered systems—such as those in nuclear power plants—make representing and forecasting their dynamic behavior challenging. Physics-based models can be overly complex and computationally intractable, whereas machine learning (ML) tools are often data-hungry and prone to unphysical solutions. This study proposes a knowledge-informed ML-aided hybrid residual modeling approach that offers accurate and efficient time series forecasting for the operation of dynamical engineered systems. Hybrid residual modeling entails a baseline solution from domain knowledge and known physics expressions about the system dynamics integrated with an ML model to capture undiscovered information from the mismatch (i.e., residuals) between true states from measurements and baseline-predicted outputs. This study further quantifies the ML model uncertainty to provide trustworthy solutions. Real-time operational data from thermal-hydraulic flow loops of the cryogenic moderator system in Oak Ridge National Laboratory’s Spallation Neutron Source facility were used to demonstrate the potential of knowledge-informed uncertainty-aware ML in real-world applications. The state variables of the cryogenic helium loop were modeled with (1) first principles–based system identification (sysID), (2) long short-term memory (LSTM) neural network, and (3) hybrid sysID (baseline) + LSTM (residual). The superior predictive capability of the sysID+LSTM model versus stand-alone sysID and LSTM is confirmed by average performance metrics and individual data points across different prediction horizons. By creating a robust representation of the underlying physical system, the widely applicable hybrid residual modeling approach will enable the future development of digital twins for performance prediction, prognostics, and operation control.

Zhao, Xingang↗
DOE OSTI2013

VEST: abstract vector calculus simplification in Mathematica

We present a new package, VEST (Vector Einstein Summation Tools), that performs abstract vector calculus computations in Mathematica. Through the use of index notation, VEST is able to reduce scalar and vector expressions of a very general type using a systematic canonicalization procedure. In addition, utilizing properties of the Levi-Civita symbol, the program can derive types of multi-term vector identities that are not recognized by canonicalization, subsequently applying these to simplify large expressions. In a companion paper, we employ VEST in the automation of the calculation of Lagrangians for the single particle guiding center system in plasma physics, a computation which illustrates its ability to handle very large expressions. VEST has been designed to be simple and intuitive to use, both for basic checking of work and more involved computations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

Mechanical Analysis of W78/88-1 Life Extension Program Warhead Design Options

Life Extension Program (LEP) is a program to repair/replace components of nuclear weapons to ensure the ability to meet military requirements. The W78/88-1 LEP encompasses the modernization of two major nuclear weapon reentry systems into an interoperable warhead. Several design concepts exist to provide different options for robust safety and security themes, maximum non-nuclear commonality, and cost. Simulation is one capability used to evaluate the mechanical performance of the designs in various operational environments, plan for system and component qualification efforts, and provide insight into the survivability of the warhead in environments that are not currently testable. The simulation efforts use several Sandia-developed tools through the Advanced Simulation and Computing program, including Cubit for mesh generation, the DART Model Manager, SIERRA codes running on the HPC TLCC2 platforms, DAKOTA, and ParaView. Several programmatic objectives were met using the simulation capability including: (1) providing early environmental specification estimates that may be used by component designers to understand the severity of the loads their components will need to survive, (2) providing guidance for load levels and configurations for subassembly tests intended to represent operational environments, and (3) recommending design options including modified geometry and material properties. These objectives were accomplished through regular interactions with component, system, and test engineers while using the laboratory's computational infrastructure to effectively perform ensembles of simulations. Because NNSA has decided to defer the LEP program, simulation results are being documented and models are being archived for future reference. However, some advanced and exploratory efforts will continue to mature key technologies, using the results from these and ongoing simulations for design insights, test planning, and model validation.

42 ENGINEERING↗
DOE OSTI2018

Final Report - Impact of Microstructure on the Containment and Migration of CO 2 in Fractured Basalts

The overall objective of the project was to advance the scientific and technical understanding of fracture microstructure on the flow and mineralization of CO 2 injected into fractured basalt. The project combined bench-scale CO 2 -water-rock testing, geochemical characterization of rock cores, and advanced characterization of the evolution of fracture structure and carbon trapping mechanisms. A series of natural and artificial basalts were acquired and characterized for use in experiments. High pressure and high temperature experiments were performed in both batch modes and in flow-through systems. The dissolution of silicate minerals in the basalts was tracked, and the timing, identity and location of carbonate minerals that trap that added CO 2 were determined. A reactive transport model was developed that generated simulations that were consistent with the major trends in the experimental data. Novel X-ray computed tomography and nuclear magnetic resonance tools were developed as part of the project. The project found that the formation of carbonate minerals in basalts subjected to injection of CO 2 -rich aqueous solutions can be rapid and extensive. The mineral trapping rate and capacity of porous basalt is much greater than that of sandstone reservoirs. Geochemical gradients that develop upon the exposure of basalt to CO 2 -rich aqueous solutions lead to spatial localization of carbonate minerals in confined environments (e.g., narrow fractures, dead-end fractures, and vesicles). While carbonate minerals form in dead-end fractures, they do not form in ways that inhibit subsequent carbonation of the basalt.

58 GEOSCIENCES↗