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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Direct observations and characterization of crack closure during microstructurally small fatigue crack growth via in-situ high-energy X-ray characterization

This study provides direct observation of the crack closure mechanism of a naturally occurring, tortuous, 3D microstructurally small fatigue crack (SFC) in additively manufactured Inconel 718. In-situ non-destructive characterization is performed using high-energy X-ray diffraction techniques to capture the evolution of the 3D microstructure and micromechanical response in the vicinity of the crack front. Based on the stress state of twelve grains analyzed at the crack tip, the crack closure events of the SFC front was found to be spatially heterogeneous with respect to loading progression governed by the local stress state of the grains (specifically the stress reversal from compression to tension). From this analysis, three grains that displayed different degrees of crack closure were further investigated, based on the orientation of the crack relative to the grains and the associated modality of crack growth. The stress normal to the crack plane and the associated degree of Mode I crack behavior were correlated with events of the crack opening earlier during the loading cycle. This was further corroborated by diffraction spot spreading analysis that quantified the crystallographic lattice distortion caused by the opening crack. Additionally, the detailed characterization of the opening behavior of the grains located at the crack tip and their associated states of stress elucidates the mechanism governing crack closure and will inform future modeling efforts of this phenomenon.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A multifidelity deep operator network approach to closure for multiscale systems

Projection-based reduced order models (PROMs) have shown promise in representing the behavior of multiscale systems using a small set of generalized (or latent) variables. Despite their success, PROMs can be susceptible to inaccuracies, even instabilities, due to the improper accounting of the interaction between the resolved and unresolved scales of the multiscale system (known as the closure problem). In the current work, we interpret closure as a multifidelity problem and use a multifidelity deep operator network (DeepONet) framework to address it. In addition, to enhance the stability and/or accuracy of the multifidelity-based closure, we employ the recently developed ``in-the-loop" training approach from the literature on coupling physics and machine learning models. The resulting approach is tested on shock advection for the one-dimensional viscous Burgers equation and vortex merging for the two-dimensional Navier-Stokes equations. Furthermore, the numerical experiments show significant improvement of the predictive ability of the closure-corrected PROM over the un-corrected one both in the interpolative and the extrapolative regimes.

42 ENGINEERING↗
DOE OSTI2020

Deep learning interfacial momentum closures in coarse-mesh CFD two-phase flow simulation using validation data

Multiphase flow phenomena have been widely observed in the industrial applications while it remains a challenging yet unsolved problems. Three-dimensional computational fluid dynamics (CFD) approaches resolve the flow fields on a finer special and temporal scales which can complement the dedicated experimental study. However, closures have to be introduced to reflect the underlying physics in multiphase flow. Among them, the interfacial forces, including drag, lift, turbulent dispersion and wall lubrication forces, play in important role on the bubble’s distribution and migration in liquid-vapor two-phase flow. Development of those closures traditionally rely on the experimental data and analytical derivation with simplified assumptions which usually cannot deliver a universal solution across wide range of flow conditions. In this paper, a data-driven approach, named as Feature Similarity Measurement (FSM), is developed and applied to improve the simulation capability of two-phase flow with coarse-mesh CFD approach. Interfacial momentum transfer in adiabatic bubbly flow serves as the focus of the present study. Both a mature and a simplified set of interfacial closures are taken as the low fidelity data. Experimental data and fine mesh CFD simulations results are adopted as high-fidelity data. Qualitative and quantitative analysis are performed in this paper which reveals that FSM can substantially improve the prediction of coarse mesh CFD model regardless of the choice of interfacial closures and it provides scalability and consistency across discontinuous flow regimes. Furthermore, it demonstrates that data-driven method can aid the multiphase flow modeling by exploring the connections between local physical features and simulation errors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Unified Entrainment and Detrainment Closures for Extended Eddy-Diffusivity Mass-Flux Schemes

The extended Eddy Diffusivity Mass Flux (EDMF) scheme is used as a unied parameterization that simulates subgrid-scale dynamics across a range of regimes.This work presents unied entrainment and detrainment closures that allow this scheme to simulate the dry convective boundary layer, shallow convection and deep convection , while adjacent works present other necessary closures for this unied scheme and test its performance for stable, stratocumulus topped and convective boundary layers. Entrainment and detrainment are modeled as a combination of turbulent and dynamical processes. The turbulent component is taken as downgradient diffusion between subdomains. The dynamical component is based on a ratio of buoyancy and vertical velocity that ensures consistent treatment of the ratio of mass-flux at vanishing area fraction. Dynamical detrainment is controlled by the difference in relative humidity between subdomains when liquid water is present. These closures are rst evaluated with entrainment and detrainment diagnosed from Large Eddy Simulations where decaying tracers are used to identify the different subdomains and mass exchanges between them. When implemented in a single column model (SCM), these closures are tested in simulations of several observationally motivated cases of dry convective boundary layer, shallow convection and deep convection. Results show good agreement between the SCM and Large Eddy Simulations in key properties for climate models and profiles of first, second and third moments. The development of diurnal cycles and the onset of and precipitation (with simplified microphysics) are well predicted by the SCM.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Neural network representability of fully ionized plasma fluid model closures

The closure problem in fluid modeling is a well-known challenge to modelers aiming to accurately describe their systems of interest. Over many years, analytic formulations in a wide range of regimes have been presented but a practical, generalized fluid closure for magnetized plasmas remains an elusive goal. Here, as a first step toward constructing a novel data-based approach to this problem, we apply ever-maturing machine learning methods to assess the capability of neural network architectures to reproduce crucial physics inherent in known magnetized plasma closures. We find encouraging results, indicating the applicability of neural networks to closure physics, but also arrive at recommendations on how one should choose appropriate network architectures for the given locality properties dictated by the underlying physics of the plasma.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Supervised machine learning-based multivariate regression of parallel closures for a high-collisionality deuterium-carbon plasma

Many plasmas of interest in laboratory experiments and space consist of multiple ion species. In tokamak edge plasmas, for instance, ionized impurities expelled from the vessel wall influence plasma transport. When describing multi-species plasmas using fluid equations, we need accurate closure relations to close the set of fluid equations. In this study, we introduce the development of fitting formulas for parallel closures using supervised machine learning, in conjunction with the recent closure theory, considering multi-ion collisions and arbitrary ion temperatures. We apply this approach to a high-collisionality deuterium-carbon plasma and demonstrate its effectiveness. As a result, the machine learning-based method for developing practical and accurate closures can be extended to a wider range of plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Ethical considerations in infectious disease modelling for public health policy: the case of school closures

Mathematical models of infectious diseases are frequently used as a tool to support public health policy and decisions around the implementation of interventions such as school closures. However, most publications on policy-relevant modelling lack an ethical framework and do not explicitly consider the ethical implications of the work. This creates a risk that the unintended consequences of interventions are overlooked or that models are used to justify decisions that are inconsistent with public health ethics. In this article, we focus on the case study of school closures as a commonly modelled intervention against pandemic influenza, COVID-19 and other infectious disease threats. We briefly review some of the key concepts in public health ethics and describe approaches to modelling the effects of school closures. We then identify a series of ethical considerations involved in modelling school closures. These include accounting for population heterogeneity and inequalities; including a diversity of viewpoints and expertise in model design; considering the distribution of benefits and harms; and model transparency and contextualization. Furthermore, we conclude with some recommendations to ensure that policy-relevant modelling is consistent with some key ethics values.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Corrective Action Management Unit Report of Post-Closure Care Activities Calendar Year 2019

The Corrective Action Management Unit (CAMU) at Sandia National Laboratories, New Mexico (SNL/NM) consists of a containment cell and ancillary systems that underwent closure in 2003 in accordance with the Closure Plan in Appendix D of the Class 3 Permit Modification (SNL/NM September 1997). The containment cell was closed with wastes in place. On January 27, 2015, the New Mexico Environment Department (NMED) issued the Hazardous Waste Facility Operating Permit (Permit) for Sandia National Laboratories (NMED January 2015) to the U.S. Department of Energy/National Nuclear Security Administration (DOE/NNSA) and its Management and Operating (M&O) contractor. The current M&O contractor is National Technology & Engineering Solutions of Sandia, LLC (NTESS). The Permit became effective February 26, 2015. The CAMU is undergoing post-closure care in accordance with the Permit, as revised and updated. This CAMU Report of Post-Closure Care Activities documents all activities and results for calendar year (CY) 2019, as required by the Permit.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Corrective Action Management Unit Report of Post-Closure Care Activities. Calendar Year 2020

he Corrective Action Management Unit (CAMU) at Sandia National Laboratories, New Mexico (SNL/NM) consists of a containment cell and ancillary systems that underwent closure in 2003 in accordance with the Closure Plan in Appendix D of the Class 3 Permit Modification (SNL/NM September 1997). The containment cell was closed with wastes in place. On January 27, 2015, the New Mexico Environment Department issued the Hazardous Waste Facility Operating Permit (Permit) for Sandia National Laboratories (NMED January 2015) to the U.S. Department of Energy/National Nuclear Security Administration (DOE/NNSA) and its Management and Operating (M&O) contractor. The current M&O contractor is National Technology & Engineering Solutions of Sandia, LLC (NTESS). The Permit became effective February 26, 2015. The CAMU is undergoing post-closure care in accordance with the Permit, as revised and updated. This CAMU Report of Post-Closure Care Activities documents all activities and results for calendar year (CY) 2020, as required by the Permit.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Improvements to CTF Closure Models for Modelling of Two-Phase Flow

This report documents the efforts to improve the CTF prediction of void fraction and two-phase pressure drop by improving two-phase closure models. Previous validation activities have revealed that CTF tends to overpredict void fraction and two-phase pressure drop. In response to this, two approaches were taken to improve CTF’s predictive capabilities. Based on findings that the interfacial drag and subcooled boiling models significantly impact void prediction, alternative closure models for these physical effects were found and implemented into the code. An extensive assessment was performed by using the existing and newly added two-phase experimental data, which showed that void prediction, wall temperature, and two-phase pressure-drop results are improved by using the newly implemented models. A second approach for improving CTF modeling accuracy involved the use of a Bayesian calibration process that uses CTF validation data to optimize selected modeling coefficients and closure model multipliers to achieve a more accurate prediction of experimental results. A similar assessment was performed with calibrated models that improved the void and pressure-drop prediction for test cases that were both included and not included in the calibration dataset. Results of this study will be used to change the models used in CTF to achieve more accurate BWR analyses moving forward. This study also identified opportunities for improving additional closure models and other opportunities to use calibration techniques to improve CTF.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

COVID-19–Related School Closures, United States, July 27, 2020–June 30, 2022

As part of a multiyear project that monitored illness-related school closures, we conducted systematic daily online searches during July 27, 2020–June 30, 2022, to identify public announcements of COVID-19–related school closures (COVID-SCs) in the United States lasting ≥1 day. We explored the temporospatial patterns of COVID-SCs and analyzed associations between COVID-SCs and national COVID-19 surveillance data. COVID-SCs reflected national surveillance data: correlation was highest between COVID-SCs and both new PCR test positivity (correlation coefficient [r] = 0.73, 95% CI 0.56–0.84) and new cases (r = 0.72, 95% CI 0.54–0.83) during 2020–21 and with hospitalization rates among all ages (r = 0.81, 95% CI 0.67–0.89) during 2021–22. The numbers of reactive COVID-SCs during 2020–21 and 2021–22 greatly exceeded previously observed numbers of illness-related reactive school closures in the United States, notably being nearly 5-fold greater than reactive closures observed during the 2009 influenza (H1N1) pandemic.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Closure Learning for Nonlinear Model Reduction Using Deep Residual Neural Network

Developing accurate, efficient, and robust closure models is essential in the construction of reduced order models (ROMs) for realistic nonlinear systems, which generally require drastic ROM mode truncations. We propose a deep residual neural network (ResNet) closure learning framework for ROMs of nonlinear systems. The novel ResNet-ROM framework consists of two steps: (i) In the first step, we use ROM projection to filter the given nonlinear system and construct a spatially filtered ROM. This filtered ROM is low-dimensional, but is not closed. (ii) In the second step, we use ResNet to close the filtered ROM, i.e., to model the interaction between the resolved and unresolved ROM modes. We emphasize that in the new ResNet-ROM framework, data is used only to complement classical physical modeling (i.e., only in the closure modeling component), not to completely replace it. We also note that the new ResNet-ROM is built on general ideas of spatial filtering and deep learning and is independent of (restrictive) phenomenological arguments, e.g., of eddy viscosity type. The numerical experiments for the 1D Burgers equation show that the ResNet-ROM is significantly more accurate than the standard projection ROM. The new ResNet-ROM is also more accurate and significantly more efficient than other modern ROM closure models.

Xie, Xuping↗
DOE OSTI2024

Scientific machine learning for closure models in multiscale problems: A review

Here, closure problems are omnipresent when simulating multiscale systems, where some quantities and processes cannot be fully prescribed despite their effects on the simulation's accuracy. Recently, scientific machine learning approaches have been proposed as a way to tackle the closure problem, combining traditional (physics-based) modeling with data-driven (machine-learned) techniques, typically through enriching differential equations with neural networks. This paper reviews the different reduced model forms, distinguished by the degree to which they include known physics, and the different objectives of a priori and a posteriori learning. The importance of adhering to physical laws (such as symmetries and conservation laws) in choosing the reduced model form and choosing the learning method is discussed. The effect of spatial and temporal discretization and recent trends toward discretization-invariant models are reviewed. In addition, we make the connections between closure problems and several other research disciplines: inverse problems, Mori-Zwanzig theory, and multi-fidelity methods. In conclusion, much progress has been made with scientific machine learning approaches for solving closure problems, but many challenges remain. In particular, the generalizability and interpretability of learned models is a major issue that needs to be addressed further.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Fabrication and Testing of DOE Standard Canister Closure Leak Test Assembly

DOE manages over 300 types of SNF, most of which are located at the INL site. “Road-ready dry storage” is a management concept where SNF is packaged into dry, sealed canisters, which are then placed in on-site storage in anticipation of later removal. The Idaho Cleanup Project and INL are collaborating on the Road-Ready Capability Demonstration Project, which will develop and demonstrate the designs, technology, processes, and regulatory framework for packaging DOE SNF for road-ready dry storage. In support of establishing a large-scale road-ready dry storage program at the INL site, the demonstration will package a select amount of DOE-managed SNF into DOE Standard Canisters. The closure process for the DOE Standard Canisters will include fuel and basket loading, welding, inspection, leak testing, and if needed, repair. As a follow-up to previous discussion on the design of the DOE Closure Leak Test Assembly, this report describes recent fabrication and testing efforts performed at INL. DOE Standard Canisters are sealed by two sequential gas tungsten arc welds. Both are performed by remotely operated and semi-autonomous welding systems. The first weld is a circumferential pipe weld that completes assembly of the canister body and lid assembly. The second and final closure weld connects the vent plug to the vent port with an identical butt joint to the circumferential pipe weld. After the second weld is performed on the vent port, these welds are helium leak tested using an inside-out technique. In addition to the commercially available vacuum and leak detector systems, the DOE Standard Canister Closure Leak Test Assembly was designed for both remote and manual operation. This report describes fabrication and performance testing associated with the inside-out technique. INL staff designed and tested systems to accomplish these tasks. Hardware fabrication occurred at INL facilities. Forthcoming work includes design optimization, integration to existing systems, and implementation to packaging demonstration operations.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Explosively Formed Helical Closure for Radioactive Material Packaging

Benefits • Allows for the creation of features in thin-walled metal parts that would otherwise be impossible or impractical to create. • Reduces margin of error during radioactive material package closure. • Increases efficiency of package closure and opening. Applications and Industries • The tooling and method could be used to create closures for any industrial application. There may be use for the geometry produced by the tooling and method that is outside of closures.

Timmons, Darren↗
DOE OSTI2025

Development of a Helical Closure for Radioactive Material Shipping Packages

The Savannah River National Laboratory (SRNL) Packaging Technology group proposed a closure design for the outer packaging of a prototype Type B shipping package being developed for the Department of Energy (DOE). The closure design provides an efficient means of securing the containment vessel (CV) within the radioactive material packaging. This design has a simplified operation, requires less components, and less maintenance when compared to current radioactive material package closure methods. This paper will review and discuss the materials, designs, processes, and testing activities that were considered and pursued in the development of the novel closure for the outer packaging of new radioactive material shipping packages.

Housley, William M. [Savannah River National Labor↗
DOE OSTI2020

An interface-aware sub-scale dynamics multi-material cell model for solids with void closure and opening at all speeds

Here, we present a multi-material cell model (closure model) for demanding arbitrary Lagrangian-Eulerian (ALE) simulations of fluids and solids. It is based on the interface-aware sub-scale dynamics (IASSD) approach which utilizes the exact material interface geometry within the computational cell to calculate internal material interactions. Our formulation of the closure model also aims to improve the accuracy in low-speed impact events. Voids are used to represent ambient vacuum and internal free boundaries of the distinct materials. Void regions can close and open at contact surfaces, allowing a transition from contact physics to free motion in vacuum. The coupling of void closure and opening with a new formulation of the IASSD model for solids is tested on several one- and two-dimensional numerical examples, ranging from gas expansion in vacuum to planar and round object impacts at various speeds.

42 ENGINEERING↗
DOE OSTI2021

Comprehensive framework for data-driven model form discovery of the closure laws in thermal-hydraulics codes

The two-phase two-fluid model is a basis of many thermal-hydraulics codes used in design, licensing, and safety considerations of nuclear power plants. Thermal-hydraulics codes rely on the closure laws to close the system of conservation equations and describe the interactions between phases. These laws, derived from years of experimental investigations, are semi-empirical correlations that lack generality and have a limited range of applicability. Increase of computational power, availability of new experiments, and development of high-fidelity simulations has increased the number of validation data. The discrepancies between the code predictions and the validation data are a great source of knowledge. Missing physics that are not included in the model but are important for the considered phenomena can be discovered by propagating the information from the experimental results through the model. Furthermore, physics-discovered data-driven model form (P3DM) methodology integrates available integral effect tests and separate effects tests to determine the necessary corrections to the model form of the closure laws. In contrast to existing calibration techniques, the methodology modifies the functional form of the closure laws. Based on the functional form of the correction, the missing physics that were not included in the original model can be discovered. The methodology provides the alternative to the machine learning approach, in which the model is discovered in the form of the intractable black-box relation. In this work, the methodology was applied to the CTF subchannel code to improve the prediction of the two-phase flow phenomena.

42 ENGINEERING↗