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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

GRIZZLY MODEL OF FULLY COUPLED HEAT TRANSFER, MOISTURE DIFFUSION, ALKALI-SILICA REACTION AND FRACTURING PROCESSES IN CONCRETE

Idaho National Laboratory (INL) has been developing a fully coupled thermo-hydro-mechanical-chemical (THMC) concrete model, referred as Grizzly, with the ultimate goal of reliably simulating and predicting the long-term performance of concrete structures subjected to various aging and degradation mechanisms. In this paper, we present a fully coupled concrete model implemented in our Grizzly code for simulating coupled moisture/heat transfer, stress-dependent ASR swelling and elastic/inelastic mechanics. Example simulation results and preliminary model validation results against experimental data reported in the literature will be provided also in this paper. The close matches between the simulation results and experiments clearly reveal the potential of the fully coupled solution strategy for solving tightly coupled THMC concrete models. We also present simulation results of using a rate-dependent damaging mechanics model for simulating the ASR swelling induced degradation of concrete structures and its feedback to moisture and heat transport processes.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2017

FIVE YEARS OF SYNTHESIS OF SOLAR SPECTRAL IRRADIANCE FROM SDID/SISA AND SDO /AIA IMAGES

In this paper we describe the synthetic solar spectral irradiance (SSI) calculated from 2010 to 2015 using data from the Atmospheric Imaging Assembly (AIA) instrument, on board the Solar Dynamics Observatory spacecraft. We used the algorithms for solar disk image decomposition (SDID) and the spectral irradiance synthesis algorithm (SISA) that we had developed over several years. The SDID algorithm decomposes the images of the solar disk into areas occupied by nine types of chromospheric and 5 types of coronal physical structures. With this decomposition and a set of pre-computed angle-dependent spectra for each of the features, the SISA algorithm is used to calculate the SSI. We discuss the application of the basic SDID/SISA algorithm to a subset of the AIA images and the observed variation occurring in the 2010–2015 period of the relative areas of the solar disk covered by the various solar surface features. Our results consist of the SSI and total solar irradiance variations over the 2010–2015 period. The SSI results include soft X-ray, ultraviolet, visible, infrared, and far-infrared observations and can be used for studies of the solar radiative forcing of the Earth’s atmosphere. These SSI estimates were used to drive a thermosphere–ionosphere physical simulation model. Predictions of neutral mass density at low Earth orbit altitudes in the thermosphere and peak plasma densities at mid-latitudes are in reasonable agreement with the observations. The correlation between the simulation results and the observations was consistently better when fluxes computed by SDID/SISA procedures were used.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Neural network kinetics for exploring diffusion multiplicity and chemical ordering in compositionally complex materials

Diffusion involving atom transport from one location to another governs many important processes and behaviors such as precipitation and phase nucleation. The inherent chemical complexity in compositionally complex materials poses challenges for modeling atomic diffusion and the resulting formation of chemically ordered structures. Here, we introduce a neural network kinetics (NNK) scheme that predicts and simulates diffusion-induced chemical and structural evolution in complex concentrated chemical environments. The framework is grounded on efficient on-lattice structure and chemistry representation combined with artificial neural networks, enabling precise prediction of all path-dependent migration barriers and individual atom jumps. To demonstrate the method, we study the temperature-dependent local chemical ordering in a refractory NbMoTa alloy and reveal a critical temperature at which the B2 order reaches a maximum. The atomic jump randomness map exhibits the highest diffusion heterogeneity (multiplicity) in the vicinity of this characteristic temperature, which is closely related to chemical ordering and B2 structure formation. The scalable NNK framework provides a promising new avenue to exploring diffusion-related properties in the vast compositional space within which extraordinary properties are hidden.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Decadal climate variability in the tropical Pacific: Characteristics, causes, predictability, and prospects

Decadal climate variability and change affects nearly every aspect of our world, including weather, agriculture, ecosystems, and the economy. Predicting its expression is thus of critical importance on multiple fronts. Power et al. review what is known about tropical Pacific decadal climate variability and change, the degree to which it can be simulated and predicted, and how we might improve our understanding of it. Here, more accurate projections will require longer and more detailed instrumental and paleoclimate records, improved climate models, and better data assimilation methods. —HJS.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

The Application of a Statistical Downscaling Process to Derive 21 st Century River Flow Predictions Using a Global Climate Simulation

The ability of water managers to maintain adequate supplies in coming decades depends, in part, on future weather conditions, as climate change has the potential to alter river flows from their current values, possibly rendering them unable to meet demand. Reliable climate projections are therefore critical to predicting the future water supply for the United States. These projections cannot be provided solely by global climate models (GCMs), however, as their resolution is too coarse to resolve the small-scale climate changes that can affect hydrology, and hence water supply, at regional to local scales. A process is needed to ‘downscale’ the GCM results to the smaller scales and feed this into a surface hydrology model to help determine the ability of rivers to provide adequate flow to meet future needs. We apply a statistical downscaling to GCM projections of precipitation and temperature through the use of a scaling method. This technique involves the correction of the cumulative distribution functions (CDFs) of the GCM-derived temperature and precipitation results for the 20 th century, and the application of the same correction to 21 st century GCM projections. This is done for three meteorological stations located within the Coosa River basin in northern Georgia, and is used to calculate future river flow statistics for the upper Coosa River. Results are compared to the historical Coosa River flow upstream from Georgia Power Company’s Hammond coal-fired power plant and to flows calculated with the original, unscaled GCM results to determine the impact of potential changes in meteorology on future flows.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Accelerating Hanford Site Cleanup through Operations Research Modeling - 20238

The Hanford Site cleanup effort will require the integration of dozens of unique facilities and processes, many of which will be first-of-a-kind in implementation and design. Each facility will be governed by its own set of operating logic, configured with a unique array of unit operations, and subject to a set of constraints that will affect its behavior. The collection of facilities have multiple points of interface, making the operations of any one facility potentially significant to the operations of other up- or downstream processes. It is therefore highly desirable to accurately predict these operations, as it allows for Site officials to identify and preempt bottlenecks and vulnerabilities before they unexpectedly inhibit the cleanup mission. With the quantity and complexity of the processes that will be on Site, building a pen-and-paper or even a spreadsheet-assisted model of the cleanup mission quickly becomes overwhelming in scope and inaccurate in execution. The Engineering organization for the Site's Tank Operations Contract (TOC) has therefore implemented the use of operations research (OR) modeling to simulate and predict future operations of Site facilities. These models are created using a discrete event simulation tool that allows for the development of detailed, versatile, and robust models. Not only can these models account for complex logical behaviors, but they can also simulate process details down to the level of vessel sizing, labor utilization, equipment reliability, and resource availability. To date, the TOC has developed OR models for several facilities on Site, including for single-shell tank (SST) farms, double-shell tank (DST) farms, the Effluent Treatment Facility (ETF), and the waste transfer system. These models have focused on identifying bottlenecks and operational constraints, and have been used to quantify the effects of implementing process changes. This latter point is particularly valuable, as it allows for several alternatives to be studied in a virtual setting before committing resources to making a change in the field. The decision to develop OR models has gained tremendous support from the Site's stakeholders and the U.S. Department of Energy (DOE) management, and has prompted the use of the tool to support additional internal and external initiatives. Recently, an initiative was proposed to use the models to help identify and provide quantitative backing for risks and opportunities for the TOC. This application of OR could not only help inform how the TOC manages its risks (e.g. quantities and types of spare parts), but could also help drive process improvements whose benefits might otherwise be hard to quantify. The models have also been used to drive the TOC's cloud computing, artificial intelligence (AI), and machine learning (ML) initiatives. These initiatives will not only improve the ability of the TOC to more rapidly respond to the needs of its customers, but it will also aid in the ability of the TOC to analyze and improve the processes it studies. Partnership with two external software development and consulting companies (Lanner and Ynformed) has furthered not only the application of AI and ML within the TOC, but has also spurred the development of new/improved software tools and platforms used by the companies. These partnerships have proven to be mutually beneficial and productive, and have set a precedent for the types of gains that can be made by exploring such options. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Structure and osmotic pressure of ionic microgel dispersions

We investigate structural and thermodynamic properties of aqueous dispersions of ionic microgels—soft colloidal gel particles that exhibit unusual phase behavior. Starting from a coarse-grained model of microgel macroions as charged spheres that are permeable to microions, we perform simulations and theoretical calculations using two complementary implementations of Poisson-Boltzmann (PB) theory. Within a one-component model, based on a linear-screening approximation for effective electrostatic pair interactions, we perform molecular dynamics simulations to compute macroion-macroion radial distribution functions, static structure factors, and macroion contributions to the osmotic pressure. For the same model, using a variational approximation for the free energy, we compute both macroion and microion contributions to the osmotic pressure. Within a spherical cell model, which neglects macroion correlations, we solve the nonlinear PB equation to compute microion distributions and osmotic pressures. By comparing the one-component and cell model implementations of PB theory, we demonstrate that the linear-screening approximation is valid for moderately charged microgels. By further comparing cell model predictions with simulation data for osmotic pressure, we chart the cell model’s limits in predicting osmotic pressures of salty dispersions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Rolling Process Modeling Report. Finite-Element Model Validation and Parametric Study on various Rolling Process parameters

Pacific Northwest National Laboratory (PNNL) has been investigating manufacturing processes for the uranium-10% molybdenum alloy plate-type fuel for high-performance research reactors in the United States. This work supports the U.S. Department of Energy National Nuclear Security Administration’s Office of Material Management and Minimization Reactor Conversion Program. This report documents modeling results of PNNL’s efforts to perform finite-element simulations to predict roll-separating forces for various rolling mill geometries for PNNL, Babcock & Wilcox Co., Y-12 National Security Complex, Los Alamos National Laboratory, and Idaho National Laboratory. The model developed and presented in a previous report has been subjected to further validation study using new sets of experimental data generated from a rolling mill at PNNL. Simulation results of both hot rolling and cold rolling of uranium-10% molybdenum coupons have been compared with experimental results. The model was used to predict roll-separating forces at different temperatures and reductions for five rolling mills within the National Nuclear Security Administration Fuel Fabrication Capability project. This report also presents initial results of a finite-element model microstructure-based approach to study the surface roughness at the interface between zirconium and uranium-10% molybdenum.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Predicting wind farm operations with machine learning and the P2D‐RANS model: A case study for an AWAKEN site

Abstract The power performance and the wind velocity field of an onshore wind farm are predicted with machine learning models and the pseudo‐2D RANS model, then assessed against SCADA data. The wind farm under investigation is one of the sites involved with the American WAKE experimeNt (AWAKEN). The performed simulations enable predictions of the power capture at the farm and turbine levels while providing insights into the effects on power capture associated with wake interactions that operating upstream turbines induce, as well as the variability caused by atmospheric stability. The machine learning models show improved accuracy compared to the pseudo‐2D RANS model in the predictions of turbine power capture and farm power capture with roughly half the normalized error. The machine learning models also entail lower computational costs upon training. Further, the machine learning models provide predictions of the wind turbulence intensity at the turbine level for different wind and atmospheric conditions with very good accuracy, which is difficult to achieve through RANS modeling. Additionally, farm‐to‐farm interactions are noted, with adverse impacts on power predictions from both models.

17 WIND ENERGY↗
DOE OSTI2025

Origin of Heating-Induced Softening and Enthalpic Reinforcement in Elastomeric Nanocomposites

Molecular simulations demonstrate that the enthalpic softening of elastomeric nanocomposites upon heating can arise naturally from a Poisson’s ratio mismatch between elastomer and nanoparticle networks, providing a more parsimonious explanation for this phenomenon than the widely accepted interpretation based on glassy interparticle bridging. Despite a century of use, the mechanism of nanoparticle-driven mechanical reinforcement of elastomers is unresolved. Here, a major hypothesis attributes it to glassy interparticle bridges, supported by an observed inversion of the variation of the modulus E(T) on heating – from entropic stiffening in elastomers to enthalpic softening in nanocomposites. Here, molecular simulations reveal that elastomer enthalpic softening can instead emerge from a competition over the preferred volumes between elastomer and nanoparticulate networks. A theory for this competition accounting for softening of the bulk modulus on heating predicts the simulated E(T) inversion, suggesting that reinforcement is driven by a volume-competition mechanism unique to cocontinuous systems of soft and rigid networks.

Biopolymers↗
DOE OSTI2019

Nanoparticle Injection Technology for Remediating Leaks of CO 2 Storage Formation (Final Report)

Leakage of a well can occur through the wellbore, the annulus between well tubing and steel casing, or on the outside of the casing. The overall goal of the project is to develop a new technology that can repair or reduce the leakage of wellbore. The new technology is based on simultaneously injecting nanoparticles electrochemically and removing some of the harmful ions in the system so the distressed cement materials can be densified and the corrosion risk of steel casing can be reduced. There were two objectives. One is the development of the injection technology for leakage repair and the other is the development of new numerical simulation models that can simulate and predict the performance of the new repair technology. An electro-migration testing unit and a in-house wellbore testing system were developed; healing agents with nanoparticles were selected, numerical models were developed to simulate the electro-migration technology, and the effectiveness of the new technology were evaluated.

42 ENGINEERING↗
DOE OSTI2019

Nanoparticle Injection Technology for Remediating Leaks of CO 2 Storage Formation (Final Technical Report)

Leakage of a well can occur through the wellbore, the annulus between well tubing and steel casing, or on the outside of the casing. The overall goal of the project is to develop a new technology that can repair or reduce the leakage of wellbore. The new technology is based on simultaneously injecting nanoparticles electrochemically and removing some of the harmful ions in the system so the distressed cement materials can be densified and the corrosion risk of steel casing can be reduced. There were two objectives. One is the development of the injection technology for leakage repair and the other is the development of new numerical simulation models that can simulate and predict the performance of the new repair technology. An electro-migration testing unit and an in-house wellbore testing system were developed; healing agents with nanoparticles were selected, numerical models were developed to simulate the electro-migration technology, and the effectiveness of the new technology was evaluated.

42 ENGINEERING↗
DOE OSTI2014

An analysis of nuclear fuel burnup in the AGR-1 TRISO fuel experiment using gamma spectrometry, mass spectrometry, and computational simulation techniques

AGR 1 was the first in a series of experiments designed to test US TRISO fuel under high temperature gas-cooled reactor irradiation conditions. This experiment was irradiated in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL) and is currently undergoing post irradiation examination (PIE) at INL and Oak Ridge National Laboratory. One component of the AGR 1 PIE is the experimental evaluation of the burnup of the fuel by two separate techniques. Gamma spectrometry was used to non destructively evaluate the burnup of all 72 of the TRISO fuel compacts that comprised the AGR 1 experiment. Two methods for evaluating burnup by gamma spectrometry were developed, one based on the Cs 137 activity and the other based on the ratio of Cs 134 and Cs 137 activities. Burnup values determined from both methods compared well with the values predicted from simulations. The highest measured burnup was 20.1% FIMA for the direct method and 20.0% FIMA for the ratio method (compared to 19.56% FIMA from simulations). An advantage of the ratio method is that the burnup of the cylindrical fuel compacts can determined in small (2.5 mm) axial increments and an axial burnup profile can be produced. Destructive chemical analysis by inductively coupled mass spectrometry (ICP MS) was then performed on selected compacts that were representative of the expected range of fuel burnups in the experiment to compare with the burnup values determined by gamma spectrometry. The compacts analyzed by mass spectrometry had a burnup range of 19.3% FIMA to 10.7% FIMA. The mass spectrometry evaluation of burnup for the four compacts agreed well with the gamma spectrometry burnup evaluations and the expected burnup from simulation. For all four compacts analyzed by mass spectrometry, the maximum range in the three experimentally determined values and the predicted value was 6% or less. Furthermore, the results confirm the accuracy of the nondestructive burnup evaluation from gamma spectrometry for TRISO fuel compacts across a burnup range of approximately 10 to 20% FIMA and also validate the approach used in the physics simulation of the AGR 1 experiment.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Monte Carlo N-Particle Transport Performance of Predicting Digital Radiographic IQI Inspection

The identification of porosity, geometric noncompliance, and other defect types are critical to the qualification of materials and components. X-ray radiographic nondestructive testing is a common industrial inspection method for process quality control and component qualification and certification. Digital radiography provides a quick and efficient alternative when compared to traditional film-based inspection. The quality of radiographic inspection is dependent on equipment specifications, such as the source spot size and detector pixel size, and the specific parameters selected for use for the radiographic technique. To evaluate if an x-ray system and technique is sufficient for a given requirement, a radiographic image quality indicator (IQI) can be used. Radiographic IQIs in hard to machine materials or hard to manufacture defects can be time consuming and expensive to manufacture. This study was conducted to evaluate current Savannah River National Laboratory (SRNL) x-ray imaging systems with a custom tantalum IQI and using Monte Carlo simulations to predict the performance of future systems. The tantalum IQI was tested using a Siefert Isovolt 420 keV x-ray tube with a Perkin Elmer XRD 1611 flat panel with 100-micron pixels. Using the Monte Carlo N-Particle transport software, the radiographic tally was used to simulate the photon flux through an identical tantalum IQI. These simulations provided a benchmark as to the best theoretical identification on a given system using our tantalum IQI. The simulations were refined to match SRNL’s current systems’ noise levels, leading to confidence in their ability to predict the performance of other systems that may be purchased and deployed in the future at the Savannah River Site. Future studies will be conducted to prove this research can be extended to artificially evaluate the ability for systems to identify critical defect sizes through x-ray radiographic inspection, drastically reducing the cost and time burdens of producing high-fidelity radiographic test articles.

digital X-ray radiography↗
DOE OSTI2025

High-Fidelity CFD Modeling of Cryogenic Hydrogen Isotope Extrusion for Fusion Reactor Pellet Fueling

This study investigates the extrusion processes of deuterium and protium using ANSYS-Polyflow. The geometries and computational fluid dynamics (CFD) settings closely replicate the experimental setups and data acquired from the extruder experiments at Oak Ridge National Laboratory (ORNL) for validation purposes. We explore the impacts of (1) slip versus non-slip boundary conditions and (2) the use of constant, temperature-, and shear rate–dependent viscosities, concluding that the implementation of non-slip wall boundary conditions combined with shear rate–dependent viscosity produced more accurate predictions. The simulations achieved excellent agreement with the experimental data, with relative differences of only 5% for deuterium, and 3% to 6% for protium. This is the first time that experimental extrusion data at ORNL have been accurately predicted through high-fidelity CFD modeling. In conclusion, the advancements offer valuable insights and a foundational modeling tool for optimizing pellet injectors for ITER and other future reactor-scale devices.

ANSYS-Polyflow↗
DOE OSTI2019

The dominant micro-turbulence instabilities in the lower q 95 high β p plasmas on DIII-D and predict-first extrapolation

Large-radius internal transport barriers (ITB) are the signature of high β p scenarios on DIII-D. Previous studies show that a large Shafranov shift, rather the E x B shear, suppresses the turbulence and helps in the formation of the large-radius ITB. New gyrokinetic simulations suggest that the remaining micro-instabilities in lower q 95 (<7.0), high β p ITB plasmas are drift wave instabilities, including the collisionless trapped electron mode in the core and ITB peak gradient region, the electron temperature gradient mode in the ITB peak gradient region and at the ITB foot and the ion temperature gradient mode at the ITB foot. Gyrokinetic simulation results qualitatively agree with the density fluctuation analysis from beam emission spectroscopy, which suggests the existence of a low-k ion mode at the ITB foot. As a result, the gyrokinetic simulations also predict that a larger Shafranov shift can overwhelm the driving sources for turbulence from the profile gradients at higher β N , leading to stronger turbulence suppression and stronger ITBs in lower q 95 , high β p plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2013

Opening new institutional spaces for grappling with uncertainty: A constructivist perspective

In the context of an increasing reliance on predictive computer simulation models to calculate potential project impacts, it has become common practice in impact assessment (IA) to call on proponents to disclose uncertainties in assumptions and conclusions assembled in support of a development project. Understandably, it is assumed that such disclosures lead to greater scrutiny and better policy decisions. This paper questions this assumption. Drawing on constructivist theories of knowledge and an analysis of the role of narratives in managing uncertainty, I argue that the disclosure of uncertainty can obscure as much as it reveals about the impacts of a development project. It is proposed that the opening up of institutional spaces that can facilitate the negotiation and deliberation of foundational assumptions and parameters that feed into predictive models could engender greater legitimacy and credibility for IA outcomes. - Highlights: Black-Right-Pointing-Pointer A reliance on supposedly objective disclosure is unreliable in the predictive model context in which IA is now embedded. Black-Right-Pointing-Pointer A reliance on disclosure runs the risk of reductionism and leaves unexamined the social-interactive aspects of uncertainty. Black-Right-Pointing-Pointer Opening new institutional spaces could facilitate deliberation on foundational predictive model assumptions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

New understanding of nano-scale interstitial dislocation loops in BCC iron

Complex states of nanoscale interstitial dislocation loop can be described by its habit plane and Burgers vector. Using atomistic simulations, we provide direct evidences on the change of the habit plane of a ½<111> loop from {111} to {110} and {211}, in agreement with TEM observations. A new {100} habit plane of this loop is also predicted by simulations. The non-conservation of the Burgers vector is approved theoretically for: (1) dislocation reactions between loops with different Burgers vectors and (2) the transition between <100> loops and ½<111> loops. The rotation from a ½<111> to a <100> loop has also been explored, which occurs at 570 K for time on the order of 10 seconds. The dislocation-precipitate phase duality and change of habit plane are then proposed as new features for nano-scale dislocation loops.

Gao, Ning↗