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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Assessment of effective elastic constants of U-10Mo fuel: A multiscale modeling and homogenization study

The significant microstructural changes that U-Mo fuel undergoes during operation degrades its mechanical properties and structural integrity. Microstructural evolution entails the formation, evolution, and redistribution of porosity in conjunction with grain refinement. In the present paper, we employ numerical approaches to assess the impact of the various microstructural features—grains, nanoscale intragranular fission gas bubbles, and mesoscale intergranular voids—on the degradation of elastic constants. Phase-field microstructure models are combined with the asymptotic expansion homogenization technique in order to derive the effective elastic constants as a function of porosity and fission density. Here the results are verified and compared against theoretical bounds. Using this approach, elastic degradation in operating nuclear fuels can be quantified when the distributions of microstructural features are known.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

The Role of Deep Convection and Large-scale Circulation in Driving Model Spread in Low Cloud Feedback and Equilibrium Climate Sensitivity

This project aims to advance the understanding of the processes that drive the large uncertainties in climate change projections, use observations to constrain model physics and reduce the inter-model spread in equilibrium climate sensitivity (ECS). There are three major goals: 1) Characterize the representation of the physical pathways that link deep convection, large-scale circulation and low cloud feedback in CMIP6 model simulations and determine the relative contribution of each pathway to the CMIP6 model spread in low cloud feedback and ECS; 2) Use process-oriented diagnostics and multiple observations to evaluate CMIP6 model performance in capturing the observed cloud-circulation relation and deep convection characteristics including convective transition statistics and the bulk properties of mesoscale convective systems (MCSs). Error decomposition in CMIP6 models will be performed. 3) Conduct E3SM short-range hindcasts following the DOE Cloud-Associated Parameterizations Testbed (CAPT) protocol to pinpoint specific model parameters/processes that are crucial to the representation of deep convection, circulation, clouds and the pathways that connect them. We will modify convective parameters in E3SM and analyze the perturbed physics experiments (PPEs) to isolate model parameters that are critical to the uncertainty of ECS.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Mesoscale interplay among composition heterogeneity, lattice deformation, and redox stratification in single-crystalline layered oxide cathode

Single-crystalline layered oxide materials for lithium-ion batteries are featured by their excellent capacity retention over their polycrystalline counterparts, making them sought-after cathode candidates. Their capacity degradation, however, becomes more severe under high-voltage cycling, hindering many high-energy applications. It has long been speculated that the interplay among composition heterogeneity, lattice deformation, and redox stratification could be a driving force for the performance decay. The underlying mechanism, however, is not well-understood. In this study, we use X-ray microscopy to systematically examine single-crystalline NMC particles at the mesoscale. This technique allows us to capture detailed signals of diffraction, spectroscopy, and fluorescence, offering spatially resolved multimodal insights. Focusing on early high-voltage charging cycles, we uncover heterogeneities in valence states and lattice structures that are inherent rather than caused by electrochemical abuse. These heterogeneities are closely associated with compositional variations within individual particles. Our findings provide useful insights for refining material synthesis and processing for enhanced battery longevity and efficiency.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Nonlocal Effect of Percolated Particle Networks on Viscoelasticity of Polymer–Filler Nanocomposites: A Mesoscale Simulation Study

With nanoparticles (NPs) as fillers, polymer nanocomposites (PNCs) usually exhibit enhanced mechanical properties. However, a direct connection between the microscopic structural relaxation and macroscopic mechanical properties of PNCs remains to be established. To investigate the micro-to-macro connection, we develop a mesoscale model, in which the NPbridging polymer chains are represented by a dynamic bonded interaction between NPs, and the bulk polymer matrix is implicitly modeled by overdamped Langevin dynamics. Extensive equilibrium simulations are performed to quantify the microscopic dynamics of model PNCs. Systematic analyses of modified Rouse dynamics, dynamic structure factor, and relaxation modulus uncover that the microscopic relaxation dynamics of PNCs are significantly decelerated across different length scales because of nonlocal effects of percolated particle networks a phenomenon that has not been adequately captured in prior simulation studies. We find that NPvolume- fraction and NP-bonding-energy barrier are the two critical variables that affect bulk viscoelasticity the most. The proposed mesoscale model is versatile and provides a powerful framework for studying structure−property relations of different PNCs.

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

Quantifying UAS Observation Error Variance Used in Data Assimilation Systems and Its Impact on Predictive Skill

Observation error determines the weights of the observations and background state used in data assimilation to generate analyses. Quantifying observation error is critical for the optimal assimilation of observational data sets. Uncrewed Aircraft System (UAS) observations have shown potential benefits in filling observational gaps in the lower atmosphere; however, characterization of their error characteristics has been limited. To optimize the use of UAS observations in numerical weather prediction, UAS observation error is estimated based on the 3‐cornered hat diagnostic approach which uses three independent estimates of the atmospheric state. This approach is applied to data from the 2018 Lower Atmospheric Profiling Studies at Elevation‐a Remotely‐piloted Aircraft Team Experiment field campaign using collocated UAS and rawinsonde observations along with output from a set of convection‐permitting model simulations. The estimated observation error values for UAS temperature, wind, and relative humidity measurements were found to be only weakly dependent on height AGL with mean values equal to 0.5°C, 0.8 m s −1 , and 3%, respectively. Only the newly estimated observation error for temperature differed from that previously used to assimilate commercial aircraft observations into global models (1.0°C). However, using this reduced temperature observation error produced more accurate mesoscale analyses and forecasts of both terrain‐driven flows and convection initiation generated by colliding outflow boundaries within the San Luis Valley of Colorado.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Soil moisture gradients strengthen mesoscale convective systems by increasing wind shear

Mesoscale convective systems are a class of storm linked to extensive flooding and other destructive hazards in many regions globally. In West Africa, soil moisture impacts provide a valuable source of predictability for mature storm hazards, but little is known about mature storm sensitivity to soil moisture in other climatic regions. Here we use a storm track dataset, satellite observations and reanalysis fields to investigate the response of mature storms to soil moisture in seven global storm hotspots—West Africa, India, South America, South Africa, Australia and the United States Great Plains. We demonstrate that mesoscale soil moisture gradients (~500 km) can enhance storms by driving increased vertical wind shear conditions, a crucial ingredient for storm organization, through the strengthening of atmospheric temperature gradients. This is evidenced by a 10–30% increase in precipitation feature size and rainfall for the largest storms on days with favourable soil moisture gradients compared with unfavourable gradients. Global simulations confirm that soil moisture gradients influence wind shear. The results demonstrate the importance of soil moisture feedbacks for accurate forecasting of mesoscale convective systems and future projections of extreme events under climate change.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2008

Evaluating Mars Science Laboratory Landing Sites with the Mars Global Reference Atmospheric Model (Mars-GRAM 2005)

The Mars Global Reference Atmospheric Model (Mars-GRAM) is an engineering-level atmospheric model widely used for diverse mission applications. Mars-GRAM s perturbation modeling capability is commonly used, in a Monte-Carlo mode, to perform high fidelity engineering end-to-end simulations for entry, descent, and landing (EDL) [1]. From the surface to 80 km altitude, Mars-GRAM is based on the NASA Ames Mars General Circulation Model (MGCM). Mars-GRAM and MGCM use surface topography from Mars Global Surveyor Mars Orbiter Laser Altimeter (MOLA), with altitudes referenced to the MOLA areoid, or constant potential surface. Traditional Mars-GRAM options for representing the mean atmosphere along entry corridors include: (1) Thermal Emission Spectrometer (TES) mapping years 1 and 2, with Mars-GRAM data coming from NASA Ames Mars General Circulation Model (MGCM) results driven by observed TES dust optical depth or (2) TES mapping year 0, with user-controlled dust optical depth and Mars-GRAM data interpolated from MGCM model results driven by selected values of globally-uniform dust optical depth. Mars-GRAM 2005 has been validated [2] against Radio Science data, and both nadir and limb data from TES [3]. There are several new features included in Mars-GRAM 2005. The first is the option to use input data sets from MGCM model runs that were designed to closely simulate conditions observed during the first two years of TES observations at Mars. The TES Year 1 option includes values from April 1999 through January 2001. The TES Year 2 option includes values from February 2001 through December 2002. The second new feature is the option to read and use any auxiliary profile of temperature and density versus altitude. In exercising the auxiliary profile Mars-GRAM option, values from the auxiliary profile replace data from the original MGCM databases. Some examples of auxiliary profiles include data from TES nadir or limb observations and Mars mesoscale model output at a particular location and time. The final new feature is the addition of two Mars-GRAM parameters that allow standard deviations of Mars-GRAM perturbations to be adjusted. The parameter rpscale can be used to scale density perturbations up or down while rwscale can be used to scale wind perturbations.

Justh, H. L.↗
DOE OSTI2024

Effective Visualization of Radar Data for Users Impacted by Color Vision Deficiency

Color vision deficiency (CVD) is a decreased ability to discern between particular colors. Eight percent of genetic males and half a percent of genetic females have some form of CVD, with many in the radar community falling into this group. When presenting data on a two-dimensional plane, it is common to use colors to represent values via a colormap. Colormap choice in the radar community is influenced by the ability to highlight scientifically interesting features in data, institutional choices, and domain dominance of legacy colormaps. The problem with these current colormaps is that many do not project well for those with CVD (i.e., green next to red). In working with the CVD community to address this problem, multiple colormaps for moments such as equivalent reflectivity factor and Doppler velocity were created for users with forms of CVD such as deuteranomaly, protanomaly, protanopia, and deuteranopia using Python tools such as colorspacious and viscm. We show how these colormaps can improve interpretability for four cases: a mesoscale convective system, a pyrocumulonimbus storm, a wintertime midlatitude cyclone, and widespread storms with a large bird migration. These new radar equivalent reflectivity factor, Doppler velocity, and polarization colormaps are designed to highlight rain, frozen precipitation, nonmeteorological targets, and velocity-based items, are perceptually uniform, and are visually friendly for those with CVD.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Engineering-scale Modeling of High-Temperature Creep and Creep Crack Growth in Alloy 316H

This document demonstrates completion of the goals described in the technical narrative of the Department of Energy’s Industry Funding Opportunity Announcement (iFOA) project entitled “Modeling and Simulation Development Pathways to Accelerating KP-FHR Licensing,” which relates to the development and demonstration of capabilities for conducting engineering-scale simulations of Alloy 316H components under high-temperature conditions. This work encompassed two major aspects: integrating and testing constitutive models for the creep response of 316H at high temperatures, and developing tools for modeling creep crack growth (CCG) in 316H. Two classes of constitutive models were used in this effort: phenomenological models based on behavior observed at the engineering scale, and reduced-order models (ROMs) that represent the nonlinear response of mesoscale models that capture the sensitivity to material microstructure and processing. Likewise, the approaches employed for CCG modeling considered both simplified engineering approaches and detailed simulations of creep and damage ahead of the crack tip. These developments, which were performed by utilizing the Grizzly code as well as the open-source libraries it depends on, strengthen Grizzly’s ability to support licensing and safety analyses of high-temperature reactor components.

316H↗
DOE OSTI2022

Multiscale investigation of the microstructural mechanisms driving ratchet growth in PBX 9502

The high explosive PBX 9502 undergoes irreversible expansion during thermal cycling (“ratchet growth”). Recent innovations in thermomechanical modeling via homogenization strategies are beginning to incorporate mesoscale information such as grain size, total porosity, and spatial distribution of voids and cracks. To generate a complete experimental data set to challenge and inform these models, PBX 9502 pellets were thermally cycled, cross-sectioned using ion polishing, and imaged in high resolution with scanning electron microscopy. Ratchet growth was found to drive expansion through microcracking. Microcracks were affected by agglomeration of crystals within the PBX. Virgin material showed greater ratchet growth than recycled material.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Characterizing Dislocation Defects in Dark Field X-ray Microscopy Images of Bulk Aluminum

Material defects play a large role in material response under shock loading, yet the process of how these defects initiate, propagate, and annihilate is not well understood. Using a newly developed diagnostic, dark field X-ray microscopy (DFXM), we can now visualize the behavior of dislocation defects in materials at the mesoscale under varying conditions. Using data from the European Synchrotron Radiation Facility, we apply a variety of image processing techniques to capture relevant features to locate and characterize size and orientation of dislocation defects in bulk aluminum DFXM images. Beyond simple visualization, this analysis drives statistical characterization of the defects and their dynamic response to further improve the relevant physics models.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Multiscale, mechanistic calculation of the effective silver diffusion coefficient in polycrystalline silicon carbide: application to silver release in AGR-1 TRISO particles

The silicon carbide (SiC) layer in tristructural isotropic (TRISO) fuel particles serves as a barrier to prevent the escape of fission from the fuel kernel. The release of silver (Ag) is a concern due to the long half-life of the 110mAg isotope. In this study, the effective diffusion coefficient of the fission product Ag through the grain boundary (GB) network is calculated using a combination of atomistic and phase-field methods. Atomistic calculations of Ag diffusivity in SiC bulk and GBs are leveraged to develop a mesoscale effective Ag diffusion coefficient (Deff) in SiC. Since GBs serve as pathways for Ag diffusion, Deff is defined as a function of temperature, microstructure variables, and fluence. Deff is implemented in the fuel performance code Bison to predict Ag release from AGR-1 TRISO fuel particles. We hereby quantify the impact of SiC grain size and irradiation on Ag release and improve Bison's predictions.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Using Satellite and ARM Observations to Evaluate Cold Air Outbreak Cloud Transitions in E3SM Global Storm‐Resolving Simulations

Abstract This study examines marine boundary layer cloud regime transition during a cold air outbreak (CAO) over the Norwegian Sea, simulated by a global storm‐resolving model (GSRM) known as the Simple Cloud‐Resolving Energy Exascale Earth System Model Atmosphere Model (SCREAM). By selecting observational references based on a combination of large‐scale conditions rather than strict time‐matched comparisons, this study finds that SCREAM qualitatively captures the CAO cloud transition, including boundary layer growth, cloud mesoscale structure, and phase partitioning. SCREAM also accurately locates the greatest ice and liquid in the mesoscale updrafts, however, underestimates supercooled liquid water in cumulus clouds. The model evaluation approach adopted by this study takes advantages of the existing computational‐expensive global simulations of GSRM and the available observations to understand model performance and can be applied to assessments of other cloud regimes in different regions. Such practice provides valuable guidance on the future effort to correct and improve biased model behaviors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials Using MALAMUTE

Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Data‐Efficient Generation of Synthetic Microstructures of Polymer‐Bonded Energetic Material With Fine‐Tuned Stable Diffusion

Among current deep learning approaches for synthetic image generation, diffusion-based models stand out in terms of algorithmic stability and ability to retain high-fidelity image features with detailed resolution. Here, in this work, we employ Dreambooth, a method for fine-tuning Stable Diffusion, on X-ray CT images of microstructure of the polymer-bonded form (PBX) of a commonly used high explosive, Pentaerythritol tetranitrate (PETN), which yields generative models for creating synthetic PBX images. The models developed here represent five classes (or ‘lots’) of microstructures and demonstrate successful generation of images of each class with high fidelity, as verified by computed classification accuracy of ∼ 94% or higher. Data augmentation afforded by such image synthesis can be used to more reliably decipher underlying statistics, build processing-structure correlations, recognize off-normal structural anomalies, and identify age-related changes. Ideas related to converting image data into appropriate density mapping and performing mesoscale simulation or surrogate modeling of detonation are also discussed.

Dreambooth↗
DOE OSTI2021

Influence of the distribution of expansive sites in aggregates on microscopic damage caused by alkali-silica reaction: Insights into the mechanical origin of expansion

The origin of damage in concrete due to the alkali-silica reaction (ASR) is attributed to the expansion site in the aggregate. To investigate the cracking process of the aggregate during ASR and its consequences on concrete damage, the effect of the distribution of the expansive sites in the aggregate on ASR expansion and the crack patterns must be evaluated. Thus, in this study, a mesoscale discrete model was applied to ASR modeling to represent the propagation of cracking during ASR accurately. The distribution of the expansive sites in the aggregate was based on the gel pocket and reaction rim models, which are two ASR mechanisms reported in the literature. These two expansion models highlight the different crack patterns obtained based on the aggregate characteristics. Further, the expansion cracking processes determined based on the gel pocket and reaction rim models are consistent with the evolution of cracking with the expansion level.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Correlating dislocation structures to basal and prismatic slip bands near grain boundaries in tensile-tested Mg–4Al using a multiscale electron microscopy approach

Dislocation structures where basal and prismatic slip bands meet grain boundaries in tensile-tested Mg with 4 wt% Al (Mg–4Al) were studied using a multiscale electron microscopy approach to explore the assumptions made for the dislocation pileup theory: a) slip bands consist of dislocation arrays piling up near grain boundaries, and b) stress concentration in the adjacent grain is solely caused by dislocation pileup. After post-testing electron backscatter diffraction (EBSD) scans, focused-ion-beam (FIB) lift-out specimens were prepared from regions of interest so that the specimen plane is parallel to the bulk sample surface to allow for direct correlation between mesoscale and microscale characterization. High dislocation density and the dislocation arrays in the slip bands corroborated with the first assumption. Evidence of plastic deformation in all grains, however, showed that the second assumption was false. Additionally, dislocation structures including crystal rotation boundaries and dislocations dissociating to the basal plane provide additional stress relief mechanisms near grain boundaries that are not accounted for in many crystal plasticity models, which requires auxiliary models to more accurately predict the stress and strain distribution in a microstructure.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Mesoscopic-scale complexity in macroscopically-uniform plastic flow of an Al 0.3 CoCrFeNi high-entropy alloy

The concept of a smooth and homogeneous plastic flow of solids is nowadays constantly challenged by various observations of the self-organization of crystal defects on mesoscopic scales pertaining, e.g., to acoustic emission or the evolution of the local strain field. Such investigations would be of particular interest for High-Entropy Alloys (HEAs) characterized by extremely complex microstructures. However, the complexity of their deformation has been only studied in relation to the jerky flow. The present work explores a mesoscale complexity of the macroscopically-smooth plastic flow of an Al 0.3 CoCrFeNi alloy. Here, an inclusive approach involving various experimental methods and complementary nonlinear analyses allowed to reveal transitions between distinct dynamical regimes, which may be assimilated with noises of different colors, in particular the blue noise that is rarely observed in complex systems, and various kinds of reddened behavior. Such findings provide significant new insights into the micro/macro transition in the deformation behavior of HEAs.

36 MATERIALS SCIENCE↗