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At least 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
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 OSTI2022

A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data

Deep learning (DL) models trained on hydrologic observations can perform extraordinarily well, but they can inherit deficiencies of the training data, such as limited coverage of in situ data or low resolution/accuracy of satellite data. In this work, we propose a novel multiscale DL scheme learning simultaneously from satellite and in situ data to predict 9 km daily soil moisture (5 cm depth). Based on spatial cross-validation over sites in the conterminous United States, the multiscale scheme obtained a median correlation of 0.901 and root-mean-square error of 0.034 m 3 /m 3 . It outperformed the Soil Moisture Active Passive satellite mission's 9 km product, DL models trained on in situ data alone, and land surface models. Our 9 km product showed better accuracy than previous 1 km satellite downscaling products, highlighting limited impacts of improving resolution. Not only is our product useful for planning against floods, droughts, and pests, our scheme is generically applicable to geoscientific domains with data on multiple scales, breaking the confines of individual data sets.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2014

Development of Dynamic Sub-Grid Models for Variational Multiscale Methods

A dynamic Variational Multiscale Method (Hughes et al. 1998) is developed by leveraging the Germano procedure from classical Large-eddy Simulations (LES). The similarity between the classical and variational approaches is analyzed in the context of incompressible flow. This analysis leads to a consistent modeling approach for both incompressible and compressible flows, the latter being demonstrated in a priori testing for low-speed attached and separated boundary layers. Similar to the classical LES procedure from which it is derived, the variational dynamic procedure does not guarantee a positive semi-definite coefficient in the general case. However, reproducing the behavior of the classical LES dynamic approach is seen as a necessary first step to develop a VMM that automatically adjusts to the local resolution and flow physics.

Variational multiscale↗
DOE OSTI2018

Multiscale topology optimization using neural network surrogate models

In this study, we are concerned with optimization of macroscale elastic structures that are designed utilizing spatially varying microscale metamaterials. The macroscale optimization is accomplished using gradient-based nonlinear topological optimization. But instead of using density as the optimization decision variable, the decision variables are the multiple parameters that define the local microscale metamaterial. This is accomplished using single layer feedforward Gaussian basis function networks as a surrogate models of the elastic response of the microscale metamaterial. The surrogate models are trained using highly resolved continuum finite element simulations of the microscale metamaterials and hence are significantly more accurate than analytical models e.g. classical beam theory. Because the derivative of the surrogate model is important for sensitivity analysis of the macroscale topology optimization, a neural network training procedure based on the Sobolev norm is described. Since the SIMP method is not appropriate for spatially varying lattices, an alternative method is developed to enable creation of void regions. Lastly, the efficacy of this approach is demonstrated via several examples in which the optimal graded metamaterial outperforms a traditional solid structure.

42 ENGINEERING↗
NASA NTRSDate not supplied

Thermoviscoelastic modelling of high strain thin-ply composites by means of multiscale plate and beam model

High strain thin-ply (HS-TPC) technology is being increasingly adopted for high-performanceaerospace applications. Albeit many of these structures such as deployable booms can bemodeled as one-dimensional beam problems, there is a lack of themoviscoelastic beam mod-els to efficiently and accurately simulate HS-TPC. This work will use mechanics of struc-ture genome (MSG) to construct linear thermoviscoelastic beam models that can homogenizethree-dimensional heterogeneous materials made of constituents with time- and temperature-dependent behavior. The formulation derives the transient strain energy based on integralformulation for thermorheologically simple materials subject to finite temperature changeswith the restriction that the strain is small. A lenticular boom is used as a numerical exampleto verify the MSG-based linear thermoviscoelastic beam model against MSG-based shell/platemodel, which has already been validated against experimental data provided by NASA, anddirect numerical simulations performed in a finite element commercial package.

Orzuri Rique Garaizar↗
NASA NTRS1997

Environmental Assessment and Monitoring with ICAMS (Image Characterization and Modeling System) Using Multiscale Remote-Sensing Data

With the rapid increase in spatial data, especially in the NASA-EOS (Earth Observing System) era, it is necessary to develop efficient and innovative tools to handle and analyze these data so that environmental conditions can be assessed and monitored. A main difficulty facing geographers and environmental scientists in environmental assessment and measurement is that spatial analytical tools are not easily accessible. We have recently developed a remote sensing/GIS software module called Image Characterization and Modeling System (ICAMS) to provide specialized spatial analytical tools for the measurement and characterization of satellite and other forms of spatial data. ICAMS runs on both the Intergraph-MGE and Arc/info UNIX and Windows-NT platforms. The main techniques in ICAMS include fractal measurement methods, variogram analysis, spatial autocorrelation statistics, textural measures, aggregation techniques, normalized difference vegetation index (NDVI), and delineation of land/water and vegetated/non-vegetated boundaries. In this paper, we demonstrate the main applications of ICAMS on the Intergraph-MGE platform using Landsat Thematic Mapper images from the city of Lake Charles, Louisiana. While the utilities of ICAMS' spatial measurement methods (e.g., fractal indices) in assessing environmental conditions remain to be researched, making the software available to a wider scientific community can permit the techniques in ICAMS to be evaluated and used for a diversity of applications. The findings from these various studies should lead to improved algorithms and more reliable models for environmental assessment and monitoring.

Lam, N.↗
DOE OSTI2015

Multiscale cohesive zone model for propagation of segmented crack fronts in mode I+III fracture

Quasistatic crack propagation in mixed-mode I+III fracture is widely observed to be unstable, the instability being characterized by the segmentation of the parent crack into a periodic array of daughter cracks shaped as flat facets rotated towards the principal stress axis. While there has been recent progress to characterize this instability, no global theory is presently available to describe all aspects of the propagation of the segmented front, including both “local” features like the angle of rotation of the facets and the ratio of their width to their spacing, and “global” ones like the effective energy-release-rate of the segmented crack front and the tendency of the facets to coarsen. This paper embarks on the development of such a theory, based on the assumption that the spacing of the facets is much smaller than their length, and asymptotic matching of outer and inner solutions for the mechanical fields on scales comparable to the facet length and spacing, respectively. Here, the inner problem is shown to reduce to a 2D linear elastic fracture mechanics problem in the plane perpendicular to the crack propagation axis. The solution of this problem is used to develop an effective cohesive zone description of the crack front on a scale much larger than the facet spacing. Such a description leads to a system of 1D integral equations for the outer mechanical fields on the cohesive zone, which may be solved numerically. Numerical examples are given that notably illustrate the prediction of the effective energy-release-rate of the segmented crack front in terms of the various geometrical parameters; this energy-release-rate is predicted to be smaller for a segmented front than for the parent planar front, with the conclusion that segmentation acts as a toughening mechanism. Implications upon the phenomenon of facet coarsening are also briefly discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Comprehensive End-to-End Design of Novel High Energy Density Materials: II. Computational Modeling and Predictions

In this work, we have proposed a holistic approach to design novel energetic materials by bridging synthesis, experimental characterization, computational modeling, and validation. Multiscale computational modeling that combines first-principles calculations, analytical theory, and empirical statistical analysis served to further advance the proposed methodology. The established materials design guiding principles led to development of a set of new energetic molecules, PHE-1, PHE-2, and PHE-3, that represent improved variations of the heterocyclic energetics and are predicted to be superior to the existing conventional energetic materials. Molecular mechanisms of the enhanced performance and sensitivity of the proposed energetic materials as a function of their chemical composition and structure are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Multiscale framework for modeling multicomponent reactive transport in stream corridors

Here, travel time–based representations of transport, a highly successful strategy for modeling conservative tracer transport in stream corridors, are extended to accommodate multicomponent reactive transport. Specifically, convolution representations used to model exchange of solute with the hyporheic zone are shown to be equivalent to solving one–dimensional subgrid models in Lagrangian form coupled to the advection dispersion equation for the stream channel. Unlike the convolution–based representations of previous travel time–based stream transport models, the subgrid model generalizes to include multicomponent reactive transport with general nonlinear reactions. An example involving biomass growth, the establishment of redox zonation, and the resulting impact on denitrification rates demonstrate reach–scale application of the new approach. Although simplified, those example simulations show some of the key phenomena associated with hyporheic–zone denitrification that are not represented with conventional first–order estimates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Assessing the Effects of Anthropogenic Aerosols on Pacific Storm Track Using a Multiscale Global Climate Model

Atmospheric aerosols impact weather and global general circulation by modifying cloud and precipitation processes, but the magnitude of cloud adjustment by aerosols remains poorly quantified and represents the largest uncertainty in estimated forcing of climate change. Here we assess the impacts of anthropogenic aerosols on the Pacific storm track using a multi-scale global aerosol-climate model (GCM). Simulations of two aerosol scenarios corresponding to the present day and pre-industrial conditions reveal long-range transport of anthropogenic aerosols across the north Pacific and large resulting changes in the aerosol optical depth, cloud droplet number concentration, and cloud and ice water paths. Shortwave and longwave cloud radiative forcing at the top of atmosphere are changed by - 2.5 and + 1.3 W m-2, respectively, by emission changes from pre-industrial to present day, and an increased cloud-top height indicates invigorated mid-latitude cyclones. The overall increased precipitation and poleward heat transport reflect intensification of the Pacific storm track by anthropogenic aerosols. Hence, this work provides for the first time a global perspective of the impacts of Asian pollution outflows from GCMs. Furthermore, our results suggest that the multi-scale modeling framework is essential in producing the aerosol invigoration effect of deep convective clouds on the global scale.

aerosol-cloud-climate interaction↗