Maximizing Operator Impact: Leveraging Machine Learning to Improve Trending Analysis for the Magnetospheric Multiscale (MMS) Mission
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Results for “multiscale analysis”
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Multiscale Neural Networks for Approximating Green’s Functions
Neural networks (NNs) have been widely used to solve partial differential equations (PDEs) in the applications of physics, biology, and engineering. One effective approach for solving PDEs with a fixed differential operator is learning Green’s functions. However, Green’s functions are notoriously difficult to learn due to their poor regularity, which typically requires larger NNs and longer training times. In this work, we address these challenges by leveraging multiscale NNs to learn Green’s functions. Through theoretical analysis using multiscale Barron space methods and experimental validation, we show that the multiscale approach significantly reduces the necessary NN size and accelerates training.
97 MATHEMATICS AND COMPUTING↗
A Multiscale, Nonlinear, Modeling Framework Enabling the Design and Analysis of Composite Materials and Structures
A framework for the multiscale design and analysis of composite materials and structures is presented. The ImMAC software suite, developed at NASA Glenn Research Center, embeds efficient, nonlinear micromechanics capabilities within higher scale structural analysis methods such as finite element analysis. The result is an integrated, multiscale tool that relates global loading to the constituent scale, captures nonlinearities at this scale, and homogenizes local nonlinearities to predict their effects at the structural scale. Example applications of the multiscale framework are presented for the stochastic progressive failure of a SiC/Ti composite tensile specimen and the effects of microstructural variations on the nonlinear response of woven polymer matrix composites.
Bednarcyk, Brett A.↗
A Multiscale, Nonlinear, Modeling Framework Enabling the Design and Analysis of Composite Materials and Structures
A framework for the multiscale design and analysis of composite materials and structures is presented. The ImMAC software suite, developed at NASA Glenn Research Center, embeds efficient, nonlinear micromechanics capabilities within higher scale structural analysis methods such as finite element analysis. The result is an integrated, multiscale tool that relates global loading to the constituent scale, captures nonlinearities at this scale, and homogenizes local nonlinearities to predict their effects at the structural scale. Example applications of the multiscale framework are presented for the stochastic progressive failure of a SiC/Ti composite tensile specimen and the effects of microstructural variations on the nonlinear response of woven polymer matrix composites.
Bednarcyk, Brett A.↗
Stiffness and Fatigue Life Estimator for Polymer Composite Laminates Using Machine Learning
Machine learning (ML) models are increasingly being used in many engineering fields due to the advancements in ML algorithms and availability of high-speed computing power. One of the most popular ML class of models is artificial neural networks (ANN). ML is increasingly being used in the design and analysis of composite materials and structures, specifically in the constitutive modeling of composite materials with the focus on greatly accelerating multiscale analyses of composite materials and structures through development of surrogate models. Towards that end, Python-based neural nets have been developed to predict initial stiffness and fatigue life of an eight-ply symmetric polymer matrix composite laminate. Two types of neural networks, a Multilayer Perceptron (MLP) and a Recurrent Neural Network (RNN), have been established. Results show that both neural net type algorithms can provide an excellent estimate of initial laminate stiffness as well as fatigue life of eight-ply symmetric polymer matrix composite laminates (PMCs). RNNs are better able to capture the shape of the fatigue curve of a laminate. The resulting tool and GUI can be very useful for system level studies to obtain an estimate of desired properties and life of PMC composite laminates. Further, the associated surrogate models can also be used in composite multiscale analyses to replace the actual physics-based calculations at lower scales and thereby significantly increase the computational efficiency of such analyses and thus make micromechanics-based multiscale analyses a viable industrial tool for large scale structural problems.
multiscale analysis↗
Stiffness and Fatigue Life Estimator for Polymer Composite Laminates Using Machine Learning
Machine learning (ML) models are increasingly being used in many engineering fields due to the advancements in ML algorithms and availability of high-speed computing power. One of the most popular ML class of models is artificial neural networks (ANN). ML is increasingly being used in the design and analysis of composite materials and structures, specifically in the constitutive modeling of composite materials with the focus on greatly accelerating multiscale analyses of composite materials and structures through development of surrogate models. Towards that end, Python-based neural nets have been developed to predict initial stiffness and fatigue life of an eight-ply symmetric polymer matrix composite laminate. Two types of neural networks, a Multilayer Perceptron (MLP) and a Recurrent Neural Network (RNN), have been established. Results show that both neural net type algorithms can provide an excellent estimate of initial laminate stiffness as well as fatigue life of eight-ply symmetric polymer matrix composite laminates (PMCs). RNNs are better able to capture the shape of the fatigue curve of a laminate. The resulting tool and GUI can be very useful for system level studies to obtain an estimate of desired properties and life of PMC composite laminates. Further, the associated surrogate models can also be used in composite multiscale analyses to replace the actual physics-based calculations at lower scales and thereby significantly increase the computational efficiency of such analyses and thus make micromechanics-based multiscale analyses a viable industrial tool for large scale structural problems.
multiscale analysis↗
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↗
Wavelet analysis applied to the IRAS cirrus
The structure of infrared cirrus clouds is analyzed with Laplacian pyramid transforms, a form of non-orthogonal wavelets. Pyramid and wavelet transforms provide a means to decompose images into their spatial frequency components such that all spatial scales are treated in an equivalent manner. The multiscale transform analysis is applied to IRAS 100 micrometer maps of cirrus emission in the north Galactic pole region to extract features on different scales. In the maps we identify filaments, fragments and clumps by separating all connected regions. These structures are analyzed with respect to their Hausdorff dimension for evidence of the scaling relationships in the cirrus clouds.
Langer, William D.↗
Stochastic Simulation of Mudcrack Damage Formation in an Environmental Barrier Coating
The FEAMAC/CARES program, which integrates finite element analysis (FEA) with the MAC/GMC (Micromechanics Analysis Code with Generalized Method of Cells) and the CARES/Life (Ceramics Analysis and Reliability Evaluation of Structures / Life Prediction) programs, was used to simulate the formation of mudcracks during the cooling of a multilayered environmental barrier coating (EBC) deposited on a silicon carbide substrate. FEAMAC/CARES combines the MAC/GMC multiscale micromechanics analysis capability (primarily developed for composite materials) with the CARES/Life probabilistic multiaxial failure criteria (developed for brittle ceramic materials) and Abaqus (Dassault Systèmes) FEA. In this report, elastic modulus reduction of randomly damaged finite elements was used to represent discrete cracking events. The use of many small-sized low-aspect-ratio elements enabled the formation of crack boundaries, leading to development of mudcrack-patterned damage. Finite element models of a disk-shaped three-dimensional specimen and a twodimensional model of a through-the-thickness cross section subjected to progressive cooling from 1,300 °C to an ambient temperature of 23 °C were made. Mudcrack damage in the coating resulted from the buildup of residual tensile stresses between the individual material constituents because of thermal expansion mismatches between coating layers and the substrate. A two-parameter Weibull distribution characterized the coating layer stochastic strength response and allowed the effect of the Weibull modulus on the formation of damage and crack segmentation lengths to be studied. The spontaneous initiation of cracking and crack coalescence resulted in progressively smaller mudcrack cells as cooling progressed, consistent with a fractal-behaved fracture pattern. Other failure modes such as delamination, and possibly spallation, could also be reproduced. The physical basis assumed and the heuristic approach employed, which involves a simple stochastic cellular automaton methodology to approximate the crack growth process, are described. The results ultimately show that a selforganizing mudcrack formation can derive from a Weibull distribution that is used to describe the stochastic strength response of the bulk brittle ceramic material layers of an EBC.
Nemeth, Noel N.↗
Detecting Risk and Anomalies in Airplane Dynamics Through Entropic Analysis of Time Series Data
Despite recent efforts to move away from traditional threshold exceedance detection methods for aircraft state monitoring, modern aircraft still rely on safety thresholds to communicate to pilots the identification of an anomaly in the aircraft when a threshold is surpassed. Current anomaly detection methods mainly depend on uninterpretable machine learning models to learn complex patterns and relationships contained in the time series data of aircraft. Although these methods are capable of identifying known anomalies, their deficiency in interpretability presents a challenge when translating them to different aircraft. To overcome this deficiency, entropic analysis of aircraft dynamics seeks to characterize the complexity, or lack thereof, of the aircraft dynamics prior to the development of a risk scenario. This complexity characterization provides a more straightforward summary of state changes in the dynamics of flight variables. To build a foundation for entropic analysis, we analyzed the complexity of unstable approaches, an anomalous event present in many of today’s aviation accidents. The analysis revealed a statistically significant difference in the complexity distribution of flight variables under a stable approach versus an unstable approach. These differences in complexity were especially notable minutes before an approach was identified as unstable. Moreover, the multiscale entropic analysis revealed the presence of signal complexity at multiple time scales across multiple time windows before landing. By capturing state changes and corrections in the aircraft dynamics using entropy, advanced, yet still interpretable, sensor systems based on entropic frameworks from this study can be constructed in the future using classical machine learning approaches.
Risk detection↗
Tracking of Ice Edges and Ice Floes by Wavelet Analysis of SAR Images
This paper demonstrates the use of wavelet transforms in the tracking of sequential ice features in the ERS-1 synthetic aperture radar (SAR) imagery, especially in situations where feature correlation techniques fail to yield reasonable results. Examples include the evolution of the St. Lawrence polynya and summer sea ice change in the Beaufort Sea. For the polynya, the evolution of the region of young ice growth surrounding a polynya can be easily tracked by wavelet analysis due to the large backscatter difference between the young and old ice. Also within the polynya, a 2D fast Fourier transform (FFT) is used to identify the extent of the Langmuir circulation region, which is coincident with the wave-agitated frazil ice growth region, where the sea ice experiences its fastest growth. Therefore, the combination of wavelet and FFT analysis of SAR images provides for the large-scale monitoring of different polynya features. For summer ice, previous work shows that this is the most difficult period for ice trackers due to the lack of features on the sea ice cover. The multiscale wavelet analysis shows that this method delineates the detailed floe shapes during this period, so that between consecutive images, the floe translation and rotation can be estimated.
Liu, Antony K.↗
Environmental analysis using integrated GIS and remotely sensed data - Some research needs and priorities
This paper discusses some basic scientific issues and research needs in the joint processing of remotely sensed and GIS data for environmental analysis. Two general topics are treated in detail: (1) scale dependence of geographic data and the analysis of multiscale remotely sensed and GIS data, and (2) data transformations and information flow during data processing. The discussion of scale dependence focuses on the theory and applications of spatial autocorrelation, geostatistics, and fractals for characterizing and modeling spatial variation. Data transformations during processing are described within the larger framework of geographical analysis, encompassing sampling, cartography, remote sensing, and GIS. Development of better user interfaces between image processing, GIS, database management, and statistical software is needed to expedite research on these and other impediments to integrated analysis of remotely sensed and GIS data.
Davis, Frank W.↗
Nucleation and growth of polar clusters with in-phase tilts into a long-range ferroelectric matrix in a sodium niobate based complex relaxor
In this study, we have investigated the temperature dependence of atomic ordering at multiple length scales in a lead-free sodium niobate-based relaxor, i.e., 0.75 NaNbO 3 -0.25 Ba 0.9 Ca 0.1 TiO 3 (NN-25BCT) via synchrotron x-ray diffraction, Raman spectroscopy, and pair distribution function analysis. High-resolution synchrotron x-ray powder diffraction (SXRD) measurements reveal a ferroelectric phase transition in the relaxor ferroelectric NN-25BCT below the Vogel-Fulcher freezing temperature (𝑇 VF ≈ 270 K). In addition, SXRD analysis demonstrates the competition between in-phase octahedral tilting and ferroelectric order at the long-range scale using mode crystallography. On the other hand, Raman spectroscopic analysis provides evidence of polar ordering for 𝑇 > 𝑇 VF (with tetragonal symmetry) persisting up to the Burns temperature (𝑇 B ). Furthermore, pair distribution function (PDF) analysis reveals the presence of a polar antiferrodistortive tetragonal phase with 𝑃4𝑏𝑚 space group at short ranges throughout the studied temperatures (i.e., 110 K ≤ 𝑇 ≤500 K), irrespective of nonpolar long-range ordering above 𝑇 VF . Therefore, our measurements provide direct evidence for the presence of polar ordering at short ranges and their gradual transformation into long-range polar ordering using an integrated multiscale structural analysis. In conclusion, as a result of a transition from relaxor to a ferroelectric phase in the vicinity of room temperature, NN-25BCT can be exploited for applications in pyroelectric detectors, electrocaloric devices, and multilayered ceramic capacitors.
36 MATERIALS SCIENCE↗
The Mechanism of Fluorine Doping for the Enhanced Lithium Storage Behavior in Cation-Disordered Cathode Oxide
Li-rich cation-disordered rock-salt (DRX) materials have emerged as promising candidates for high-capacity oxide cathodes. Their fluorinated variants have shown improved cycling stability with effectively suppressed oxygen loss. However, a comprehensive understanding of how fluorination impacts the multiscale structure and lithium transportation in DRX remains elusive in experiments. In this study, the neutron total scattering technique in conjunction with the advanced reverse Monte Carlo (RMC) fitting method is employed to characterize the intricate structure of Li 1.16 Ti 0.37 Ni 0.37 Nb 0.1 O 2 (LTNNO) and the fluorinated Li 1.2 Ti 0.35 Ni 0.35 Nb 0.1 O 1.8 F 0.2 (LTNNOF). Through rigorous statistical analysis, the multiscale structural evolution upon fluorination is quantified from atomic (≤5 Å) to long-range scale (≈100 Å). The local Li-rich environments around F induce a modest 2.4% increment in the number of fast Li 0TM (transition metal) channels. Crucially, at a broader scale, the proportion of 0TM channels participating in percolation increases significantly from 2.9% in LTNNO to 8.7% in LTNNOF. Fluorination improves the capacity release mainly through merging isolated fast Li channels into the percolation network. This work experimentally unravels the multiscale mechanism of fluorination-induced performance improvement in DRX materials and highlights the necessity of adopting an advanced RMC fitting method to obtain a full view of the complex structural features in developing high-capacity DRX cathodes.
25 ENERGY STORAGE↗
Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials using MALAMUTE
The 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. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing 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 microstructural variability 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 models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s 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 additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation 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 helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.
36 - MATERIALS SCIENCE↗
Numerical validation of scaling laws for stratified turbulence
Recent theoretical progress using multiscale asymptotic analysis has revealed various possible regimes of stratified turbulence. Notably, buoyancy transport can either be dominated by advection or diffusion, depending on the effective Péclet number of the flow. Two types of asymptotic models have been proposed, which yield measurably different predictions for the characteristic vertical velocity and length scale of the turbulent eddies in both diffusive and non-diffusive regimes. The first, termed a ‘single-scale model’, is designed to describe flow structures having large horizontal and small vertical scales, while the second, termed a ‘multiscale model’, additionally incorporates flow features with small horizontal scales, and reduces to the single-scale model in their absence. By comparing predicted vertical velocity scaling laws with direct numerical simulation data, we show that the multiscale model correctly captures the properties of strongly stratified turbulence within regions dominated by small-scale isotropic motions, whose volume fraction decreases as the stratification increases. Meanwhile its single-scale reduction accurately describes the more orderly, layer-like, quiescent flow outside those regions.
Mechanics↗
Clustering of inertial particles in turbulent flow through a porous unit cell
We report direct numerical simulation is used to investigate effects of turbulent flow in the confined geometry of a face-centred cubic porous unit cell on the transport, clustering and deposition of fine particles at different Stokes numbers (St = 0.01, 0.1, 0.5, 1, 2) and at a pore Reynolds number of 500. Particles are advanced using one-way coupling and the collision of particles with pore walls is modelled as perfectly elastic with specular reflection. Tools for studying inertial particle dynamics and clustering developed for homogeneous flows are adapted to take into account the embedded, curved geometry of the pore walls. The pattern and dynamics of clustering are investigated using the volume change of Voronoi tesselation in time to analyse the divergence and convergence of the particles. Similar to the case of homogeneous, isotropic turbulence, the cluster formation is present at large volumes, while cluster destruction is prominent at small volumes and these effects are amplified with the Stokes number. However, unlike homogeneous, isotropic turbulence, the formation of a large number of very small volumes was observed at all Stokes numbers and attributed to the collision of particles with the pore wall. Multiscale wavelet analysis of the particle number density indicates that the peak of the energy density spectrum, representative of enhanced particle clustering, shifts towards larger scales with an increase in the Stokes number. Scale-dependent skewness and flatness quantify the intermittent void and cluster distribution, with cluster formation observed at small scales for all Stokes numbers, and void regions at large scales for large Stokes numbers.
42 ENGINEERING↗
Resolving Discrepancies between State-of-the-Art Theory and Experiment for HO 2 + HO 2 via Multiscale Informatics
Recent high-level theoretical calculations predict a mild temperature dependence for HO 2 + HO 2 inconsistent with state-of-the-art experimental determinations that upheld the stronger temperature dependence observed in early experiments. Via MultiScale Informatics analysis of the theoretical and experimental data, we identified an alternative interpretation of the raw experimental data that uses HO 2 + HO 2 rate constants nearly identical to theoretical predictions---implying that the theoretical and experimental data are actually consistent, at least when considering the raw data from experimental studies. Here, similar analyses of typical signals from low-temperature experiments indicate that an HOOOOH intermediate---identified by recent theory but absent from earlier interpretations---yields modest effects that are smaller than, but may have contributed to, the scatter in data among different experiments. More generally, the findings demonstrate that modern chemical theories and experiments have progressed to a point where meaningful comparison requires joint consideration of their data simultaneously.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗