Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Cluster Analysis”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

On the interplay between relaxation, defect formation, and atomic Sn distribution in Ge{sub (1−x)}Sn{sub (x)} unraveled with atom probe tomography

Ge{sub (1−x)}Sn{sub (x)} has received a lot of interest for opto-electronic applications and for strain engineering in advanced complementary-metal-oxide-semiconductor technology, because it enables engineering of the band gap and inducing strain in the alloy. To target a reliable technology for mass application in microelectronic devices, the physical problem to be addressed is to unravel the complex relationship between strain relaxation (as induced by the growth of large layer thicknesses or a thermal anneal) and defect formation, and/or stable Sn-cluster formation. In this paper, we study the onset of Sn-cluster formation and its link to strain relaxation using Atom Probe Tomography (APT). To this end, we also propose a modification of the core-linkage [Stephenson et al., Microsc. Microanal. 13, 448 (2007)] cluster analysis method, to overcome the challenges of limited detection efficiency and lateral resolution of APT, and the quantitative assessment for very small clusters (<40 atoms) embedded in a random distribution of Sn-atoms. We concluded that the main relaxation mechanism for these layers is defect generation (misfit dislocations, threading dislocations, etc.), irrespective of the cause (thickness of layer or thermal anneal) of relaxation and is independent of the cluster formation. The low thermodynamic solubility limit of Sn in Ge seems to be the driving force for Sn-cluster formation. Finally, we also discuss the spatial distribution of Sn in clusters and relate them to the theoretically predicted stable Sn clusters [Ventura et al., Phys. Rev. B 79, 155202 (2009)].

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Evapotranspiration simulations in ISIMIP2a—Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets

Actual land evapotranspiration (ET) is a key component of the global hydrological cycle and an essential variable determining the evolution of hydrological extreme events under different climate change scenarios. However, recently available ET products show persistent uncertainties that are impeding a precise attribution of human-induced climate change. Here, we aim at comparing a range of independent global monthly land ET estimates with historical model simulations from the global water, agriculture, and biomes sectors participating in the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). Among the independent estimates, we use the EartH2Observe Tier-1 dataset (E2O), two commonly used reanalyses, a pre-compiled ensemble product (LandFlux-EVAL), and an updated collection of recently published datasets that algorithmically derive ET from observations or observations-based estimates (diagnostic datasets). A cluster analysis is applied in order to identify spatiotemporal differences among all datasets and to thus identify factors that dominate overall uncertainties. The clustering is controlled by several factors including the model choice, the meteorological forcing used to drive the assessed models, the data category (models participating in the different sectors of ISIMIP2a, E2O models, diagnostic estimates, reanalysis-based estimates or composite products), the ET scheme, and the number of soil layers in the models. By using these factors to explain spatial and spatio-temporal variabilities in ET, we and that the model choice mostly dominates (24%{40% of variance explained), except for spatio-temporal patterns of total ET, where the forcing explains the largest fraction of the variance (29%). The most dominant clusters of datasets are further compared with individual diagnostic and reanalysis-based estimates to assess their representation of selected heat waves and droughts in the Great Plains, Central Europe and Western Russia. Although most of the ET estimates capture these extreme events, the generally large spread among the entire ensemble indicates substantial uncertainties.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

GazeAppraise v. 0.1

GazeAppraise advances the state of the art of gaze pattern analysis using methods that simultaneously analyze spatial and temporal characteristics of gaze patterns. GazeAppraise enables novel research in visual perception and cognition; for example, using shape features as distinguishing elements to assess individual differences in visual search strategy. Given a set of point-to-point gaze sequences, hereafter referred to as scanpaths, the method constructs multiple descriptive features for each scanpath. Once the scanpath features have been calculated, they are used to form a multidimensional vector representing each scanpath and cluster analysis is performed on the set of vectors from all scanpaths. An additional benefit of this method is the identification of causal or correlated characteristics of the stimuli, subjects, and visual task through statistical analysis of descriptive metadata distributions within and across clusters.

Wilson, Andrew↗
DOE OSTI2014

Faint dwarfs in nearby groups

The number and distribution of dwarf satellite galaxies remain a critical test of cold dark matter-dominated structure formation on small scales. Until recently, observational information about galaxy formation on these scales has been limited mainly to the Local Group. We have searched for faint analogues of Local Group dwarfs around nearby bright galaxies, using a spatial clustering analysis of the photometric catalog of the Sloan Digital Sky Survey (SDSS) Data Release 8. Several other recent searches of SDSS have detected clustered satellite populations down to Δm{sub r} ≡ (m{sub r,} {sub sat} – m{sub r,} {sub main}) ∼ 6-8, using photometric redshifts to reduce background contamination. SDSS photometric redshifts are relatively imprecise, however, for faint and nearby galaxies. Instead, we use angular size to select potential nearby dwarfs and consider only the nearest isolated bright galaxies as primaries. As a result, we are able to detect an excess clustering signal from companions down to Δm{sub r} = 12, 4 mag fainter than most recent studies. We detect an overdensity of objects at separations <400 kpc, corresponding to about 4.6 ± 0.5 satellites per central galaxy, consistent with the satellite abundance expected from the Local Group, given our selection function. Although the sample of satellites detected is incomplete by construction, since it excludes the least and most compact dwarfs, this detection provides a lower bound on the average satellite luminosity function, down to luminosities corresponding to the faintest ''classical'' dwarfs of the Local Group.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

The importance of accounting for landscape position when investigating grasslands: A multidisciplinary characterisation of a Californian coastal grassland

Data from the characterisation of the Point Reyes Field Site, published in AGU Earth's Future under the title: The importance of accounting for landscape position when investigating grasslands: A multidisciplinary characterisation of a Californian coastal grassland. This paper explored the effect of landscape position on the response of a Californian grassland to seasonal changes. All files are csv files. The EMI data contains 8 csv files with a metadata csv explaining the columns. The dataset also includes soil variables including total concentrations calculated from fused samples, then dissolved and measured on ICP-AES for whole-rock elements and ICP-MS for trace elements. Mineral composition was attained using X-ray diffraction at BL 11-3 at SSRL . Data was then run through the High Score database to characterise different mineral phases. total It also includes a table with bulk soil characteristics such as soil pH, cation exchange capacity, and soil textural data. Data from Teros 12 Meter soil moisture, electrical conductivity and temperature sensors are presented in SMS Csv file. While the WL bottom and top files contain data from Piezometers measuring the ground water table. We have included a csv file that contains soil CO2 efflux data from Feb 2021-Oct 2021 in the Point Reyes Grassland Experiment We have included the spatially orientated (easting northing) remotely sensed datasets that were used in the K-means clustering analysis conducted on our site with electrical conductivity, normalised difference vegetation index, elevation, slope, solar radiation, topographic position and wetness index, and a clustering score. Finally there is a list of all the identified grassland species at the site.For more information on flux data, please contact the corresponding author.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1974

NCUBE - A clustering algorithm based on a discretized data space

Cluster analysis involves the unsupervised grouping of data. The process provides an automatic procedure for generating known training samples for pattern classification. NCUBE, the clustering algorithm presented, is based upon the concept of imposing a gridwork on the data space. The NCUBE computer implementation of this concept provides an easily derived form of piecewise linear discrimination. This piecewise linear discrimination permits the separation of some types of data groups that are not linearly separable.

Eigen, D. J.↗
DOE OSTI2020

Evaluation of genetic diversity, agronomic traits, and anthracnose resistance in the NPGS Sudan Sorghum Core collection

The United States Department of Agriculture (USDA) National Plant Germplasm System (NPGS) sorghum core collection contains 3011 accessions randomly selected from 77 countries. Genomic and phenotypic characterization of this core collection is necessary to encourage and facilitate its utilization in breeding programs and to improve conservation efforts. In this study, we examined the genome sequences of 318 accessions belonging to the NPGS Sudan sorghum core set, and characterized their agronomic traits and anthracnose resistance response. We identified 183,144 single nucleotide polymorphisms (SNPs) located within or in proximity of 25,124 annotated genes using the genotyping-by-sequencing (GBS) approach. The core collection was genetically highly diverse, with an average pairwise genetic distance of 0.76 among accessions. Population structure and cluster analysis revealed five ancestral populations within the Sudan core set, with moderate to high level of genetic differentiation. In total, 171 accessions (54%) were assigned to one of these populations, which covered 96% of the total genomic variation. Genome scan based on Tajima’s D values revealed two populations under balancing selection. Phenotypic analysis showed differences in agronomic traits among the populations, suggesting that these populations belong to different ecogeographical regions. A total of 55 accessions were resistant to anthracnose; these accessions could represent multiple resistance sources. Genome-wide association study based on fixed and random model Circulating Probability (farmCPU) identified genomic regions associated with plant height, flowering time, panicle length and diameter, and anthracnose resistance response. Integrated analysis of the Sudan core set and sorghum association panel indicated that a large portion of the genetic variation in the Sudan core set might be present in breeding programs but remains unexploited within some clusters of accessions. The NPGS Sudan core collection comprises genetically and phenotypically diverse germplasm with multiple anthracnose resistance sources. Population genomic analysis could be used to improve screening efforts and identify the most valuable germplasm for breeding programs. The new GBS data set generated in this study represents a novel genomic resource for plant breeders interested in mining the genetic diversity of the NPGS sorghum collection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Elemental Mixing State of Aerosol Particles Collected in Central Amazonia during GoAmazon2014/15

Two complementary techniques, Scanning Transmission X-ray Microscopy/Near Edge Fine Structure spectroscopy (STXM/NEXAFS) and Scanning Electron Microscopy/Energy Dispersive X-ray spectroscopy (SEM/EDX), have been quantitatively combined to characterize individual atmospheric particles. This pair of techniques was applied to particle samples at three sampling sites (ATTO, ZF2, and T3) in the Amazon basin as part of the Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) field campaign during the dry season of 2014. The combined data was subjected to k-means clustering using mass fractions of the following elements: C, N, O, Na, Mg, P, S, Cl, K, Ca, Mn, Fe, Ni, and Zn. Cluster analysis identified 12 particle types, across different sampling sites and particle sizes. Samples from the remote Amazon Tall Tower Observatory (ATTO, also T0a) exhibited less cluster variety and fewer anthropogenic clusters than samples collected at the sites nearer to the Manaus metropolitan region, ZF2 (also T0t) or T3. Samples from the ZF2 site contained aged/anthropogenic clusters not readily explained by transport from ATTO or Manaus, possibly suggesting the effects of long range atmospheric transport or other local aerosol sources present during sampling. In addition, this data set allowed for recently established diversity parameters to be calculated. All sample periods had high mixing state indices (χ) that were >0.8. Two individual particle diversity (D i ) populations were observed, with particles <0.5 μm having a D i of ~2.4 and >0.5 μm particles having a D i of ~3.6, which likely correspond to fresh and aged aerosols respectively. The diversity parameters determined by the quantitative method presented here will serve to aid in the accurate representation of aerosol mixing state, source apportionment, and aging in both less polluted and more industrialized environments in the Amazon Basin.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1976

Variations in chemical composition of Apollo 15 mare basalts

Chemical analyses of 30 different Apollo 15 mare basalts were examined to evaluate the effects of closure on the pearson moment correlation coefficient. It is shown possible to describe the Apollo 15 mare basalts in terms of an opaque, an olivine/pyroxene, an anorthite, and a KREEP component, if significant correlations are identified using the expected correlations as null values. Using Q-mode cluster analysis and nonlinear mapping, it is possible to recognize three groups of the mare basalts, groups 1 and 2 belonging to the olivine normative basalt cluster and group 3 to the quartz normative cluster.

Butler, J. C.↗
NASA NTRS1994

Temperature and iron abundance variation of the gas in the Perseus cluster

We present the first two-dimensional map of the temperature and iron abundance in the Perseus cluster. Analysis of spectra obtained using the Gas Imaging Spectrometer on ASCA shows nonaxisymmetric variations in both the temperature and iron abundance. Traveling west from the cluster center, the temperature increases to 9 keV at 20 min and then decreases rapidly to 5 keV at 40 min. There is a hot (greater than 10 keV) region to the northwest of the cluster center. The abundance is approximately constant over much of the surveyed region, but there is evidence for an increased abundance in the northwest hot area and a gradual decrease in a westerly direction.

Arnaud, K. A.↗
DOE OSTI2024

Chemical properties and single-particle mixing state of soot aerosol in Houston during the TRACER campaign

Abstract. A high-resolution soot particle aerosol mass spectrometer (SP-AMS) was used to selectively measure refractory black carbon (rBC) and its associated coating material using both the ensemble size-resolved mass spectral mode and the event trigger single particle (ETSP) mode in Houston, Texas, in summer 2022. This study was conducted as part of the Department of Energy Atmospheric Radiation Measurement (ARM) program's TRacking Aerosol Convection interactions ExpeRiment (TRACER) field campaign. The study revealed an average (±1σ) rBC concentration of 103 ± 176 ng m−3. Additionally, the coatings on the BC particles were primarily composed of organics (59 %; 219 ± 260 ng m−3) and sulfate (26 %; 94 ± 55 ng m−3). Positive matrix factorization (PMF) analysis of the ensemble mass spectra of BC-containing particles resolved four distinct types of soot aerosol, including an oxidized organic aerosol (OOABC,PMF) factor associated with processed primary organic aerosol, an inorganic sulfate factor (SO4,BC,PMF), an oxidized rBC factor (O-BCPMF), and a mixed mineral dust–biomass burning aerosol factor with significant contribution from potassium (K-BBBC,PMF). Additionally, K-means clustering analysis of the single-particle mass spectra identified eight different clusters, including soot particles enriched in hydrocarbon-like organic aerosol (HOABC,ETSP), sulfate (SO4,BC,ETSP), two types of rBC, OOA (OOABC,ETSP), chloride (ClBC,ETSP), and nitrate (NO3,BC,ETSP). The single-particle measurements demonstrate substantial variation in BC coating thickness with coating-to-rBC mass ratios ranging from 0.1 to 100. The mixing state index (χ), which denotes the degree of homogeneity of the soot aerosol, varied from 4 % to 94 % with a median of 40 %, indicating that the aerosol population lies in between internal and external mixing but has large temporal and source type variability. In addition, a significant fraction of BC-containing particles, a majority enriched with oxidized organics and sulfate, exhibit sufficiently high κ values and diameters conducive to activation as cloud nuclei under atmospherically relevant supersaturation conditions. This finding bears significance in comprehending the aging processes of rBC-containing particles and their activation into cloud droplets. Our analysis highlights the complex nature of soot aerosol and underscores the need to comprehend its variability across different environments for accurate assessment of climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Agronomic performance of 27 Populus clones evaluated after two 3–year coppice rotations in Henan, China

Selecting superior clones is the first step for commercial short–rotation coppice cultures to provide biomass and bioenergy. Till date, such selection for hybrid Populus clones in middle China is absent. Here we describe the growth, aboveground biomass production and cell wall composition of 27 hybrid poplar clones in Henan, China for two 3–year rotations. Significant variation in these three characteristics over two triennial rotation coppices among the 27 poplar clones was observed. During two 3–year rotation coppices, clones ‘276’, ‘02–17’, and ‘599’ showed relatively higher tree heights and larger basal diameters than those of the other clones. However, the most productive clones were ‘36’ and ‘01–30’. At the end of the second triennial rotation, the aboveground biomass production reached 18 Mg ha–1 year–1. For the cell wall composition analysis, the cellulose contents of clones ‘01–243’ and ‘2001’ were relatively high, while the xylose contents of clones ‘01–30’ and ‘65’ were relatively high. Cluster analysis based on height, basal diameter, biomass, heat value, cellulose content, and survival rate revealed five growth potential classes. Accordingly, clones ‘03–332’, ‘36’, and ‘599’ exhibited high biomass and growth and had the greatest potential to serve as excellent biomass producers in Henan, China. In addition, the expression patterns of 20 key regulatory genes were analyzed, and an integrated coexpression network was constructed. This field trial provides a comprehensive quantification and evaluation of the agronomic performance of 27 poplar clones in Henan, China. The results of this study and the analytical strategies provide an efficient mechanism for selecting clones that will perform well agronomically in local environments. The expression of key genes and the integrated coexpression network provide the molecular mechanisms of poplar biomass performance.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2016

Cloud Regimes as a Tool for Systematic Study of Various Aerosol-Cloud-Precipitation Interactions

Systematic changes of clouds and precipitation are notoriously difficult to ascribe to aerosols. This presentation will showcase yet one more attempt to at least credibly detect the signal of aerosol-cloud-precipitation interactions. We surmise that the concept of cloud regimes (CRs) is appropriate to conduct such an investigation. Previous studies focused on what we call here dynamical CRs, and while we continue to adopt those too for our analysis, we have found that a different way of organizing cloud systems, namely via microphysical regimes is also promising. Our analysis relies on MODIS Collection 6 Level-3 data for clouds and aerosols, and TRMM-TMPA data for precipitation. The regimes are derived by applying clustering analysis on MODIS joint histograms, and once each grid cell is assigned a regime, aerosol and precipitation data can be spatiotemporally matched and composited by regime. The composites of various cloud and precipitation variables for high (upper quartile of distribution) and low (lower quartile) aerosol loadings can then be contrasted. We seek evidence of aerosol effects both in regimes with large fractions of deep ice-rich clouds, as well as regimes where low liquid phase clouds dominate. Signals can be seen, especially when the analysis is broken by land-ocean and when additional filters are applied, but there are of course caveats which will be discussed.

cloud regimes↗
DOE OSTI2019

Weak Lensing Analysis of SPT selected Galaxy Clusters using Dark Energy Survey Science Verification Data

We present weak-lensing (WL) mass constraints for a sample of massive galaxy clusters detected by the South Pole Telescope (SPT) via the Sunyaev-Zel'dovich effect (SZE). We use griz imaging data obtained from the Science Verification (SV) phase of the Dark Energy Survey (DES) to fit the WL shear signal of 33 clusters in the redshift range 0.25 <= z <= 0.8 with NFW profiles and to constrain a four-parameter SPT mass-observable relation. To account for biases in WL masses, we introduce a WL mass to true mass scaling relation described by a mean bias and an intrinsic, lognormal scatter. We allow for correlated scatter within the WL and SZE mass-observable relations and use simulations to constrain priors on nuisance parameters related to bias and scatter from WL. We constrain the normalization of the zeta-M-500 relation, A(SZ) = 12.0(-6.7)(+2.6) when using a prior on the mass slope B-SZ from the latest SPT cluster cosmology analysis. Without this prior, we recover A(SZ) = 10.8(-5.2)(+2.3) and B-SZ = 1.30(-0.44)(+0.22). Results in both cases imply lower cluster masses than measured in previous work with and without WL, although the uncertainties are large. The WL derived value of B-SZ is approximate to 20 per cent lower than the value preferred by the most recent SPT cluster cosmology analysis. The method demonstrated in this work is designed to constrain cluster masses and cosmological parameters simultaneously and will form the basis for subsequent studies that employ the full SPT cluster sample together with the DES data.

cosmology: observations↗
DOE OSTI2016

SU-D-207A-03: Potential Role of BOLD MRI in Discrimination of Aggressive Tumor Habitat in Prostate Cancer

Purpose: To determine whether blood oxygenation level dependent (BOLD) MRI signal measured in prostate cancer patients, in addition to quantitative diffusion and perfusion parameters from multiparametric (mp)MRI exams, can help discriminate aggressive and/or radioresistant lesions. Methods: Several ongoing clinical trials in our institution require mpMRI exam to determine eligibility (presence of identifiable tumor lesion on mpMRI) and prostate volumes for dose escalation. Upon consent, patients undergo fiducial markers placement and a T2*-weighted imaging at the time of CT sim to facilitate the fusion. In a retrospective analysis eleven clinical trial patients were identified who had undergone mpMRI on GE 3T magnet, followed by T2*-weighted imaging (time-period mean±SD = 48±20 days) using a consistent protocol (gradient echo, TR/TE=30/11.8ms, flip angle=12, matrix=256×256×75, voxel size=1.25×1.25×2.5mm). ROIs for prostate tumor lesions were automatically determined using ADC threshold ≤1200 µm2/s. Although the MR protocol was not intended for BOLD analysis, we utilized the T2*-weighted signal normalized to that in nearby muscle; likewise, T2-weighted lesion signal was normalized to muscle, following rigid registration of the T2 to T2* images. The ratio of these normalized signals, T2*/T2, is a measure of BOLD effect in the prostate tumors. Perfusion parameters (Ktrans, ve, kep) were also calculated. Results: T2*/T2 (mean±SE) was found to be substantially lower for Gleason score (GS) 8&9 (0.82±0.04) compared to GS 7 (1.08±0.07). A k-means cluster analysis of T2*/T2 versus kep = Ktrans/ve revealed two distinct clusters, one with higher T2*/T2 and lower kep, containing only GS 7 lesions, and another with lower T2*/T2 and higher kep, associated with tumor aggressiveness. This latter cluster contained all GS 8&9 lesions, as well as some GS 7. Conclusion: BOLD MRI, in addition to ADC and kep, may play a role (perhaps orthogonal to Gleason score) in identifying prostate lesions that would benefit from more aggressive radiotherapy.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1986

Spectral characteristics and the extent of paleosols of the Palouse formation

Spectral relationships were investigated for several bare soil fields which were in summer fallow rotation on the date of the imagery. Printouts of each band were examined and compared to aerial photography. Bands with dissimilar reflectance patterns for known areas were then combined using ratio techniques which were proven useful in other studies (Williams, 1983). Selected ratios were Thematic Mapper (TM) 1/TM4, TM3/TM4, and TM5/TM4. Cluster analyses and Baysian and Fastclass classifier images were produced using the three ratio images. Plots of cluster analysis outputs revealed distinct groupings of reflectance data representing green crops, ripened crops, soil and green plants, and bare soil. Bare soil was represented by a line of clusters on plots of the ratios TM5/TM4 and TM3/TM4. The soil line was investigated further to determine factors involved in the distributin of clusters alone the line. The clusters representing the bare soil line were also studied by plotting the Tm5/TM4, TM1/TM4 dimension. A total of 76 soil samples were gathered and analyzed for organic carbon.

Frazier, B. E.↗
DOE OSTI2019

Biomarker Mapping on Skin Tape Strips Using MALDI Mass Spectrometry Imaging

Keratinocyte organization and biochemistry are important in forming the skin’s protective barrier. Intrinsic and extrinsic factors can affect skin barrier function at the cellular and molecular levels. Matrix-assisted laser desorption/ionization (MALDI) mass spectrometric imaging, a technique which combines both molecular aspects and histological details, has proven to be a valuable method in various disciplines including pharmacology, dermatology and cosmetology. It typically requires ex vivo samples, prepared following frozen tissue sectioning. This paper demonstrates the feasibility of performing MALDI analysis on tape strips collected non-invasively on skin. The aim is to obtain molecular imaging of corneocytes on tapes towards novel biological insights. Tapes were collected from two skin sites (volar forearm and cheek) of human volunteers. Ten molecules relating to skin barrier function were detected with a single mode of acquisition at high spatial resolution with a 7 T MALDI-Fourier transform ion cyclotron resonance (FTICR) instrument. The method sensitivity was adequate to create molecular maps which could be overlaid on transmission microscopy images of the same area of the tape. Analysis of the molecular distributions from tapes at the two skin sites was consistent with the known skin properties of the two sites, confirming the validity of the observations. Hierarchical clustering analysis was used to differentiate corneocyte populations based on their molecular profiles. Furthermore, morphological analysis provided a new way of considering statistical populations of corneocytes on the same tape, rather than measuring a single averaged value, providing additional useful information relating to their structure-function relationship. .

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

A generalizable machine learning-assisted fast Fourier transform algorithm to simulate the large strain phenomena in polycrystalline materials

Machine learning methods have shown initial promise in constitutive modeling for single crystals or homogenized polycrystals, delivering notable computational efficiency. However, existing machine learning-based constitutive models often lack generalizability, limiting their application across diverse boundary value problems. This study introduces a thermodynamics-informed artificial neural network model to accelerate rate-tangent crystal plasticity fast Fourier transform simulations for cross-scale deformation behaviors of polycrystals under complex loading. Our model integrates microstructural variability and local interactions effectively. To address local effects in each grain, we employ K-means clustering to group Gauss points within the microstructure into clusters assumed to be in similar mechanical states. This approach, based on self-clustering analysis, extends model scope from macroscopic stress response to the granular level, capturing mechanical responses and orientation evolution across grains. This reduces the number of nonlinear problems to solve, with cluster responses propagated throughout each group. The thermodynamics-based artificial neural network-extracted features are further processed using local material state clusters to account for history-dependent deformation and evolving microstructures. Additionally, representative volume element simulations with rate-tangent crystal plasticity fast Fourier transform provide reliable datasets for model training. The proposed model demonstrates high efficiency, accuracy, self-consistency, and enhanced generalizability in predicting strain–stress responses and orientation evolution at both individual grain and aggregate scales under complex loading conditions, such as biaxial tension and arbitrary loading scenarios.

36 MATERIALS SCIENCE↗