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Results for “Voronoi partitioning”

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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Graph neural networks for mechanical property prediction of 2D fiber composites

This work investigates the ability of graph neural networks (GNNs) to homogenize 2D fiber composite microstructures. We use different inhomogeneity and anisotropy indices to motivate and show that the Volume Elements (VEs) used in ML methods should ideally be far from their Representative Volume Element (RVE) size limit and, consequently, are notably anisotropic. Hence, training only the isotropic limit properties may not be acceptable. Another aspect is the need to normalize elastic stiffness values for ML, especially when high elastic contrast ratios are encountered between composite phases or in the material set. We introduce a normalization technique based on the mean-field method (MFM) to handle such high contrast ratios and train for the entire stiffness tensor. We show that the proposed GNN approaches exhibit high accuracy and efficiency compared to traditional methods and convolutional neural networks, utilizing unstructured graphs constructed from microstructure topology. Our model successfully predicts the stiffness tensor, peak strength under bulk damage, and brittle fracture initiation strength across diverse microstructure configurations while maintaining high accuracy even for extreme material contrasts and volume fractions. We also present a method to improve prediction accuracy for small dataset sizes using Voronoi partitioning.

Brittle strength↗
DOE OSTI2026

Many-Body Benchmark of Electronic Charge and Spin Densities for Li 1–x NiO 2

Accurate benchmarks are particularly important for highly correlated oxides as mean-field approximations often fail to describe the subtle balance of charge transfer and magnetism in these materials with an accuracy comparable to experimental needs. Here we present accurate diffusion Monte Carlo (DMC) results of the electronic charge and spin densities for the tunable highly correlated oxide Li 1–x NiO 2 for x = 0, 1/2, and 1. To enable quantitative comparisons, we introduce a robust density-partitioning scheme, extending Voronoi analysis to assign atomic charges from spatially noisy DMC densities. We then benchmark common approximations used in density functional theory (DFT). Comparison against DMC shows that r 2 SCAN delivers the most balanced performance across charge, spin, and radial density descriptors, nearly reproducing DMC results for LiNiO 2 and apical Ni sites in Li 0.5 NiO 2 . Hybrid functionals (PBE0, SCAN0) perform unexpectedly poorly, and PBE + U + V yields inconsistent trends between charge and spin densities. Therefore, the r 2 SCAN functional minimizes errors relative to DMC while capturing the variable valence of the Ni ion and also retaining the computational efficiency of DFT for large-scale simulations of the tunable structural and electronic phases of Li1−xNiO2. Our study highlights the importance of accurate benchmarking of the fundamental quantities involved in DFT to select appropriate DFT approximations in order to advance the predictive modeling of charge-transfer-driven phenomena in correlated electron systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Development and testing of coarse-grained models for ultrasonic simulations of cast austenitic stainless steel

Ultrasonic inspection of cast austenitic stainless steel (CASS) in the nuclear industry is particularly challenging because of sound field scatter and attenuation caused by the coarse-grained microstructure. Modeling and simulation are important tools in ultrasonic testing, as they can be used to help address key aspects of inspections, such as developing new probe designs, predicting inspection reliability, and testing phased-array focal laws. However, developing a useful and reliable CASS model is challenging due to the many grain interfaces and crystalline orientations that must be captured. We demonstrate a method of creating a realistic CASS model that is usable in CIVA, a commercially available modeling and simulation software platform. Using polished and chemically etched sections, we generate models of a coarse-grained equiaxed specimen and a columnar specimen. We also test an alternative method of generating a coarse-grained model using Voronoi regions. We qualitatively compare sound field scatter and quantitatively compare sound field attenuation and beam partitioning in simulated sound fields to those of laboratory-measured sound fields. Results show that the Voronoi models perform as well as or better than the models based on actual grain morphology. Here we also show that model-to-model randomness in Voronoi grain structure can impact the magnitude of a simulated echo response by a factor of two or more. Although CASS models are potentially a good depiction of reality for a given scenario, they should not be considered representative since CASS morphology can change significantly from specimen to specimen or within the same specimen.

36 MATERIALS SCIENCE↗