Results for “Complex Systems Characteristics” — Page 6

DOE OSTI2025

Modeling of melting and erosion in high velocity microparticle impacts

High velocity collisions can cause melting, a situation involving elasticity, plasticity, and phase transformations in tandem, which is very challenging for mechanical modeling. Here, this paper advances the use of smoothed particle hydrodynamics (SPH) simulations for this complex situation, investigating stainless steel microspheres impacting a tin substrate. After extensive validation against a range of mechanical and geometrical measurements of experimental impacts, the model provides a detailed, quantitative view of melt volume production over a broad range of impact velocities. It also enables direct decomposition of the impact energy partitioning, including analysis of the energy absorbed by plasticity in the impactor, the system kinetic energy, and the adiabatic heating of the substrate in addition to the melting itself. Since these quantities are not available experimentally, SPH modeling substantially enhances the quantitative understanding of such impacts. Finally, the model speaks to the phase characteristics of the ejecta formed during impact, confirming the experimental inference that the ejected material likely contains a significant fraction of solid material.

Constitutive modeling↗

DOE OSTI2025

A statistical and simulation-informed model for estimating permeability from pore size distribution in saturated geomaterials

Accurate permeability estimation is essential across subsurface engineering applications but remains challenging due to the complex pore structures of natural geomaterials. Traditional empirical methods and simplified theoretical models often inadequately capture the role of pore size distribution and connectivity. Here, this study develops a statistical and simulation-informed permeability model that collapses pore-scale complexity into a compact scaling of the form k = αϕμ d 2 , where ϕ is porosity, μ d is mean pore size, and α is a weakly varying coefficient. By combining pore network simulations with statistical analysis of unimodal and bimodal pore size distributions, we identify three key findings: (i) permeability is much more sensitive to mean pore size than to porosity; (ii) across extensive datasets, the ratio σ d /μ d (standard deviation to mean) clusters around a characteristic value ∼0.4, allowing the effects of the full pore size distribution to be represented by μ d and a narrowly varying α ≈ 0.05; and (iii) for bimodal systems, there exists a critical fraction of small pores ∼0.78 above which flow becomes small-pore dominated, enabling the definition of an effective flow-controlling pore population and facilitating simplified permeability estimation for such systems. The resulting model, which requires only porosity and a representative mean pore size as inputs, is validated against comprehensive experimental datasets (>1700 samples) spanning diverse soils and rocks and achieves good predictive accuracy. Overall, this work provides a physically grounded yet practically simple permeability estimator suitable for subsurface engineering, environmental protection, and resource management applications.

Permeability↗

DOE OSTI2026

Direct Measurement of Cl − Activity in Metal Chloride Molten Salts using a Cl 2 /Cl − Electrode

Molten metal chloride salts are promising candidates for advanced heat transfer fluids in generation IV nuclear reactors and beyond. The activity of the chloride ion in the salt has a large influence on the redox characteristics of the corresponding molten salt. There exists a knowledge gap for the direct measurement of chlorobasicity (Lewis basicity) in molten-salt mixtures. Here, the focus of this work was to develop a Cl 2 /Cl − electrode for the direct measurement of chloride activity in molten metal-chloride salt mixtures. The LiCl–KCl eutectic system was utilized as the reference melt to determine the change in chloride ion activity as increasing concentrations of MgCl 2 are added to the LiCl–KCl eutectic working electrode. The results of the measurements indicated a reduction in chloride ion activity as the concentration of magnesium chloride increased, consistent with the complexation of free chlorides by Lewis acidic magnesium cationic species. The Temkin model was used to estimate the thermodynamic properties of MgCl 4 2− complex.

Chloride↗

DOE OSTI2025

FY25 Task 5: Small-Scale Mixing

The U.S. Department of Energy (DOE) Hanford Site has 177 underground storage tanks that contain a complex and diverse mix of chemical and radioactive wastes from past nuclear fuel reprocessing and waste management operations. The strategy of the DOE Hanford Field Office is to retrieve this waste, ~20 vol% of which is in the form of insoluble undissolved solids (UDS) or sludge, and treat it via immobilization at the Hanford Waste Treatment and Immobilization Plant (WTP). The diverse properties and characteristics of Hanford tank waste lead to major challenges related to its transport from the underground tanks to the WTP. These challenges, however, can be addressed by investigating the behavior of tank waste samples and simulant materials and evaluating their behavior against the capabilities of mixing and transport system designs that may be incorporated to retrieve and treat the waste.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Digital camera imagery for vegetation phenology, Seward Peninsula, Alaska, 2022-2023

Timelapse camera images from Council Mile Marker (MM) 71, Kougarok MM 64, Kougarok Fire Complex (KFC), and Teller MM 27 NGEE-Arctic field sites on the Seward Peninsula, Alaska, captured from July 2022 to July 2023. Eight Wingscape Timelapse Pro cameras, and thirty-one Power-interval Camera Automation Modules (PiCAMs) designed by Brookhaven National Laboratory?s Terrestrial Ecosystem Science and Technology (TEST) group were deployed targeting patches of low and tall shrubs (including Alnus sp. and Salix sp.) and general vegetation and landscape views. Images from Wingscape cameras were recorded at hourly intervals from 11 AM to 2 PM, and images from PiCAMs were recorded at 5 hourly intervals from 12 AM to 8 PM, continuously for 12 months and capture vegetation phenology, snow accumulation and snow melt events. This data package includes images (*.jpg), organized by site and camera ID, and metadata with details of the cameras used, number of images recorded, start and end dates, GPS locations and example fields of view. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Mapping in situ the assembly and dynamics in aqueous supramolecular polymers

Supramolecular polymers, bonded through directional non-covalent interactions, closely mimic dynamic behaviors of biological nanofibers. However, the complexity of assembly pathways makes it highly challenging to unravel the nature of supramolecular dynamics in aqueous environments. Here we introduce a precise combinatorial titration methodology to probe in situ the assembly of peptide amphiphiles (PAs). This approach reveals a binary assembly mechanism governed by equilibrium between spheroidal micelles and β-sheet polymers. Weakening hydrogen bonding shifts the equilibrium towards micelles and decreases the internal structural order of filamentous polymers, promoting supramolecular dynamics. Extending this methodology to two-component copolymerization systems, we find a surprising tendency to form blocky nanostructures with reduced internal phase separation as the mismatch in peptide sequence decreases. Interestingly, while well-mixed copolymers acquire different dynamics, mismatched ones retain the characteristic supramolecular motion of their homopolymer counterparts. These critical insights into supramolecular dynamics offer strategies to tailor the dynamic functions of supramolecular nanomaterials.

Molecular self-assembly↗

DOE OSTI2024

Modeling hyperbranched polymer formation via ATRP using dissipative particle dynamics

Hyperbranched polymers (HBPs) offer distinguishing, advantageous properties that arise from their distinctive complex topology. One of the effective approaches to the synthesis of hyperbranched structures involves the use of a branching initiator (inibramer) that is activated only after incorporation into a polymer chain. There remain, however, challenges in determining and characterizing the structures of the synthesized HBPs. Dissipative particle dynamics (DPD) was used to probe the effects of inibramer concentration, solvent concentration, and inibramer reactivity on the kinetics, molecular weight, and dispersity of HBPs. Additionally, DPD allows for direct observation of branched structures, which was not possible in previously reported Monte Carlo type simulations. It was found that higher inibramer concentrations led to faster monomer consumption while forming more dendritic structures with fewer defects. Additionally, high dispersities characteristic of HBP systems were found to originate from asymmetric propagation rates between inibramer-inibramer and inibramer-monomer reactions.

Biswas, Santidan↗

DOE OSTI2024

Optimizing DOE Opportunities to Research Land–Atmosphere Interactions in the U.S. Southeast (Workshop Report)

The southeastern United States (Southeast), with its complex and varied environments, is an area of tremendous economic, ecological, and societal importance to the country. The region is characterized by heterogeneous landscapes (i.e., geology and soil type) and a long history of human land use coupled with a warm temperature regime and high precipitation. As a result, soil erosion and deposition are pronounced, vegetation recovery is rapid, and human modification is extensive across the region. To better understand land–atmosphere interactions in this important and complex region, research communities supported by the U.S. Department of Energy’s Biological and Environmental Research (BER) program identified the Southeast as a priority region of interest. In fall 2024, the third Atmospheric Radiation Measurement Mobile Facility (AMF3), one of three mobile monitoring facilities designed to collect atmospheric and climate data from undersampled regions around the world, will begin operations in northwestern Alabama’s Bankhead National Forest (BNF). The AMF3-BNF 5-year deployment, from 2024–2029, will monitor the effects of feedbacks among aerosols, clouds, and precipitation on plant physiology and canopy-scale fluxes. It will also focus on scale aggregation to resolve the role of local forcing on larger-scale processes. To enable broader AMF3 involvement by the science community, the BER Environmental System Science (ESS) program organized the Southeast Land– Atmosphere Research Opportunities (SELARO) workshop in August 2023. The purpose was to identify gaps in scientific understanding of terrestrial processes in the Southeast (defined as states bounded by the Gulf of Mexico to the south, the Atlantic Ocean to east, the Mississippi River to the west, and extending through Tennessee and North Carolina to the north) and explore opportunities to use the AMF3-BNF deployment to coordinate and leverage research efforts across the region. Many parts of the Southeast have experienced repeated anthropogenic forcings. Farming, hunting, burning, and settlement of the region by Indigenous Peoples first shaped the distribution of plant communities, which in turn influenced European colonization patterns. Timber harvesting was common during the expansion of European settlements, and production forestry continues today. Agricultural production was extensive and then waned through the 20th century, creating a period of afforestation following agricultural abandonment. Today, many formerly agricultural landscapes are undergoing rapid urbanization and suburbanization. Overlying these patterns of anthropogenic land use are frequent disturbances from hurricanes, tornadoes, wildfires, drought, flooding, ice storms, and the occasional blizzard. An additional characteristic of the Southeast is its overall landscape complexity. Unlike the western United States, where broad expanses may share similar characteristics, Southeast topography, drainage patterns, vegetation, and development patterns vary widely across relatively small spatial scales (<1 km). This is due to the region’s underlying geology and soil development, species biodiversity patterns, and land ownership and use coupled with strong forces of erosion, weathering, and rapid plant growth in the warm, wet climate.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Robust Spectral Anomaly Detection in EELS Spectral Images via 3D Convolutional Variational Autoencoders

Abstract A 3D Convolutional Variational Autoencoder (3D‐CVAE) is introduced for automated anomaly detection in electron energy‐loss spectroscopy spectrum imaging (EELS‐SI) data. This approach leverages the full 3D structure of EELS‐SI data to detect subtle spectral anomalies while preserving both spatial and spectral correlations across the datacube. By employing cross‐entropy loss and training on bulk spectra, the model learns to reconstruct bulk features characteristic of the defect‐free material. In exploring methods for anomaly detection, both the 3D‐CVAE approach and principal component analysis (PCA) are evaluated, testing their performance using FeL‐edge ΔEpeak shifts designed to simulate material defects. These results show that 3D‐CVAE achieves superior anomaly detection and maintains consistent performance across various shift magnitudes. The method demonstrates clear bimodal separation between bulk and anomalous spectra, enabling reliable classification. Further analysis verifies that lower‐dimensional representations are robust to anomalies in the data. While performance advantages over PCA diminish with decreasing anomaly concentration, our method maintains high reconstruction quality even in challenging, noise‐dominated spectral regions. This approach provides a robust framework for unsupervised automated detection of spectral anomalies in EELS‐SI data, particularly valuable for analyzing complex material systems.

Chemistry↗

DOE OSTI2024

Machine Learning Classification Strategy to Improve Streamflow Estimates in Diverse River Basins in the Colorado River Basin

Streamflow in the Colorado River Basin (CRB) is significantly altered by human activities including land use/cover alterations, reservoir operation, irrigation, and water exports. Climate is also highly varied across the CRB which contains snowpack-dominated watersheds and arid, precipitation-dominated basins. Recently, machine learning methods have improved the generalizability and accuracy of streamflow models. Previous successes with LSTM modeling have primarily focused on unimpacted basins, and few studies have included human impacted systems in either regional or single-basin modeling. We demonstrate that the diverse hydrological behavior of river basins in the CRB are too difficult to model with a single, regional model. We propose a method to delineate catchments into categories based on the level of predictability, hydrological characteristics, and the level of human influence. Lastly, we model streamflow in each category with climate and anthropogenic proxy data sets and use feature importance methods to assess whether model performance improves with additional relevant data. Overall, land use cover data at a low temporal resolution was not sufficient to capture the irregular patterns of reservoir releases, demonstrating the importance of having high-resolution reservoir release data sets at a global scale. On the other hand, the classification approach reduced the complexity of the data and has the potential to improve streamflow forecasts in human-altered regions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Classification of Cloud Particle Imagery and Thermodynamics (COCPIT): A New Databasing Tool for the Characterization of Cloud Particle Images Captured During DOE Field Campaigns

The Department of Energy for decades has explored the earth system and atmosphere through research and deployment of in-situ and remote sensing platforms during field campaigns. Among these datasets exists a vast supply of cloud particle images that provide visual insight into the complex microphysics in the clouds that span our globe. The millions of images collected over decades of deployments provides a unique opportunity to further our understanding of our atmosphere down to the crystal size. This work over the past 5 years has sought to organize these images into digestible datasets that can then be used by scientists to further our understanding of microphysics. A machine learning model was developed that categorizes over 1.5 million images across 11 weather events with over 90% accuracy according to particle type. The database was then extended to include dimensional characteristics of the particle as well as co-location of environmental properties, such as temperature and water content. Then, to initialize the connection between these data and our understanding of how crystals form and grow, weather research and forecasting simulations were run to generate the growth histories of the classified crystals. This research culminates with 2 databases per event: (1) a database of all classified crystals and their dimensional and environmental properties and (2) simulated growth histories of each crystal. Finally, a user interface was created to allow researchers to explore data statistics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Growth Strategy of a Hybrid Yb3Rh4Sn13/LaRuSn3 Structure Type: Integrating Thermal Analysis, In Situ Diffraction, and Geometric Descriptors

Remeika phases form a versatile family of cage like intermetallics built from transition metal–centered trigonal prisms linked into three dimensional frameworks that generate two characteristic voids: a large [AX₁₂] cuboctahedral cage hosting the rare earth ion and an [XX′₁₂] icosahedral cage accommodating the tetrel atom. In this work, we present a tool driven approach for targeted solid state synthesis of Remeika phases, integrating differential scanning calorimetry, in situ neutron diffraction, and a geometric tolerance factor that quantifies rare earth size compatibility within these polyhedral cages. When combined with arc melting and metallic flux crystal growth, this framework enables prediction, verification, and isolation of specific Remeika phases. Applying this strategy, we obtain single crystals of the predicted pseudo-perovskite Fe based Remeika compound Yb0.9FeGe3.1. More broadly, this perspective highlights how coupling real time structural probes with simple geometric descriptors provides a general pathway for designing synthesis conditions and accessing potentially metastable structure types in complex intermetallic systems.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Investigating Spatial Variability of Aerosol, Cloud Condensation Nuclei, and Ice Nucleating Particles in Mountainous Terrain Field Campaign Report

The U.S. Department of Energy Atmospheric System Research (ASR)-supported Surface Atmosphere Integrated Field Laboratory (SAIL) campaign in the East River Watershed (ERW) of the Upper Colorado River Basin in southwestern Colorado ran from fall 2021 to spring 2023. Two monitoring sites were deployed in the ERW as part of SAIL. The two sites were the Aerosol Observation System (AOS) located on Crested Butte Ski Mountain, and the second ARM Mobile Facility (AMF2), located at the Rocky Mountain Biological Laboratory in Gothic, Colorado. To gain a more comprehensive understanding of aerosols in complex, mountainous terrain, Handix Scientific deployed SAIL-Net, a distributed network of six measurement nodes spanning the domain of the SAIL research area from October 2021 to July 2023. Each node measured aerosol particles between 140 nm and 3.4 μm in diameter using a small portable optical particle spectrometer (POPS; Gao et al. 2016), cloud condensation nuclei (CNN) using a miniature CCN counter (CloudPuck), and ice nucleating particles (INP) using the time-resolved aerosol filter sampler (TRAPS; Creamean et al. 2018). Our approach was similar to other studies that aimed to better characterize and understand aerosols and gas-phase pollutants using networks of lower-cost sensors (Caubel et al. 2019, Kelly et al. 2021, Asher et al. 2022). Such studies have identified neighborhood-level variations in pollutant concentrations (Schneider et al. 2017, Popoola et al. 2018, Caubel et al. 2019). Small-scale variations such as this are poorly represented in models and poorly measured by a single monitoring system (Caubel et al. 2019). Previous work has shown the representation error (the ability of measurements to represent a larger area) increases with complex orography, leading to decreases in model accuracy (Schutgens et al. 2017). The overall goal of SAIL-Net was to improve our understanding of the variability of aerosol in the ERW, thus increasing our knowledge of aerosol-cloud interactions in this region and informing the usefulness of distributed networks of measurements for future studies. We met this goal by answering the following science questions: 1. What is the aerosol temporal variability, and how does aerosol inhomogeneity vary seasonally? Is there significant seasonal variability in sources, or are short-term meteorological conditions the most important determining factor in sources for cloud nuclei? 2. What is the aerosol spatial variability? What are the aerosol characteristics at cloud base, presumably the particles most representative of those acting as cloud nuclei? 3. How should measurement networks be designed to capture aerosol-cloud interactions, and what do they need to measure? Can a single measurement site accurately represent aerosol properties in regions of complex terrain? SAIL-Net consisted of six measurement nodes spread across the ERW near Crested Butte, Colorado. The primary objective in site placement was to select locations that captured the vertical variation in aerosol properties while also spanning the domain of the SAIL campaign. The elevation of the sites ranged from roughly 2750 m along the valley floor of the ERW to approximately 3500 m near the top of Crested Butte Mountain, which is one of the taller peaks in the ERW. The farthest distance between sites was 14 km, while the closest two sites were approximately 1 km apart. Two of the sites were collocated with the ARM SAIL sites; our instruments sat on top of one of the trailers at AOS and another one of our sites was located in a meadow just above AMF2.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Laser Powder Directed Energy Deposition of Steels for Nuclear Applications

This comprehensive investigation examines the structure–property relationships in two nuclear alloy systems—Alloy 709 (A709) austenitic stainless steel and Grade 92 (G-92) ferritic/martensitic (F/M) steel—manufactured via directed energy deposition (DED) for sodium-cooled fast reactor applications. This study establishes the fundamental mechanisms for controlling microstructures for optimizing the mechanical performance of additively manufactured nuclear materials through systematic heat treatment optimization and multiscale characterization. As-deposited A709 steel develops a complex multiscale strengthening architecture consisting of a fine cellular solidification structure with diameter of 2-3 µm within10–50 µm grains, elevated dislocation densities from rapid thermal cycling, and grain boundary precipitates that activate concurrent Hall–Petch, dislocation, and precipitation hardening mechanisms to achieve exceptional properties [yield strength (YS): 603 MPa, ultimate tensile strength (UTS): 844 MPa, Vickers hardness: 220 HV] that achieve a 44% superior strength compared to that of the wrought material. Heat treatments produce different results. Solution annealing (SA) dissolves the cellular structure and reduces the hardness to 190 HV. Precipitation treatment (PT) keeps the cellular structure but adds carbides, allowing the hardness to reach 205 HV. The best approach combines both treatments (SA+PT) and creates uniform precipitate distributions with M 23 C 6 carbides at the grain boundaries and MX carbonitrides in the matrix, achieving a hardness of 195 HV. However, directional differences persist, with a 12%–15% strength variation between orientations due to the inherited layered microstructural architecture that survives aggressive heat treatment. While tensile testing at 550°C demonstrates 40%–50% thermal softening with dynamic strain aging, DED A709 steel still maintains a 71% higher YS than that of the wrought material. Ion irradiation studies (100–400 dpa) of DED A709 steel reveal progressive radiation damage with increasing void density and radiation-induced segregation causing nickel enrichment and chromium depletion, which will ultimately compromise mechanical properties. As-deposited G-92 exhibits exceptional strength (UTS: 1650–1700 MPa, 430 HV) through a complex microstructure containing both ferrite and martensite phases, a high geometrically necessary dislocation (GND) density (17.04×10 14 /m 2 ), and fine carbides. Heat treatments create distinct changes. Normalizing produces fresh martensite with the highest hardness (460 HV) and an increased GND density (20.23×10 14 /m 2 ). Tempering develops dual precipitation systems and reduces the hardness to 290 HV. The optimal approach uses sequential normalizing plus tempering, achieving balanced properties with the lowest hardness (250 HV) and a reduced GND density (11.01×10 14 /m 2 ). A processing-dependent anisotropy is observed: horizontal specimens achieve superior ductile behavior, while vertical specimens exhibit brittle failure. A tempering heat treatment successfully mitigates this anisotropic behavior by transforming the hard martensitic as-deposited structure into tempered martensite enabling both horizontal and vertical specimens to exhibit similar stress–strain characteristics with visible necking behavior. Remarkably, testing at 550°C reveals a reversal in the anisotropy, where as-deposited specimens achieve near isotropy with superior thermal stability (a 15%–20% strength reduction), while tempered specimens develop an orientation dependence with a 25%–30% strength reduction. Both alloy systems demonstrate that DED processing creates specimens with a superior strength through refined microstructural features, though with distinct strengthening mechanisms—austenitic through cellular structures and precipitates versus F/M through phase transformations and precipitates. Heat treatment optimization requires alloy-specific approaches, with A709 benefiting from controlled precipitation while G-92 requires careful phase transformation control. The results show that DED manufacturing can produce nuclear materials with exceptional performance, but directional effects and temperature-dependent behavior must be carefully considered for reactor component design and qualification.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Library of Advanced Materials for Engineering (LAMÉ) 5.20

Accurate and efficient constitutive modeling remains a cornerstone issue for solid mechanics analysis. Over the years, the LAMÉ advanced material model library has grown to address this challenge by implementing models capable of describing material systems spanning soft polymers to stiff ceramics including both isotropic and anisotropic responses. Inelastic behaviors including (visco)plasticity, damage, and fracture have all incorporated for use in various analyses. This multitude of options and flexibility, however, comes at the cost of many capabilities, features, and responses and the ensuing complexity in the resulting implementation. Therefore, to enhance confidence and enable the utilization of the LAMÉ library in application, this effort seeks to document and verify the various models in the LAMÉ library. Specifically, the broader strategy, organization, and interface of the library itself is first presented. The physical theory, numerical implementation, and user guide for a large set of models is then discussed. Importantly, a number of verification tests are performed with each model to not only have confidence in the model itself but also highlight some important response characteristics and features that may be of interest to end-users. Finally, in looking ahead to the future, approaches to add material models to this library and further expand the capabilities are presented.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Library of Advanced Materials for Engineering (LAMÉ) 5.22

Accurate and efficient constitutive modeling remains a cornerstone issue for solid mechanics analysis. Over the years, the LAMÉ advanced material model library has grown to address this challenge by implementing models capable of describing material systems spanning soft polymers to stiff ceramics including both isotropic and anisotropic responses. Inelastic behaviors including (visco)plasticity, damage, and fracture have all incorporated for use in various analyses. This multitude of options and flexibility, however, comes at the cost of many capabilities, features, and responses and the ensuing complexity in the resulting implementation. Therefore, to enhance confidence and enable the utilization of the LAMÉ library in application, this effort seeks to document and verify the various models in the LAMÉ library. Specifically, the broader strategy, organization, and interface of the library itself is first presented. The physical theory, numerical implementation, and user guide for a large set of models is then discussed. Importantly, a number of verification tests are performed with each model to not only have confidence in the model itself but also highlight some important response characteristics and features that may be of interest to end-users. Finally, in looking ahead to the future, approaches to add material models to this library and further expand the capabilities are presented.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Library of Advanced Materials for Engineering (LAMÉ) 5.28

Accurate and efficient constitutive modeling remains a cornerstone issue for solid mechanics analysis. Over the years, the LAMÉ advanced material model library has grown to address this challenge by implementing models capable of describing material systems spanning soft polymers to stiff ceramics including both isotropic and anisotropic responses. Inelastic behaviors including (visco)plasticity, damage, and fracture have all incorporated for use in various analyses. This multitude of options and flexibility, however, comes at the cost of many capabilities, features, and responses and the ensuing complexity in the resulting implementation. Therefore, to enhance confidence and enable the utilization of the LAMÉ library in application, this effort seeks to document and verify the various models in the LAMÉ library. Specifically, the broader strategy, organization, and interface of the library itself is first presented. The physical theory, numerical implementation, and user guide for a large set of models is then discussed. Importantly, a number of verification tests are performed with each model to not only have confidence in the model itself but also highlight some important response characteristics and features that may be of interest to end-users. Finally, in looking ahead to the future, approaches to add material models to this library and further expand the capabilities are presented.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Library of Advanced Materials for Engineering (LAMÉ) 5.30

Accurate and efficient constitutive modeling remains a cornerstone issue for solid mechanics analysis. Over the years, the LAMÉ advanced material model library has grown to address this challenge by implementing models capable of describing material systems spanning soft polymers to stiff ceramics including both isotropic and anisotropic responses. Inelastic behaviors including (visco)plasticity, damage, and fracture have all incorporated for use in various analyses. This multitude of options and flexibility, however, comes at the cost of many capabilities, features, and responses and the ensuing complexity in the resulting implementation. Therefore, to enhance confidence and enable the utilization of the LAMÉ library in application, this effort seeks to document and verify the various models in the LAMÉ library. Specifically, the broader strategy, organization, and interface of the library itself is first presented. The physical theory, numerical implementation, and user guide for a large set of models is then discussed. Importantly, a number of verification tests are performed with each model to not only have confidence in the model itself but also highlight some important response characteristics and features that may be of interest to end-users. Finally, in looking ahead to the future, approaches to add material models to this library and further expand the capabilities are presented.

36 MATERIALS SCIENCE↗