Elucidating the Influence of Side-Chain Circular Distribution on the Crack Onset Strain and Hole Mobility of Near-Amorphous Indacenodithiophene Copolymers
Abstract not provided
Sommerville, Parker J.W.↗
SEARCH · Engineering Papers
Results for “properties and materials”
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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Property optimized energy absorber for automotive bumpers utilizing multi-material and structural design strategies
This study proposes a novel design for automotive bumper using optimized lattice structures and multi-materials to balance low-speed collision and high-speed pedestrian impact performance. Different blends of 20 % carbon fiber-reinforced acrylonitrile butadiene styrene with thermoplastic polyurethane were used to tailor material properties. The energy absorber features lattice structures with customized mechanical responses, created by varying the incline angle θ from 0 to 180°. We conducted 576 finite element simulations on a half-scale model to optimize energy absorption and stiffness, leading to 66 optimized designs that met both low-speed and high-speed impact criteria. Two sub-scale optimized energy absorbers with different peak forces—both meeting low-speed impact requirements—were 3D printed and validated through drop-weight testing. The one with lower peak stress demonstrated a more compliant response, exhibiting approximately 90 % lower initial peak force and an increase in energy absorption of around 33 % (from 24 J to 32 J). Compared to the baseline triangular lattice, the optimized absorber increased energy absorption by 68 % from (19 J to 32 J) and reduced peak stress by 70 %. It also showed near-complete recovery with minimal fractures, making it suitable for repeated use. This design improves safety while offering a lightweight, durable, and cost-effective bumper system.
36 MATERIALS SCIENCE↗
Compute in‐Memory with Non‐Volatile Elements for Neural Networks: A Review from a Co‐Design Perspective
Abstract Deep learning has become ubiquitous, touching daily lives across the globe. Today, traditional computer architectures are stressed to their limits in efficiently executing the growing complexity of data and models. Compute‐in‐memory (CIM) can potentially play an important role in developing efficient hardware solutions that reduce data movement from compute‐unit to memory, known as the von Neumann bottleneck. At its heart is a cross‐bar architecture with nodal non‐volatile‐memory elements that performs an analog multiply‐and‐accumulate operation, enabling the matrix‐vector‐multiplications repeatedly used in all neural network workloads. The memory materials can significantly influence final system‐level characteristics and chip performance, including speed, power, and classification accuracy. With an over‐arching co‐design viewpoint, this review assesses the use of cross‐bar based CIM for neural networks, connecting the material properties and the associated design constraints and demands to application, architecture, and performance. Both digital and analog memory are considered, assessing the status for training and inference, and providing metrics for the collective set of properties non‐volatile memory materials will need to demonstrate for a successful CIM technology.
36 MATERIALS SCIENCE↗
An asynchronous parallel high-throughput model calibration framework for crystal plasticity finite element constitutive models
Crystal plasticity finite element model (CPFEM) is a powerful numerical simulation in the integrated computational materials engineering toolboxes that relates microstructures to homogenized materials properties and establishes the structure–property linkages in computational materials science. However, to establish the predictive capability, one needs to calibrate the underlying constitutive model, verify the solution and validate the model prediction against experimental data. Bayesian optimization (BO) has stood out as a gradient-free efficient global optimization algorithm that is capable of calibrating constitutive models for CPFEM. Here in this paper, we apply a recently developed asynchronous parallel constrained BO algorithm to calibrate phenomenological constitutive models for stainless steel 304 L, Tantalum, and Cantor high-entropy alloy.
304L stainless steel↗
High-Throughput Microstructural Characterization and Process Correlation Using Automated Electron Backscatter Diffraction
The need to optimize the processing conditions of additively manufactured (AM) metals and alloys has driven advances in throughput capabilities for material property measurements such as tensile strength or hardness. High-throughput (HT) characterization of AM metal microstructure has fallen significantly behind the pace of property measurements due to intrinsic bottlenecks associated with the artisan and labor-intensive preparation methods required to produce highly polished surfaces. This inequality in data throughput has led to a reliance on heuristics to connect process to structure or structure to properties for AM structural materials. In this study, we show a transformative approach to achieve laser powder bed fusion (LPBF) printing, HT preparation using dry electropolishing and HT electron backscatter diffraction (EBSD). This approach was used to construct a library of > 600 experimental EBSD sample sets spanning a diverse range of LPBF process conditions for AM Kovar. This vast library is far more expansive in parameter space than most state-of-the-art studies, yet it required only approximately 10 labor hours to acquire. Build geometries, surface preparation methods, and microscopy details, as well as the entire library of >600 EBSD data sets over the two sample design versions, have been shared with intent for the materials community to leverage the data and further advance the approach. Using this library, we investigated process–structure relationships and uncovered an unexpected, strong dependence of microstructure on location within the build, when varied, using otherwise identical laser parameters.
Characterization and Analytical Technique↗
Estimating viscoelastic compliance of desiccating cementitious materials using drying prism tests
Highlights: • Determining relative humidity and degree of reaction of desiccating cementitious materials via an integrated method • Extracting moisture diffusion coefficient of cement mortar using the mass loss data of the prism specimens • Estimating uniaxial viscoelastic compliance of cement mortar using the shrinkage strain data of the prism specimens An integrated modeling approach to determining internal relative humidity and degree of reaction of desiccating cementitious materials has been developed. By simultaneously accounting for both self-desiccation and external drying, the model is capable of predicting the evolution of material properties of desiccating cementitious materials. Using the mass loss data of the prism specimens, the moisture diffusion coefficient of a nonlinear model for cement mortar was determined. Additionally, a previous poroviscoelastic shrinkage model was modified and extended. Through comparison of predicted and measured axial strain of the prism specimens, the viscoelastic compliance of cement mortar was extracted. The present work revealed that it is possible to use a simple drying prism test to fit the moisture diffusion coefficient and creep compliance of cementitious materials, though the comparison of the extracted viscoelastic compliance with measured compliance was only reasonable if the outer layer of the drying mortar was presumed to have microcracking.
36 MATERIALS SCIENCE↗
Evaluating generalized feature importance via performance assessment of machine learning models for predicting elastic properties of materials
Not Available
Banik, Suvo (ORCID:0000000172398853)↗
Phase field modeling of hydrogen release in nitrile-butadiene rubber composites after high-pressure hydrogen exposure
Experimental data showed that a simple diffusion model doesn’t suffice to explain the hydrogen (H 2 ) release kinetics in nitrile butadiene rubber (NBR) composites (one with no filler or plasticizer, one with plasticizer only, one with fillers and plasticizer, and one with fillers only) after exposed to hydrogen gas at a pressure of 27.6 MPa. In this work, we developed a phase field model that considers the effect of H 2 gas bubble evolution on H 2 release kinetics. With the model, we simulated the effect of thermodynamic and kinetics properties on the nucleation, growth, and shrinkage of gas bubbles and H 2 release kinetics during and after decompression. Here, the results demonstrated that 1) the model built upon certain thermodynamic and kinetics properties of a given material can well describe its H 2 release kinetics measured in experiments, and 2) the model can predict the gas bubble evolution, which is associated with the material property degradation and failures, with accurate thermodynamic and kinetics properties or H 2 release data from experiments.
08 HYDROGEN↗
The effect of loading direction on the fracture behaviors of cortical bone at a dynamic loading rate
Here, the dynamic cracking processes in porcine cortical bone were visualized in real-time using the high-speed synchrotron X-ray phase-contrast imaging (PCI) technique in three osteon orientations: in-plane transverse, out-of-plane transverse and in-plane longitudinal. The dynamic flexural loading applied on the pre-notched bone specimens was introduced by a modified Kolsky compression bar. High-speed X-ray images of the entire loading events were documented with a high-speed camera. Three-dimensional X-ray micro-computed tomography was conducted to examine the intact microstructures and obtain the basic material properties of the bone material used for mechanical characterizations. The onset location, where crack initiated, and the subsequent direction, along which the incipient crack propagated, were measured quantitatively using the high-speed X-ray images and the latter was found dependent on the osteon direction significantly. The crack propagation velocities were dependent on crack extension over the entire crack path significantly for all the three directions while the initial velocity for in-plane longitudinal direction was lower than the other two directions. Straight-through crack paths were observed for in-plane longitudinal specimens while the cracks were deflected and twisted in the in-plane transverse direction. For out-of-plane transverse direction, the cracks follow paths with tortuosity fall in between the other two directions, showing a mixed mode of fractures of the former two extreme cases. The toughening mechanisms, visualized by the high-speed X-ray images, and the corresponding fracture toughness, evaluated in terms of fracture initiation toughness and crack growth resistance curve (R-curve), were also found significantly different among the three osteon directions, suggesting an overall transition from brittle to ductile-like fracture behaviors at the dynamic displacement rate (5.4 m/s) as the osteon orientation varies from in-plane longitudinal to out-of-plane transverse, and to in-plane transverse eventually.
42 ENGINEERING↗
Modulation of apparent optical properties using arrayed mesoscale structures
In this study, a method for using arrays of mesoscale structures to modify the apparent optical properties of an opaque composite surface has been theoretically demonstrated to both raise and lower the apparent emissivity as compared to the intrinsic properties of the constitutive materials. For design problems where thermomechanical and optical material properties are both of importance, mesoscale surface structuring can greatly expand the design space. Analysis via the net radiosity method herein illustrates the ability to achieve a wide range of spectral apparent optical properties. Notably, a hexagonal array of spheres on a planar surface can raise the apparent emissivity of a planar surface by 50%. Conversely, a hexagonal enclosure of reradiating surfaces, realized by thin adiabatic walls, can reduce the apparent emissivity of a blackbody by half. As this method of modifying apparent optical properties utilizes structures much larger than the wavelengths of interest, the relationship between intrinsic planar emissivity, geometry, and apparent emissivity can be computed semi-analytically at low computational expense. Passive solar cooling, thermophotovoltaic cells, aerodynamic surfaces exposed to intense heating, and solar absorbers are presented as case studies that could benefit from the use of mesoscale structures on opaque surfaces to modify the apparent optical properties.
42 ENGINEERING↗
Dataset of tensorial optical and transport properties of materials from the Wannier function method
Not provided.
Science & Technology - Other Topics↗
Understanding dynamic properties of materials using neutron spectroscopy and atomistic simulation
Recent developments in first-principles lattice dynamics and classical force field based molecular dynamics are revolutionising the field of neutron spectroscopy. Herein we present a short review of these methods, their critical role in the supporting of cutting-edge experiments, and how they are improved by matching experimental data. We begin with a brief overview of how lattice dynamics calculations can be compared to inelastic neutron scattering (INS) and molecular dynamics simulations to both INS and quasi-elastic neutron scattering (QENS). We then provide a series of exemplar applications where lattice dynamics and molecular dynamics have been used in conjunction with neutron spectroscopy to bring significant understanding to topical areas of materials science namely: (i) lattice dynamics and INS for the study of hybrid organic-inorganic perovskites (ii) lattice dynamics and INS for the study of flexible porous solids and (iii) molecular dynamics and QENS for probing molecular behaviour in zeolite catalysis. In all three cases, the understanding gained through the synergy of experiment and computation would have been significantly reduced using either in isolation. Finally, we consider the current state of the art, describing outstanding challenges and suggesting future directions in this exciting and fertile area of physical science.
36 MATERIALS SCIENCE↗
AL-ASMR: Active Learning of Atomistic Surrogate Models for Rare Events
Atomistic simulation with artificial intelligence (AI) is an emerging tool for understanding materials' properties and behaviors and predicting novel materials with optimized/targeted properties. Neural network potentials (NNPs) are outstanding in this field as they have shown a comparable accuracy to ab initio electronic structure calculations for reproducing potential energy surfaces while being several orders of magnitude faster. However, such NNPs can perform poorly outside of their training domain and typically fail catastrophically in the prediction of rare events in molecular dynamics (MD) simulations. For effective AL loops to distinguish the informative data from enhanced sampled configurations, we developed a decision engine by configurational similarity and uncertainty quantification (UQ) with data augmentation.
Jung, Gang Seob↗
Progress Report on the Assessment of the Material Performance for TCR Applications
The objective of Argonne’s materials research activities for the Transformational Challenge Reactor (TCR) program is to improve the understanding of material properties from additive manufacturing with the focus on understanding the creep and fatigue properties of additively manufactured materials. The material work conducted by Argonne provides support in developing and qualifying advanced materials and manufacturing processes to allow for innovative reactor design and licensing for the TCR. In this report, we present the creep data at temperatures of 550, 600 and 650°C and stresses between 175 and 300 MPa for 316L stainless steel produced by a laser powder bed fusion process. We conducted five different post-build heat treatments, namely 650°C/1h, 750°C/1h, 800°C/1h, 900°C/1h and 1050°C/1h. Creep tests of the heat-treated specimens were performed under the same test condition, 550°C/275 MPa to evaluate and understand the effect of post-build heat treatment on the creep behavior of additively-manufactured 316L SS. Creep tests were conducted to ASME NQA-1 or its lab equivalent for quality assurance.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Tomographic imaging of atmospheric pressure plasma on complex surfaces
Many plasma types and behaviors such as streamer, arcs, cathode spots, anode spots, ionization waves, and magnetic field interactions create non-symmetric, fully 3D plasma structures. The plasma distribution in 3D space is heavily influenced by complex surfaces and the coupling interactions between plasma properties and the interfacing material properties. For example, ionization waves propagate in directions where ionization rates are highest, leading to complex configurations that are not fully understood or well characterized. Recent advances in laser diagnostics and models have been able to investigate well-controlled idealized plasmas in 2D fashion, but the complex structure in actual plasmas requires a technique than can provide a more complete 3D picture. However, 3D plasma diagnostics do not currently exist. To address this limitation, this activity will leverage available equipment to build a new tomographic optical imaging capability and advance the state-of-the-art in plasma diagnostics to investigate 3D phenomena on complex surfaces.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Multi-fidelity equations of state and transport coefficient datasets for pulsed-power applications
Reliably simulating experiments relevant to the National Nuclear Security Administration (NNSA) requires a detailed description of material properties across a wide range of conditions. Such properties include the equations of state, charged-particle transport coefficients, and optical properties like the opacity. Together, these properties make up the material models used in radiation-magnetohydrodynamic simulations of nuclear fusion experiments. Many of these models do not incorporate uncertainties in the data used to produce them. It is unknown whether these uncertainties significantly impact the interpretation of simulation results and diagnostics. The purpose of this work is to quantify how such uncertainties impact simulations of pulsed-power experiments. We accomplished this task by first assessing discrepancies between approaches used to generate the data. This included bringing together members of the high-energy-density community spanning the three NNSA laboratories and multiple universities. Then, using these data, we developed a general framework that systematically incorporates physical uncertainties within the material models suitable for uncertainty quantification analyses. The framework utilizes machine learning, Bayesian inference, and incorporates multi-fidelity datasets. We demonstrated the framework by quantifying the impact that material model uncertainties have on simulations of pulsed-power experiments underway on Z at Sandia National Laboratories. As a result of this work, we discovered that modest uncertainties in material models (roughly 20%) correspond to significant uncertainties in the outputs from simulations. Our framework has enabled rapid construction of material models through an automated procedure and allows for the generation of material models of interest to the NNSA.
36 MATERIALS SCIENCE↗
3013 Baseline Inner Container Report: Description, Imaging, and Analysis
This report presents consolidated findings from three microscopy studies of baseline Bagless Transfer Containers (BTCs), which are the inner containers used in DOE Standard 3013-compliant plutonium storage packages. The studies were conducted by Los Alamos National Laboratory (LANL), Savannah River National Laboratory (SRNL), and DNV, a subcontractor specializing in corrosion science. The baseline BTCs examined had not been exposed to corrosive environments, serving as control samples for comparison with destructively examined (DE) containers. Additionally, the Background section provides an overview of the BTC design, including manufacturing methods, material properties, and container configurations that may influence corrosion behavior. The primary objective of the LANL and SRNL studies was to establish reference characteristics of BTCs to distinguish manufacturing artifacts from corrosion-induced features observed in DE containers. Both laboratories used advanced microscopy techniques, including Wide Area Microscopy (WAMS), Laser Confocal Microscopy (LCM), Scanning Electron Microscopy (SEM), and Focused Ion Beam (FIB), to reveal that most surface anomalies were shallow and attributable to flow-forming processes, not corrosion. Subsurface impurities, identified as non-metallic inclusions, were also observed. DNV’s investigation focused on characterizing the mechanical and chemical properties of the BTC material to support crack growth modeling. Their findings indicate higher hardness and strength but reduced ductility—consistent with flow-formed 304L stainless steel and a higher-than-expected martensite content, in contrast to manufacturer claims. Ongoing and future studies, including Electron Backscatter Diffraction (EBSD), aim to further understand the microstructural factors influencing crack initiation and propagation. The combined studies provide a crucial baseline for distinguishing corrosion effects in DE containers, contributing to the safe and reliable long-term storage of plutonium-bearing materials.
36 MATERIALS SCIENCE↗
Multi-Scale Modeling of the Evolution of Structure and Properties in Materials for Nuclear Energy Applications [Slides]
Nuclear energy is an important component of an overall strategy to address climate change. Idaho National Laboratory (INL) is the U.S. Department of Energy’s primary facility for research and development in nuclear science and technology for energy generation, supporting the improvement and life extension of the existing reactor fleet and the development and licensing of new reactor designs. Computational modeling is an important component of these activities, particularly in the area of materials for nuclear applications, where experimental data can be very challenging and expensive to acquire, and where data is especially scarce for new reactor designs. INL has used multi-scale modeling – linking atomistic, mesoscale, and engineering scales – to improve the ability to predict the performance of materials for nuclear energy applications. In this talk, I will give an overview of the approach and tools used, and several examples of application, including performance of nuclear fuels, understanding radiation-driven formation of nanoscale void and gas bubble superlattices, and powder densification through electric field assisted sintering.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗