Evaluation of nano- and mesoscale structural features in composite materials through hierarchical decomposition of the radial distribution function
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The objective of this project was to reveal how we can design materials with a tunable mechanical behavior through interface engineering. To do so we focused on understanding slip transmission across internal surfaces and how it relates to the strength and chemistry of the internal surface. In doing so we followed an interdisciplinary approach that employed theory, experiments and molecular dynamic (MD) simulations. Our guideline was that interfaces are characterized by their own mechanically induced interface parameter. New dislocation-grain boundary interaction mechanisms were revealed and the strengthening effects of non-traditional interfaces. We began studying simple grain boundaries in Fe systems, and then moved on to newly explored systems such as graphene-metal and nanolayer composites.
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Outline: Introduction and motivation - Correlated electron materials; Theoretical approaches to strongly correlated materials - First-principles dynamical mean-field theory (DMFT) framework - Applications to f -electron correlation effects; Summary and outlook.
In this report we demonstrate some relatively simple and inexpensive methods to effectively account for various sources of epistemic lack-of-knowledge type uncertainty in inverse problems. The demonstration problem involves inverse estimation of six parameters of a bolted joint that attaches a kettlebell shaped object to a thick plate. The parameters are efficiently inverted in a modal-based model calibration using gradient-based optimization. Two material properties of the kettlebell are treated as uncertain to within given epistemic uncertainty bounds. We apply and test interval and sparse-sample probabilistic approaches to account for uncertainty in the estimated parameters (and various scalar functionals of the parameters as generic quantities of interest, QOIs) due to uncertainties in the material properties. We also investigate the error effects of limited numbers of vibration sensors (accelerometers) on the kettlebell and plate, and therefore abbreviated excitation/response information in the parameter inversions. We propose and demonstrate a Leave-K-Sensors-Out “cross-prediction” UQ approach to estimate related uncertainties on the parameters and QOI functionals. We indicate how uncertainties from material properties and limited sensors are treated in a combined manner. The economical combined UQ approach involves just three to five samples (i.e. three to five inverse simulations), with no added complication or error/uncertainty from use of surrogate models for affordability. Finally, we describe a related economical UQ approach for handling potential parameter solution non-uniqueness and numerical optimization related precision uncertainties in the estimated parameter values. Indicated further research is identified.
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.
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. These modeling efforts make extensive of MOOSE (Multiphysics Object-Oriented Simulation Environment), a general-purpose open source finite element framework developed at INL. 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.
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. These modeling efforts make extensive of MOOSE (Multiphysics Object-Oriented Simulation Environment), a general-purpose open source finite element framework developed at INL. 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.
Materials development is needed to support the design and deployment of advanced nuclear reactors that will operate at higher temperatures and in harsher environments than the current light water reactor fleet. High entropy alloys have been identified as a class of alloy that could address the needs of advanced reactors, such as the molten salt reactors, due to their potential strength, radiation resistance, and corrosion resistance. This project summarizes the work done in fiscal year 2024 for the Additive Manufacturing of High Entropy Alloys for Nuclear Applications Project. This work has focused on examination of potential alloy compositions, down selection based on simulated results, and preliminary testing of down selected alloys.
The Marine Energy Advanced Materials project is an ongoing multi-year, multi-lab project with the main goals of addressing barriers and uncertainties facing marine energy developers in adopting advanced materials for structural applications. NREL's goals of the project were to address subcomponents testing needs for marine energy materials, to improve understanding of design allowables at the full-scale and provide near net-scale static and fatigue data of composite subcomponents using materials applicable to the marine energy industry. In the long term, the test method development and data generated would be used to inform standards development. This report outlines perhaps one of the largest-scale studies conducted with regards to saltwater conditioning of various composite material subcomponents and their subsequent structural validation, specifically directed at the marine renewable energy industry. A variety of fiberglass composite panels with epoxy and vinyl ester epoxy resin systems were manufactured at Montana State University, which were then used to manufacture an array of different types of subcomponent test specimens at the National Renewable Energy Laboratory's Flatirons Campus. These subcomponents were in the form of T-bolt and double-ended-insert specimens, which were intended to represent bonded and mechanical bolted connections for thick composite laminates, metal-metal and composite lap shear specimens to evaluate adhesion of constituent materials, and adhesive beam-shear specimens as part of an effort to better evaluate the characteristics of thick adhesive bondlines. Overall, the materials used were fiberglass reinforced epoxy and vinyl ester matrix composites, epoxy and methacrylate adhesives, and 316 and 2507 stainless steels. Specimens were then conditioned in salt water at various temperatures and for various periods of time at Florida Atlantic University and Pacific Northwest National Laboratory. All specimens were then mechanically characterized and validated using various test methods under static and fatigue loading conditions at NREL's Structural Technology Laboratory. Throughout the conditioning and mechanical validation process, valuable experience was gained, which will help guide future test method development for marine energy materials. In many instances, the results indicated similar observations as to what had been observed during previous coupon scale characterization efforts that provided a vital understanding of the scale up process. However in some instances, unexpected phenomena were observed, such as interactions between the adhesives and 316 steel. Furthermore, some materials exhibited significant degradation due to the saltwater conditioning. Ultimately, this report provides a detailed summary of the specimens that were designed, the subcomponent test methods that were developed, and the results that were generated, which will serve as important guidance for marine renewable energy developers and researchers for future structural designs and validation.
The Marine Energy Advanced Materials project addresses the barriers and uncertainties facing marine renewable energy developers in using composite materials for load-bearing structures. Sponsored by the U.S. Department of Energy's Water Power Technologies Office, the multiyear project comprises of collaborators from the National Renewable Energy Laboratory (NREL), Sandia National Laboratories, Pacific Northwest National Laboratory (PNNL), Montana State University (MSU), Florida Atlantic University (FAU), and industry stakeholders. As part of the Marine Energy Advanced Materials project, marine renewable energy industry surveys and assessments were conducted to identify key materials and knowledge gaps that hinder the adoption of composite materials in marine renewable energy structures. Specific knowledge gaps highlighted for composite materials were environmental effects, fatigue strengths, and bonded and bolted interconnects (composite/composite and composite/metal). It was concluded that many of these gaps could be addressed through subcomponent validation; consequently, a program was developed at NREL with the goals of developing subcomponent validation methods for appropriate marine energy materials, which would improve the understanding of design allowables for full-scale structural components and joints. Ultimately, the aim is to reduce timelines and costs associated with full-scale structural validation efforts while also providing near-net-scale static and fatigue data of composite/metal subcomponents for marine renewable energy systems. To approach these goals, a testing program was developed at NREL to investigate a variety of materials and structural design details at the subcomponent scale to understand (a) the effects of harsh and corrosive marine energy environments and (b) the static and fatigue strengths of the complex geometries. The recent study from this testing program is perhaps the largest that has ever been conducted with respect to specimen scale and geographic diversity of underwater environmental conditions that the specimens were subjected to. A variety of specimen geometries were designed by NREL to highlight key features of multimaterial (composite/composite or metal/composite) interconnects that may be used in marine renewable energy structural designs. The designs used several different composite matrices, adhesives, and marine-grade steels, which were highlighted in the surveys as being the most appropriate for harsh marine environments. Composite panels were then manufactured at MSU, and were subsequently manufactured into test specimens by NREL. The specimens were shipped to FAU and PNNL for conditioning in ocean water tanks at various temperatures for an extended period. The specimens were then returned to NREL for structural validation. This presentation will provide an overview of recent advances within the testing program in terms of specimen design and test methods, and will discuss results and key findings of the subcomponent testing program to date at NREL.
This paper presents a predictive multiscale modeling scheme for Unidirectional (UD) Carbon Fiber Reinforced Polymers (CFRP). A bottom-up modeling procedure is discussed for predicting the performance of UD structures. UD material responses are computed from high fidelity Representative Volume Elements (RVEs). A data-driven Reduced Order Modeling approach compresses RVEs into Reduced Order Models so material responses can be computed in a concurrent fashion along with the structural level simulation. The approach presented in this paper is validated against experimental data and is believed to provide design guidance for future fiber reinforced polymers development.
Speckle patterns produced by coherent X-ray have a close relationship with the internal structure of materials but quantitative inversion of the relationship to determine structure from speckle patterns is challenging. Here, in this work, we investigate the link between coherent X-ray speckle patterns and sample structures using a model 2D disk system and explore the ability of machine learning to learn aspects of the relationship. Specifically, we train a deep neural network to classify the coherent X-ray speckle patterns according to the disk number density in the corresponding structure. It is demonstrated that the classification system is accurate for both non-disperse and disperse size distributions.
Ultra High Temperature Metal Matrix Composites (UHT-MMCs) are of interest to the due to their potential defense and aerospace applications. These materials consist of a metal matrix reinforced with a stiff ceramic or cermet. Based on a preliminary literature survey, this project will focused on a pure titanium (Ti) matrix reinforced with tungsten carbide-cobalt (WC-Co). UHT-MMC technology is still at a nascent stage of development and requires extensive experimental work to optimize the structure and resulting material properties of a particular UHT-MMC material system. An accurate and complete high-performance computing (HPC) model of the Ti/WC-Co material system could greatly accelerate the development and deployment Ti/WC-Co materials by connecting processing parameters to material properties. This type of model could then be used in an integrated computational materials engineering approach to optimize the material structure to achieve a targeted level of performance.
The low thermal conductivity of Phase Change Materials (PCM) can be improved with extended surfaces such as additively manufactured 3D periodic lattice structures. Three different aluminum alloy-based lattices (base sizes 10, 20, and 40 mm) with average porosity of 0.95 filled with paraffin wax, with a nominal phase change temperature of 55°C, were experimentally investigated. In this work, a computationally efficient 2D Resistance Capacitance-based model (RCM) was developed for predicting the thermal characteristics of these geometries. Non-uniform porosity in the PCM-metal domain was estimated using image processing and served as model input. The solver does not solve for higher-order physics as in CFD but still can provide a good prediction of thermal resistance and energy storage at a very low computational cost. The simulation-to-real-time factor for this geometry is of the order of 10-4, while CFD simulations typically have a real-time factor greater than 1. The model was validated against the experimental data for melting under three different heat fluxes (6250 W/m2, 12500 W/m2, and 18750 W/m2). The mean deviation of the predicted average PCM temperature was between 1.34 K-2.81 K for different cases. The maximum average temperature deviation of 5.45 K was observed for the 20 mm geometry at the highest heat flux test condition. The effects of natural convection were neglected in the model, but the predicted PCM temperature and energy storage still showed good agreement with the experimental data.
Over the last couple of decades, increasingly larger rotor diameters have enabled significant reductions in the levelized cost of energy for wind through higher energy capture and improved capacity factors. But for onshore turbines, this has been met with manufacturing and transportation challenges. To enable larger onshore rotors, an innovative inflatable blade concept is investigated that employs both traditional and onsite manufacturing. The design architecture consists primarily of an inflatable aerodynamic shell attached to a factory-manufactured primary structure. Multiple architectures, cross-sectional topologies, and material combinations for the primary structure are investigated, as well as multiple skin materials. Structural cross-sectional characteristics are compared to a traditional blade, and inflatability and cost evaluations are presented. Results show that a box-beam architecture using only composite materials yields significant mass reduction with mature technologies/processes and modest cost decrease compared to traditional blade construction. This low-TRL study demonstrates the inflatable blade concept's techno-economic potential.
Abstract Interfaces play a decisive role in perovskite solar cells’ power conversion efficiency and their long‐term durability. Small‐molecule hole‐transporting materials (HTMs) have grabbed enormous attention due to their structural flexibility, material properties, and stabilities, allowing for improved operational durability in perovskite photovoltaics. This study synthesizes and investigates a new class of benzimidazole‐based small molecules, named YJS001 and YJS003 , serving as the HTMs to enable high‐efficiency mixed‐cation mixed‐halide perovskite solar cells. The benzimidazole‐based materials are dopant‐free HTMs composed of donor and acceptor building blocks that are designed to engineer the energy level alignment near the HTM/perovskite interface. Mixed‐cation mixed‐halide perovskites can be grown uniformly on both HTMs with large crystalline grains. It is discovered that the donor‐rich YJS003 ‐based solar cell exhibits a high open‐circuit voltage of 1.09 V with a champion power conversion efficiency of over 20%. Power‐dependent current–voltage characteristics of the solar cells are analyzed, from which the high performance of YJS003's excellent hole mobility and well‐aligned energy level is attributed. This work introduces a new class of benzimidazole‐based small molecules as HTMs, that paves the path for dopant free interface material development for commercialization of perovskite solar cells.