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Presentation to be given at the FY 2020 SDRD Annual Meeting, to be held Sept. 23-24, on WebEx. Strategic Studies in Dynamic Material Response of Weapons-Relevant Materials, LAO-015-18, Year 3
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In this LDRD “Materials response under hypersonic flow conditions probed by multi-modal diagnostics in a benchtop wind tunnel” a peer-reviewed lab-scale hypersonic wind tunnel (WT) was planned to be assembled in-house, along with time and space resolved and related flow and materials diagnostics to derive high quality measurements and the science that is able to inform the development of our modeling and simulation codes for the hypersonic regime. The baseline capability to achieve Mach 3 flow was scheduled to be completed in Q4 2020, in the first year of the LDRD. However due to the COVID-19 delays and travel restrictions starting in March, 2020 this task could not be completed to carry out the materials response studies in-house, or those planned with external partners that were engaged during this LDRD (NASA Langley, NASA Ames, Texas A&M university, and Virginia Tech). Still, using our hypersonic computational fluid simulation codes, many of the hypersonic components have been designed (for Mach 3, 5, and 7) and procured (or in procurement phase), and a new dedicated hypersonic laboratory was facilitized, 2 WT test sections have been manufactured, and a Mach 3 nozzle was 3D printed for prototyping. In parallel, unique materials diagnostics have been tested under static conditions and fully specified and awaiting validation in our partner facilities. Due to the new hypersonic initiative announced in January, 2020 we proposed a related scope increase in hypersonics research requiring energy interactions and new diagnostics well beyond what could be supported by an LDRD ER. A decision was made to end the current LDRD ER and formally propose a new LDRD SI that leverages the current effort, which will be starting in FY21. As a result of the uniqueness of this LDRD work, our early efforts have been encouraged and well received in the hypersonic research community, and is now part of multiple unsolicited internal and external project proposals expected to be funded in the coming years, which is encouraging to make LLNL a leader in experimentally probing extreme physics of local energetics critical to hypersonics.
This paper presents an uncertainty quantification (UQ) framework for the physics-based model prediction of material response with a large number of parameters. The application problem presented in this work is that of predicting creep in Grade 91 steel at 600°C. The material response is defined with a physically based microstructural model with constitutive equations emulating several observed phenomena in Grade 91 and embodied into an explicit geometry mesoscale finite element model for prior austenite grains and grain boundaries. Creep within the grains and in grain boundaries are represented by crystal plasticity for dislocation motion and a physics-based model for cavity growth and nucleation, respectively. The creep behavior of this material is influenced by several parameters, some of which have a wide range of variation based on experimental data. UQ combined with microstructural modeling can discover the core microstructural causes of experimental variability, leading to improved materials with lower variability in critical long-term material properties. In this study, we investigate the model's uncertainty to identify material properties that may be modified during production to increase creep life and analyze different components of the crystal plasticity model for improvements. For this purpose, a quantity of interest is defined as time to minimum creep rate, which correlates well to the creep failure of the material. A deep neural network model was trained and validated to be used as a surrogate for the finite element model. Then, a variance-based sensitivity analysis is performed on the surrogate model to find the Sobol indices of the input parameters in respect to the output quantity of interest. The Sobol indices are used to reduce the dimensionality of the model. Generalized polynomial chaos expansion is used on the reduced basis models to propagate the uncertainty from the input parameters to the quantity of interest using the deep neural network surrogate model. These results are benchmarked against uncertainty propagation using Monte Carlo simulations. In conclusion, the UQ performed through the reduced basis model captures almost all the uncertainty in the model with significantly fewer simulations, making it possible to perform the UQ directly via simulations with the finite element model rather than surrogate machine-learned models.
Manipulating polymer interfaces is crucial for understanding how structure influences function in applications spanning biofouling prevention to energy storage. Moreover, observing how polymers adapt their microscopic structure to changes in their local environment can reveal essential properties that govern their performance in such applications, providing key insights into how to design more effective interfaces. Here, in this study, a series of “grafting-from” polymer brushes with side chains varying in charge, are probed by sum frequency generation (SFG) and modeled using all-atom molecular dynamics (MD) simulations to elucidate how chemical makeup and charge mediate interfacial restructuring in dry versus hydrated states. Results show that charge, in progressing from nonpolar to cationic to zwitterionic, results in dramatic changes in interfacial structure and overall hydration. While net neutral systems, regardless of bulk phase polarity, show minimal interfacial water structuring, the cationic species exhibits strong bulk water signals from the surface potential. Meanwhile, the polymer brushes themselves restructure in water differently independent of charge, impacting the functional groups that are presented to the aqueous phase. Nonpolar and cationic species for instance undergo a change in alkyl group orientations to accommodate hydrating water molecules, whereas the zwitterionic polymer becomes completely disordered in water. Overall, the structure-based behavior trends presented herein have implications in antifouling applications and responsive material interfaces.
Neural operators, which can act as implicit solution operators of hidden governing equations, have recently become popular tools for learning the responses of complex real-world physical systems. Nevertheless, most neural operator applications have thus far been data-driven and neglect the intrinsic preservation of fundamental physical laws in data. Here, in this work, we introduce a novel integral neural operator architecture called the Peridynamic Neural Operator (PNO) that learns a nonlocal constitutive law from data. This neural operator provides a forward model in the form of state-based peridynamics, with objectivity and momentum balance laws automatically guaranteed. As applications, we demonstrate the expressivity and efficacy of our model in learning complex material behaviors from both synthetic and experimental data sets. We also compare the performances with baseline models that use predefined constitutive laws. We show that, owing to its ability to capture complex responses, our learned neural operator achieves improved accuracy and efficiency. Moreover, by preserving the essential physical laws within the neural network architecture, the PNO is robust in treating noisy data. The method shows generalizability to different domain configurations, external loadings, and discretizations.
Sandia Materials Science Investment Area contributed to the SARS-CoV-2 virus and COVID-19 disease which represent the most significant pandemic threat in over 100 years. We completed a series of 7, short duration projects to provide innovative materials science research and development in analytical techniques to aid the neutralization of COVID-19 on multiple surfaces, approaches to rapidly decontaminate personal protective equipment, and pareto assessment of construction materials for manufacturing personal protective equipment. The developed capabilities and processes through this research can help US medical personnel, government installations and assets, first responders, state and local governments, and multiple federal agencies address the COVID-19 Pandemic.
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Finite element solutions are only valid if the information inputted into the models is a valid assumption. This is why it is important to determine if the model behaves as expected especially in the material response. Material models that consider deformation in the elastic and plastic regions are desirable. As a material deforms plastically, hardening will occur until the ultimate strength is reached due to the reduction of the cross sectional area in a uniaxial tensile test environment. A phenomenological approximation of this behavior which is known as power law hardening (plh) can be expressed as σ = σ y + K $ϵ^{n}_{p}$ where K and n are empirically determined constants, and σ y is the proportional limit of the material. The first objective of this study is to verify the behavior for a calibrated plh material model for selective deviatoric hexahedral elements when applied to a different material stock. The Sierra SM input for this calibrated model can be found in the appendix. The second objective of this study is to determine the differences in failure characteristics for considerably large shell elements versus that of fine hexahedral elements.
Abstract not provided
Stimuli-responsive changes in lanthanide-based materials are a promising research direction. In this study, [DBTTF] 4 [Ce 2 Cl 10 ] DBTTF = dibenzotetrathiafulvalene (1) was synthesized by a light-induced crystallization, where photo-oxidation of DBTTF enables formation of the cerium dimer [Ce 2 Cl 10 ] 4− . Intermolecular interactions between the stacked organic units of the crystal result in charge transfer bands in the visible-NIR (near-infrared) region, evident in the solid-state absorption spectrum upon comparison with the solution spectrum. The assignments of the sublattice oxidation states were made with single-crystal X-ray diffraction (SC-XRD) structural characterization, Raman spectroscopy, X-ray absorption spectroscopy, and magnetometry. Continuous 532 nm laser irradiation of the microcrystalline solid modulates the redox states in 1, leading to ∼40% reduction in the observed magnetization at 2 K. Density functional theory PBE+U/HSE06 band structure calculations predict Mott insulating behavior in 1, with a bandgap of 0.54/0.81 eV, and further support the conjecture that the observed photo-induced change in magnetization results from electron transfer from the [Ce 2 Cl 10 ] 4− anions to the π-stacked [DBTTF] 2 2+ organic dimer subunits. An enhancement in conductivity is similarly observed upon 532 nm irradiation, determined by single-crystal transport measurements. The findings reveal that photo-responsive lanthanide-based materials can be achieved by integration of redox-active organic moieties with redox-active lanthanide cations for the realization of switchable, photo-magnetic materials.
Neither of us can remember people talking about stimuli-responsive polymers when we were students. Even though this was still in the last century, it was almost 70 years after Staudinger's seminal paper "uber Polymerisation"![1] By the time we entered graduate school in the early 1990 s, the first papers on polymer light emitting diodes appeared, while research on electrically conducting, nonlinear optical, and other "functional" polymers was already in full swing. Looking back, there were some stimuli-responsive materials that were around at the time, but it took the better part of the last 30 years until the field had developed into what we think is one of the most vibrant areas of polymer science. Before the potential usefulness of this class of materials was recognized, researchers were focused on developing polymeric materials that would not respond to external influences, and in fact maintain (in particular) their mechanical properties in a wide range of environments. This has led to the development of many environmentally robust polymers, which have come to impact basically every aspect of our daily life. Ironically, this long-sought stability now comes back to haunt us in the form of plastic pollution, but this is another story. The concept of materials that adapt their properties in response to changing environmental conditions might appear like a complete reversal with respect to the original goals of creating stable materials. However, eventually a) it was recognized that materials that respond to a specific stimulus in a predictable and useful manner would significantly broaden the potential usefulness of polymers, and b) the field of polymer science had developed to the point where the rational design, preparation, and characterization of such materials became possible. The expression 'stimuli-responsive polymer' had been first used in the 1980's, however its use really only gained popularity a few decades later. In the early days, terms such as environmentally-sensitive or environmentally-responsive polymers, stimuli-reversible or stimuli-sensitive polymers, or polymers with phase transitions have also been used.
A method of turning a catalytic material by altering the charge state of a catalyst support. The catalyst support is intercalated with a metal ion, altering the charge state to alter and/or augment the catalytic activity of the catalyst material.
In one aspect the present disclosure relates to a system for forming a three dimensional (3D) object from a volume of photo-responsive material contained within a material container. A plurality of optical projection subsystems are adapted to be arranged circumferentially around a material container containing the volume of photo-responsive material, and are controlled by a controller. The optical projection subsystems direct optical projections at a plurality of angles θ through the volume of photo-responsive material. The optical projections are further directed about a z axis extending through the volume of photo-responsive material. The plurality of optical subsystems are further controlled by the controller to provide each of the optical projections with a calculated three-dimensional intensity distribution acting over a fixed temporal exposure period, which is sufficient to at least one of cure or remove selected portions of the volume of photo-responsive material, and leave other portions unmodified, to form a desired 3D part.
In one aspect the present disclosure relates to a system for forming a three dimensional (3D) object from a volume of photo-responsive material contained within a material container. A plurality of optical projection subsystems are adapted to be arranged at least partially circumferentially around a material container containing the volume of photo-responsive material, and are controlled by a controller. The optical projection subsystems direct optical projections at a plurality of angles θ through the volume of photo-responsive material. The optical projections are further directed about a z axis extending through the volume of photo-responsive material. The plurality of optical subsystems are further controlled by the controller to provide each of the optical projections with a calculated three-dimensional intensity distribution acting over a fixed temporal exposure period, which is sufficient to at least one of cure or remove selected portions of the volume of photo-responsive material, and leave other portions unmodified, to form a desired 3D part.
Mesoscale imperfections, such as pores and voids, can strongly modify the properties and the mechanical response of materials under extreme conditions. Tracking the material response and microstructure evolution during void collapse is crucial for understanding its performance. In particular, imperfections in the ablator materials, such as voids, can limit the efficiency of the fusion reaction and ultimately hinder ignition. To characterize how voids influence the response of materials during dynamic loading and seed hydrodynamic instabilities, we, in this paper, have developed a tailored fabrication procedure for designer targets with voids at specific locations. Our procedure uses SU-8 as a proxy for the ablator materials and hollow silica microspheres as a proxy for voids and pores. By using photolithography to design the targets’ geometry, we demonstrate precise and highly reproducible placement of a single void within the sample, which is key for a detailed understanding of its behavior under shock compression. This fabrication technique will benefit high-repetition rate experiments at x-ray and laser facilities. Insight from shock compression experiments will provide benchmarks for the next generation of microphysics modeling.
Abstract Understanding the physical and chemical response of materials to impulsive deformation is crucial for applications ranging from soft robotic locomotion to space exploration to seismology. However, investigating material properties at extreme strain rates remains challenging due to temporal and spatial resolution limitations. Combining high-strain-rate testing with mechanochemistry encodes the molecular-level deformation within the material itself, thus enabling the direct quantification of the material response. Here, we demonstrate a mechanophore-functionalized block copolymer that self-reports energy dissipation mechanisms, such as bond rupture and acoustic wave dissipation, in response to high-strain-rate impacts. A microprojectile accelerated towards the polymer permanently deforms the material at a shallow depth. At intersonic velocities, the polymer reports significant subsurface energy absorption due to shockwave attenuation, a mechanism traditionally considered negligible compared to plasticity and not well explored in polymers. The acoustic wave velocity of the material is directly recovered from the mechanochemically-activated subsurface volume recorded in the material, which is validated by simulations, theory, and acoustic measurements. This integration of mechanochemistry with microballistic testing enables characterization of high-strain-rate mechanical properties and elucidates important insights applicable to nanomaterials, particle-reinforced composites, and biocompatible polymers.