Peridynamic Model for Graphene Derived from Molecular Dynamics
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AIAA Scitech 2021 Forum
Aeroengines ingest foreign object debris such as sand, which eventually erode components through repeated impacts. Due to the wide feature space, modeling and simulations are needed to rapidly assess the erosion behavior of materials such as composites. Peridynamic simulations were performed to analyze erosion of SiC/SiC composite due to sand impacts, which gives direct insight into the impact erosion mechanism and amounts. The erosion data was strongly correlated to impact velocity and angle, providing predictive equations.
AIAA Scitech 2021 Forum
Environmental Barrier Coatings (EBC) protect ceramic matrix composites from exposure to high temperature moisture present in turbine operation through their dense top coats. However, moisture is able to diffuse and oxidize the Si bond coat to form the Thermally Grown Oxide (TGO), a layer of SiO2 where the incorporation of O causes swelling and stress. At sufficient TGO-based swelling, the EBC will fail due to increased damage such as delamination. A multiscale simulation framework has been developed to link operating conditions of a high-performance turbine to the failure modes of the EBC. Computational fluid dynamics (CFD) simulations of the E3 turbine were performed and compared to prior literature data to demonstrate the fidelity of the Loci/CHEM software to determine the flow conditions on the turbine blade surface. Boundary condition data of pressure and heat flux were then determined with the CFD simulations, providing the temperature at the bond coat. Peridynamics was used to model the microscale TGO growth. A swelling model that links moisture concentration to strain at the TGO due to the volume increase from oxidation was demonstrated, coupling moisture transport to localized strain and directly observing TGO growth and the corresponding damage. This framework is generalized and can be adapted to a range of EBC microstructures and operating conditions.
CEUR Workshop Proceedings
We show that machine learning can improve the accuracy of simulations of stress waves in one-dimensional composite materials. We propose a data-driven technique to learn nonlocal constitutive laws for stress wave propagation models. The method is an optimization-based technique in which the nonlocal kernel function is approximated via Bernstein polynomials. The kernel, including both its functional form and parameters, is derived so that when used in a nonlocal solver, it generates solutions that closely match high-fidelity data. The optimal kernel therefore acts as a homogenized nonlocal continuum model that accurately reproduces wave motion in a smaller-scale, more detailed model that can include multiple materials. We apply this technique to wave propagation within a heterogeneous bar with a periodic microstructure. Several one-dimensional numerical tests illustrate the accuracy of our algorithm. The optimal kernel is demonstrated to reproduce high-fidelity data for a composite material in applications that are substantially different from the problems used as training data.
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Journal of the Mechanics and Physics of Solids
This article concerns modeling unsaturated deformable porous media as an equivalent single-phase and single-force state peridynamic material through the effective force state. The balance equations of linear momentum and mass of unsaturated porous media are presented by defining relevant peridynamic states. The energy balance of unsaturated porous media is utilized to derive the effective force state for the solid skeleton that is an energy conjugate to the nonlocal deformation state of the solid, and the suction force state. Through an energy equivalence, a multiphase constitutive correspondence principle is built between classical unsaturated poromechanics and peridynamic unsaturated poromechanics. The multiphase correspondence principle provides a means to incorporate advanced constitutive models in classical unsaturated porous theory directly into unsaturated peridynamic poromechanics. Numerical simulations of localized failure in unsaturated porous media under different matric suctions are presented to demonstrate the feasibility of modeling the mechanical behavior of such three-phase materials as an equivalent single-phase peridynamic material through the effective force state concept.
We show that machine learning can improve the accuracy of simulations of stress waves in one-dimensional composite materials. We propose a data-driven technique to learn nonlocal constitutive laws for stress wave propagation models. The method is an optimization-based technique in which the nonlocal kernel function is approximated via Bernstein polynomials. The kernel, including both its functional form and parameters, is derived so that when used in a nonlocal solver, it generates solutions that closely match high-fidelity data. The optimal kernel therefore acts as a homogenized nonlocal continuum model that accurately reproduces wave motion in a smaller-scale, more detailed model that can include multiple materials. We apply this technique to wave propagation within a heterogeneous bar with a periodic microstructure. Several one-dimensional numerical tests illustrate the accuracy of our algorithm. The optimal kernel is demonstrated to reproduce high-fidelity data for a composite material in applications that are substantially different from the problems used as training data.
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A technique called the splice method for coupling local to peridynamic subregions of a body is described. The method relies on ghost nodes, whose values of displacement are interpolated from nearby physical nodes, to make each subregion visible to the other. In each time step, the nodes in each subregion treat the nodes in the other subregion as boundary conditions. Adaptively changing the subregions is possible through the creation and deletion of ghost nodes. Example problems in 2D and 3D illustrate how the method is used to perform multiscale modeling of fracture and impact events within a larger structure.
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The propagation of a wave pulse due to low-speed impact on a one-dimensional, heterogeneous bar is studied. Due to the dispersive character of the medium, the pulse attenuates as it propagates. This attenuation is studied over propagation distances that are much longer than the size of the microstructure. A homogenized peridynamic material model can be calibrated to reproduce the attenuation and spreading of the wave. The calibration consists of matching the dispersion curve for the heterogeneous material in the limit of long and moderately long wavelengths. It is demonstrated that the peridynamic method reproduces the attenuation of wave pulses predicted by an exact microstructural model over large propagation distances.
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The peridynamic theory of solid mechanics is applied to the continuum modeling of the impact of small, high-velocity silica spheres on multilayer graphene targets. The model treats the laminate as a brittle elastic membrane. The material model includes separate failure criteria for the initial rupture of the membrane and for propagating cracks. Material variability is incorporated by assigning random variations in elastic properties within Voronoi cells. The computational model is shown to reproduce the primary aspects of the response observed in experiments, including the growth of a family of radial cracks from the point of impact.
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This report outlines the fiscal year (FY) 2019 status of an ongoing multi-year effort to develop a general, microstructurally-aware, continuum-level model for representing the dynamic response of material with complex microstructures. This work has focused on accurately representing the response of both conventionally wrought processed and additively manufactured (AM) 304L stainless steel (SS) as a test case. Additive manufacturing, or 3D printing, is an emerging technology capable of enabling shortened design and certification cycles for stockpile components through rapid prototyping. However, there is not an understanding of how the complex and unique microstructures of AM materials affect their mechanical response at high strain rates. To achieve our project goal, an upscaling technique was developed to bridge the gap between the microstructural and continuum scales to represent AM microstructures on a Finite Element (FE) mesh. This process involves the simulations of the additive process using the Sandia developed kinetic Monte Carlo (KMC) code SPPARKS. These SPPARKS microstructures are characterized using clustering algorithms from machine learning and used to populate the quadrature points of a FE mesh. Additionally, a spall kinetic model (SKM) was developed to more accurately represent the dynamic failure of AM materials. Validation experiments were performed using both pulsed power machines and projectile launchers. These experiments have provided equation of state (EOS) and flow strength measurements of both wrought and AM 304L SS to above Mbar pressures. In some experiments, multi-point interferometry was used to quantify the variation is observed material response of the AM 304L SS. Analysis of these experiments is ongoing, but preliminary comparisons of our upscaling technique and SKM to experimental data were performed as a validation exercise. Moving forward, this project will advance and further validate our computational framework, using advanced theory and additional high-fidelity experiments. ACKNOWLEDGEMENTS The authors greatly appreciate the support of Mike Saavedra in machining the experimental samples. The authors would also like to thank the Dynamic Integrated Compression facility (DICE) staff for executing the Thor experiments: Brian Stoltzfus, Randy Hickman, Keith Hodge, Joshua Usher, Lena Pacheco, and Eric Breden. The authors would also like to thank the staff at the Shock Thermodynamics Applied Research (STAR) facility for executing the plate impact experiments: Scott Alexander, Bill Reinhart, Bernardo Farfan, Rocky Palomino, John Martinez, and Rafael Sanchez. Lastly, the authors would like to acknowledge the development support of Jason Sanchez in ALEGRA to incorporate our upscaling method and Michael Powell for helping with post processing scripts for results analysis.
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Under high-rate loading in tension, metals can sustain much larger tensile stresses for sub-microsecond time periods than would be possible under quasi-static conditions. This type of failure, known as spall, is not adequately reproduced by hydrocodes with commonly used failure models. The Spall Kinetics Model treats spall by incorporating a time scale into the process of failure. Under sufficiently strong tensile states of stress, damage accumulates over this time scale, which can be thought of as an incubation time. The time scale depends on the previous loading history of the material, reflecting possible damage by a shock wave. The model acts by modifying the hydrostatic pressure that is predicted by any equation of state and is therefore simple to implement. Examples illustrate the ability of the model to reproduce the spall stress and resulting release waves in plate impact experiments on stainless steel.
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