Implementation of Predictive Joint Models in Finite Element Codes.
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Lightweight structural materials are required to increase the mobility of fission batteries. The materials must feature a robust combination of mechanical properties to demonstrate structural resilience. The primary objective of this project is to produce lightweight structural materials whose strength-to-weight ratios exceed those of the current widely used structural materials such as 316L stainless steels (316L SS). To achieve this, advanced modeling and simulation tools were employed to design lattice structures with different lattice parameters and different lattice types. A process was successfully developed for transforming lattice-structures models into Multiphysics Object Oriented Simulation Environment (MOOSE) inputs. Finite element modeling (FEM) was used to simulate the uniaxial tensile testing of the lattice-structured parts to investigate the stress distribution at a given displacement. The preliminary results showed that the lattice-structured sample displayed a lower Young’s modulus in comparison with the solid material and that the unit cell size of the lattice had a minimal effect. The novelty here is to apply up-front modeling to determine the best structure for the application before actually producing the sample. The approach of using modeling as a guiding tool for preliminary material design can significantly save time and cost for material development.
Here, we present a machine-learning strategy for finite element analysis of solid mechanics wherein we replace complex portions of a computational domain with a data-driven surrogate. In the proposed strategy, we decompose a computational domain into an “outer” coarse-scale domain that we resolve using a finite element method (FEM) and an “inner” fine-scale domain. We then develop a machine-learned (ML) model for the impact of the inner domain on the outer domain. In essence, for solid mechanics, our machine-learned surrogate performs static condensation of the inner domain degrees of freedom. This is achieved by learning the map from displacements on the inner-outer domain interface boundary to forces contributed by the inner domain to the outer domain on the same interface boundary. We consider two such mappings, one that directly maps from displacements to forces without constraints, and one that maps from displacements to forces by virtue of learning a symmetric positive semi-definite (SPSD) stiffness matrix. We demonstrate, in a simplified setting, that learning an SPSD stiffness matrix results in a coarse-scale problem that is well-posed with a unique solution. We present numerical experiments on several exemplars, ranging from finite deformations of a cube to finite deformations with contact of a fastener-bushing geometry. We demonstrate that enforcing an SPSD stiffness matrix drastically improves the robustness and accuracy of FEM–ML coupled simulations, and that the resulting methods can accurately characterize out-of-sample loading configurations with significant speedups over the standard FEM simulations.
A finite element model of a four-cell photovoltaic mini-module was developed and compared to experimental results from an accelerated stress test protocol in order to validate that computational models can accurately represent their physical counterparts when subjected to mechanical loading and to assess mini-module representativeness against full scale photovoltaic modules. Deflected shapes across the simulated mini-modules were compared to measured mini-module shapes when subjected to various pressure loads. Displaced mini-module shape results constrained to the experimental protocols of 0.4 mm and 1.1 mm of displacement at the mini-module center were compared to experimental results of full-size modules subjected to module qualification test load levels of 1.0 kPa and 2.4 kPa, to assess if the bending of mini-modules was representative of full-sized modules under the load. Temperature cycling was incorporated into the model to simulate the impacts of stress due to thermal expansion of the backsheet and cells. A preliminary uncertainty analysis was performed to show how variations in material properties and geometric parameters change the simulation results.
A multi-scale finite element modeling approach was developed to study the deformation and fracture behavior of Al-steel resistance spot welds. First, a micro-scale model was applied to simulate the mechanical responses of the intermetallic compound (IMC) layer having various morphologies and thicknesses under tensile and shear loading conditions. Second, the predicted tensile and shear strength of the IMC layer, that varied along the joint interface per the IMC layer morphology and thickness variation, was then introduced into 3D macro-scale models to predict the overall mechanical performance of weld coupons under coach peel, lap shear and cross-tension testing conditions. In conclusion, the numerical predictions agreed reasonably well with the experimental data.
Here, a particle finite element method-based model is proposed to analyze droplet dynamics problems, particularly droplet spreading on solid substrates (wetting). The model uses an updated Lagrangian framework to formulate the governing equations of the liquid. The curvature of the liquid surface is tracked accurately using a deforming boundary mesh. In order to predict the spreading rate of the droplet on the solid substrate and track the corresponding contact angle evolution, dissipative forces at the contact line are included in the formulation in addition to the Navier-slip boundary conditions at the solid–liquid interface. The inclusion of these boundary conditions makes it possible to account for the induced Young’s stress at the contact line and for the viscous dissipation along the solid–liquid interfacial region. These are found to be essential to obtain a mesh-independent physical solution. The temporal evolution of the contact angle and the contact line velocity of the proposed model are compared with spreading droplets and micro-sessile droplet injection experiments and are shown to be in good agreement.
A descriptive review is presented of the most recent methods for performing sensitivity analysis of the structural behavior of discretely-modeled systems. The methods are generally but not exclusively aimed at finite element modeled structures. Topics included are: selections of finite difference step sizes; special consideration for finite difference sensitivity of iteratively-solved response problems; first and second derivatives of static structural response; sensitivity of stresses; nonlinear static response sensitivity; eigenvalue and eigenvector sensitivities for both distinct and repeated eigenvalues; and sensitivity of transient response for both linear and nonlinear structural response.
Flexible body modeling presents a large challenge in the development of simulations to aid in design of flight control systems for launch and landing vehicles. Typically, the flexible body model is not a single continuous model but are rather discrete sets of Linear Time Invariant (LTI) Finite Element Models (FEM) incremented by propellant levels. This introduces the problem of smoothly transitioning modal and physical states of the vehicle when switching from one FEM to the next. This paper introduces a new approach to optimally transition flexible body states with weighted least squares, building off previous methods.
Flexible body modeling presents a large challenge in the development of simulations to aid in design of flight control systems for launch and landing vehicles. Typically, the flexible body model is not a single continuous model but are rather discrete sets of Linear Time Invariant (LTI) Finite Element Models (FEM) incremented by propellant levels. This introduces the problem of smoothly transitioning modal and physical states of the vehicle when switching from one FEM to the next. This paper introduces a new approach to optimally transition flexible body states with weighted least squares, building off previous methods.
Numerical simulations, irrespective of the discipline or application, are often plagued by arbitrary numerical and modeling choices. Arbitrary choices can originate from kinematic assumptions, for example the use of 1D beam, 2D shell, or 3D continuum elements, mesh discretization choices, boundary condition models, and the representation of contact and friction in the simulation. This works takes a step toward understanding the effect of arbitrary choices and model-form assumptions on the accuracy of numerical predictions. The application is the simulation of the first four resonant frequencies of a one-story aluminum portal frame structure under free-free boundary conditions. The main challenge of the portal frame structure resides in modeling joint connections, for which different modeling assumptions are available. To study this model-form uncertainty, and compare it to other types of uncertainty, two finite element models are developed using solid elements, and with differing representations of the beam-to-column and column-to-base plate connections using: (i) contact stiffness, and (ii) tied nodes. Test-analysis correlation is performed by comparing the range of numerical predictions obtained from parametric studies of the joint modeling strategies to the range of experimentally obtained natural frequencies. The approach proposed is, first, to characterize the experimental variability of the joints by varying the bolt torque, method of bolt tightening, and the sequence in which the bolts are tightened. The second step is to convert what is learned from these experimental studies to models that bound the range of observed bolt behavior. We show that this approach, that combines small-scale experiments, sensitivity analysis studies, and bounding-case models, successfully produces bounds of numerical predictions that match those measured experimentally on the frame structure.
In support of the Space Station Freedom (SSF) Solar Dynamic Power Module effort, structural design studies were performed to investigate issues related to the design of the power module, its pointing capabilities, and the integration of the module into the SSF infrastructure. Of particular concern from a structural viewpoint are the dynamics of the power module, the impact of the power module on the Space Station dynamics and controls, and the required control effort for obtaining the specified Solar Dynamic Power Module pointing accuracy. Structural analyses were performed to determine the structural dynamics attributes of both the existing and the proposed structural dynamics module designs. The objectives of these analyses were to generate validated Solar Dynamic Power Module NASTRAN finite element models, combine Space Station and power module models into integrated system models, perform finite element modal analyses to assess the effect of the relocations of the power module center of mass, and provide modal data to controls designers for control systems design.
The use of structural mechanics models during the design process often leads to the development of models of varying fidelity. Often low-fidelity models are efficient to simulate but lack accuracy, while the high-fidelity counterparts are accurate with less efficiency. Here, this paper presents a multi-fidelity surrogate modeling approach that combines the accuracy of a high-fidelity finite element model with the efficiency of a low-fidelity model to train an even faster surrogate model that parameterizes the design space of interest. The objective of these models is to predict the nonlinear frequency backbone curves of the Tribomechadynamics Research Challenge benchmark structure which exhibits simultaneous nonlinearities from frictional contact and geometric nonlinearity. The surrogate model consists of an ensemble of neural networks that learn the mapping between low and high-fidelity data through nonlinear transformations. Bayesian neural networks are used to assess the surrogate model's uncertainty. Once trained, the multi-fidelity neural network is used to perform sensitivity analysis to assess the influence of the design parameters on the predicted backbone curves. Additionally, Bayesian calibration is performed to update the input parameter distributions to correlate the model parameters to the collection of experimentally measured backbone curves.
This paper reports on plastic recovery and self-healing behavior in longitudinally-twinned and [112] orientated SiGe nanowire (NW) beams when they are subjected to large bending strains. The NW alloys are comprised of lamellar nanotwin platelet(s) sandwiched between two semi-cylindrical twins. The loading curves, which are acquired from atomic force microscope (AFM) based three-point bending tests, reveal the onset of plastic deformation at a characteristic stress threshold, followed by further straining of the NWs. This ductility is attributed to dislocation activity within the semi-cylindrical crystal portions of the NW, which are hypothesized to undergo a combination of elastic and plastic straining. On the other hand, the lamellar nanoplatelets undergo purely elastic stretching. During the unloading process, the release of internal elastic stresses enables dislocation backflow and escape at the surface. As a result, the dislocations are predominantly annihilated and the NW samples evidenced self-healing via plastic recovery even at ultra-large strains, which are estimated using finite-element models at 16.3% in one of the tested devices. Finite element analysis also establishes the independence of the observed nanomechanical behavior on the relative orientation of the load with respect to the nanoplatelet. This first observation of reversible plasticity in the SiGe material system, which is aided by a concurrent evolution of segmented elastic and plastic deformation within its grains during the loading process, presents an important new pathway for mechanical stabilization of technologically important group-IV semiconductor nanomaterials.
Suitable formulation of finite-element model speeds computation. Improved method developed for analysis of stress and compatibility of components in multicomponent rotating assembly. In method, single finite-element mathematical model represents all of components. Made possible by use of gap elements to represent contact surfaces between components.
Driven by the exceedingly high computational demands of simulating mechanical response in complex engineered systems with finely resolved finite element models, there is a critical need to optimally reduce the fidelity of such simulations. The minimum required fidelity is constrained by error tolerances on the simulation results, but error bounds are often impossible to obtain a priori. One such source of error is the variability of material properties within a body due to spatially non-uniform processing conditions and inherent stochasticity in material microstructure. This study seeks to quantify the effects of microstructural heterogeneity on component- and system-scale performance to aid in the choice of an appropriate material model and spatial resolution for finite element analysis.