A unified constitutive model is presented to predict the recently observed“multi-stage”creep behavior of Al−Li−S4 *** corresponding microstructural variables related to the yield strength and creep deformation of th...
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A unified constitutive model is presented to predict the recently observed“multi-stage”creep behavior of Al−Li−S4 *** corresponding microstructural variables related to the yield strength and creep deformation of the alloy during the creep ageing process,including dislocations and multiple precipitates,have been characterized in detail by X-ray diffraction(XRD)and transmission electron microscopy(TEM).For the yield strength,the model considers the multiphase strengthening behavior of the alloy based on strengthening mechanisms,which includes shearable T1 precipitate strengthening,non-shearable T1 precipitate strengthening andθ′precipitate *** on creep deformation mechanism,the“multi-stage”creep behavior of the alloy is predicted by introducing the effects of interacting microstructural variables,including the radius of multiple precipitates,dislocation density and solute concentration,into the creep stress−strain *** is concluded that the results calculated by the model are in a good agreement with the experimental data,which validates the proposed model.
For optical monitoring of layer thickness in the deposition of multilayer optical coatings, a stable method is proposed that completely eliminates the cumulative effect of errors in the thicknesses of deposited layers...
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For optical monitoring of layer thickness in the deposition of multilayer optical coatings, a stable method is proposed that completely eliminates the cumulative effect of errors in the thicknesses of deposited layers. The considered monitoring method relies on a nonlocal algorithm for analyzing data measured in the course of coating deposition monitoring. Computer simulation of coating deposition is used to demonstrate the effectiveness of the proposed type of monitoring as compared with other optical monitoring methods.
We investigate tracking tasks for an automatic mobile robot with obstacle avoidance. To this end we apply a linear model-predictive control (LMPC) method to the nonlinear robot model. The LMPC uses a linearized robot ...
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We investigate tracking tasks for an automatic mobile robot with obstacle avoidance. To this end we apply a linear model-predictive control (LMPC) method to the nonlinear robot model. The LMPC uses a linearized robot model around the reference track and takes into account (fixed or moving) obstacles, which the robot has to avoid. The resulting discretized linear-quadratic optimal control problems are solved numerically by a semismooth Newton method, which turns out to be fast and robust. Furthermore, we propose a structure exploitation strategy to reduce the computational effort of the semismooth Newton method. Simulation results for a two-wheeled robot are presented to validate the control algorithm.
A technique for solving dynamic problems of the behavior of compound shell structures (solving the equations of motion subject to appropriate boundary and initial conditions) is developed. The structurally orthotropic...
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A technique for solving dynamic problems of the behavior of compound shell structures (solving the equations of motion subject to appropriate boundary and initial conditions) is developed. The structurally orthotropic model of a three-layer shell structure with a cellular core is used, for which the integral values of the elastic modulus and Poisson's ratios are determined experimentally. numerical algorithms are developed, and the corresponding problems of mathematical theory of elasticity are solved. The numerical results are analyzed.
Solving linear system(LS) is a common and important issue in both academic and industrial *** are many methods to solve LS,including neural network(specifically,neural dynamics) and traditional numerical computation *...
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Solving linear system(LS) is a common and important issue in both academic and industrial *** are many methods to solve LS,including neural network(specifically,neural dynamics) and traditional numerical computation ***,traditional numerical computation algorithms comprise,but not limited to,Jacobi iteration and Gauss-Seidel iteration *** the other hand,neural network algorithms have been the hot topics in research for a long time,which are applied in various *** addition,standard Zhang neural dynamics(ZND) is a special kind of neural network to solve some time-variant problems *** this paper,an elegant-formula ZND(EFZND) algorithm is obtained through theoretical ***,the standard ZND and different EFZND models for solving LS are ***,the possible relationship between neural dynamics and traditional numerical computation algorithms,especially Jacobi iteration algorithm,is discussed from two specific EFZND models for solving ***,the feasibility and efficiency of standard ZND and EFZND models for solving LS are proved through derivation.
Computation of derivatives (gradient and Hessian) of a fidelity function is one of the most crucial steps in many optimization algorithms. Having access to accurate methods for computing these derivatives is even more...
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Computation of derivatives (gradient and Hessian) of a fidelity function is one of the most crucial steps in many optimization algorithms. Having access to accurate methods for computing these derivatives is even more desirable where the optimization process requires propagation of these computations over many steps, which is particularly important in optimal control of spin systems. Here we propose a novel numerical approach, ESCALADE (Efficient Spin Control using Analytical Lie Algebraic Derivatives), that offers the exact first and second derivatives of the fidelity function by taking advantage of the properties of the Lie group of 2 x 2 unitary matrices, SU(2), and its Lie algebra, the Lie algebra of skew-Hermitian matrices, su(2). A full mathematical treatment of the proposed method along with some numerical examples are presented. (C) 2021 Published by Elsevier Ltd.
Viscoelastic material properties at high strain rates are needed to model many biological and medical systems. Bubble cavitation can induce such strain rates, and the resulting bubble dynamics are sensitive to the mat...
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Viscoelastic material properties at high strain rates are needed to model many biological and medical systems. Bubble cavitation can induce such strain rates, and the resulting bubble dynamics are sensitive to the material properties. Thus, in principle, these properties can be inferred via measurements of the bubble dynamics. Estrada et al. (2018) demonstrated such bubble-dynamic high-strain-rate rheometry by using least-squares shooting to minimize the difference between simulated and experimental bubble radius histories. We generalize their technique to account for additional uncertainties in the model, initial conditions, and material properties needed to uniquely simulate the bubble dynamics. Ensemble-based data assimilation minimizes the computational expense associated with the bubble cavitation model , providing a more efficient and scalable numerical framework for bubble-collapse rheometry. We test an ensemble Kalman filter (EnKF), an iterative ensemble Kalman smoother (IEnKS), and a hybrid ensemble-based 4D-Var method (En4D-Var) on synthetic data, assessing their estimations of the viscosity and shear modulus of a Kelvin-Voigt material. Results show that En4D-Var and IEnKS provide better moduli estimates than EnKF. Applying these methods to the experimental data of Estrada et al. (2018) yields similar material property estimates to those they obtained, but provides additional information about uncertainties. In particular, the En4D-Var yields lower viscosity estimates for some experiments, and the dynamic estimators reveal a potential mechanism that is unaccounted for in the model, whereby the apparent viscosity is reduced in some cases due to inelastic behavior, possibly in the form of material damage occurring at bubble collapse.
This paper formulates stress-assisted and strain-induced austenite to martensite transformation kinetics laws within a crystal plasticity framework to enable modeling of strain path sensitive elasto-plastic deformatio...
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This paper formulates stress-assisted and strain-induced austenite to martensite transformation kinetics laws within a crystal plasticity framework to enable modeling of strain path sensitive elasto-plastic deformation of austenitic steels taking into account the evolution of crystallographic texture and the directionality of deformation mechanisms in the constituent phases. Consistent with experimental observations for mechanically induced martensitic transformation, the stress-assisted transformation is modeled as direct from gamma-austenite to alpha'-martensite, while the strain-induced transformation is modeled as indirect through an intermediate epsilon-martensite phase, which subsequently transforms to alpha'-martensite. While the stress-assisted transformation law is conceived based on an energy criterion, the strain-induced transformation law relies on the local stress state sensitive motion of partial dislocations forming shear bands of epsilon-martensite phase, which after intersecting with other shear bands give rise to alpha'-martensite. The kinetic models are implemented in the elasto-plastic self-consistent polycrystal plasticity model to facilitate modeling of strain path and crystallographic texture dependence of martensitic transformation, while predicting deformation behavior of metastable austenitic steels. Due to its morphology, the epsilon-martensite is modeled using a flat ellipsoid approximation, which is a new numerical feature in the model. Simple tension, simple compression, and simple shear data of an austenitic steel have been used to calibrate and to illustrate predictive characteristics of the overall implementation. In doing so, stress-strain response, texture, and phase fractions of gamma-austenite, intermediate epsilon-martensite, and alpha '-martensite are all calculated, while fully accounting for the crystallography of the transformation mechanisms. It is demonstrated that the appropriate modeling of phase fractions and crystallog
In this paper, the numerical algorithms for solution of pore volume and surface diffusion model of adsorption systems are constructed and investigated. The approximation of PDEs is done by using the finite volume meth...
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In this paper, the numerical algorithms for solution of pore volume and surface diffusion model of adsorption systems are constructed and investigated. The approximation of PDEs is done by using the finite volume method for space derivatives and ODE15s solvers for numerical integration in time. The analysis of adaptive in time integration algorithms is presented. The main aim of this work is to analyze the sensitivity of the solution with respect to the main parameters of the mathematical model. Such a control analysis is done for a linearized and normalized mathematical model. The obtained results are compared with simulations done for a full nonlinear mathematical model.
Crystal plasticity models evolve a polycrystalline yield surface using meso-scale descriptions of deformation mechanisms. The activation of deformation mechanisms is governed by crystallography and a set of model para...
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Crystal plasticity models evolve a polycrystalline yield surface using meso-scale descriptions of deformation mechanisms. The activation of deformation mechanisms is governed by crystallography and a set of model parameters, which are typically calibrated through the fitting of mechanical data such as stress-strain curves and elastic lattice strains. Microstructural data such as phase fractions and texture evolution are used for verifying crystal plasticity parameters. In this work, we use a multi-objective genetic algorithm to identify hardening parameters from flow stress curves with an option to incorporate texture into the optimization approach. Robust, generalized objective functions are developed and used to identify sets of parameters pertaining to dislocation density-based hardening laws in visco-plastic and elasto-plastic self-consistent (VPSC and EPSC) homogenization models. First, the parameters are identified for pure Nb directly from texture using an objective function based on generalized spherical harmonics. Since texture evolution is driven by the relative contribution of active slip systems, the parameters governing the evolution of slip resistance ratios can be recovered from fitting discrete textures at a series of strains. Next, a comprehensive set of load reversal data for dual phase (DP) 780 steel is used to fit a hardening law and a back-stress law in EPSC. Finally, parameters pertaining to a complex hardening law for the evolution of slip and twinning in pure alpha-Ti are identified. Remarkably, using texture as an objective in combination with stress-strain objectives constrains the model of Ti to fully reproduce not only stress-strain and texture evolution but also hierarchical twinning measurements as a function of initial grain size and texture. Furthermore, given an appropriate model fit to representative experimental texture evolution, underlying twin volume fractions contributing to texture evolution can be predicted. (C) 2021 Elsevier
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