Granular materials: Parameter identification and modelling of localisation problems

The prediction of landsliding requires an exact knowledge of the mechanical behaviour of granular materials. This kind of materials, e. g., sand, have a very complex deformation behaviour, which depend on the stress state and on the loading history. In this work, the deformation behaviour of the solid skeleton is characterised via homogeneous triaxial tests on dry sand specimens. Additionally, an appropriate elasto-plastic material law to describe the solid skeleton in the frame of Theory of Porous Media (TPM) is used, which is implemented in the FE tool PANDAS. Furthermore, a single-surface yield criterion with isotropic hardening, which limits the elastic domain, and a non-associated plastic flow are employed. The determination of the material parameters of the linear elasticity law as well as the single-surface yield criterion are based on test data of triaxial experiments. The material parameters are identified using a derivative-based optimisation method (donlp2), which is coupled with PANDAS. Finally, a simulation of a benchmark test is presented to show shear band localisation effects, where the material behaviour is described by a triphasic porous media model based on the TPM, where the constituents are a deformable solid skeleton and two pore fluids, water and air. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Phase transition processes of pore fluids in porous materials

Taking a closer look on, e. g., storage processes of greenhouse gases in deep geological aquifers or pressure decreases in dilatant shear bands, the observation can be made that pressure and temperature changes in porous materials can induce phase transition processes of the respective pore fluids. For a numerical simulation of this behaviour, a continuum mechanical model based on a multiphasic formulation embedded in the well-founded framework of the Theory of Porous Media (TPM) is presented in this contribution. The single phases are an elasto-viscoplastic solid skeleton, a materially compressible pore gas consisting of the components air and gaseous pore water (water vapour) and a materially incompressible pore liquid, i. e., liquid pore water. The numerical treatment is based on the weak formulations of the governing equations, whereas the primary variables are the temperature of the mixture, the displacement of the solid skeleton and the effective pressures of the pore fluids. An initial boundary-value problem is discussed in detail, where the resulting system of strongly coupled differential-algebraic equations is solved by the FE tool PANDAS. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

A computer‐aided methodology for the optimization of electrostatic separation processes in recycling

The rapid growth of technological products has led to an increasing volume of waste electrical and electronic equipments (WEEE), which could represent a valuable source of critical raw materials. However, current mechanical separation processes for recycling are typically poorly operated, making it impossible to modify the process parameters as a function of the materials under treatment, thus resulting in untapped separation potentials. Corona electrostatic separation (CES) is one of the most popular processes for separating fine metal and nonmetal particles derived from WEEE. In order to optimize the process operating conditions (i.e., variables) for a given multi‐material mixture under treatment, several technological and economical criteria should be jointly considered. This translates into a complex optimization problem that can be hardly solved by a purely experimental approach. As a result, practitioners tend to assign process parameters by few experiments based on a small material sample and to keep these parameters fixed during the process life‐cycle. The use of computer experiments for parameter optimization is a mostly unexplored area in this field. In this work, a computer‐aided approach is proposed to the problem of optimizing the operational parameters in CES processes. Three metamodels, developed starting from a multi‐body simulation model of the process physics, are presented and compared by means of a numerical and simulation study. Our approach proves to be an effective framework to optimize the CES process performance. Furthermore, by comparing the predicted response surfaces of the metamodels, additional insight into the process behavior over the operating region is obtained. Copyright © 2015 John Wiley & Sons, Ltd.     

Comparison of a biphasic and a multicomponent model describing viscoelastic swelling phenomena

Biological soft tissues like articular cartilage and their artificial replacement hydrogel have a multicomponent microstructure, consisting of a charged viscoelastic solid matrix saturated by a fluid, which is composed of the liquid solvent and the dissolved anions and cations. Such charged multiphasic materials exhibit a swelling behaviour under varying chemical conditions. These materials are best described by a macroscopic approach like the Theory of Porous Media (TPM). Starting from this point, a standard two-phase model is extended by dividing the fluid into the above mentioned components. Therein, the chemical relations describing the behaviour of the ions and their interaction with the other mixture constituents are incorporated. The resulting model covers mechanical as well as osmotic and electrostatic effects. For numerical and simplicity reasons, it is possible to describe the swelling phenomena by a simplified biphasic model, where the ions as a third degree of freedom and their time-dependent diffusion are neglected. Furthermore, the viscoelastic solid matrix can be replaced by an elastic material. Note that using the multicomponent model generally results in numerical problems, since the boundary conditions depend on the internal fixed charge density. It is shown that this problem can be solved by including the boundary conditions into the weak formulation. Finally, to compare the different behaviour of the above mentioned models by means of an swelling example, they are implemented into the FE tool PANDAS using stable Taylor-Hood elements for the spatial discretization. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Construction of the Equivalent Plastic Strain Increment

Strain hardening plastic deformation of a material possessing a yield locus (f(σ_ij)) which may be written as a homogeneous function of the stress components (σ_ij), and which obeys the classical associated flow rule for metals (e.g. Bishop and Hill, 1951) is considered. The material may be anisotropic and may display plastic dilatation. A method is given for constructing the equivalent plastic strain increment in such a way that the increment of plastic work is always equal to the product of the equivalent plastic strain increment and the equivalent yield stress. Construction of the equivalent plastic strain at a corner in the yield locus is given. The method given here is implied in classical treatments of hardening (e.g. Hill, 1950) but seems not to have been given explicitly heretofore.     

Acceleration of uncertainty updating in the description of transport processes in heterogeneous materials

The prediction of thermo-mechanical behaviour of heterogeneous materials such as heat and moisture transport is strongly influenced by the uncertainty in parameters. Such materials occur e.g., in historic buildings, and the durability assessment of these therefore needs a reliable and probabilistic simulation of transport processes, which is related to the suitable identification of material parameters. In order to include expert knowledge as well as experimental results, one can employ an updating procedure such as Bayesian inference. The classical probabilistic setting of the identification process in Bayes’ form requires the solution of a stochastic forward problem via computationally expensive sampling techniques, which makes the method almost impractical.     

Solving stochastic structural optimization problems by RSM-based stochastic approximation methods — gradient estimation in case of intermediate variables

Reliability-based structural optimization methods use mostly the following basic design criteria: I) Minimum weight (volume or costs) and II) high strength of the structure. Since several parameters of the structure, e.g. material parameters, loads, manufacturing errors, are not given, fixed quantities, but random variables having a certain probability distribution P,stochastic optimization problems result from criteria (I), (II), which can be represented by (1) $$\mathop {\min }\limits_{x \in D} F(x)withF(x): = Ef(\omega ,x).$$ Here,f=f(ω,x) is a function on ? ^r depending on a random element ω, “E” denotes the expectation operator andD is a given closed, convex subset of ? ^r . Stochastic approximation methods are considered for solving (1), where gradient estimators are obtained by means of the response surface methodology (RSM). Moreover, improvements of the RSM-gradient estimator by using “intermediate” or “intervening” variables are examined.     

Modelling of Swelling Phenomena in Charged Hydrated Porous Media

Biological tissues like articular cartilage and geomaterials like clay have a multicomponent microstructure. The charged solid is saturated by a viscous fluid, which itself is composed of several components: the liquid solvent and the dissolved ions, namely, water, anions and cations. These charged multiphase materials exhibit a swelling behaviour under varying chemical conditions. The model describing such materials combines electrochemical and mechanical effects like osmosis and electrostatics within a macroscopic formulation. Starting from the Theory of Porous Media (TPM), a four component model is presented, wherein all constituents are materially incompressible and mass exchanges are excluded. This isothermal model leads to a set of equations which consists of three primary variables: the solid displacement u _S, the pore‐pressure p and the molar ion concentration c_m, since the ion concentrations always depend on each other because of the electroneutrality condition. For the numerical treatment, the weak formulations of governing equations are implemented in the FE tool PANDAS, wherein Taylor‐Hood elements are used for the spatial discretization. Finally, a simulation of a 3‐d swelling experiment is shown. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Non-linear Stochastic Finite Element

This paper investigates the uncertainty of physically non-linear problems by modeling the elastic random material parameters as stochastic fields. For its stochastic discretization a polynomial chaos (PC) is used to expand the coefficients into deterministic and stochastic parts. Then, from experimental data for an adhesive material the distribution of the random variables, i.e. Young's modulus E(θ), the static yield point Y₀ and the nonlinear hardening parameters q and b, are known. In the numerical example the distribution of the stresses obtained by the PC based SFEM and Monte Carlo simulation is compared. (© 2015 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Inverse Parameter Identification for Orthotropic Elasto-Plastic Sheet-Steel

In this contribution the Finite Element Model Updating (FEMU) approach is utilized to determine the material parameters of sheet-steel. From experimental testing it is observed that the considered cold-rolled steel exhibits orthotropic behaviour. To regard this in the simulation, a user-implemented material model based on a quadratic yield function is used. Via the method of digital image correlation (DIC), the displacement field of a biaxially loaded specimen is measured from images taken at different stages of loading. Comparing the experimentally determined displacements to those obtained by simulation an error measure is defined which can be minimized by optimization algorithms. Starting with initial values for the orthotropic elasto-plastic material parameters, the FEM model is thus updated consecutively until a specified error margin is reached. (© 2015 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Gradient‐based optimization of a viscoelastic Ogden type model for cellular polymers

The parameter identification is the interface between a theoretical material model and its application in numerical computations. Only by an accurate identification of the theoretically introduced material parameters, an applicable simulation of the material is achieved. An increasing standard of the parameter identification is set by the requirements of complex material models used in computer‐aided engineering. A common identification strategy is a gradient‐based optimization of a least‐squares functional, e. g. the sequential quadratic programming (SQP) technique. In this paper, the SQP method is used to optimize material models of cellular polymers. In particular, the optimization is shown for a viscoelastic polyurethane (PU) foam. Due to the high‐grade nonlinear material behaviour, the foam is modelled by a finite viscoelastic Ogden type law in the framework of the Theory of Porous Media (TPM).     

Non-monotonic penalizing for the number of structural breaks

This paper first reduces the problem of detecting structural breaks in a random walk to that of finding the best subset of explanatory variables in a regression model and then tailors various subset selection criteria to this specific problem. Of particular interest are those new criteria, which are obtained by means of simulation using the efficient algorithm of Bai and Perron (J Appl Econom 18:1–22, 2003). Unlike conventional variable selection methods, which penalize new variables entering a model either in the same way (e.g., AIC and BIC) or milder (e.g., MRIC and $\mathrm {FPE}_\mathrm{{sub}}$ ) than already included variables, they do not follow any monotonic penalizing scheme. In general, their non-monotonicity is more pronounced in the case of fat tails. The characteristics of the different criteria are illustrated using bootstrap samples from the Nile data set.     

Optimal Fine‐Scale Structures in Compliance Minimization for a Shear Load

We return to a classic problem of structural optimization whose solution requires microstructure. It is well‐known that perimeter penalization assures the existence of an optimal design. We are interested in the regime where the perimeter penalization is weak; i.e., in the effect of perimeter as a selection mechanism in structural optimization. To explore this topic in a simple yet challenging example, we focus on a two‐dimensional elastic shape optimization problem involving the optimal removal of material from a rectangular region loaded in shear. We consider the minimization of a weighted sum of volume, perimeter, and compliance (i.e., the work done by the load), focusing on the behavior as the weight ɛ of the perimeter term tends to 0. Our main result concerns the scaling of the optimal value with respect to ɛ. Our analysis combines an upper bound and a lower bound. The upper bound is proved by finding a near‐optimal structure, which resembles a rank‐2 laminate except that the approximate interfaces are replaced by branching constructions. The lower bound, which shows that no other microstructure can be much better, uses arguments based on the Hashin‐Shtrikman variational principle. The regime being considered here is particularly difficult to explore numerically due to the intrinsic nonconvexity of structural optimization and the spatial complexity of the optimal structures. While perimeter has been considered as a selection mechanism in other problems involving microstructure, the example considered here is novel because optimality seems to require the use of two well‐separated length scales.© 2016 Wiley Periodicals, Inc.     

Coexistence of domains of attraction of nonautonomous neural networks with time-varying delays

This paper presents new dynamical behavior, i.e., the coexistence of 2^N domains of attraction of N-dimensional nonautonomous neural networks with time-varying delays. By imposing some new assumptions on activation functions and system parameters, we construct 2^N invariant basins for neural system and derive some criteria on the boundedness and exponential attractivity for each invariant basin. Particularly, when neural system degenerates into periodic case, we not only attain the coexistence of 2^N periodic orbits in bounded invariant basins but also give their domains of attraction. Moreover, our results are suitable for autonomous neural systems. Our new results improve and generalize former ones. Finally, computer simulation is performed to illustrate the feasibility of our results.     

Quantile regression metamodeling: Toward improved responsiveness in the high-tech electronics manufacturing industry

Both technology and market demands within the high-tech electronics manufacturing industry change rapidly. Accurate and efficient estimation of cycle-time (CT) distribution remains a critical driver of on-time delivery and associated customer satisfaction metrics in these complex manufacturing systems. Simulation models are often used to emulate these systems in order to estimate parameters of the CT distribution. However, execution time of such simulation models can be excessively long limiting the number of simulation runs that can be executed for quantifying the impact of potential future operational changes. One solution is the use of simulation metamodeling which is to build a closed-form mathematical expression to approximate the input–output relationship implied by the simulation model based on simulation experiments run at selected design points in advance. Metamodels can be easily evaluated in a spreadsheet environment “on demand” to answer what-if questions without needing to run lengthy simulations. The majority of previous simulation metamodeling approaches have focused on estimating mean CT as a function of a single input variable (i.e., throughput). In this paper, we demonstrate the feasibility of a quantile regression based metamodeling approach. This method allows estimation of CT quantiles as a function of multiple input variables (e.g., throughput, product mix, and various distributional parameters of time-between-failures, repair time, setup time, loading and unloading times). Empirical results are provided to demonstrate the efficacy of the approach in a realistic simulation model representative of a semiconductor manufacturing system.