GAMM Mitteilungen最新文献

This contribution proposes a multiscale modeling approach, ranging from the macromolecular behavior of tropocollagen over collagen fibrils and the interfibrillar matrix up to bundles of collagen fibers. Two damage mechanisms are described: intramolecular damage inside the tropocollagen molecules based on a permanent opening of the triple helical conformation and damage in the interfibrillar matrix restricting the recovery of interfibrillar sliding. Both intramolecular and interfibrillar damage is considered as a probabilistic process based on detachment of adhesive bonds, where the probability of failure depends on the full load history of the bond. The presented modeling concept is based on generalized assumptions valid for most soft fibrous tissues, and can therefore be applied for a variety of tissues and load-cases. The final constitutive equations are validated against recent experimental data from uniaxial tension tests of rat tail tendon. All utilized material constants have a clear physical interpretation.

Engineers are faced with the challenge of supporting decision making under uncertainty. Engineering decisions often depend on model-based predictions of the performance of the engineering system of interest. Input uncertainties of models can be categorized into two distinct types: aleatory (random/irreducible) or epistemic (reducible). Polymorphic uncertainty quantification (UQ) can be used to treat aleatory and epistemic uncertainties in a unified framework. The polymorphic UQ framework employs probability theory to model aleatory variables and alternative approaches (interval, fuzzy, Bayesian probabilistic, and combinations thereof) to model epistemic variables. This paper compares different polymorphic UQ approaches with respect to their ability to support a simple engineering decision. The comparison is based on a test-bed example, whereby aleatory variables are defined in terms of probability distributions and epistemic variables are described based on limited information (sparse data or intervals). Two challenges related to common engineering decisions (safety assessment and reliability-based design) serve as a basis for the comparison. Five independent research groups applied different models to describe the epistemic parameters based on a subjective interpretation of the given information. The comparison of the results reveals a strong influence of both the subjective choices on the models of the epistemic variables and the chosen basis for assessing the performance of the structure on the obtained decision outcomes.

Modeling of mechanical systems with uncertainties is extremely challenging and requires a careful analysis of a huge amount of data. Both, probabilistic modeling and nonprobabilistic modeling require either an extremely large ensemble of samples or the introduction of additional dimensions to the problem, thus, resulting also in an enormous computational cost growth. No matter whether the Monte-Carlo sampling or Smolyak's sparse grids are used, which may theoretically overcome the curse of dimensionality, the system evaluation must be performed at least hundreds of times. This becomes possible only by using reduced order modeling and surrogate modeling. Moreover, special approximation techniques are needed to analyze the input data and to produce a parametric model of the system's uncertainties. In this paper, we describe the main challenges of approximation of uncertain data, order reduction, and surrogate modeling specifically for problems involving polymorphic uncertainty. Thereby some examples are presented to illustrate the challenges and solution methods.

The aim of this paper is to give an overview of different multifidelity uncertainty quantification (UQ) schemes. Therefore, different views on multifidelity UQ approaches from a frequentist, Bayesian, and possibilistic perspective are provided and recent developments are discussed. Differences as well as similarities between the methods are highlighted and strategies to construct low-fidelity models are explained. In addition, two state-of-the-art examples to showcase the capabilities of these methods and the tremendous reduction of computational costs that can be achieved when using these approaches are provided.

In this contribution, several case studies with data uncertainties are presented which have been performed in individual projects as part of the DFG (German Research Foundation) Priority Programme SPP 1886 “Polymorphic uncertainty modelling for the numerical design of structures.” In all case studies numerical models with uncertainties are derived from engineering problems describing concepts for handling and incorporating measurement data, either of model input parameters or of the system response. The first case study deals with polymorphic uncertain data based on computer tomographic scans with respect to air voids which are acquired, simplified and integrated in numerical models of adhesive bonds. In the second case study, the variation sensitivity analysis is presented to provide suitable prior knowledge for numerical soil analyses, for example, in order to reduce required input data. The uncertainty in friction processes is treated in case study 3 whereby measurement data are used in data driven methods to improve the numerical predictions. In case study 4, the failure behavior of die-cast window hinges, which is affected by an uncertain initial pore distribution, is investigated by means of a Markov chain approach. In the last two case studies, mathematical methods of statistical inference and updating algorithms for uncertainty models are shown. Due to the heterogeneous spectrum of problems, a generalized strategy for data modeling, acquisition, and assimilation is developed and applied on each case study.

In structural analysis with multivariate random fields, the underlying distribution functions, the autocorrelations, and the crosscorrelations require an extensive quantification. While those parameters are difficult to measure in experiments, a lack of knowledge is included. Therefore, polymorphic uncertainty models are attained by involving uncertainty models with epistemic characteristic for the quantification of the stochastic models in this contribution. Three extensions for random fields with polymorphic uncertainty modeling are introduced. Interval probability based random fields, fuzzy probability based random fields, and structural dependent autocorrelations for random fields are shown. Applications for engineering problems are shown for each extension, where uncertainty analysis of structures with different materials is performed. In this contribution, a damage simulation of a concrete beam with interval valued parametrization of stochastic models, an application for porous media in a multiphysical structural analysis with fuzzy valued parametrization and an uncertainty analysis with structural dependent autocorrelations for timber structures are presented.

To account for the natural variability of material parameters in multiphasic and hydro-mechanical coupled finite element analyses of soil and earth structure applications, the use of probabilistic methods may be effective. Here, selecting the appropriate soil auto-correlation functions for random field realizations plays an essential role. In a joint study, the general influence of auto-correlation lengths on the results of strongly coupled models is determined. Subsequently, a polymorphic approach using fuzzy probability based random fields is used to capture the solution space for fuzzy auto-correlation lengths. To adequately describe the behavior of the soil the theory of porous media is implemented, which uses a homogenization approach for the multiple phases on the soil microstructure. Its foundations and the differentiated methods used for the polymorphic uncertainty quantification are explained in this contribution. Based on two representative examples, the requirements and advantages of a polymorphic uncertainty model are worked out30.

In this contribution, a numerical design strategy for the optimization under polymorphic uncertainty is introduced and applied to a self-weight minimization of a framework structure. The polymorphic uncertainty, which affects the constraint function of the optimization problem, is thereby modeled in terms of stochastic variables, fuzzy sets, and intervals to account for variability, imprecision and insufficient information. The stochastic quantities are computed using polynomial chaos expansion resulting in a purely fuzzy-valued formulation of the constraint functions which can be computed using α-cut optimization. Afterward, the constraint function can be interpreted in a possibilistic manner, resulting in a flexible formulation to include expert knowledge and to achieve a robust design.