Bauhaus University, Weimar

About: Bauhaus University, Weimar is a education organization based out in Weimar, Thüringen, Germany. It is known for research contribution in the topics: Finite element method & Isogeometric analysis. The organization has 1421 authors who have published 2998 publications receiving 104454 citations. The organization is also known as: Bauhaus-Universität Weimar & Hochschule für Architektur und Bauwesen.

Isogeometric analysis suitable trivariate NURBS representation of composite panels with a new offset algorithm

Abstract: Trivariate NURBS (non-uniform rational B-splines) representation of composite panels which is suitable for three-dimensional isogeometric analysis (IGA) is constructed with a new curve/surface offset algorithm. The proposed offset algorithm, which is required by IGA, is non-existent in the CAD literature. Using the presented approach, finite element analysis of composite panels can be performed with the only input being the geometry representation of the composite surface. The method proposed provides a bi-directional system in which one can go forward from CAD to analysis and backwards from analysis to CAD. This is believed to facilitate the design of composite structures. Different parts (patches) can be parametrized independently of each other and glued together, in the finite element solver, by a discontinuous Galerkin method. A stress analysis of curved composite panel with stiffeners is provided to demonstrate the proposed framework.

A comparative study of two molecular mechanics models based on harmonic potentials

Abstract: We show that the two molecular mechanics models, the stick-spiral and the beam models, predict considerably different mechanical properties of materials based on energy equivalence. The difference between the two models is independent of the materials since all parameters of the beam model are obtained from the harmonic potentials. We demonstrate this difference for finite width graphene nanoribbons and a single polyethylene chain comparing results of the molecular dynamics (MD) simulations with harmonic potentials and the finite element method with the beam model. We also find that the difference strongly depends on the loading modes, chirality and width of the graphene nanoribbons, and it increases with decreasing width of the nanoribbons under pure bending condition. The maximum difference of the predicted mechanical properties using the two models can exceed 300% in different loading modes. Comparing the two models with the MD results of AIREBO potential, we find that the stick-spiral model overestimates and the beam model underestimates the mechanical properties in narrow armchair graphene nanoribbons under pure bending condition.

Attributed variability models: outside the comfort zone

Abstract: Variability models are often enriched with attributes, such as performance, that encode the influence of features on the respective attribute. In spite of their importance, there are only few attributed variability models available that have attribute values obtained from empirical, real-world observations and that cover interactions between features. But, what does it mean for research and practice when staying in the comfort zone of developing algorithms and tools in a setting where artificial attribute values are used and where interactions are neglected? This is the central question that we want to answer here. To leave the comfort zone, we use a combination of kernel density estimation and a genetic algorithm to rescale a given (real-world) attribute-value profile to a given variability model. To demonstrate the influence and relevance of realistic attribute values and interactions, we present a replication of a widely recognized, third-party study, into which we introduce realistic attribute values and interactions. We found statistically significant differences between the original study and the replication. We infer lessons learned to conduct experiments that involve attributed variability models. We also provide the accompanying tool Thor for generating attribute values including interactions. Our solution is shown to be agnostic about the given input distribution and to scale to large variability models.