Technische Universität Dresden

Permanent URI for this community

http://opara.zih.tu-dresden.de/handle/123456789/10
Research data repository of Dresden University of Technology.

Browse

Browsing Technische Universität Dresden by Subject "4::41::402::402-02"

Now showing 1 - 2 of 2
  • Item
    Open Access
    Data: Neural networks meet anisotropic hyperelasticity: A framework based on generalized structure tensors and isotropic tensor functions
    (Technische Universität Dresden, 2024-11-21) Kalina, Karl Alexander; Brummund, Jörg; Sun, WaiChing; Kästner, Markus
    This collection provides homogenized datasets including deformation gradient, free energy, stress tensor and material tangent for anisotropic hyperelastic composites. Five different representative volume elements (RVEs) are included: An RVE of a fiber reinforced material with stochastic fiber distribution (stochastic fibers), a unit cell with a hexagonal fiber arrangement (hexagonal fibers), a unit cell with one spherical inclusion (cubic sphere), an RVE with a plane-like arrangement of particles (plane spheres), and an RVE with an arrangement of particles in a chain-like structure (chain spheres). All components, i.e., matrix, particles and fibers are assumed to be compressible and isotropic. For all, a two-parametric neo-Hookean model was chosen. The data have been generated by using an in-house finite element code based on Matlab. The data belongs to the work "Neural networks meet anisotropic hyperelasticity: A framework based on generalized structure tensors and isotropic tensor functions" by Kalina et al.; https://doi.org/10.48550/arXiv.2410.03378. Further information on the data can be found there.
  • Item
    Open Access
    Dual testing field for studies of environmental and operational effects in structural damage localization of mechanical structures
    (Technische Universität Dresden, 2024-07-09) Rohrer, Maximilian
    This dataset accompanies the research on the impact of Environmental and Operational Conditions (EOC) on vibration-based Structural Health Monitoring (SHM) methods. It includes comprehensive acceleration data collected from a novel experimental testing field consisting of two identical mechanical structures. One structure operates in a controllable laboratory environment, while the other is subjected to real-world EOC in a field setup. The dataset captures mass along with various environmental factors affecting the field setup. This modular measurement system ensures the collection of high-quality data, making this dataset a valuable benchmark for researchers studying the effects of EOC on SHM. The dataset provides a unique opportunity for validating and developing robust SHM techniques that can adapt to varying EOC, fostering advancements in the field.