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  • Self-oscillating Gel Simulation Snapshot Dataset

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    Creator:
    Aksoy, Doruk and Kim, Donghak
    Description:
    This dataset contains snapshots from simulations of a hexagonal self oscillating gel sheet defined via a triangular lattice. The lattice has stretching springs between neighboring vertices and bending springs with energy proportional to the square of the angle between neighboring traingular faces. The motion of the lattice is driven by time- and space-varying distributions of the rest lengths of the stretching springs. In the motivating experiments on thin gel sheets, there are chemical waves, radial or spiral in form, that induce local swelling of the sheets. As a simple model, this dataset considers radial or planar (unidirectional) traveling waves in the simulations. The sheet is modeled as a flat hexagon of radius 1 with an equilateral triangular triangle lattice mesh, with initially uniform mesh spacing of 1/33, resulting in 3367 mesh points. A small out-of-plane perturbation is applied and the motion evolves over the sheet over time. The sheet is modeled to have damped dynamics. However for large enough wave amplitudes, the sheet rapidly buckles into shapes with time-varying distributions of curvature, large in magnitude. For more information on the simulation that generated the data, please refer to "Semi-implicit methods for the dynamics of elastic sheets,” at Journal of Computational Physics by Alben et al. For an example SciML application that considers this dataset, please refer to "Inverse design of self-oscillatory gels through deep learning." Neural Computing and Applications by Aksoy et al.
    Keyword:
    Soft robotics, Partial Differential Equations, Scientific Simulations, and Chaotic Systems
    Citation to related publication:
    Alben, Silas, et al. "Semi-implicit methods for the dynamics of elastic sheets." Journal of Computational Physics 399 (2019): 108952., Aksoy, Doruk, et al. "Inverse design of self-oscillatory gels through deep learning." Neural Computing and Applications 34.9 (2022): 6879-6905., Aksoy, Doruk, et al. "An incremental tensor train decomposition algorithm." SIAM Journal on Scientific Computing 46.2 (2024): A1047-A1075., and Aksoy, Doruk, and Alex A. Gorodetsky. "Incremental Hierarchical Tucker Decomposition." arXiv preprint arXiv:2412.16544 (2024).
    Discipline:
    Engineering and Science