TOMAS: Topology Optimization of Multiscale Fluid Devices using Variational Autoencoders and Super-Shapes

Rahul Kumar Padhy, Krishnan Suresh, Aaditya Chandrasekhar

Abstract

In this paper, we present a framework for multiscale topology optimization of fluid-flow devices. The objective is to minimize dissipated power, subject to a desired contact-area. The proposed strategy is to design optimal microstructures in individual finite element cells, while simultaneously optimizing the overall fluid flow. In particular, parameterized super-shape microstructures are chosen here to represent microstructures since they exhibit a wide range of permeability and contact area. To avoid repeated homogenization, a finite set of these super-shapes are analyzed a priori, and a variational autoencoder (VAE) is trained on their fluid constitutive properties (permeability), contact area and shape parameters. The resulting differentiable latent space is integrated with a coordinate neural network to carry out a global multi-scale fluid flow optimization. The latent space enables the use of new microstructures that were not present in the original data-set. The proposed method is illustrated using numerous examples in 2D.

Citation

@article{padhy2024tomas,
  title={TOMAS: topology optimization of multiscale fluid flow devices using variational auto-encoders and super-shapes},
  author={Padhy, Rahul Kumar and Suresh, Krishnan and Chandrasekhar, Aaditya},
  journal={Structural and Multidisciplinary Optimization},
  volume={67},
  number={7},
  pages={119},
  year={2024},
  publisher={Springer}
}