Multi-fidelity reduced-order model for GPU-enabled microfluidic concentration gradient design (original) (raw)

Abstract

This paper presents a multi-fidelity reduced-order model (MFROM) and global optimization method for rapid and accurate simulation and design of microfluidic concentration gradient generators (µCGGs). It divides the entire process into two stages: the offline ROM construction and the online ROM-based design optimization. In the offline stage, proper orthogonal decomposition is used to obtain the low-dimensional representation of the high-fidelity CFD data and the low-fidelity physics-based component model (PBCM) data, and a kriging model is developed to bridge the fidelity gap between PBCM and CFD in the modal subspace, yielding compact MFROM applicable within broad trade space. The GPU-enabled genetic algorithm is utilized to optimize µCGG design parameters through massively parallelized evaluation of the fast-running MFROM. The numerical results show that MFROM is a feasible and accurate multi-fidelity modeling approach to replace costly CFD simulation for rapid global optimization (up to 11 s/optimization). The design parameters obtained by MFROM-based optimization produce CGs that match the prescribed ones very well with an average error < 6%.

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Authors and Affiliations

  1. Department of Mechanical Engineering, University of South Carolina, Columbia, SC, 29208, USA
    Haizhou Yang, Seong Hyeon Hong & Yi Wang
  2. Department of Mechanical and Aerospace Engineering, University of Alabama in Huntsville, Huntsville, AL, 35899, USA
    Gang Wang

Authors

  1. Haizhou Yang
  2. Seong Hyeon Hong
  3. Gang Wang
  4. Yi Wang

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Correspondence toYi Wang.

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Yang, H., Hong, S.H., Wang, G. et al. Multi-fidelity reduced-order model for GPU-enabled microfluidic concentration gradient design.Engineering with Computers 39, 2869–2887 (2023). https://doi.org/10.1007/s00366-022-01672-z

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