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Flapping Soft Fin Deformation Modeling using Planar Laser-Induced Fluorescence Imaging
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Multiobjective optimization for flapping foil hydrodynamics with a multitask and multifidelity approach.

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  • 1Zhejiang University, Hangzhou 310027, China.

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|February 17, 2024
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Summary
This summary is machine-generated.

We developed a multitask and multifidelity Gaussian process (MMGP) model for optimizing flapping foil performance. This approach efficiently uses varied data fidelity to reduce costs and enhance multiobjective predictions.

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Area of Science:

  • Fluid dynamics
  • Machine learning
  • Computational physics

Background:

  • Flapping foils are complex systems with multiobjective performance characteristics.
  • High-fidelity data acquisition for optimizing these systems is often costly and time-consuming.
  • Existing models may struggle to efficiently integrate data of varying fidelity levels.

Purpose of the Study:

  • To develop a novel multitask and multifidelity Gaussian process (MMGP) model.
  • To accurately predict and optimize the multiobjective performance of flapping foils.
  • To minimize the computational cost associated with high-fidelity data.

Main Methods:

  • Utilized a spectral mixture kernel after comparing three kernel options.
  • Implemented a linear prior formula-based multifidelity framework to incorporate varying data fidelities.
  • Employed Bayesian optimization with a multiacquisition function for multitask active learning.

Main Results:

  • The MMGP model demonstrated robust and effective performance.
  • The multiacquisition function proved to be effective for active learning.
  • Successful integration of data with different fidelity levels was achieved.

Conclusions:

  • The MMGP model is a capable and efficient framework for addressing multiobjective challenges in flapping foil optimization.
  • This approach offers a cost-effective solution for complex fluid dynamics problems.
  • The study highlights the potential of multitask and multifidelity learning in engineering applications.