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<p>Gas turbine combustors commonly feature swirling flows. The swirl is usually characterized by the swirler geometry. The development of swirl-stabilized burners includes the experimental assessment of the resulting flow field and the quantification of the swirl, e.g. through Laser Doppler Anemometry (LDA). LDA accurately acquires flow velocity components in a small probing volume. Nevertheless, the measurement quality represents a trade-off between invested measurement time and spatial resolution. In this work, the potential of Physics-Informed Neural Networks (PINNs) to assimilate flow fields from sparse LDA measurements is investigated. A novel burner is employed in which the swirl is fluidically adjustable from a non-swirled jet to a fully swirled flow through a secondary air flow injection. Data is acquired through LDA within spatial measurement grids at multiple axial distances from the swirler. Assuming symmetry, axial, tangential, and radial velocity components are determined. A PINN is subsequently trained with the acquired data, creating a continuous and differentiable flow field representation by evaluating RANS equations. By systematically reducing the training data while evaluating the physical validity of the reconstructed field, a minimum training data requirement is identified. As a result, for three operating conditions, the flow field is adequately characterized by a minimum of measurement points.</p>

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Reconstruction of a Continuous Flow Field from Discrete Experimental Data Points using Physics-Informed Neural Networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月26日

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  • Mattias E. G. Eck,
  • Jakob G. R. von Saldern,
  • Philipp zur Nedden,
  • Alessandro Orchini,
  • Christian Oliver Paschereit

摘要

Gas turbine combustors commonly feature swirling flows. The swirl is usually characterized by the swirler geometry. The development of swirl-stabilized burners includes the experimental assessment of the resulting flow field and the quantification of the swirl, e.g. through Laser Doppler Anemometry (LDA). LDA accurately acquires flow velocity components in a small probing volume. Nevertheless, the measurement quality represents a trade-off between invested measurement time and spatial resolution. In this work, the potential of Physics-Informed Neural Networks (PINNs) to assimilate flow fields from sparse LDA measurements is investigated. A novel burner is employed in which the swirl is fluidically adjustable from a non-swirled jet to a fully swirled flow through a secondary air flow injection. Data is acquired through LDA within spatial measurement grids at multiple axial distances from the swirler. Assuming symmetry, axial, tangential, and radial velocity components are determined. A PINN is subsequently trained with the acquired data, creating a continuous and differentiable flow field representation by evaluating RANS equations. By systematically reducing the training data while evaluating the physical validity of the reconstructed field, a minimum training data requirement is identified. As a result, for three operating conditions, the flow field is adequately characterized by a minimum of measurement points.