A new facility for studying turbulent boundary layer flows has been developed at the Delft University of Technology and is referred to as the DU-BLF: Delft University Boundary Layer Facility. Its design and boundary layer data characteristics are presented in the current work. The DU-BLF can be employed for a range of studies revolving around boundary layer flows, e.g., covering fundamentals of boundary layer development, flow control with passive surface modifications, and control efforts with active technologies. A modular setup of the test section allows for a relatively long development length, with both physical and optical access over its complete extent. For the present characterisation, a turbulent boundary layer was developed under a zero (streamwise) pressure gradient, with the aid of a flexible ceiling. We establish the general flow characteristics, including freestream turbulence intensity levels, acoustic noise characteristics, boundary layer-integral parameters, and wall-normal profiles of the first and second-order turbulence statistics. Results are validated by employing multiple measurement techniques, namely, hot wire anemometry, particle image and tracking velocimetry, and wall-pressure measurements. Results are shown for friction Reynolds numbers up to Re _τ≈5100, and reveal that the boundary layer flow adheres to the expected behaviour of canonical wall-bounded turbulence. Data of the current turbulent boundary layer measurements are made available online.

Phase-resolved volumetric velocity measurements of a pulsed jet are conducted by means of three-dimensional particle tracking velocimetry (PTV). The resulting scattered and relatively sparse data are densely reconstructed by adopting physics-informed neural networks (PINNs), here regularized by the Navier-Stokes equations. It is shown that the assimilation yields a higher spatial resolution, and the process remains robust, even at low particle densities ( 𝑝𝑝𝑝 < 0.001). This is achieved by enforcing compliance with the governing equations, thus leveraging the spatiotemporal evolution of the measured flow field. The results indicate that the PINN reconstructs unambiguously velocity, vorticity and pressure fields with a level of detail not attainable with conventional methods (binning) or more advanced data assimilation techniques (vortex-in-cell). The results of this article support the findings of Clark di Leoni (2023) suggesting that the PINN methodology is inherently suited to the assimilation of PTV data, in particular under conditions of severe sparsity or during experiments with limited control of seeding concentration.

We investigate the impact of a single miniature Helmholtz resonator on wall-bounded turbulence using time-resolved planar particle image velocimetry. A particular aim is to explain the mechanism by which a resonator alters the turbulent velocity fluctuations of different scales. A grazing flow configuration is studied in which the resonator is embedded in the wall beneath a turbulent boundary layer at a friction Reynolds number of Reτ≈2300; the resonator is designed so that its resonance frequency matches the peak frequency of the wall-pressure spectrum. It is found that the resonator amplifies velocity fluctuations near its resonance frequency, while it attenuates the energy of subresonance scales. Underlying mechanisms responsible for these changes in energy are discussed in view of the resonator's local impedance condition. It is posited that large-scale velocity fluctuations in the wall-normal velocity, at temporal frequencies below resonance, are subject to a phase-opposed wall-normal velocity perturbation when the TBL flow convects over the resonator's orifice. This yields a decrease of large-scale energy in u′u′¯,-u′v′¯, and v′v′¯. In addition, modifications of the wall-shear stress field downstream of the resonator are addressed. Insights from this research will contribute to the development of surface designs for passive skin-friction control using arrays of miniature resonators.

Phase-resolved volumetric velocity measurements of a pulsed jet are conducted by means of three-dimensional particle tracking velocimetry (PTV). The resulting scattered and relatively sparse data are densely reconstructed by adopting physics-informed neural networks (PINNs), here regularized by the Navier-Stokes equations. It is shown that the assimilation remains robust even at low particle densities ( ppp < 10 − 3 ) where the mean particle distance is larger than 10% of the outlet diameter. This is achieved by enforcing compliance with the governing equations, thereby leveraging the spatiotemporal evolution of the measured flow field. Thus, the PINN reconstructs unambiguously velocity, vorticity, and pressure fields, enabling a robust identification of vortex structures with a level of detail not attainable with conventional methods (binning) or more advanced data assimilation techniques (vortex-in-cell). The results of this article suggest that the PINN methodology is inherently suited to the assimilation of PTV data, in particular under conditions of severe data sparsity encountered in experiments with limited control of the seeding concentration and/or distribution.