Uniform blowing in wall bounded shear flows is well known for its drag reducing effects and has long been investigated ever since. However, many contemporary and former research on the present topic has confirmed the drag reducing effect but very less is known regarding how blowing mechanism is effecting the coherent structures, more importantly, their influence on the Reynolds stresses at high Reynolds number. Therefore, effect of uniform blowing has been experimentally investigated in a zero pressure gradient turbulent boundary layer (TBL). The wind tunnel used for the measurement was particularly suitable to obtain high resolution data (Boundary layer thickness 1 , δ > 0.24m) at high Reynolds number with Stereo Particle Image Velocimetry (SPIV) measurements. The data presented in this literature covers a large range of high Reynolds number flow e.g. Re θ = 7500 ∼ 19763 where Reynolds number is based on the momentum thickness. Upstream effect of blowing was varied from 1% ∼ 6% of free stream velocity by tuning the flow rate of the compressed air and measurements were taken downstream after a short interval. In order to access statistics and turbulence properties of the TBL with focus on the logarithmic and outer region, the streamwise SPIV plane (Vertical plane parallel to flow direction) configuration was used to obtain velocity fields.

Physics-informed neural networks (PINN) are machine-learning methods that have been proved to be very successful and effective for solving governing equations of fluid flow. In this work we develop a robust and efficient model within this framework and apply it to a series of two-dimensional three-component (2D3C) stereo particle-image velocimetry (SPIV) datasets, to reconstruct the mean velocity field and correct measurements errors in the data. Within this framework, the PINNs-based model solves the Reynolds-averaged-Navier-Stokes (RANS) equations for zero-pressure-gradient turbulent boundary layer (ZPGTBL) without a prior assumption and only taking the data at the PIV domain boundaries. The TBL data has different flow conditions upstream of the measurement location due the effect an applied flow control via uniform blowing. The developed PINN model is very robust, adaptable and independent of the upstream flow conditions due to different rates of wall-normal blowing while predicting the mean velocity quantities simultaneously. Hence, this approach enables improving the mean flow quantities by reducing errors in the PIV data. The PINNs-predicted data have less than 1\% error and is in excellent agreement with reference data. This shows that PINNs has potential applicability to shear-driven turbulent flows with different flow histories for predicting high-fidelity data.

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To study airborne transport of aerosol particles by mixed convection and dynamic situations within a closed room, the Cottbus Aerosol Particle Reference Experiment (CARE) was built and equipped, which includes thermal manikins and a spreader dummy. For various flow configurations (location of spreader, heating bodies, windows opened, air ventilation with and without air purification systems) flow visualisation was performed, particulate matter sensors (PMS) measured local particle concentrations, head-mounted camera systems counted particle concentrations of individuals and finally, large field of view Shake-The-Box Particle Tracking delivered velocity fields. The comprehensive experimental configuration of different measurement systems are discussed in terms of their aerosol transport properties and quantitative results, effective application and comparative efficiency explaining the flow dynamics. The findings from these experiments also provide information under which circumstances particularly high concentrations of aerosol particles can be found on which locations.

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