The working conditions of the Scaled Convective Airflow Laboratory Experiment (SCALEX) at Technische Universität Ilmenau and sample experiments are reported. The SCALEX facility is a pressure vessel which allows for downscaling of laboratory experiments up to a factor of 20 by compression of gaseous working fluids, air or sulfur hexafluoride, to change the material properties of the fluid. The requirements and conditions for downscaling of fluid dynamical problems are discussed in detail. Long-term high and low pressure tests are conducted to screen the stability of the experimental environment inside the vessel against pressure and temperature fluctuations. Finally, a Rayleigh–Bénard convection experiment at an aspect ratio 10 is performed inside the SCALEX facility as a proof of concept. The reference experiment was conducted under 4.5 bar pressure for Ra = 1.9 × 105. However, the Rayleigh number could be varied in a wide range of Ra = 104 … 108. The flow investigation was pursued with stereoscopic particle image velocimetry in horizontal mid-plane through the convection cell. To improve the image quality the cameras were placed inside the pressure cell and tested up to 6 bar. Thus the feasibility of optical flow measurements at elevated pressures is shown.

The prediction of turbulent flow by the application of machine learning (ML) algorithms to big data is a concept currently in its infancy which requires further development. It is of special importance if the aim is a prediction that is good in a statistical sense or if the vector fields should be predicted as good as possible. For this purpose, the deterministic and statistical prediction of the unsteady but periodic flow of the von Kármán Vortex Street (KVS) was examined using an Echo State Network (ESN) which is well suited for learning from time series due to its recurrent connections. The experimental data of the velocity field of the KVS were collected by Particle Image Velocimetry (PIV). Then, the data were reduced by Proper Orthogonal Decomposition (POD) and the flow was reconstructed by the first hundred modes. An ESN with 3000 neurons was optimized with respect to its three main hyperparameters to predict the time coefficients of the POD modes. For the deterministic prediction, the aim was to maximize the correct direction of the vertical velocities. The results indicate that the ESN can mimic the periodicity and the unsteadiness of the flow. It is also able to predict the sequence of the upward and downward directed velocities for longer time spans. For the statistical prediction, the similarity of the probability density functions of the vertical velocity fields between the predicted and actual flow was achieved. The leaking rate of the ESN played a key role in the transition from deterministic to statistical predictions.

, 84 pages Flow characterization over delta wings have gained attention in recent decades due to their prevailing usage in designs of unmanned air vehicles (UAVs). In literature, only a few studies have reported wing thickness effect on both the aerodynamic performance and detailed flow structure over delta wings. In the present investigation, the effect of thickness-to-chord (/) ratio on aerodynamics of a non-slender delta wing with 45 degree sweep angle is characterized in a low-speed wind tunnel using laser illuminated smoke visualization, surface pressure measurements, particle image velocimetry, and force measurements. The delta wings with / ratios varying from 2 % to 15 % are tested at broad ranges of angle of attack and Reynolds number. The results indicate that the effect of / ratio on flow structure is quite substantial. Considering the low angles of attack where the wings experience leading edge vortex structure, the strength of the vortex structure increases as the / ratio increases. However, low / ratio wings have pronounced surface separations at higher angle of attack compared to the high / ratio wings. These results are well supported by the force measurements such that high / ratio wings induce higher lift coefficients, CL, at vi low angles of attack, whereas maximum CL values are higher and appear at higher angle of attack for low / ratio wings. This indicates that low / ratio wings are more resistive to the stall condition. Considering the lift-to-drag ratio, CL/CD, increase in / ratio induces remarkable drop in CL/CD values.

The flow behind nine different arrays of cylinders is experimentally investigated via Particle Image Velocimetry (PIV) at a Reynolds number of Re ∼100 based on the diameter of the cylinders. Each array consists of a column of four cylinders in front and three in the rear. The horizontal distance between the two columns and the vertical distance between the cylinders within each column are varied for H/D=[2,4,8] and V/D=[2,4,6], resulting in nine different arrays denoted as mVnH, where m corresponds to V/D and n stands for H/D. The PIV measurements are conducted for 15 s at 200 Hz frequency, corresponding to 39 to 360 vortex shedding events for the wakes in this study. Then, proper orthogonal decomposition is applied to the velocity fields to analyze the flow dynamics. All arrays show unsteady flow, and based on their flow structures, they are classified in to three main categories of single bluff body (SBB), transitional (TR), and co-shedding (CS) flow. SBB characteristics can be seen for 2V2H and 2V4H arrays, but the latter has more steady vortex shedding as the H/D increases from 2 to 4. Then, 2V8H and 4V2H have an asymmetric flow with several vortex streets and act as an intermediary stage in the shift from SBB to CS flow structure when the distances are increased. The highest total kinetic energy values and widest probability density functions of the velocity components are observed for this group. The five remaining arrays in the CS group have symmetric flow, with three or five vortex streets present behind. However, based on the distances, the frequency and phase synchronization of the vortex streets change considerably, which might have an important effect on, for example, the heat transfer or the structural load of the cylinders.