Flush-mounted cavity hot-wire probes have emerged as an alternative to classical hot-wire probes mounted several diameters above the surface for wall-shear stress measurements. They aim at increasing the frequency response and accuracy by circumventing the well-known issue of heat transfer to the substrate that hot-wire and hot-film probes possess. Their use, however, depends on the assumption that the cavity does not influence the flow field. In this study, we show that this assumption does not hold, and that turbulence statistics are modified by the presence of the cavity with sizes that are practically in use. The mean velocity and fluctuations increase near the cavity while the shear stress decreases in its surroundings, all seemingly stemming from the fact that the no-slip condition is not present anymore and that flow reversal occurs. Overall, the energy spectra and the probability density function of the wall shear stress fluctuations indicate a change of nature of turbulence by the presence of the cavity.

High-Performance Computing (HPC) systems provide input/output (IO) performance growing relatively slowly compared to peak computational performance and have limited storage capacity. Computational Fluid Dynamics (CFD) applications aiming to leverage the full power of Exascale HPC systems, such as the solver Nek5000, will generate massive data for further processing. These data need to be efficiently stored via the IO subsystem. However, limited IO performance and storage capacity may result in performance, and thus scientific discovery, bottlenecks. In comparison to traditional post-processing methods, in-situ techniques can reduce or avoid writing and reading the data through the IO subsystem, promising to be a solution to these problems. In this paper, we study the performance and resource usage of three in-situ use cases: data compression, image generation, and uncertainty quantification. We furthermore analyze three approaches when these in-situ tasks and the simulation are executed synchronously, asynchronously, or in a hybrid manner. In-situ compression can be used to reduce the IO time and storage requirements while maintaining data accuracy. Furthermore, in-situ visualization and analysis can save Terabytes of data from being routed through the IO subsystem to storage. However, the overall efficiency is crucially dependent on the characteristics of both, the in-situ task and the simulation. In some cases, the overhead introduced by the in-situ tasks can be substantial. Therefore, it is essential to choose the proper in-situ approach, synchronous, asynchronous, or hybrid, to minimize overhead and maximize the benefits of concurrent execution.