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This paper explores integrating artificial intelligence (AI) segmentation models, particularly the Segment Anything Model (SAM), into fluid mechanics experiments. SAM's architecture, comprising an image encoder, prompt encoder, and mask decoder, is investigated for its application in detecting and segmenting objects and flow structures. Additionally, we explore the integration of natural language prompts, such as BERT, to enhance SAM's performance in segmenting specific objects. Through case studies, we found that SAM is robust in object detection in fluid experiments. However, segmentations related to flow properties, such as scalar turbulence and bubbly flows, require fine-tuning. To facilitate the application, we have established a repository (https://github.com/AliRKhojasteh/Flow%5fsegmentation) where models and usage examples can be accessed. [ABSTRACT FROM AUTHOR]

An unstable density stratification between two fluids mixes spontaneously under the effect of gravity, a phenomenon known as Rayleigh--Taylor (RT) turbulence. If the two fluids are immiscible, for example, oil and water, surface tension prevents intermixing at the molecular level. However, turbulence fragments one fluid into the other, generating an emulsion in which the typical droplet size decreases over time as a result of the competition between the rising kinetic energy and the surface energy density. Even though the first phenomenological theory describing this emulsification process was derived many years ago, it has remained elusive to experimental verification, hampering our ability to predict the fate of oil in applications such as deep-water spills. Here, we provide the first experimental and numerical verification of the immiscible RT turbulence theory, unveiling a unique turbulent state that originates at the oil--water interface due to the interaction of multiple capillary waves. We show that a single, non-dimensional, and time-independent parameter controls the range of validity of the theory. Our findings have wide-ranging implications for the understanding of the mixing of immiscible fluids. This includes in particular oil spills, where our work enables the prediction of the oil--water interface dynamics that ultimately determine the rate of oil biodegradation by marine bacteria. [ABSTRACT FROM AUTHOR]

In the design of pipelines for the transport of fluids, different equations are used to determine the friction factor in laminar and turbulent regimes. However, it is possible that there are important variations of the different parameters involved in the physical phenomenon, which significantly change the laminar flow regime to turbulent or vice versa. Therefore, it is practical to have unified expressions that are applicable regardless of the flow pattern. In this order of ideas, over time some researchers have proposed formulations for this purpose, however, it is absolutely necessary to validate, especially the modern equations, in order to establish the accuracy and, therefore, the reliability of the results that each expression provides. [ABSTRACT FROM AUTHOR]

Turbulence is a longstanding problem in fluid mechanics for which experimentation remains unavoidable. In contrast to conventional experimental techniques that inevitably require the introduction of probes into the flow, a very convenient technique would be one in which there is no contact between the measuring sensors and the flow. The laser-based diagnostic technique reported in this work is described as an estimation of a large number of parameters defining the diffusion coefficient profile in the heated turbulent wind tunnel jet, which is required in the formula of the Karman turbulence spectrum for the jet under study. For this purpose, some required experiments in the jet are carried out. A laser beam is then sent perpendicular to the jet exhaust, and measurements of the probabilities of the position of the laser beam's impact on a photocell placed outside the jet are performed. Using the Markovian model, the same probabilities are calculated numerically. For these numerical results to agree with the experimental results, a numerical optimal-control strategy is applied. Due to the large number of unknown parameters searched, a genetic algorithm (GA) computation is performed. A good agreement observed between the GA results and those derived from the previously published cold-wire-anemometer data, combined with the use of the Dale-Gladstone law, proves the validity and accuracy of the laser-based genetic measurement technique. [ABSTRACT FROM AUTHOR]