Cavitating jets have been widely studied for over a century, but despite the extensive literature on this subject, the implementation of cavitating jets in many industries is still very limited due to technical challenges. The main purpose of the present paper is to provide recommendations on using the cavitating jets based on a comprehensive literature review on the erosion mechanism in these jets. Self-resonating jets are extensively discussed in the present paper due to their importance in amplifying the erosion effect of cavitating jets. The influence of different jet nozzle geometric parameters and the operating conditions of the cavitating jet flow on the erosion mechanism is also discussed. Finally, well drilling in addition to multiple other industrial applications of cavitating jets are examined.

Fluid mechanics often involves complex systems characterized by a large number of physical parameters, which are usually described by experimental and numerical sparse data (temporal or spatial). The difficulty of obtaining complete spatio-temporal datasets is a common issue with conventional approaches, such as computational fluid dynamics (CFDs) and various experimental methods, particularly when evaluating and modeling turbulent flows. This review paper focuses on the integration of machine learning (ML), specifically physics-informed neural networks (PINNs), as a means to address this challenge. By directly incorporating governing physical equations into neural network training, PINNs present a novel method that allows for the reconstruction of flow from sparse and noisy data. This review examines various applications in fluid mechanics where sparse data is a common problem and evaluates the effectiveness of PINNs in enhancing flow prediction accuracy. An overview of diverse PINNs methods, their applications, and outcomes is discussed, demonstrating their flexibility and effectiveness in addressing challenges related to sparse data and illustrating that the future of fluid mechanics lies in the synergy between data-driven approaches and established physical theories.

Hydrodynamic cavitation is the formation, growth and subsequent collapse of vapor bubbles in a moving liquid. It is extremely important to determine conditions of cavitation inception and when it starts damaging industrial equipment. In some cases, such as hydrodynamic cleaning it is important to understand how to improve the cavitation phenomenon in order to enhance cleaning properties. The cavitation number is a parameter used to predict cavitation and its potential effects. In this paper we discuss limitations of this parameter and demonstrate that it cannot be considered sufficient to predict cavitation inception and development in the fluid flow. The experimental setup was designed and built to study cavitation inception in various nozzles. RANS SST k–ω turbulence model was used in this study to model turbulent flow in ANSYS Fluent. CFD calculations were compared to experimental results. It was shown that cavitation inception was sensitive to change in nozzle geometry and, since geometrical parameters are not included in cavitation number formula, scenarios of cavitation inception can be different at the same cavitation number.

Flow patterns of a Taylor-Couette-Poiseuille flow were studied at low axial Reynolds and rotational Taylor numbers (Re ≤ 10.5, Ta ≤ 319). The radius ratio of the inner and outer cylinders was 0.804 and the ratio of the length of the annulus to the gap width was 44.5. Complete map of the studied flow regimes was elaborated. The axial and azimuthal components of the wall shear rate γ were measured at the outer fixed cylinder using a three-segment electrodiffusion probe. The components of the wall shear rate of helices have never been measured in previous investigations. Spatio-temporal description of multiple flow patterns was obtained using flow visualizations and simultaneous measurements of wall shear rate components. The flow structures include Taylor vortices, helices winding in the same direction as the base flow or in the opposite direction, helices that were stagnant or moving in the axial direction, smooth or with superposed azimuthal waves, among others. The influence of different flow structures on the wall shear stress components is discussed with and without axial base flow. It was found that the wall shear stress is a function of Ta but no significant dependence on Re was observed for the studied flow regimes and that the mean wall shear stress increases with the number of azimuthal waves. It was also noted that the ED probes provide a more detailed information about flow patterns than torque measurements and visualizations described in the literature.