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.

Ejectors are simple mechanical devices with no moving parts which convert the pressure energy of a motive fluid to kinetic energy and generate suction of the secondary fluid. The ability to recover waste heat, to operate using solar power and the ability to use geothermal energy make ejector-based systems attractive in different industrial applications. The main challenge of ejector-based refrigeration systems is their relatively low coefficient of performance (COP). In order to increase the ejector performance, two chemically distinct fluids can be used in the refrigeration cycle. It is suggested that a higher molecular mass be used for the motive fluid to improve the entrainment ratio of the binary fluid ejector (BFE) and thus the system COP. Inert gas combinations of argon–helium and krypton–air are studied in this paper using computational fluid dynamics (CFDs) and experimental measurements. All CFD cases were axisymmetric and the appropriate turbulence model was selected based on experimental validation. Specifically, the entrainment ratio and the static pressure along the ejector wall were measured to validate the CFD predictions. It was found that the molar entrainment ratio was significantly higher in argon–helium compared to krypton–air. The static pressure measurements along the wall, in addition, exhibited good agreement with the results obtained via computational fluid dynamics (CFDs).