A creative low-cost and compact mechanical device that mimics the rapid closure of the pistol shrimp claw was used to conduct electrochemical experiments, in order to study the effects of hydrodynamic cavitation on the corrosion of aluminum and steel samples. Current–time curves show significant changes associated with local variations in dissolved O₂ concentration, cavitation-induced erosion, and changes in the nature of the surface corrosion products.

A computationally-supported experimental technique is presented, to measure height of luminous flames, using convolution and density-based spatial clustering for image processing. The experimental setup employs a high-definition camera array to capture flame imagery from 0°, 45° and 90° visualization planes. The volumetric fuel flow was ranged from 350 to 1800 cc min ⁻¹ and images of the resulting flame structure were captured and measured. Results show that output measurements are affected by both the volumetric fuel flow and the visualization plane. Whilst the former is evidently the most significant factor, the latter was found to be relevant, since several flame features, particularly, flame tilt and flickering, are only perceivable through specific visualization planes; attributed to uneven flame structure due non-homogeneous thermal stress. The experimental technique proposed yields an overall statistical tolerance of 1.29 cm and an expanded uncertainty of 0.609 cm (∼11.5%). From these results, the test is considered successful and the proposed experimental technique is deemed to be on par with the already existing ones. The utilization of spatial density clustering of image gridded data has only been tested for this implementation; being severely constrained by the sample size and density variability of the data. Consequently, care should be exercised. Nonetheless, this approach was found to inherently recognize flame front edge, and mitigate variations of pixel value due change in flame intensity, normalizing image processing, hence, it is proposed as a viable alternative for flame feature/structure visualization and estimation, and further research is encouraged.

We characterize the long-term development of high-viscosity gas–liquid intermittent flows by means of a detrended fluctuation analysis (DFA). To this end, the pressures measured at different locations along an ad hoc experimental flow line are compared. We then analyze the relevant time-series to determine the evolution of the various kinds of intermittent flow patterns associated with the mixtures under consideration. Although no pattern transitions are observed in the presence of high-viscosity mixtures, we show that the dynamical attributes of each kind of intermittence evolves from one point to another within the transport system. The analysis indicates that the loss of a long-range correlation between the pressure responses are due to the discharge processes.

The flow of viscous and highly viscous fluids in straight and bent pipes and channels is a fundamental process in a wide variety of industrial applications and is, therefore, of great interest in science and engineering. Understanding the physics behind such flows has a direct impact on the design of efficient, safe and reliable systems. The type of fluid, which can be viscous or even highly viscous, and the pipe geometry can affect the flow dynamics, the pressure loss and the overall efficiency of the process. In this paper, we provide an extensive review of the state-of-the-art research concerning the flow of Newtonian and non-Newtonian, single-phase fluids in straight and bent pipes. Since a big amount of work in the literature is devoted to the study of Newtonian pipe flows, the paper starts with a brief outline of the nonlinear theory of viscous Newtonian fluid flow in pipes, including a survey of early and recent analytical solutions to the Navier–Stokes equations. The central part of the paper deals with an extensive overview of existing experimental and numerical research work on viscous Newtonian pipe flows. Separate sections are devoted to non-Newtonian fluid flows, the problem of entropy generation due to irreversible processes in the flow and hydromagnetic Newtonian and non-Newtonian pipe flow. The review closes with a brief survey of machine learning and artificial intelligence modeling applied to pipe flow along with future trends and challenges in pipe flow research.