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This numerical study investigates the dynamics around a single Taylor bubble rising with small-dispersed bubbles in a vertical pipeline. The Volume-of-fluid (VOF) method in Ansys Fluent 18.1 is used to track the Taylor bubble, and the Discrete Particle Method (DPM) is used to track the small-dispersed bubbles, for the following parameter range; $$812 \leqslant \text{Re} \leqslant 5684$$ , $${\kern 1pt} Eo = 94$$ and $$\log (Mo) = - 10.6$$ . The coupling strategies for both the continuous gas–liquid phase as well as the discrete bubble phase are all accounted for in the numerical calculations. The dispersed bubbles significantly affect the behaviour of the rising Taylor bubble in numerous ways. The terminal velocity increases with an increase in the number of dispersed bubbles. The rise velocity is, however directly correlated with the deformation of the nose of the bubble. The flow dynamics around the nose of the Taylor bubble are stronger for low velocities due to lower inertia. The computations also demonstrated that there are both acceleration and deceleration effects on the Taylor bubble. Our predictions are in quantitative agreement with published experimental results by Cerqueira and Paladino (Int. J. Multiph. Flow, vol. 133, p. 103,450, Dec. 2020).

The study is influenced by the significant progress made in the field of hydrodynamics and heat transfer enhancement of Taylor flow in vertical and horizontal pipelines. It maps the hydrodynamics and thermal properties of Taylor flow using 752 scientific articles published in refereed journals, extracted from the Web of Science Core Collection (WoSCC), using bibliometric analysis. The study identifies clusters that constitute the intellectual structure of the study domain, and investigates their linkages, by incorporating all citations from the Science Citation Index (SCI) from 2000–2021. The study highlights the most active, impactful, and influential parameters surrounding Taylor flow research. This analysis is critical for expanding our understanding of the intellectual network and identifying new research directions.

This is an incompressible numerical study of the hydrodynamics and heat transfer characteristics of Taylor flow in vertical oil and gas pipelines under constant heat flux using the Volume-of-fluid (VOF) method in ANSYS Fluent, covering a wide range of Re(0.22 ≤ Re ≤ 800) and Ca(0.0075 ≤ Ca ≤ 0.35). Nusselt number (Nu) correlations were used to examine the heat transfer characteristics based on a set of flow parameters. A comparison of the predictions of the void fraction, average velocity, pressure drop and the mean Nusselt number was made with available experimental observations, with most of the experimental data falling within 15.540% of the current study. The bubble increases in length with increasing capillary number and the wall of the tube at the confines of the gas phase leads to asymmetric and axisymmetric bubbles at low and high capillary numbers respectively. The transition region between the edge of the bubble and the film thickness increases with an equivalent increase in Ca and more evident at high Re. The study revealed that, Taylor flow plays a more significant role on the pressure drop increase and, provided the mechanisms and theoretical guidance for heat transfer characteristics in oil and gas pipelines.

This paper proposes an approach which safeguards confidentiality to avoid disclosures of features within a multiple IoT situation, that is, a setup of objects in networks that communicate with each other. Two ideas derived from the theory of databases, namely k-anonymity and t-certitude, form our basis. They are used to cluster the objects to provide a unitary view of them and their characteristics. In fact, the use of anonymity and t-closeness robustly ensures privacy for derived groups. Furthermore, description of the object grouping scheme that preserves privacy, which represents the core of our approach was studied. Eventually, we illustrated the corresponding security model and analyzed the associated properties. The study also provided important advantages for the protection of user privacy in all those situations where knowledge of object features may help an attacker to obtain information about user habits and behavior. This study prevents not only the disclosure of information but also the divulgation of features. This is a major strength of our approach as malicious analyzes of the characteristics of objects can interfere with the privacy of people.

Navigating the turbulent waters of the doctoral voyage Navigating the turbulent waters of the doctoral voyage

There has been a major concern in the utilization of an efficient routing protocol that should be versatile and ready to maintain router regardless of the changing network connectivity. Parallel expansion in the interest for fast deployable, short-term systems that work with no centralized base station have been studied. MANETs venture to turn the imagination of getting connected anywhere and at any time into a reality, in this work, we examined a cluster-based routing by utilizing a multi-meshed tree model. The key objective is to decrease the control overheads needed in the organization and maintenance of network while providing a robust connectivity. We proposed a (HMMT) Hybrid Multi-Meshed Tree, routing protocol features a combination of Proactive and Reactive (Hybrid) routing. This proposed protocol solves the challenges associated with dynamic adaptation and improved connectivity via a Hierarchical Cluster based method along with a Virtual Identifier. The simplicity of its algorithm and reduced message complexity are suitable for limited power, resource constrained networks and limited bandwidths are its major advantages.