Explore the vast range of titles available.

This study employs a numerical simulation approach to investigate argon bubble flow behavior within a steel continuous casting mold, with a focus on the impact of bubble swarm correction models. Three scenarios are compared: one without any correction and two incorporating drag coefficient corrections, specifically designed for bubble swarm effects. The results demonstrate that incorporating these correction models significantly improves the predictive accuracy of simulations. In particular, the inclusion of a bubble swarm correction model reduces the error in predicted bubble trajectories by 51.7% and 23.0%, respectively, when measured by Hausdorff distances against experimental trajectory data, compared to the scenario without corrections. These findings underline the importance of selecting an appropriate drag correction model for accurate simulations of bubble dynamics and their interaction with the liquid steel in continuous casting molds. This study highlights that drag correction models tailored to the specific conditions of the continuous casting process are essential for achieving realistic predictions.

The present study used a sparged bubble column to study the mixing of a passive scalar under bubble-induced diffusion. The effect of gas superficial velocity (up to 69 mm/s) and external vertical vibrations (amplitudes up to 10 mm, frequency <23 Hz) on the mixing time scale were investigated. The bubble-induced mixing was characterized by tracking the distribution of a passive scalar within a sparged swarm of bubbles. Void fraction and bubble size distribution were also measured at each test condition. Without vibrations (static), the bubble column operated in the homogenous regime and the mixing time scale was insensitive to void fraction, which is consistent with the literature. In addition, the temporal evolution of the static column mixing was well approximated as an error function. With vertical vibrations at lower amplitudes tested, the bubble-induced mixing was restrained due to the suppression of the liquid velocity agitations in the bubble swarm wake, which decelerates mixing. Conversely, at higher amplitudes tested, vibration enhanced the bubble-induced mixing; this is attributed to bubble clustering and aggregation that produced void fraction gradients, which, in turn, induced a mean flow and accelerated the mixing. The vibration frequency for the range studied in the present work did not produce a significant effect on the mixing time. Analysis of the temporal evolution of the concentration of the passive scalar at a fixed point within the column revealed significant fluctuations with vibration. A dimensionally reasoned correlation is presented that scales the non-dimensional mixing time with the transient buoyancy number.

Removing inclusions from the melt is an important task in metallurgy with critical impact on the quality of the final alloy. Processes employed with this purpose, such as flotation, crucially depend on the particle size. For small inclusions, the aggregation kinetics constitute the bottleneck and, hence, determine the efficiency of the entire process. If particles smaller than all flow scales are considered, the flow can locally be replaced by a plane shear flow. In this contribution, particle interactions in plane shear flow are investigated, computing the fully resolved hydrodynamics at finite Reynolds numbers, using a lattice Boltzmann method with an immersed boundary method. Investigations with various initial conditions, several shear values and several inclusion sizes are conducted to determine collision efficiencies. It is observed that although finite Reynolds hydrodynamics play a significant role in particle collision, statistical collision efficiency barely depends on the Reynolds number. Indeed, the particle size ratio is found to be the prevalent parameter. In a second step, modeled collision dynamics are applied to particles tracked in a fully resolved bubbly flow, and collision frequencies at larger flow scale are derived.

An analytical model for the convective heat transfer coefficient and the two-phase bubble size of a three-phase direct contact heat exchanger was developed. Until the present, there has only been a theoretical model available that deals with a single two-phase bubble and a bubble train condensation in an immiscible liquid. However, to understand the actual heat transfer process within the three-phase direct contact condenser, characteristic models are required. A quasi - steady energy equation in a spherical coordinate system with a potential flow assumption and a cell model configuration has been simplified and solved analytically. The convective heat transfer in terms of Nu number has been derived, and it was found to be a function to Pe number and a system void fraction. In addition, the two-phase bubble size relates to the system void fraction and has been developed by solving a simple energy balance equation and using the derived convective heat transfer coefficient expression. Furthermore, the model correlates well with previous experimental data and theoretical results. nema

Whale optimization algorithm (WOA) is a recently developed swarm-based meta-heuristic algorithm that is based on the bubble-net hunting maneuver technique—of humpback whales—for solving the complex optimization problems. It has been widely accepted swarm intelligence technique in various engineering fields due to its simple structure, less required operator, fast convergence speed and better balancing capability between exploration and exploitation phases. Owing to its optimal performance and efficiency, the applications of the algorithm have extensively been utilized in multidisciplinary fields in the recent past. This paper investigates further into WOA of its applications, modifications, and hybridizations across various fields of engineering. The description of the strengths, weaknesses and opportunities to support future research are also explored. The Systematic Literature Review is opted as a method to disseminate the findings and gap from the existing literature. The authors select eighty-two (82) articles as a primary studies out of nine hundred and thirty-nine (939) articles between 2016 and 2020. As per our result, WOA-based techniques are applied in 5 fields and 17 subfields of various engineering domains. 61% work has been found on modification, 27% on hybridization and 12% on multi-objective variants of WOA techniques. The growing research trend on WOA is expected to continue into the future. The review presented in the paper has the potential to motivate expert researchers to propose more novel WOA-based algorithms, and it can serve as an initial reading material for a novice researcher.

In this study, we explored the occurrence of near-sunrise equatorial plasma bubbles (EPBs) and inhibition of dusk-time EPBs during the geomagnetic storm (SYM-Hmin= −139 nT) of 19–20 April 2024 using multi-instrument observations over the Indian and Southeast Asian longitude sectors. The initial phase of this storm commenced around 0530 UT on 19 April 2024 and did not manifest any visible alterations in the ionospheric electric fields during the main phase of the storm, which corresponded to a period between post-sunset to midnight over the study region. However, during the recovery phase of the storm, the IMF Bz suddenly flipped northward and was associated with an overshielding of the penetrating electric fields, which triggered the formation of near-sunrise EPBs. Interestingly, the persistence of EPBs was also noticed for more than three hours after the sunrise terminator. Initially, sunrise EPBs were developed in the Southeast Asian region and later drifted toward the Indian longitude region, along with the sunrise terminator. Moreover, this study suggested that the occurrence of EPBs was suppressed due to the altered storm time electric fields at the dip equatorial region across the 70–90°E longitude sector in the recovery period. This study highlighted that even moderate geomagnetic storms can generate near-sunrise EPBs in a broader longitude sector due to penetrating electric fields in overshielding conditions, which can significantly affect trans-ionospheric signals.

ESA's Swarm constellation entered in a “overfly” configuration in the period between September and October 2021, when the longitudinal distance between the lower pair and the upper satellite was at its minimum since the launch of the spacecrafts. In addition, the local time of the nighttime tracks was favorable to detect and study the morphology of post‐sunset equatorial plasma bubbles (EPBs). In this study, we focus on the Swarm overfly occurring between 00:41 UT and 00:59 UT on 30 September 2021, which covered one of the most densely instrumented regions for the study of the ionospheric irregularities embedded in the EPBs: the South American sector. By exploiting the use of ground‐based receivers of Global Navigation Satellite System (GNSS) signals in combination with the Swarm plasma density measurements, we study the irregularities in the EPB formed at ∼60°W and investigate the different scales of the irregularities and the cascading processes along the magnetic flux tubes. We also highlight how diffusion along the magnetic field lines occurs simultaneously with the plasma uplift, contributing then to the correct interpretation of the EPB evolution and decay process. The precious overfly conditions also allow the introduction of ionosphere‐related quantities, evaluated across the tracks at satellite altitudes enlarging the possibilities given by the same quantities already available along the tracks. Such opportunity envisages the possibility to proxy the impact of EPBs on GNSS signals with Low‐Earth Orbit satellite data provided by future missions specifically dedicated to the characterization of the near‐Earth environment and ionospheric studies.

An axial piston pump can produce a serious cavitation phenomenon in the high- and low-pressure ‎transition process. Cavitation bubbles expand, compress, rebound and collapse when they enter the high-‎pressure oil drainage area. This affects the outlet flow ripple as well as the pressure pulsation of the ‎piston pump. However, the effect of the cavitation bubbles is ignored in the current outlet flow ripple ‎model of axial piston pumps. It affects the optimization design of the axial piston pump distribution area ‎structure parameters with the objective of reducing the pressure and flow rate. Therefore, a method of ‎optimizing the fluid dynamic characteristics and the flow distribution area structure parameters of an axial ‎piston pump considering the cavitation bubble evolution is proposed. A single-cavity dynamic model was ‎established to study the bubble evolution as the piston chamber pressure changes. According to the ‎cavitation cloud (group cavitation) characteristics of the axial piston pump, theoretical models of the ‎outlet flow ripple and the pressure pulsation of a piston pump were established considering the cavitation ‎bubble characteristics. The influence of cavitation characteristics on the outlet flow ripples and pressure ‎pulsation of the axial piston pump was analyzed and compared with that without cavitation. Comparison ‎with the experimental results, verified that the outlet flow ripple model becomes more accurate when ‎cavitation bubble characteristics are considered. Based on the multi-agent particle swarm optimization ‎‎(MAPSO) algorithm, an optimization model of the piston pump outlet flow ripple was established ‎considering the cavitation bubble characteristics. The optimized design parameters for the flow ‎distribution area of the axial piston pump were evaluated. The proposed method can provide theoretical ‎guidance for the design of a low flow ripple axial piston pump.‎

Equatorial plasma irregularities in the ionospheric F‐region proliferate after sunset, causing the most apparent radio scintillation “hot‐spot” in geospace. These irregularities are caused by plasma instabilities, and appear mostly in the form of under‐densities that rise up from the F‐region's bottomside. After an irregularity production peak at sunset, the amplitude of the resulting turbulence decays with time. Analyzing a large database of irregularity spectra observed by one of the European Space Agency's Swarm satellites, we have applied a novel but conceptually simple statistical analysis to the data, finding that turbulence in the F‐region tends to decay with a uniform, scale‐independent rate, thereby confirming and extending the results from an earlier case study. We find evidence for two regimes, one valid post‐sunset (1.4 hr decay rate) and one valid post‐midnight (2.6 hr). Our results should be of utility for large‐scale space weather modeling efforts that are unable to resolve turbulent effects. Plain Language Summary After sunset in the equatorial region of Earth, GPS devices frequently experience service interruption due to space weather. The signal disruptions that cause these interruptions are in turn caused by plasma turbulence in Earth’s ionosphere, a layer of ionized gas that covers Earth like a blanket of electrical currents. The growth of such turbulence has been studied for decades, but little is still known about how such plasma irregularity structures decay with time. We elucidate the topic, showing that turbulence cause structures to decay at the same rate regardless of size. This important result will have consequences for large‐scale space weather modeling efforts, since such models rarely have the capability to resolve turbulence. Turbulence is an enigmatic chaotic behavior often that is often present in astrophysical processes, but also on Earth’s oceans and in its atmosphere. Key Points Turbulence forces equatorial irregularities to decay with a scale‐independent rate Equatorial irregularities of scale‐sizes between 500 m and 75 km are not dissipating by chemical recombination or perfect ambipolar diffusion Decay rates depend on solar local time, with post‐sunset decay rates around 1.4 hr, increasing to 2.6 hr post‐midnight

In this paper, a multilayer perceptron (MLP)-type artificial neural network model with a back-propagation training algorithm is utilized to model the bubble growth and bubble dynamics parameters in nucleate boiling with a non-uniform electric field. The influences of the electric field on different parameters that describe bubble’s behaviors including bubble waiting time, bubble departure frequency, bubble growth time, and bubble departure diameter are considered. This study models single bubble dynamic behaviors of R113 created on a heater in an inconsistent electric field by utilizing a MLP neural network optimized by four different swarm-based optimization algorithms, namely: Salp Swarm Algorithm (SSA), Grey Wolf Optimizer (GWO), Artificial Bee Colony (ABC) algorithm, and Particle Swarm Optimization (PSO). For evaluating the model effectiveness, the MSE value (Mean-Square Error) of the artificial neural network model with various optimization algorithms is measured and compared. The results suggest that the optimal networks in the two-hidden layer and three-hidden layer models for the bubble departure diameter improve MSE by 33.85% and 35.27%, respectively, when compared with the best response in the one-hidden layer model. Additionally, for bubble growth time, the networks with two hidden layers and three hidden layers have the 44.51% and 45.85% reduction in error, when compared with the network with one hidden layer, respectively. For the departure frequency, the error reduction in the two-layer and three-layer networks is 46.85% and 62.32%, respectively. For bubble waiting time, the best networks in the two hidden-layer and three hidden-layer models improve MSE by 52.44% and 62.27% compared with the best 1HL model response, respectively. Also, the two algorithms of SSA and GWO are able to compete well (comparable MSE) with the PSO and ABC algorithms.