Gas hydrates are solid compounds that form under high-pressure and low-temperature conditions, posing a major threat to oil and gas operations because of their tendency to agglomerate and block pipelines. One mitigation strategy is to allow hydrates to form under controlled conditions, enabling their transport as a slurry within the liquid phase. However, limited research on how such particles affect key multiphase flow parameters has been conducted. This study investigates the influence of particle concentration on slug flow characteristics, a common flow regime in oil and gas production. Experiments using air–water and air–oil systems with model polyethylene particles mimicking hydrate density were performed in a flow loop. The test section was composed of 50-mm ID, 34-m long horizontal pipe. Four particle concentrations were tested: 0%, 5%, 10% and 20% v/v. Except under flow conditions near the stratified–slug transition line, which lead to long elongated bubbles and low slug frequencies, particles were effectively dispersed and transported in both the film and slug regions. The presence of particles had a weak effect on the slug flow topology – structure lengths, flow frequency, bubble velocity and phase fraction remained almost unchanged. This was attributed to the minimal impact of the particles on the thermophysical properties of the mixture. In contrast, particles significantly increased the pressure drops in the oil system because of a higher mixture density and a particle size comparable to the viscous sublayer, what affects the apparent viscosity. An empirical correlation for pressure drop prediction was proposed, achieving deviations of about 5% compared to experimental data.
Optical skyrmions, particle-like topological textures of light with resilience to perturbation, hold great promise for next-generation robust information carriers. Recent advances have enabled their efficient generation and modulation in both classical and quantum regimes via artificial nanostructures with certain topological landscapes. However, existing studies are largely confined to (quasi)monochromatic domain, because the resonance-based linear and nonlinear light-matter interactions are constrained by narrowband response and strong spectral dispersion. Therefore, the exploration of coloured and white light skyrmions is virtually zero, blocking their extension to broadband information technologies. Here, we present an ultra-compact micro-generator that generates ultra-broadband coloured skyrmions from a natural ferroelectric spherulite crystal through the combined action of the photonic spin-orbital coupling and optical focusing. This approach circumvents the resonance effects intrinsic to artificially nanostructured optical systems. The resulting polychromatic skyrmions cover the entire visible spectral range and can propagate over an appreciable distance in free space. Their topological textures can be continually modulated by tuning the polarization of the incident beam, enabling switching among multiple topologies, such as skyrmions, biskyrmions, and quadrumerons. Furthermore, we reported the experimental evidence of spontaneous parametric down conversion process occurring within the ferroelectric spherulite, which implies the possibility to generate the correlated topological quantum states in further research. These distinctive features will revolutionize modern informatic applications spanning optical communication, data storage, and topological photonic devices.
The one-dimensional (1D) Stefan problem is a prototypical heat and mass transfer problem that analyzes the temperature distribution in a material undergoing phase change. In addition, it describes the evolution of the phase change front within the phase change material (PCM). Analytical solutions to the two-phase Stefan problem that describe melting of a solid or boiling of a liquid have been extensively discussed in the literature. Density change effects and associated fluid flow phenomena during phase change are typically ignored to simplify the analysis. As the PCM boils or condenses, it undergoes a density change of 1000 or more. The effects of density changes and convection cannot be ignored when dealing with such problems. In our recent work, we found analytical solutions to the two-phase Stefan problem that account for a jump in the thermophysical properties of the two phases, including density. In the present work, we extend our prior analyses to obtain analytical solutions to the three-phase Stefan problem in which an initially solid PCM melts and boils under imposed temperature conditions. This scenario is typical of metal additive manufacturing (AM) and welding processes, wherein a high-power laser melts and boils the metal powder or substrate. While deriving the analytical solution, all relevant jump conditions, including density and kinetic energy, are accounted for. It is shown that the three-phase Stefan problem admits similarity transformations and similarity solutions. To our knowledge, this is the first work that presents an analytical solution to the three-phase Stefan problem with simultaneous melting, solidification, boiling, and condensation (MSNBC). Furthermore, we describe a numerical method for solving the three-phase Stefan problem with second-order accuracy.
Hamilton-Jacobi partial differential equations (HJ PDEs) play a central role in many applications such as economics, physics, and engineering. These equations describe the evolution of a value function that encodes valuable information about the system, such as action, cost, or level sets of a dynamic process. Their importance lies in their ability to model diverse phenomena, ranging from the propagation of fronts in computational physics to optimal decision-making in control systems. This paper provides a review of some recent advances in numerical methods to address challenges such as high dimensionality, nonlinearity, and computational efficiency. By examining these developments, this paper sheds light on important techniques and emerging directions in the numerical solution of HJ PDEs.
Image-based quality assessment (QA) in additive manufacturing (AM) often relies heavily on the expertise and constant attention of skilled human operators. While machine learning and deep learning methods have been introduced to assist in this task, they typically provide black-box outputs without interpretable justifications, limiting their trust and adoption in real-world settings. In this work, we introduce a novel QA-VLM framework that leverages the attention mechanisms and reasoning capabilities of vision-language models (VLMs), enriched with application-specific knowledge distilled from peer-reviewed journal articles, to generate human-interpretable quality assessments. When evaluated on 24 single-bead samples produced by laser wire direct energy deposition (DED-LW), our framework demonstrates higher validity and consistency in explanation quality than off-the-shelf VLMs. These findings indicate that the literature-supported quality assessment model has the potential to improve reliability for QA tasks, motivating future validation on larger, multi-layer, and multi-pass builds, and broader process/material conditions.
