Experimental and numerical investigations are performed to provide an assessment of the transport behavior of an ultrasonic oscillatory air–water two-phase flow in a microchannel. The flow exhibits highly unsteady behavior as the water and air interact with each other during the vibration cycles, making it significantly different from the well-studied steady flow in microchannels. The computational fluid dynamics (CFD) modeling is realized by combing the turbulence Reynolds-averaged Navier–Stokes $k-\omega$ model with the phase-field method in the Eulerian framework to resolve the dynamics of the two-phase flow. The numerical results are qualitatively validated by the experiment. Through parametric studies achieved by CFD simulation, we specifically examined the effects of vibration conditions (i.e., frequency and amplitude), microchannel taper angle, and wall surface contact angle (i.e., wettability) on the flow rate through the microchannel. The configuration for the highest flow rate is identified, which will help optimize the efficiency of a novel ultrasonic drying device. The described CFD modeling approach may also advance the potential applications where oscillatory or general unsteady microchannel two-phase flows may be present.

Corrugated pipes are extensively used in engineering applications that require flexibility and enhanced heat exchange, such as drainage and compact heat exchangers, and recently as inner layers in cryogenic flexible hoses for offshore liquid ship-to-ship transfer. The great flexibility of these hoses makes them well-suited for deployment in dynamic and harsh marine environments. However, the corrugated geometry also induces flow separation, elevated turbulence, and intricate heat transfer behaviors. This study focuses on the flow and heat transfer characteristics in corrugated pipes with various geometries, addressing the current lack of systematic comparative studies on the performance of different Reynolds-Averaged Navier–Stokes (RANS) models in such configurations. Despite their limitations in accuracy compared to high-fidelity methods, RANS models remain the workhorse for engineering analysis due to their computational efficiency. This study employs several RANS models to simulate flow and heat transfer in three corrugated pipe geometries—sinusoidal (Sin), C-type, and U-type—over a Reynolds number range of O(10⁴) to O(10⁵) and assesses their performance against high-fidelity Large Eddy Simulation benchmarks. The results show that prediction accuracy decreases with increasing corrugation depth, with the most significant errors in trough regions where reverse flow dominates, and that the choice of turbulence model has a strong influence on the predicted flow and heat transfer behavior. Among all models, the $k-ϵ$ models overall provide the most consistent and accurate predictions for friction factor, velocity distribution, and Nusselt number, while the $k-\omega$ models perform the worst. The Reynolds Stress Model improves friction factor prediction accuracy at high Reynolds numbers and provides marginally better accuracy in mean Nusselt number prediction, but its advantages are limited relative to its substantially higher computational cost. The Standard $k-ϵ$ model with Enhanced Wall Treatment demonstrates robust and balanced performance across geometries and flow regimes, making it a practical choice for engineering use. This work provides engineers and researchers guidance for choosing RANS models that balance accuracy and computational efficiency in simulations of LNG ship-to-ship transfer, compact heat exchangers, and other industrial systems that employ corrugated pipes.

With the increasing severity of climate change, Carbon Capture, Utilization, and Storage (CCUS) technology has become essential for reducing atmospheric CO₂. Marine carbon sequestration, which stores CO₂ in seabed geological structures, offers advantages such as large storage capacity and high stability. Cryogenic hoses are critical for the ship-to-ship transfer of liquid CO₂ from transportation vessels to offshore carbon sequestration platforms, but their design methods and mechanical analysis remain inadequately understood. This study reviews existing cryogenic hose designs, including reinforced corrugated hoses, vacuum-insulated hoses, and composite hoses, to assess their suitability for liquid CO₂ transfer. Based on CO₂’s physicochemical properties, a conceptual composite hose structure is proposed, featuring a double-spring-supported internal composite hose, thermal insulation layer, and outer sheath. Practical recommendations for material selection, corrosion prevention, and monitoring strategies are provided to improve flexibility, pressure resistance, and thermal insulation, enabling reliable long-distance tandem transfer. A mechanical analysis framework is developed to evaluate structural performance under conditions including mechanical loads, thermal stress, and dynamic responses. This manuscript includes an introduction to the background, the methodology for data collection, a review of existing designs, an analysis of CO₂ characteristics, the proposed design methods, the mechanical analysis framework, a discussion of challenges, and the conclusions.

The prediction of surface ship response to a far‐field underwater explosion (UNDEX) requires the simulation of shock wave propagation in the fluid, cavitation, fluid‐structure interaction, and structural response. Effective approaches to model the fluid include cavitating acoustic finite element (CAFE) and cavitating acoustic spectral element (CASE) methods. Although the spectral element method offers the potential for greater accuracy at lower computational cost, it also generates more spurious oscillations around discontinuities which are difficult to avoid in shock‐related problems. Thus, the advantage of CASE remains unproven. In this paper, we present a 3D‐partitioned FSI framework and investigate the application of CAFE and CASE to a surface ship early‐time far‐field UNDEX problem to determine which method has the best computational efficiency for this problem. We also associate the accuracy of the structural response with the modeling of cavitation distribution. A further contribution of this work is the examination of different nonmatching mesh information exchange schemes to demonstrate how they affect the structural response and improve the CAFE/CASE methodologies.

Time-domain numerical simulation is generally considered an accurate method to predict the mooring system performance, but it is also time and resource-consuming. This paper attempts to completely replace the time-domain numerical simulation with machine learning approaches, using a catenary anchor leg mooring (CALM) system design as an example. An adaptive sampling method is proposed to determine the dataset of various parameters in the CALM mooring system in order to train and validate the generated machine learning models. Reasonable prediction accuracy is achieved by the five assessed machine learning algorithms, namely random forest, extremely randomized trees, K-nearest neighbor, decision tree, and gradient boosting decision tree, among which random forest is found to perform the best if the sampling density is high enough.