Traditional hybrid RANS/LES methods often struggle to accurately capture both the boundary layer transition and flow separation simultaneously due to their reliance on fully turbulent RANS models. To address this limitation, the present study first evaluates three transitional RANS models (γ-Reθt-SST, γ-SST, and Kγ-SST) on the E387 airfoil. The results demonstrate that the γ-SST model offers the best balance of accuracy and computational efficiency in predicting laminar separation bubbles (LSBs) and transition points. Building on this, we implement the γ-SST-IDDES model into OpenFOAM-v2312, which integrates the γ-SST transitional RANS model with the Improved Delayed Detached Eddy Simulation (IDDES) approach. This coupling allows for the simultaneous prediction of the laminar-turbulent transition and high-fidelity resolution of separated flows. The γ-SST-IDDES model is rigorously validated across three airfoil cases with distinct separation characteristics: E387 (small separation), DBLN-526 (moderate separation), and NACA 0021 (massive separation). The results show that the γ-SST-IDDES model outperforms conventional methods, capturing leading-edge LSBs with high accuracy compared to fully turbulent IDDES. Additionally, it successfully resolves complex 3D vortical structures in separated regions, whereas unsteady URANS provides only quasi-2D results.

This paper investigates the influence of airfoil parameters on the cavitation performance of water jet propulsion pumps through numerical simulation methods. The effects of a varying inlet pressure and different airfoil structures on the critical net positive suction head (NPSH), head, and efficiency were systematically studied. Subsequently, the impact pattern of the airfoil structure on the cavitation performance was analyzed. The results demonstrate that the NACA0009-16_0004-16 airfoil exhibited the lowest required NPSH and superior cavitation resistance relative to the other tested airfoils. Nevertheless, the NACA0009-13_0004-13 airfoil demonstrated an optimal comprehensive performance, balancing the efficiency, head, and cavitation resistance. By extracting a water velocity isosurface of 23.6 m/s, we further investigated the flow characteristics of the suction surfaces of different airfoils at different cavitation conditions and found that the cavitation mainly includes TIP cavitation and sheet cavitation. With an increasing cavitation intensity, the sheet cavitation region progressively develops axially from the blade tip towards the blade outlet, extends radially from the shroud to the hub, and eventually nearly extends over the entire blade surface. The area of the TIP cavitation also expands, spreading downward in the same direction as the impeller rotation. The velocity vector exhibits a significantly higher density near the shroud and blade tips, suggesting potential flow separation and complex vortex structures in these regions. Near the blade leading edge, the water velocity isosurface area contracts, whereas near the trailing edge, it expands. These alterations indicate that the cavitation development modifies the flow field velocity distribution and adversely affects the impeller performance. This study establishes a theoretical foundation and offers practical guidelines for the multi-objective collaborative design of water jet propulsion pumps.

This article discusses contemporary strategies to deal with immersed boundary (IB) frameworks useful for analyzing flow–structure interaction in complex settings. It focuses on immense advancements in various fields: biology, oscillation of structures due to fluid flow, deformable materials, thermal processes, settling particles, multiphase systems, and sound propagation. The discussion also involves a review of techniques addressing moving boundary conditions at complex interfaces. Evaluating practical examples and theoretical challenges that have been addressed by these frameworks are another focus of the article. Important results highlight the integration of IB methods with adaptive mesh refinement and high-order accuracy techniques, which enormously improve computational efficiency and precision in modeling complex solid–fluid interactions. The article also describes the evolution of IB methodologies in tackling problems of energy harvesting, bio-inspiration propulsion, and thermal-fluid coupling, which extends IB methodologies broadly in many scientific and industrial areas. More importantly, by bringing together different insights and paradigms from across disciplines, the study highlights the emerging trends in IB methodologies towards solving some of the most intricate challenges within the technical and scientific domains.

Snow and ice accumulation on transportation infrastructure presents significant safety and maintenance challenges in cold regions, while conventional removal methods are both energy-intensive and environmentally detrimental. This study proposes a passive Heat Pipe–Coupled Geothermal Snow Melting System (HP-GSMS) that harnesses shallow geothermal energy to maintain snow-free surfaces without external energy input. Using Fluent-based CFD simulations, the system’s thermal performance was evaluated under various working fluids (ammonia, carbon dioxide, water) and pipe materials (stainless steel, aluminum). A one-dimensional thermal resistance model validated the CFD results under ammonia–stainless steel conditions, predicting a heat flux of 358.6 W/m2 compared to 361.0 W/m2 from the simulation, with a deviation of only 0.66%, confirming model accuracy. Ammonia demonstrated superior phase-change efficiency, with the aluminum–ammonia configuration yielding the highest heat flux (up to 677 W/m2), surpassing typical snow-melting thresholds. Aluminum pipes enhanced radial heat conduction without compromising phase stability, while water exhibited poor phase-change performance and CO2 showed moderate but stable behavior. Additionally, a dynamic three-node RC thermal network was employed to assess transient performance under realistic diurnal temperature variations, revealing surface heat fluxes ranging from 230 to 460 W/m2, with a daily average of approximately 340 W/m2. These findings demonstrate the HP-GSMS’s practical viability in cold climates and underscore the importance of selecting low-boiling-point fluids and high-conductivity materials for scalable, energy-efficient, and low-carbon snow-melting applications in urban infrastructure.

Computational Fluid Dynamics (CFD) is regarded as an important tool for analyzing the flow field, thermal environment, and air quality around the built environment. However, for built environment applications, the high computational cost of CFD hinders large-scale scenario simulation and efficient design optimization. In the field of built environment research, surrogate modeling has become a key technology to connect the needs of high-fidelity CFD simulation and rapid prediction, whereas the low-dimensional nature of traditional surrogate models is unable to match the physical complexity and prediction needs of built flow fields. Therefore, combining machine learning (ML) with CFD to predict flow fields in built environments offers a promising way to increase simulation speed while maintaining reasonable accuracy. This review briefly reviews traditional surrogate models and focuses on ML-based surrogate models, especially the specific application of neural network architectures in rapidly predicting flow fields in the built environment. The review indicates that ML accelerates the three core aspects of CFD, namely mesh preprocessing, numerical solving, and post-processing visualization, in order to achieve efficient coupled CFD simulation. Although ML surrogate models still face challenges such as data availability, multi-physics field coupling, and generalization capability, the emergence of physical information-driven data enhancement techniques effectively alleviates the above problems. Meanwhile, the integration of traditional methods with ML can further enhance the comprehensive performance of surrogate models. Notably, the online ministry of trained ML models using transfer learning strategies deserves further research. These advances will provide an important basis for advancing efficient and accurate operational solutions in sustainable building design and operation.

Numerous dark linear recurrent features called Recurring Slope Lineae (RSL) are observed on Martian surfaces, hypothesized as footprints of high-salinity liquid flow. This paper experimentally examined this “wet hypothesis” by analyzing the aspect ratios (length/width) of the flow traces on the granular material column to investigate how they vary with the granular material column, liquid and its flow rate, and inclination. While pure water produced low aspect ratios (<1.0) on the Martian regolith simulant column, high-salinity fluid (CaCl2(aq)) traces exhibited significantly higher aspect ratios (>4.0), suggesting that pure water alone is insufficient to explain RSL formulation. Furthermore, the aspect ratios of high-salinity fluid traces on Martian regolith simulants were among the highest observed across all studied granular materials with similar particle sizes, aligning closely with actual RSL observed on Martian slopes. The results further suggest that variable ARs of actual RSL at the given slope can partly be explained by variable flow rates of high-salinity flow as well as salinity (i.e., viscosity) of flow. The results can be attributed to the unique granular properties of Martian regolith, characterized by the lowest permeability and Beavers–Joseph slip coefficient among the studied granular materials. This distinctive microstructure surface promotes surface flow over Darcy flow within the regolith column, leading to a narrow and long-distance feature with high aspect ratios observed in Martian RSL. Thus, our findings support that high-salinity flows are the primary driver behind RSL formation on Mars. Our study suggests the presence of salts on the Martian surface and paves the way for further investigation into RSL formulation processes.

Cardiovascular diseases remain the leading cause of morbidity and mortality worldwide, underscoring the need for continuous innovation in diagnostics and treatment. Mock circulation loops (MCLs) systems have recently emerged as new research platforms capable of replicating the hemodynamics of the human cardiovascular system. This review explores the expanding applications of MCLs to cardiovascular diseases beyond their traditional role in testing ventricular assist devices and heart failure management. We focus on their versatility in simulating various cardiovascular conditions, particularly arterial diseases such as atherosclerosis, stenosis, and aneurysms. This review traces the evolution of MCLs and their integration with computational simulations and real-time data acquisition systems. MCLs provide detailed insights into hemodynamic responses under diverse conditions, enhancing the precision and safety of cardiovascular interventions. This comprehensive review emphasizes the critical role of MCLs in advancing cardiovascular research, refining clinical interventions, and improving patient outcomes.

In this work, experimental results on the generation of acoustic contributions during the discharge process of different granular materials from both vertical and inclined silos are presented. The experiments show that the generation of acoustic emissions associated with the “silo music” phenomenon occurs not only in vertical silos but also in inclined ones. The acoustic signals produced during the silo discharge process are recorded and analysed in both time and frequency domains. The frequency analysis focuses on low frequencies near the lower auditory threshold of 20 Hz, demonstrating that the spectral components of the acoustic signals are related to the mass flow rate and the discharge velocity of the granular material.

This paper simulates the blood clot extraction process inside an idealized cylindrical blood vessel model using the aspiration-based thrombectomy technique. A fully Eulerian technique is used within the finite volume method where incompressible Navier–Stokes equations are solved in the fluid region. In contrast, the Cauchy stress equation is solved in the clot region. Blood is assumed to be a Newtonian fluid, while the clot is either hyperelastic or viscoelastic material. In the hyperelastic formulation, the clot deformation is calculated based on the left Cauchy–Green deformation tensor, while the stresses are based on the linear Mooney–Rivlin model. In the viscoelastic formulation, the Oldroyd B model is used within the log conformation approach to calculate the viscoelastic stresses in the clot. The interface between the blood and the clot is tracked with the help of the geometric volume-of-fluid method. We focus on the role of flow variables like the pressure, velocity, and proximity between the clot and the catheter tip to successfully capture the clot under catheter suction. We observe that, once the clot is attracted to the catheter port due to pressure forces, the viscous stresses try to drag it inside the catheter. On the other hand, if the clot is not initially attracted, it is carried downstream by the viscous stresses. If the suction velocity is low (∼0.2 m/s), the clot cannot be sucked inside the catheter, even if it is touching the catheter. At a higher suction velocity of 0.4 m/s, the suction effect is strong enough to capture the clot despite the larger initial distance from the catheter. Hence, the pressure distribution and viscous stresses play essential roles in the suction or escape of the clot during the thrombectomy process. Also, the viscoelastic model predicts the rupture of the clot inside the catheter during suction.

This work introduces a 0.6-scale water model of the continuous slab-casting process and a MATLAB-based model to study the effects of non-primed and multiphase flow on pressure and flow rate. The water model uses stopper-rod flow control and features pressure and velocity measurements at multiple locations. The new computational model, PFSR V4 (Pressure-drop Flow-rate model of Stopper Rod metal delivery systems, Version 4), improves upon a prior one-dimensional Bernoulli-based framework by incorporating a bubble accumulation zone. This zone represents a region of bubbly flow with an intermediate gas fraction between constant-pressure gas pockets below the stopper tip and the downstream bubbly flow regime. Parametric studies with the water model show that flow remains fully primed at low gas flow rates but transitions to non-primed flow as the gas flow rate exceeds 10–16 SLPM. Three different flow regions are observed inside the water model nozzle: air pocket, bubble accumulation, and bubbly flow, which are also captured by the new computational model. Above a critical gas flow rate, the flow becomes unstable and difficult to control, though higher hot gas flow rates are expected for similar transitions in a real steel caster due to gas expansion at high temperatures. Pressure changes are minimal in the air pocket region and increase significantly in the upper bubble accumulation zone, where liquid velocity is much higher than in the classic bubbly-flow region, found lower in the nozzle. The new model was successfully calibrated to match the observed flow regimes and shows good agreement with the water-model measurements.