Search Results (227)

The flow around circular cylinders is a classic problem in fluid mechanics with significant implications for offshore engineering. While extensive numerical and experimental research has focused on the subcritical and critical Reynolds regimes, the supercritical and postcritical regimes remain challenging and relatively unexplored, primarily due to the complex nature of turbulence and the high computational requirements. In this study, we perform three-dimensional detached eddy simulations using the finite volume method in OpenFOAM v1906, employing Menter’s k-$\omega$ SST turbulence model, to systematically investigate the flow past an infinitely long smooth cylinder from the subcritical through the postcritical regimes. The numerical setup ensures accurate near-wall resolution and reliable representation of unsteady flow features. We present a detailed analysis of vortex shedding patterns, wake evolution, and statistical properties of lift and drag coefficients for selected Reynolds numbers representative of each regime. The simulation results are benchmarked against experimental data from the literature, demonstrating good agreement for Strouhal number and mean drag. Special emphasis is placed on the evolution of wake topology and force coefficients as the flow transitions from laminar to fully turbulent conditions. The findings contribute to the limited numerical literature on flow around circular cylinders across subcritical, critical, supercritical, and postcritical Reynolds number regimes, providing insights that are fundamentally relevant to the broader scope of understanding vortex shedding phenomena. Full article

Molten salts are widely used in high-temperature energy systems because of their thermal properties. In such applications, natural circulation provides a passive means of heat transport in systems that require passive safety features. Many studies have examined the thermal–hydraulic behavior of molten salts in natural circulation configurations. This work develops a two-dimensional CFD model of a molten salt natural circulation loop and evaluates two formulations—a laminar model and the Transition SST (γ–Reθ) model. The models were verified through mesh-independence studies and validated against experimental benchmark data. Both models reproduced the measured temperature rise across the loop, but significant differences appeared in velocity and Reynolds-number prediction. The laminar model underpredicted circulation by about 30%, whereas the Transition SST model shows 4.2% for velocity and 11.8% for Reynolds number. Local comparison showed that the Transition SST model captured developing wall-peaked structures in the vertical legs, whereas the laminar model misinterprets these regions as stagnant core flow. These findings apply only to the 2D model, and the use of the CFD models follows a benchmark experiment rather than universal validation for all molten salt loops. Overall, the results show that transitional turbulence modeling is needed to capture the mixed-regime behavior in molten salt natural circulation. Full article

This study provides a comprehensive three-dimensional Computational Fluid Dynamics analysis of airflow distribution in a surgical operating room under realistic occupancy and equipment conditions. Using integrated modelling in SolidWorks and a subsequent analysis in ANSYS Fluent, a full-scale Operating Room geometry was simulated to assess the effectiveness of a laminar airflow system. The model includes surgical staff mannequins, thermal loads from surgical lights, and medical equipment that commonly disrupt unidirectional flow patterns. A polyhedral mesh with over 2.8 million nodes was employed, and a grid independence study confirmed solution reliability. The realisable k–ε turbulence model with enhanced wall treatment was used to simulate steady-state airflow, thermal stratification, and pressure variation due to door opening. Results highlight significant flow disturbances and recirculation zones caused by the shear zone created by supply air, overhead lights and heat plumes, particularly outside the core laminar air flow zone. The most important area, 10 cm above the surgical site, shows a maximum velocity gradient of 0.09 s⁻¹ while the temperature gradient shows 6.7 K.m⁻¹ and the pressure gradient, 0.0167 Pa.m⁻¹. Streamline analysis reveals potential re-entrainment of contaminated air into the sterile field. Full article

Coarctation of the aorta is a localized narrowing of the aortic lumen. This pathology leads to hypertension in upper extremity vessels, left ventricular hypertrophy and to impaired perfusion of the abdominal cavity and lower extremities. Along with traditional diagnostic methods, mathematical modeling is used for risk assessment and the prediction of disease outcomes. However, when applying numerical models to describe hemodynamic parameters, the choice of turbulence model to describe swirling flow occurring in the aorta in this pathology must be justified. Thus, three turbulence models, namely k-ε, k-ω, and SST were analyzed for the description of swirling flows in the study of coarctation’s effect on hemodynamic parameters and analysis of the mechanisms leading to various cardiovascular diseases caused by altered hemodynamics. The results revealed significant differences in swirling flow patterns between the k-ε and k-ω models, while the k-ω and SST models showed consistent results over the cardiac cycle. In the peak systolic phase, average velocity rises to 1.07–1.98 m·s⁻¹ for the k-ε model, 0.82–2.12 m·s⁻¹ for the k-ω model, 1.22–2.12 m·s⁻¹ for the SST model and 0.8–2.12 m·s⁻¹ for laminar flow. WSS values increase rapidly to 11–22 Pa in k-ε, 25–50 Pa in k-ω and SST models of turbulence, and 30–55 Pa for laminar flow. Significant differences were also evident in the prediction of wall shear stress, with the k-ε model giving values more than twice as high as the k-ω and SST models. The data obtained confirm the necessity of careful model selection for accurate hemodynamic parameter estimation, especially in coarctation. The findings of this study can be used for further physics-informed neural network analysis of evaluation of treatment evaluations for congenital heart disease patients. Full article

This paper proposes a novel convention-based subgrid scale (SGS) model for large eddy simulation (LES) by using the Liutex concept. Conventional SGS models typically rely on the eddy viscosity assumption, which uses the linear eddy viscosity terms to approximate the nonlinear effects of unresolved turbulent eddies, that should be measured by unresolved Liutex. However, the eddy viscosity assumption is empirical but lacks a scientific foundation, which limits its predictive accuracy. The proposed model in this paper directly models the convective terms and demonstrates several key advantages: (1) the new model gets rid of isotropic assumption for the unresolved SGS eddies which are, in general, anisotropic, (2) the new model contains no empirical coefficients which need to be adjusted case by case, (3) the new model explicitly captures nonlinear convective effects by the SGS eddies and (4) the new model is consistent with the physics for boundary layer as the model becomes zero in the laminar sublayer, where Liutex becomes zero automatically. This new model has been applied in the flat plate boundary transition flow, and the results show that it outperforms the popular and widely adopted wall-adapting local eddy (WALE) model. This new model is a conceptual breakthrough in SGS modeling and has the potential to open a new direction for more accurate SGS models and future LES applications. Full article

Laminar–turbulent transition is a phenomenon that extensively exists in many fluid flows. Accurate and cost-effective modelling of the transition is of fundamental importance for the design and diagnosis of relevant flow processes and industry systems. Existing transition turbulence models were mostly developed for high-speed aerodynamics applications. Their suitability for buoyant low-speed flows, such as natural and mixed convection flows, has been rarely assessed. This study aimed to bridge this gap through comparing the velocity and temperature fields yielded from various transition turbulence models against the experimental data of natural convection flow in a differentially heated cavity. The results showed that Wilcox’s low-Re modification to the SST k-ω model and the transport γ-equation had good accuracies for low-speed natural convection flows. Other models, including the algebraic γ-equation, γ-Re_θ model and k_t-k_l-ω model, overpredicted the turbulence quantities, resulting in significant predictive errors in velocity and temperature simulations. Full article

The topic of the present study is the aerodynamic performance of a Natural Laminar Flow (NLF) wing for UAVs at low speed. The basis is a thoroughly tested NLF airfoil in the wind tunnel of NASA which is well-customized for light aircrafts. The aim of this work is the numerical verification that a typical wing design (tapered with moderate aspect ratio and wash-out), being constructed out of aerodynamically highly efficient NLF airfoils during cruise, can deliver high aerodynamic loading under minimal freestream turbulence as well as realistic atmospheric conditions of intermediate turbulence. Thus, high mission flexibility is achieved, e.g., short take off/landing capabilities on the deck of ship where moderate air turbulence is prevalent. Special attention is paid to the effect of the Wing Tip Vortex (WTV) under minimal inflow turbulence regimes. The flight conditions are take off or landing at moderate Reynolds number, i.e., one to two millions. The numerical simulation is based on an open source CFD code and parallel processing on a High Performance Computing (HPC) platform. The aim is the identification of both mean flow and turbulent structures around the wing and subsequently the formation of the wing tip vortex. Due to the purely three-dimensional character of the flow, the turbulence is resolved with advanced modeling, i.e., the Improved Delayed Detached Eddy Simulation (IDDES) which is well-customized to switch modes between Delayed Detached Eddy Simulation (DDES) and Wall-Modeled Large Eddy Simulation (WMLES), thus increasing the accuracy in the shear layer regions, the tip vortex and the wake, while at the same time keeping the computational cost at reasonable levels. IDDES also has the capability to resolve the transition of the boundary layer from laminar to turbulent, at least with engineering accuracy; thus, it serves as a high-fidelity turbulence model in this work. The study comprises an initial benchmarking of the code against wind tunnel measurements of the airfoil and verifies the adequacy of mesh density that is used for the simulation around the wing. Subsequently, the wing is positioned at near-stall conditions so that the aerodynamic loading, the kinematics of the flow and the turbulence regime in the wing vicinity, the wake and far downstream can be estimated. In terms of the kinematics of the WTV, a thorough examination is attempted which comprises its inception, i.e., the detachment of the boundary layer on the cut-off wing tip, the roll-up of the shear layer to form the wake and the motion of the wake downstream. Moreover, the effect of inflow turbulence of moderate intensity is investigated that verifies the bibliography with regard to the performance degradation of static airfoils in a turbulent atmospheric regime. Full article

Cardiovascular disease diagnosis heavily relies on accurate blood flow assessments, traditionally performed using invasive and often uncomfortable methods like catheterization. This research introduces PPG-Net 4, an innovative deep learning approach for non-invasive blood flow pattern classification using dual photoplethysmography (PPG) signals. By leveraging advanced machine learning techniques, the proposed method addresses critical limitations in current diagnostic technologies. The study employed a novel dual-sensor arrangement capturing PPG signals from two body locations, generating a comprehensive dataset from 75 participants. Advanced signal processing techniques, including mel spectrogram generation and mel-frequency cepstral coefficient extraction, enabled sophisticated feature representation. The deep learning model, PPG-Net 4, demonstrated good capability at classifying the following five distinct blood flow patterns: laminar, turbulent, stagnant, pulsatile, and oscillatory. The experimental results revealed strong classification performance, with F1-scores ranging from 0.86 to 0.92 across different flow patterns. The highest accuracy was observed for pulsatile flow (F1-score: 0.92), underscoring the model’s precision and reliability. This approach not only provides a non-invasive alternative to traditional diagnostic methods but also offers a potentially useful technique for early cardiovascular disease detection and continuous monitoring. Full article

In industrial equipment containing hydraulic lines for power transmission, the lines have boundary conditions defined by components such as pumps, valves, and actuators located at the ends of the lines. Sudden changes in any of the boundary conditions may result in significant pressure/flow dynamics (fluid transients) in the lines that may be detrimental or favorable to the performance of the equipment. Accurate models for line transients are defined by the exact solution to a set of simultaneous partial differential equations. In this paper, analytical solutions to the partial differential equations provide Laplace transform transfer functions applicable to any set of boundary conditions yet to be specified that satisfy the requirements of causality. Analytical solutions of these partial differential equations from previous publications are reviewed for cases of laminar and turbulent flow for Newtonian and a class of non-Newtonian fluids. This paper focuses on a method for obtaining total system analytical models and time domain solutions for cases in which the end-of-line components can be modeled with linear equations for perturbations relative to pre-transient flow conditions. Examples with pumps, valves, and actuators demonstrate the process of coupling equations for components at the ends of a line to obtain total system transfer functions and then obtain time domain solutions for outputs of interest associated with system inputs and load variations. Full article

The presence of solid particles (or droplets) in a flow leads to a significant increase in heat fluxes, the occurrence of chemical reactions, and erosive surface wear of various aircraft moving in the dusty (or rainy) atmosphere of Earth or Mars. A review of computational, theoretical, and experimental work devoted to the study of the characteristics of the boundary layers (BL) of gas with solid particles was performed. The features of particle motion in laminar and turbulent boundary layers, as well as their inverse effect on gas flow, are considered. Available studies on the stability of the laminar boundary layer and the effect of particles on the laminar–turbulent transition are analyzed. At the end of the review, conclusions are drawn, and priorities for future research are discussed. Full article

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-Re_θt-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. Full article

Hollow-fiber water gap membrane distillation (HF-WGMD) modules are gaining attention for desalination applications due to their compact design and high surface-area-to-volume ratio. This study presents a comprehensive CFD model to analyze and compare the performance of two HF-WGMD module configurations: one with a conventional stagnant water gap (WG) and the other incorporating water gap flow circulation. The model was validated against experimental data, showing excellent agreement, and was then used to simulate flow patterns in the feed, water gap, and coolant domains. Results indicate that, at a feed temperature of 80 °C with a stagnant WG, employing a turbulent flow scheme in the feed side increases water flux by 20.7% compared to laminar flow, while increasing coolant flow rate has a minor impact. In contrast, introducing circulation within the water gap significantly enhances performance, boosting water flux by 30.1%. This effect becomes more pronounced with rising feed temperature: increasing from 50 °C to 80 °C leads to a flux increase from 6.74 to 27.89 kg/(m²h) under circulating WG conditions. However, in multistage systems, the energy efficiency trade-off becomes evident. Water gap circulation is more energy-efficient than the stagnant configuration only for systems with fewer than 20 stages. At higher stage counts, the stagnant WG setup proves more efficient. For example, at 80 °C and 50 stages, the stagnant configuration consumes just 793 kWh/m³, representing a 47.3% reduction in energy consumption compared to the circulating WG setup. These findings highlight the performance benefits and energy trade-offs of water gap circulation in HF-WGMD systems, providing valuable guidance for optimization and scalability of high-efficiency desalination module designs. Full article

The effect of surface roughness in laminar flow has been the focus of recent research related to drag reduction. However, although particle transport is governed by laminar flow in most applications, the effect of surface texture on the drag of a sphere has mostly been addressed in the transitional and turbulent regimes. The aim of the present study is to explore the drag behavior of rough spherical micro-particles in laminar flow. The spheres’ roughness has been structured based on a 3D complex Weaire–Phelan model, as well as on a simpler orthogonal lattice one, and quantified as per various definitions. The emerging surface roughness comprises irregular elements in terms of shape and size. The investigation has been performed at Reynolds numbers ranging from 2 to 8. The drag coefficient is found to drop quadratically with increasing roughness. Relative roughness can reduce the total drag on the particle by over 21%. The key physical mechanism is explained by the particles’ surface cavities, which contain recirculating, nearly stagnant fluid, thus creating a self-lubricating effect that reduces skin friction, as the main flow skims over the top without entering the cavities. A reduction in total drag arises when skin friction drag reduction is larger than the increase in form drag. Understanding the drag behavior of spherical particles with irregular surface texture provides new and useful insight into low Reynolds number transport phenomena related to a variety of engineering applications. Full article

The numerical modelling of the melt flow in Si crystal growth plays an important role for improving the resistivity distribution of crystals grown in industrial processes. However, recent series of experiments have shown that the existing numerical model—a finite volume solver with incompressible laminar approximation of the melt flow—is not always accurate enough to describe the experimental results for 4″ crystals. To improve the simulation results, material properties have been revised. For some of them, such as the Marangoni or thermal expansion coefficients, the literature suggests different values varying by more than a factor of two. Therefore, simulations using different combinations of parameters were run to perform parameter calibration. The study demonstrated that the description of induced heat on the open melting front needs to be modified to obtain the shape of phase boundaries that provides the best agreement to the experiment. It was concluded that new values should be assigned to several material properties in the model, most importantly the Marangoni coefficient $M=-1.2·{10}^{-4}{\displaystyle \frac{\mathrm{N}}{\mathrm{m}·\mathrm{K}}}$, and that an appropriate turbulence model may help to describe the dopant transport more precisely. Full article

This study investigates the application of deep learning (DL) techniques for predicting temperature fields in the SM1 swirl-stabilized turbulent non-premixed flame. Two distinct DL approaches were developed using a comprehensive CFD database generated via the steady laminar flamelet model coupled with the SST k-ω turbulence model. The first approach employs a fully connected dense neural network to directly map scalar input parameters—fuel velocity, swirl ratio, and equivalence ratio—to high-resolution temperature contour images. In addition, a comparison was made with different deep learning networks, namely Res-Net, EfficientNetB0, and Inception Net V3, to better understand the performance of the model. In the first approach, the results of the Inception V3 model and the developed Dense Model were found to be better than Res-Net and Efficient Net. At the same time, file sizes and usability were examined. The second framework employs a U-Net-based convolutional neural network enhanced by an RGB Fusion preprocessing technique, which integrates multiple scalar fields from non-reacting (cold flow) conditions into composite images, significantly improving spatial feature extraction. The training and validation processes for both models were conducted using 80% of the CFD data for training and 20% for testing, which helped assess their ability to generalize new input conditions. In the secondary approach, similar to the first approach, studies were conducted with different deep learning models, namely Res-Net, Efficient Net, and Inception Net, to evaluate model performance. The U-Net model, which is well developed, stands out with its low error and small file size. The dense network is appropriate for direct parametric analyses, while the image-based U-Net model provides a rapid and scalable option to utilize the cold flow CFD images. This framework can be further refined in future research to estimate more flow factors and tested against experimental measurements for enhanced applicability. Full article