Search Results (221)

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

This study presents a numerical comparison between Direct numerical simulation (DNS) and the standard κ-ε turbulence model to evaluate natural convection in a two-dimensional, differentially heated, air-filled cavity over the Rayleigh number range 10³ to 10¹⁰. The objective is to assess the predictive capabilities of both methods across laminar and turbulent regimes, with a particular emphasis on the quantitative comparison of thermal characteristics under high Rayleigh number conditions. The Navier–Stokes and energy equations were solved using the finite element method with Boussinesq approximation, employing refined meshes near the hot and cold walls to resolve thermal and velocity boundary layers. The results indicate that for Ra ≤ 10⁶, the κ-ε model significantly underestimates temperature gradients, maximum velocities, and average Nusselt numbers, with errors up to 19.39%, due to isotropic assumptions and empirical formulation. DNS, in contrast, achieves global energy balance errors of less than 0.0018% across the entire range. As Ra increases, the κ-ε model predictions converge to DNS, with Nusselt number deviations dropping below 1.2% at Ra = 10¹⁰. Streamlines, temperature profiles, and velocity distributions confirm that DNS captures flow dynamics more accurately, particularly near the wall vortices. These findings validate DNS as a reference solution for high-Ra natural convection and establish benchmark data for assessing turbulence models in confined geometries Full article

Stenosis geometries are constrictions of a biological tube that can be found in many forms in the human body. Capturing the flow field in such geometries is important. For this purpose, simulations were performed using the generalised k-$\omega$ (GEKO) turbulence model to study flow through stenosis geometries with throat constrictions of 75, 50 and 25% area reduction. Laminar flow conditions of Re = 2000 and 1000 were applied and the results were compared with experimental data. The effect of four GEKO parameters ($C_{SEP}$, $C_{NW}$, $C_{JET}$ and $C_{MIX}$) on flow in the post-stenotic region was investigated by simulating a wide range of parameter values. Results showed that the $C_{MIX}$ parameter, combined with a modified GEKO blending function, had the greatest effect on axial velocity, velocity fluctuations and the location of the jet breakdown region. A $C_{MIX}$ value of 0.4 closely matched the experimental results for a 75% area reduction stenosis at $Re=2000$ and showed significant improvements over existing Reynolds-averaged Navier–Stokes models. The GEKO model was also able to closely match the axial velocity results predicted by previously published large-eddy simulation models under the same flow conditions. Furthermore, the GEKO model was applied to a realistic oral-to-tracheal airway model for a Reynolds number of 2000 and produced results consistent with the idealised stenotic tube. Full article

This study investigates the aerodynamic performance of a symmetric NACA 0018 airfoil under harmonic pitching motions at low Reynolds numbers, a regime characterized by the presence of laminar separation bubbles and their impact on aerodynamic forces. The analysis encompasses oscillation frequencies of 1 Hz, 2 Hz, and 13.3 Hz, with amplitudes of 4° and 8°, along with steady-state simulations conducted for angles of attack up to 20° to validate the numerical model. The results reveal that the γ-Re_θ turbulence model provides improved predictions of aerodynamic forces at higher Reynolds numbers but struggles at lower Reynolds numbers, where laminar flow effects dominate. The inclusion of the 13.3 Hz frequency, relevant to Darrieus vertical-axis wind turbines, demonstrates the effectiveness of the model in capturing dynamic hysteresis loops and reduced oscillations, in contrast to the k-ω SST model. Comparisons with XFOIL further highlight the challenges in accurately modeling laminar-to-turbulent transitions and dynamic flow phenomena. These findings offer valuable insights into the aerodynamic behavior of thick airfoils under low Reynolds number conditions and contribute to the advancement of turbulence modeling, particularly in applications involving vertical-axis wind turbines. Full article

In the present study, a thermal–electromagnetic hydrodynamics model has been used to study welding temperature and melt flow characteristics during the laser welding of 316L steel. This welding was performed using an assisted electromagnetic field. In addition, a Monte Carlo model was used to study grain growth during solidification with the purpose of achieving a better understanding of the control of the microstructure. Based on the numerical model, which has been validated by experimental data, the effects of the current intensity of the electromagnetic field on the temperature distribution, melt flow characteristics, and grain growth are discussed here in detail. The simulation results showed that both Marangoni convection and welding temperature could be controlled by the magnetic damping effect, and that they increased due to the electromagnetic heating effect when using an electromagnetic field. Furthermore, when controlling the temperature distribution and melt flow velocity in the laminar flow of the melt pool, which was assisted by a 30 A current intensity of the electromagnetic field, the temperature gradient decreased by 13.5%. This decrease could be even larger than 50% when a turbulent flow was formed in the melt pool, which has here been demonstrated for a current intensity of 100 A. In addition, undercooling was found to decrease because of the increase in the melt flow velocity when using an assistive electromagnetic field. This led to a longer nucleation time in the melt pool. Furthermore, more and larger directional columnar grains, grown by the driving force of the temperature gradient, could be formed after the consumption of the small, nucleated grains near the solid–liquid interface. In short, by controlling the temperature distribution and melt flow velocity, the required grain morphology (equiaxed or columnar) and dimension (radius, length, or width) can be controlled by coarsening and epitaxial growth. Full article

This study investigates the organized flow structures and turbulence quantities in a transitional oscillatory boundary-layer flow over a smooth bed using a DNS model set up by the open-source framework Nektar++ (v5.2.0). The present model was validated against the results of a previous study involving a bypass transition mechanism in the intermittently turbulent regime. To trigger the initial perturbations, a roughness element was placed on the bed and removed at the very moment a two-dimensional vortex tube, caused by an inflectional-point shear-layer instability, was observed on it. Then, the turbulent spots where the flow experienced intense fluctuations in an otherwise laminar boundary layer were identified from the bed shear-stress distribution on the bed, which served as a reliable indicator of turbulence. These flow features emerged as the first sign of the initiation of turbulence. Several measurement points were selected to follow the bed shear-stress variations and to observe the spatial and temporal development of turbulent spots at a low-wave Reynolds number, $Re=1.8\times {10}^5$. Along with these observations, phase-resolved turbulence quantities were also investigated over successive half-cycles for the first time in the literature to understand how turbulence develops and spreads over the flow domain. The results show that the turbulence generated in the near-bed region becomes stronger in the deceleration stage due to the adverse pressure gradient and diffuses away from the bed during the subsequent phases of the developing oscillatory boundary-layer flow. The findings related to the turbulence quantities also indicate that the turbulence gradually evolves and spreads into the fluid domain in successive half-cycles. Full article

Soil particle size distribution is a critical parameter in geotechnical and hydraulic engineering, particularly in applications such as dam seepage monitoring, building foundation assessments, and sediment transport. This study presents a novel algorithm for estimating soil particle sizes by analyzing their falling velocities in water, combining optical flow computation with chaotic motion analysis. To address the limitations of the classical Horn and Schunck method, particularly its sensitivity to large displacements and brightness variations, we introduced a coarse-to-fine warping strategy, an image decomposition step to separate dominant structures from fine textures, and the Charbonnier penalty function. The improved model achieved competitive accuracy compared to advanced optical flow algorithms. To manage turbulence and motion noise during particle settling, we incorporated a global flow analysis framework using streaklines, streak flow, and potential functions. This enabled the segmentation of laminar, turbulent, and rebound flow regions without requiring individual particle tracking. Soil particle sizes were then back-calculated from laminar flow velocities using Stokes’ Law. Experimental results confirmed the method’s accuracy for particle sizes ranging from 20 mm to 0.7 mm, significantly extending the measurable range of Sedimaging systems. The proposed approach shows strong potential for integration into dam-related particle monitoring applications and building-related monitoring systems requiring fine-resolution analysis. Full article