Search Results (49)

Polymeric nanoparticles based on poly(lactic-co-glycolic acid) (PLGA) are widely used in drug delivery, yet scalable and reproducible production methods remain a major challenge. In this study, we combine experimental nanoprecipitation and computational fluid dynamics (CFD) modeling to optimize PLGA nanoparticle formulation using both traditional batch and microfluidic methods. While Design of Experiments (DoE) was used to optimize the batch process, microfluidic mixing was systematically explored by varying flow parameters such as the flow rate ratio (FRR) and total flow rate (TFR). We compared two microfluidic mixer designs with Y-junction and three-inlet junction geometries to evaluate their impact on the mixing efficiency and nanoparticle formation. Experimental results revealed that the three-inlet design produced smaller, more uniform nanoparticles with superior post-lyophilization stability. CFD simulations confirmed these findings by displaying velocity fields and PLGA concentration gradients, demonstrating significantly more homogeneous mixing and efficient interfacial contact in the three-inlet configuration. Furthermore, simulated outlet concentrations were used to predict the nanoparticle size via theoretical modeling, which closely agreed with the experimental data. This integrated approach highlights the importance of microfluidic geometry in controlling nanoparticle nucleation dynamics and provides a framework for rational design of scalable nanomedicine production systems. Full article

In response to the growing demand for lightweight and high-efficiency industrial equipment, this study addresses the critical issue of lubrication failure in high-speed, heavy-duty gear reducers, which often leads to reduced transmission efficiency and premature mechanical damage. A three-dimensional transient multiphysics-coupled model of oil-jet lubrication is developed based on computational fluid dynamics (CFD). The model integrates the Volume of Fluid (VOF) multiphase flow method with the shear stress transport (SST) k−ω turbulence model. This framework enables the accurate capture of oil-jet interface fragmentation, reattachment, and turbulence-coupled behavior within the gear meshing region. A parametric study is conducted on oil injection velocities ranging from 20 to 50 m/s to elucidate the coupling mechanisms between geometric configuration and flow dynamics, as well as their impacts on oil film evolution, energy dissipation, and thermal management. The results reveal that the proposed method can reveal the dynamic evolution characteristics of the gear injection lubrication. Adopting an appropriately moderate injection velocity (30 m/s) improves oil film coverage and continuity, with the lubricant transitioning from discrete droplets to a dense wedge-shaped film within the meshing zone. Optimal lubrication performance is achieved at this velocity, where oil shear-carrying capacity and kinetic energy utilization efficiency are maximized, while excessive turbulent kinetic energy dissipation is effectively suppressed. Dynamic monitoring data at point P further corroborate that a well-tuned injection velocity stabilizes lubricant-velocity fluctuations and improves lubricant oil distribution, thereby promoting consistent oil film formation and more efficient heat transfer. The proposed closed-loop collaborative framework—comprising model initialization, numerical solution, and post-processing—together with the introduced quantitative evaluation metrics, provides a solid theoretical foundation and engineering reference for structural optimization, energy control, and thermal reliability design of gearbox lubrication systems. This work offers important insights into precision lubrication of high-speed transmissions and contributes to the sustainable, green development of industrial machinery. Full article

This study investigates the thermal dynamics and material behavior involved in the baking process for Lebanese flatbread, focusing on the heat transfer mechanisms, water loss, and dough expansion under high-temperature conditions. Despite previous studies on flatbread baking using impingement or conventional ovens, this work presents the first experimental investigation of the traditional Lebanese flatbread baking process under realistic industrial conditions, specifically using a high-temperature tunnel oven with direct flame heating, extremely short baking times (~10–12 s), and peak temperatures reaching ~650 °C, which are essential to achieving the characteristic pocket formation and texture of Lebanese bread. This experimental study characterizes the baking kinetics of traditional Lebanese flatbread, recording mass loss pre- and post-baking, thermal profiles, and dough expansion through real-time temperature measurements and video recordings, providing insights into the dough’s thermal response and expansion behavior under high-temperature conditions. A custom-designed instrumented oven with a steel conveyor and a direct flame burner was employed. The dough, prepared following a traditional recipe, was analyzed during the baking process using K-type thermocouples and visual monitoring. Results revealed that Lebanese bread undergoes significant water loss due to high baking temperatures (~650 °C), leading to rapid crust formation and pocket development. Empirical equations modeling the relationship between baking time, temperature, and expansion were developed with high predictive accuracy. Additionally, an energy analysis revealed that the total energy required to bake Lebanese bread is approximately 667 kJ/kg, with an overall thermal efficiency of only 21%, dropping to 16% when preheating is included. According to previous CFD (Computational Fluid Dynamics) simulations, most heat loss in similar tunnel ovens occurs via the chimney (50%) and oven walls (29%). These findings contribute to understanding the broader thermophysical principles that can be applied to the development of more efficient baking processes for various types of bread. The empirical models developed in this study can be applied to automating and refining the industrial production of Lebanese flatbread, ensuring consistent product quality across different baking environments. Future studies will extend this work to alternative oven designs and dough formulations. Full article

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. Full article

Simulative and experimental studies were carried out to address multi-dimensional particle fractionation of non-biological particles according to size, shape, and density inside a high-throughput DLD array. Density sensitive separation was achieved for melamine and polystyrene particles at a diameter of 5 µm at a Reynolds number (Re) of 82, corresponding to an overall flow rate of 11.3 mL/min. This process is very sensitive, as no fractionation occurred for Re = 85 (11.7 mL/min). For the first time, the fractionation of elliptical polystyrene particles (5 × 10 µm) at Re > 1 was investigated up to Re = 80 (11 mL/min). A separation of elliptical particles from spherical melamine particles (5 µm) was observed in single experiments at all investigated Reynolds numbers. However, the separation is not reliably repeatable due to partial clogging of ellipsoidal particles along the posts. In addition, higher concentrations of polydisperse silica suspensions were experimentally investigated by using polydisperse silica particles at concentrations up to 0.4% (m/V) up to Re = 80 (20 mL/min). The separation size generally decreased with increasing Reynolds number and increased with increasing concentration. Separation efficiency decreased with increasing concentration, independent of the Reynolds number. In order to investigate the material-dependent separation in a contactless dielectrophoresis system (cDEP), the resolved CFD-DEM software was extended to calculate dielectrophoretic forces on particles. With this, the second stage of a serial-combined DLD-DEP system was simulated, showing good separation at lower flow rates. For these systems, different fabrication methods to minimize the distance between the electrodes and the fluid as well as the requirement to withstand high-throughput applications, were investigated. Full article

Creating and updating safety regulations and standards for industrial processes and end-uses related to hydrogen demand a solid scientific foundation, which requires extensive research on unignited hydrogen releases from high-pressure systems across different situations. This study focuses on high-pressure hydrogen releases along a horizontal plate to investigate the surface effects on hydrogen dispersion. Hydrogen releases from high-pressure sources up to 30 MPa were modeled using a computational fluid dynamics (CFD) method, with the CFD models validated by experimental data. The hydrogen dispersion characteristics along the plate were studied for various source pressures and leak nozzle diameters. The results show that the maximum flammable extent along the plate increases linearly with both the source pressure and nozzle diameter, while the combustible mass increases to the power of 1.5 with the increase in leakage flow rate. The locations where the jet centerline attach to the plate are identical (about 0.41 m away from the nozzle exit in the axial direction) for different source pressures (10~30 MPa) and nozzle diameters (0.5~1.5 mm). The flow region was divided into pre-attachment and attachment zones by the attachment point, and the self-similarity characteristics of both zones were analyzed. Finally, correlations for the centerline and lateral concentration distributions were developed for both the pre- and post-attachment zones. The results can help users quickly assess safety distance when hydrogen leaks along the plate. Full article

In this work, a statistical method is applied to a multiphase CFD simulation of concrete mixing performed in a truck mixer. The numerical model is based on an Eulerian–Eulerian approach in a transient regime. The aggregate materials are simulated as dispersed solid particles of various diameters, while the cement paste is simulated as a non-Newtonian continuous fluid. The first ten drum revolutions are analyzed from the condition of the completely segregated materials. The cell mixing index, defined by a statistical method in terms of mean, variance, and density probability function, is applied to the analysis of the simulation results. The statistical variables are implemented using the fluid dynamics code in the post-processing result analyses. The method predicts the distribution efficiency of the materials within a truck mixer as a function of its internal geometry, rotation speed, and mixture composition. As the number of revolutions increases, the distribution qualitatively improves, as shown by the motion fields, velocities, and vortices of the various materials, quantified through the calculation of the mixing index. The illustrated method can be used to predictively calculate the distribution effectiveness of new truck mixer designs before prototyping them and can be applied to other types of mixers. Furthermore, this study can be applied to liquid–solid mixing processes analyzed via the Eulerian multiphase numerical approach. Full article

Adapting to climate change and controlling carbon emissions have emerged as significant challenges faced by the international community. The high-quality pedestrian space system of TOD blocks, as an important means for carbon reduction and carbon sink increase in cities, showcases the effect of green intensification and low-carbon sustainable urban space development. In this study, by combining the research on low-carbon block creation and urban microclimate, focusing on the technical process of the three stages of pre-treatment, core calculation, and post-treatment, comprehensively considering the three elements of microclimate, namely wind, heat, and carbon, and their influencing parameters, and introducing a CFD simulation method for porous media, a CFD simulation technology framework for microclimate improvement in urban design is constructed. Through the spatial visualization of the software solution calculation results and the correlation and comparative analysis of the measured data, we quantitatively analyze the coupling relationship between the block morphology and the comprehensive environment of wind, heat, and carbon. The research results indicate that by rationally adjusting indicator elements such as the height-to-width ratio of streets and entrance forms, it is possible to effectively facilitate cooling, ventilation, and air circulation within blocks and dilute the CO₂ concentration. Finally, from the urban design element systems at the micro, meso, and macro levels, the adaptive planning strategies in the three dimensions of the spatial form, constituent elements, and planning guidelines of TOD blocks are summarized and refined, with the aim of achieving the low-carbon transformation of cities through the creation of a healthy microclimate environment. Full article

A batch mixer is an important piece of equipment for polymer filling modification, and the kinematics of agglomerate breakup and distribution are necessary for the structure design and mixing process optimization of the rotor, particularly in light of the cohesive forces that exist within the agglomerate. In this paper, computational fluid dynamics (CFD) was coupled with discrete element method (DEM) to simulate the mixing process, including breakup and distribution, which was further quantitatively evaluated by the post-processing involving numerical method. To study the mixing process of an agglomerate composed of massive spherical particles (individual particle ratio was r), the coordinates of the particles were exported from the CFD-DEM simulation results. Then, the coordinate data were automatically processed with an automate custom-built post-processing program to obtain the average radius of gyration (R_gy) and the particle distribution density (ε). The kinematics analyzation of breakup and distribution was represented by curve of R_gy/r versus mixing time (t) and curve of ε versus t, respectively. The value of R_gy/r and ε decreased over time until they reached an equilibrium and vibrated around a certain value. In particular, a notable decline in the value of R_gy/r was observed following an increase prior to critical time. The increase in R_gy/r stated that the agglomerate or aggregates undergo stretching deformation. Additionally, mixing processes of rotors with different pressurization coefficients (S) and rotation speeds could be facilitated and intensified by large S and high rotation speed. Finally, a “breakup-line” was developed by considering the influence of cohesive force and rotation speed on the agglomerate breakup process. The agglomerate could be broken if the combination of rotation speed and bonding strength was above the “breakup line”, otherwise the agglomerate was not broken. Furthermore, rotors with larger slopes exhibited stronger breakup ability. Full article

Gravel soil faces significant engineering challenges such as leakage erosion and soil flow due to its complex composition and susceptibility to groundwater effects. This study integrates the entire machine learning process, including pre- and post-processing of images, WGAN implementation, and validation of hydraulic and morphological properties. Obtaining intact gravel soil samples is difficult and costly due to their erodible nature in the Li River, China. A μ-CT scanning series is employed to capture detailed images with three microstructural characteristics of gravel soil, forming the basis for training datasets using WGANs. This approach allows the generation of similar 3D realizations that replicate the microstructural characteristics and hydraulic behaviors of a prototype of gravel soils. Through computational fluid dynamics (CFD) simulations, the effectiveness of the realizations in hydraulic behavior within reconstructed porous structures is verified. This process indirectly validates the consistency between the realization′s microstructure and the prototype. This integrated methodology not only enhances understanding but also aids in the optimization of engineering designs and applications in geotechnical and materials science disciplines. Full article

Automatic and blind parameter estimation based on the inherent features of wireless signals is a major research area due to the fact that these techniques lead to the simplification of receivers, especially in terms of coarse synchronization, and more importantly reduce the signaling load at the control channels. Thus, in the literature, many techniques are proposed to estimate a vast set of parameters including modulation types and orders, data and chip rates, phase and frequency offsets, and so on. In this paper, a cyclostationary feature detection (CFD) based method is proposed to estimate the carrier numbers and carrier spacing of carrier-aggregated direct sequence spread spectrum (DSSS) cellular signals blindly. The particular chip rate of the signal is also estimated through the process jointly. The proposed CFD-based method unearths the inhered and hidden second-order periodicities of carrier-aggregated DSSS signals, particularly targeting repeated pseudorandom noise sequences of users over the carriers. Throughout the paper, after the proposed method is formulated, the measurement setup that is developed to collect the data for the validation of the method is introduced. The measurement results are post-processed for performance analysis purposes. To that end, the method is investigated in terms of signal-to-noise ratio (SNR) values, different channel conditions, and measurement durations. Furthermore, the performance of the proposed method is compared with that of energy detection. The measurement results indicate superior performance of the proposed method under significant wireless channel impairments and in low-SNR regions, e.g., for 0 dB the proposed method provides more than $0.9$ detection performance for the case of $0.1$ false alarm rate, while the performance of ED is $0.6$ under the same wireless channel impairments. The raw outputs of the method can be utilized to train a convolutional neural network to eliminate the statistical estimation process in future work. Full article

Cardiovascular diseases (CVD) remain the leading cause of mortality among older adults. Early detection is critical as the prognosis for advanced-stage CVD is often poor. Consequently, non-invasive diagnostic tools that can assess hemodynamic function, particularly of the aorta, are essential. Computational fluid dynamics (CFD) has emerged as a promising method for simulating cardiovascular dynamics efficiently and cost-effectively, using increasingly accessible computational resources. This study developed a CFD model to assess the aorta geometry using tetrahedral and polyhedral meshes. A healthy aorta was modeled with mesh sizes ranging from 0.2 to 1 mm. Key hemodynamic parameters, including blood pressure waveform, pressure difference, wall shear stress (WSS), and associated wall parameters like relative residence time (RRT), oscillatory shear index (OSI), and endothelial cell activation potential (ECAP) were evaluated. The performance of the CFD simulations, focusing on accuracy and processing time, was assessed to determine clinical viability. The CFD model demonstrated clinically acceptable results, achieving over 95% accuracy while reducing simulation time by up to 54%. The entire simulation process, from image construction to the post-processing of results, was completed in under 120 min. Both mesh types (tetrahedral and polyhedral) provided reliable outputs for hemodynamic analysis. This study provides a novel demonstration of the impact of mesh type in obtaining accurate hemodynamic data, quickly and efficiently, using CFD simulations for non-invasive aortic assessments. The method is particularly beneficial for routine check-ups, offering improved diagnostics for populations with limited healthcare access or higher cardiovascular disease risk. Full article

Although the impact of local fluid dynamics in the biodegradation of magnesium is well known, currently no studies in the literature address the degradation effects of ocular vitreous on bioresorbable devices made of magnesium, which could be developed as drug delivery carriers. The aim of this study was to investigate the flow-induced corrosion mechanism of magnesium in an ophthalmological environment for future applications in ophthalmic drug delivery. To achieve this, experimental and computational methods were combined. Specifically, a CFD model was employed to design experimental conditions that replicate the ocular flow-induced shear stress (FISS) on manufactured magnesium samples. Pure Mg samples were tested in a bioreactor system capable of imposing the ocular CFD calculated values of FISS on the Mg samples’ surface by varying the pump flow rate. Optimal flow rates for a range of different FISS values specific to the ophthalmological fluid dynamics affecting the device were indeed determined before running the experiments. After conducting customized corrosion tests, morphological observations and profilometric maps of the eroded surfaces of Mg samples were obtained using scanning electron microscopy (SEM) and confocal laser scanning microscopy (CLSM). These maps were then post-processed for the parametric evaluation of corrosion rates. Pre-existing localized superficial defects did affect the final corrosion pattern. SEM images and CLSM data confirmed a uniform corrosion mechanism, with corrosion rates of 1.9, 2.7, and 3.4 μm/day under different shear stress conditions (0, 0.01, and 0.032 Pa, respectively). More generally, uniform corrosion on pure Mg samples increased with higher FISS values, and at higher shear stress values (FISS = 0.032 Pa), a notable washing-out effect of the corrosion products was observed. The removal of corrosion products at higher shear stresses suggests that the dynamic ocular environment, influenced by saccadic movements, plays a significant role in the corrosion mechanism of pure magnesium. The corrosion rates determined in this study, in conjunction with clinical drug release requirements, are crucial for designing potential drug-release devices for ocular applications. Full article

This study aimed to analyze the aerodynamic characteristics of Chinese Nordic combined athletes during the ski jump take-off process, comparing them with elite athletes from the 2009 Nordic World Ski Championships using computational fluid dynamics (CFD) methods. Methods: Using 3D model analysis and continuous relative phase analysis, CFD methods were utilized to assess the mechanical characteristics of athletes during the take-off phase. Results: The analysis revealed that Chinese athletes displayed a lower dominance of the knee joint during the take-off phase, leading to increased air drag. Conclusion: Reduced knee joint dominance and an excessive ankle angle at the initiation of the ski jump take-off contribute to higher air drag. The lean angle of the body and the ankle angle post-take-off significantly affect the resultant lift and drag forces. Full article

Inflammation of the bile ducts and surrounding tissues can impede bile flow from the liver into the intestines. If this occurs, a plastic or self-expanding metal (SEM) stent is placed to restore bile drainage. United States (US) Food and Drug Administration (FDA)-approved plastic biliary stents are less expensive than SEMs but have limited patency and can occlude bile flow if placed spanning a duct juncture. Recently, we investigated the effects of variations to post-processing and autoclaving on a commercially available stereolithography (SLA) resin in an effort to produce a suitable material for use in a biliary stent, an FDA Class II medical device. We tested six variations from the manufacturer’s recommended post-processing and found that tripling the isopropanol (IPA) wash time to 60 min and reducing the time and temperature of the UV cure to 10 min at 40 °C, followed by a 30 min gravity autoclave cycle, yielded a polymer that was flexible and non-cytotoxic. In turn, we designed and fabricated customizable, SLA 3D-printed polymeric biliary stents that permit bile flow at a duct juncture and can be deployed via catheter. Next, we generated an in silico stent 3-point bend test to predict displacements and peak stresses in the stent designs. We confirmed our simulation accuracy with experimental data from 3-point bend tests on SLA 3D-printed stents. Unfortunately, our 3-point bend test simulation indicates that, when bent to the degree needed for placement via catheter (~30°), the peak stress the stents are predicted to experience would exceed the yield stress of the polymer. Thus, the risk of permanent deformation or damage during placement via catheter to a stent printed and post-processed as we have described would be significant. Moving forward, we will test alternative resins and post-processing parameters that have increased elasticity but would still be compatible with use in a Class II medical device. Full article