Search Results (5,287)

Energetic powder processing includes comminution, sieving, drying, conveying, mixing, and packaging, all of which determine product performance and safety. With growing requirements for efficiency and reliability, numerical simulation has become essential for analyzing mechanisms, optimizing parameters, and supporting equipment design. This review summarizes recent progress in simulation techniques such as the discrete element method (DEM), computational fluid dynamics (CFD), and multi-scale coupling while also evaluating their predictive capabilities and limitations across various unit operations and safety concerns such as electrostatic hazards. It, thus, establishes the core “property–parameter–performance” relationships and clarifies mechanisms in multiphase flow, energy transfer, and charge accumulation, and highlights the role of symmetry in improving simulation efficiency. By highlighting persistent challenges, this work lays a foundation for future research, guiding the development of theoretical frameworks and practical solutions for advanced powder processing. Full article

Synthetic jets, generated through the periodic suction and ejection of fluid without net mass addition, offer distinct benefits, such as compactness, ease of integration, and independence from external fluid sources. These characteristics make them well-suited for flow control and convective heat transfer applications. However, conventional single-actuator configurations are constrained by limited jet formation, narrow surface coverage, and diminished effectiveness in the far field. This review critically evaluates the key limitations and explores four advanced configurations developed to mitigate them: dual-cavity synthetic jets, single-actuator multi-orifice jets, coaxial synthetic jets, and synthetic jet arrays. Dual-cavity synthetic jets enhance volume flow rate and surface coverage by generating multiple vortices and enabling jet vectoring, though they remain constrained by downstream vortex diffusion. Single-actuator multi-orifice designs enhance near-field heat transfer through multiple interacting vortices, yet far-field performance remains an issue. Coaxial synthetic jets improve vortex dynamics and overall performance but face challenges at high Reynolds numbers. Synthetic jet arrays with independently controlled actuators offer the greatest potential, enabling jet vectoring and focusing to enhance entrainment, expand spanwise coverage, and improve far-field performance. By examining key limitations and technological advances, this review lays the foundation for expanded use of synthetic jets in practical engineering applications. Full article

In this study, a high-fidelity, full-scale three-dimensional Computational Fluid Dynamics (CFD) model was developed to analyze the effects of U-type and Z-type interconnection configurations on flow and distribution uniformity within a 4 × 1 kW planar solid oxide fuel cell (SOFC) stack composed of 40 unit cells. Mesh independence was verified using the Richardson extrapolation method. The results reveal that on the anode (fuel inlet) side, the Z-type configuration exhibits significantly better flow and pressure uniformity than the U-type configuration and shows low sensitivity to variations in fuel utilization (${\mathrm{U}}_f$ = 0.3–0.8), maintaining stable flow distribution under different conditions. On the cathode (air inlet) side, however, the U-type configuration demonstrates superior flow stability at an air utilization rate of 0.3. Therefore, it is recommended to employ the Z-type configuration for the anode and the U-type configuration for the cathode to achieve more uniform gas distribution and enhanced operational stability. These findings provide valuable insights for optimizing the design and operation of solid oxide fuel cells (SOFCs) and offer guidance for the development of more efficient fuel cell systems. Full article

Radial piston motors are expected to expand their applications in hydraulic drive systems due to their high torque density and mechanical robustness. However, its volumetric efficiency can be significantly affected by the multi-stroke operating characteristics and leakage occurring in the micro-clearances of the valve plate. In this study, a detailed modeling procedure for a multi-stroke radial piston motor is proposed using the 1D system simulation software Amesim. In particular, the dynamic interaction between the ports and pistons inside the motor is formulated using mathematical function-based expressions, enabling a more precise representation of the driving behavior and torque generation process. Furthermore, to characterize the leakage flow occurring in the micro-clearance between the fluid distributor and cylinder housing, the commercial CFD software Simerics MP+ was employed to analyze the three-dimensional flow characteristics within the leakage gap. Based on these CFD results, a leakage-path function was constructed and implemented in the Amesim model. As a result, the developed model exhibited strong agreement with reference data from an actual motor in terms of overall operating performance, including volumetric and mechanical efficiencies while consistently reproducing the leakage behavior observed in the CFD analysis. The simulation approach presented in this study demonstrates the capability to reliably capture complex fluid–mechanical interactions at the system level, and it can serve as an effective tool for performance prediction and optimal design of hydraulic motors. Full article

Accurate identification of oil–water two-phase flow patterns is essential for ensuring the safety and operational efficiency of oil and gas extraction systems. While traditional methods using empirical models and sensor technologies have provided basic insights, they often struggle to capture the nonlinear features of complex operational conditions. To address the challenge of data scarcity commonly found in experimental settings, this study employs a data augmentation strategy that combines the Synthetic Minority Over-sampling Technique (SMOTE) with Gaussian noise injection, effectively expanding the feature space from 60 original experimental nodes. Next, a physics-constrained attention mechanism model was developed that incorporates a physical constraint matrix to effectively mask irrelevant feature interactions. Experimental results show that while the standard attention model (83.88%) and the baseline BP neural network (84.25%) have limitations in generalizing to complex regimes, the proposed physics-constrained model achieves a peak test accuracy of 96.62%. Importantly, the model demonstrates exceptional robustness in identifying complex transition regions—specifically Dispersed Oil-in-Water (DO/W) flows—where it improved recall rates by about 24.6% compared to baselines. Additionally, visualization of attention scores confirms that the distribution of attention weights aligns closely with fluid-dynamic mechanisms—favoring inclination for stratified flows and flow rate for turbulence-dominated dispersions—thus validating the model’s interpretability. This research offers a novel, interpretable approach for modeling dynamic feature interactions in multiphase flows and provides valuable insights for intelligent oilfield development. Full article

Modern processes to produce rare-earth elements, strategic metals, and nuclear fuel reprocessing require highly efficient liquid–liquid extraction in systems characterized by high viscosity, elevated interfacial tension, and small density differences. Traditional gravity-driven extractors exhibit low performance under these conditions, whereas centrifugal extractors enable rapid mass transfer and nearly complete phase separation. Differential-contact annular centrifugal contactors offer the highest flexibility and efficiency, but their optimization is limited by the lack of experimental data on the hydrodynamics of liquid flow through perforated nozzles in a rotating field. This limitation hinders the development of accurate computational fluid dynamics (CFD) models (e.g., ANSYS Fluent), reliable equipment scale-up, and the design of optimized contactor configurations. The present study addresses this gap by experimentally determining the flow velocity of liquids through nozzles of various geometries across a wide range of centrifugal accelerations. From these data, a universal power-law correlation was derived, linking the flow rate to rotor speed, nozzle geometry, and the physicochemical properties of the phases. The proposed correlation provides a robust experimental basis for numerical model validation, computational design, and optimization of next-generation differential-contact centrifugal extractors. Full article

This paper presents a unified aerodynamic design and optimization framework for morphing Blended-Wing-Body (BWB) Unmanned Aerial Vehicles (UAVs) operating in subsonic and near-transonic regimes. The proposed framework integrates parametric CAD modeling, Computational Fluid Dynamics (CFD), and surrogate-based optimization using Response Surface Methodology (RSM) to establish a generalized approach for geometry-driven aerodynamic design under multi-Mach conditions. The study integrates classical aerodynamic principles with modern surrogate-based optimization to show that adaptive morphing geometries can maintain efficiency across varied flight conditions, establishing a scalable and physically grounded framework that advances real-time, high-performance aerodynamic adaptation for next-generation BWB UAVs. The methodology formulates the optimization problem as drag minimization under constant lift and wetted-area constraints, enabling systematic sensitivity analysis of key geometric parameters, including sweep, taper, and twist across varying flow regimes. Theoretical trends are established, showing that geometric twist and taper dominate lift variations at low Mach numbers, whereas sweep angle becomes increasingly significant as compressibility effects intensify. To validate the framework, a representative BWB UAV was optimized at Mach 0.2, 0.4, and 0.8 using a parametric ANSYS Workbench environment. Results demonstrated up to a 56% improvement in lift-to-drag ratio relative to an equivalent conventional UAV and confirmed the theoretical predictions regarding the Mach-dependent aerodynamic sensitivities. The framework provides a reusable foundation for conceptual design and optimization of morphing aircraft, offering practical guidelines for multi-regime performance enhancement and early-stage design integration. Full article

The depletion of fossil fuel resources and the growing need for sustainable energy solutions have increased interest in vertical axis wind turbines (VAWTs), which offer advantages in urban and variable-wind environments but often exhibit limited performance at low tip speed ratios (TSRs). This study optimizes VAWT aerodynamic behavior across a wide TSR range by varying three geometric parameters: maximum thickness position ($a/b$), relative thickness (m), and pitch angle ($\beta$). A two-dimensional computational fluid dynamics (CFD) framework, combined with the Metamodel of Optimal Prognosis (MOP), was used to build surrogate models, perform sensitivity analyses, and identify optimal profiles through gradient-based optimization of the integrated $C_p$–$\lambda$ curve. The Joukowsky transformation was employed for efficient geometric parameterization while maintaining aerodynamic adaptability. The optimized airfoils consistently outperformed the baseline NACA 0021, yielding up to a 14.4% improvement at $\lambda =2.64$ and an average increase of 10.7% across all evaluated TSRs. Flow-field analysis confirmed reduced separation, smoother pressure gradients, and enhanced torque generation. Overall, the proposed methodology provides a robust and computationally efficient framework for multi-TSR optimization, integrating Joukowsky-based parameterization with surrogate modeling to improve VAWT performance under diverse operating conditions. Full article

Under the condition of gas–liquid two-phase flow, traditional sucker rod pumps are prone to gas locking due to the high compressibility of gas, and their volumetric efficiency is usually less than 30%, which seriously restricts the exploitation benefits of oil wells. To solve this difficult problem, this study proposes a variable-diameter tube pump structure that adopts an optimized cone angle of the pump cylinder. The results of computational fluid dynamics simulations using dynamic mesh modeling indicate that the stepped change in the pump barrel diameter can enhance the gas–liquid separation effect caused by vortices, while the flow-guiding grooves on the valve seat can reduce the response delay. Comparative calculations and analyses show that compared with the traditional design, its head increases to 13.89 m, and the hydraulic power rises to 1431.01 W, respectively, representing an increase of 17%. This is attributed to the reduction in the gas retention time during piston reciprocation and the stability of the flow field. This structural innovation effectively alleviates the gas lock problem and provides a feasible approach for improving energy efficiency in oil wells prone to vaporization, which is of great significance in oilfield development operations. Full article

Nowadays various coupling strategies have been developed to combine the strengths of different numerical methods in computational fluid dynamics (CFD), among which the coupled algorithm of the lattice Boltzmann-finite volume method (LBM-FVM) has gained widespread attention. However, research on parallel optimization of LBM-FVM coupled solvers remains limited, mostly focused on independent solvers. In this work, we proposed a flexible framework and optimization schemes to explore the coordinated balance of accuracy-efficiency-hardware adaptability. First, we designed a processor layout strategy to address load imbalance and communication redundancy in the coupled solver. We then developed several parallelization techniques, including LBM restructuring, data reuse, and SIMD optimization for targeted kernels on the most advanced architecture of the Sunway series in China, namely SW26010P heterogeneous many-core processors, which provide hardware architectural advantages well suited for large-scale parallel computational fluid dynamics. Finally, the accuracy of the LBM-FVM coupling simulations was validated through benchmark simulations of 2D/3D lid-driven cavity flow. The results show that our LBM-FVM coupling solver can accurately capture flow characteristics, with vortex structures consistent with experimental data. Additionally, we achieved a 152× speedup for the LBM solver and a 126× speedup for the coupled simulation compared to the standalone FVM simulation on the New Sunway supercomputer system. Our approach marks a milestone in the field of LBM implementations and provides a promising future for coupled algorithms in CFD. Full article

Spunbonding drafters play a decisive role in determining fiber attenuation, morphology, and final nonwoven quality; however, their internal airflow behavior remains poorly characterized due to limited physical accessibility and historically empirical design practices. This work employs high-fidelity computational fluid dynamics (CFD) to systematically resolve the airflow field inside a laboratory-scale drafter and to quantify the impact of geometry on fiber drawing conditions. The simulations reveal a previously unreported “braking effect,” where adverse flow structures reduce effective shear drag, limit drawability, and increase the likelihood of fiber breakage. Parametric virtual experimentation across seven geometric variables demonstrates that the drafter configuration strongly governs shear distribution, flow uniformity, and energy consumption. Using a performance-oriented optimization framework, three key processing objectives were targeted: (i) maximizing shear drag to promote stable fiber attenuation, (ii) improving axial drawing uniformity, and (iii) minimizing pressurized-air demand. CFD-guided design modifications—including controlled widening, tailored wall divergence and convergence, and an extensible lower section—were implemented and subsequently validated using a newly constructed prototype. Experimental measurements on polypropylene (PP) and high-density polyethylene (HDPE) fibers confirm substantial reductions in fiber breakage and improvements in drawing stability, thereby demonstrating the effectiveness of simulation-driven process optimization in spunbonding equipment design. Full article

Research shows that neurodegenerative processes do not develop from a single “broken” biochemistry process; rather, they develop when a complex multi-physics environment gradually loses its ability to stabilize the neuron via a collective action between the protein, ion, field and fluid dynamics of the neuron. The use of new technologies such as quantum-informed molecular simulation (QIMS), dielectric nanoscale mapping, fluid dynamics of the cell, and imaging of perivascular flow are allowing researchers to understand how the collective interactions among proteins, membranes and their electrical properties, along with fluid dynamics within the cell, form a highly interconnected dynamic system. These systems require fine control over the energetic, mechanical and electrical interactions that maintain their coherence. When there is even a small change in the protein conformations, the electric properties of the membrane, or the viscosity of the cell’s interior, it can cause changes in the high dimensional space in which the system operates to lose some of its stabilizing curvature and become prone to instability well before structural pathologies become apparent. AI has allowed researchers to create digital twin models using combined physical data from multiple scales and to predict the trajectory of the neural system toward instability by identifying signs of early deformation. Preliminary studies suggest that deviations in the ergodicity of metabolic–mechanical systems, contraction of dissipative bandwidth, and fragmentation of attractor basins could be indicators of vulnerability. This study will attempt to combine all of the current research into a cohesive view of the role of progressive loss of multi-physics coherence in neurodegenerative disease. Through integration of protein energetics, electrodynamic drift, and hydrodynamic irregularities, as well as predictive modeling utilizing AI, the authors will provide mechanistic insights and discuss potential approaches to early detection, targeted stabilization, and precision-guided interventions based on neurophysics. Full article

Micro-combustion for micro-thermophotovoltaic (MTPV) and micro-thermoelectric (MTE) systems is gaining renewed interest as a pathway toward compact power generation with high energy density. This review examines how emerging artificial intelligence (AI) methodologies can accelerate the development of such systems by addressing longstanding modeling, optimization, and design challenges. We analyze four major research areas: artificial neural network (ANN)-based design optimization, AI-driven prediction of micro-scale flow variables, Physics-Informed Neural Networks for combustion modeling, and surrogate models that approximate high-fidelity computational fluid dynamics (CFD) and detailed chemistry solvers. These approaches enable faster exploration of geometric and operating spaces, improved prediction of nonlinear flow and reaction dynamics, and efficient reconstructions of thermal and chemical fields. The review outlines a wide range of future research directions motivated by advances in high-fidelity modeling, AI-based optimization, and hybrid data-physics learning approaches, while also highlighting key challenges related to data availability, model robustness, validation, and manufacturability. Overall, the synthesis shows that overcoming these limitations will enable the development of micro-combustors with higher energy efficiency, lower emissions, more stable and controllable flames, and the practical realization of commercially viable MTPV and MTE systems. Full article

This study employed numerical simulations to investigate the aerodynamic characteristics of a flapping wing by solving the governing incompressible Navier–Stokes equations. Using computational fluid dynamics (CFD), the effect of frequency acceleration on the aerodynamic performance of a two-degrees-of-freedom (DoF) flapping wing in hovering was examined. The results indicate that the pitching frequency acceleration significantly influences the aerodynamic force: positive acceleration enhances lift by up to 2.0 times while maintaining propulsion compared to the case under negative acceleration. This mechanism is attributed to the delayed shedding of the leading-edge vortex (LEV) and the shedding of the trailing-edge vortex (TEV). Moreover, aerodynamic forces are also affected by plunge acceleration, with both negative and positive acceleration contributing to performance improvement. An increase in the acceleration coefficient leads to a notable enhancement in the aerodynamic force; however, the improvement becomes marginal when the coefficient n exceeds 0.4. The underlying flow evolution is illustrated and analyzed through pressure and vorticity contours. These findings on the acceleration effect will be applied to optimize the kinematics and design of flapping wing drones. Full article

To address the challenges of high computational cost and lengthy design cycles in the high-precision optimization of ray-like underwater gliders, this study proposes a high-accuracy, low-cost parametric modeling and optimization method. The proposed framework begins by extracting the characteristic contours of the manta ray and reconstructing the airfoil sections using the Class-Shape Transformation (CST) method, resulting in a flexible parametric geometry capable of smooth deformation. High-fidelity Computational Fluid Dynamics (CFD) simulations are employed to evaluate the hydrodynamic characteristics, and detailed flow field analyses are conducted to identify the most influential geometric features affecting lift and drag performance. On this basis, a Kriging-based sequential optimization framework is developed. The surrogate model is adaptively refined through dynamic infilling of sample points based on combined Mean Squared Prediction (MSP) and Expected Improvement (EI) criteria, thus improving optimization efficiency while maintaining predictive accuracy. Comparative case studies demonstrate that the proposed method achieves a 116% improvement in lift-to-drag ratio and a more uniform flow distribution, confirming its effectiveness in enhancing both design accuracy and computational efficiency. The results indicate that this approach provides a practical and efficient tool for the parametric design and hydrodynamic optimization of bio-inspired underwater vehicles. Full article