Search Results (1,947)

Debris accumulation critically impacts hydraulic structures by altering approach flow, amplifying hydrodynamic forces, and inducing backwater rise. While previous research has extensively examined drag forces due to debris, the effects of debris porosity, its proximity to the channel bed, and upstream–downstream water level difference on hydrodynamic loadings are still not fully understood. To address these gaps, 336 experiments were conducted under subcritical flow conditions, involving nine debris configurations, characterized by different geometries and porosities. Drag and lift forces were measured to quantify debris–flow–structure interactions. The results show that drag and lift coefficients increase with blockage ratio and water level difference, whereas they decrease with Froude number and proximity ratio. Moreover, debris porosity and geometry have a negligible effect on drag coefficient but significantly influence lift coefficient. In the tested range of Reynolds numbers, both coefficients are not affected by the flow regime, with all other parameters being constant. Based on experimental evidence and dimensional analysis, empirical equations were derived for estimating drag and lift coefficients. To the best of the authors’ knowledge, for the first time, the proposed predictive relationships account for all the above-mentioned hydraulic and geometric variables, providing useful tools for improving the design and resilience of bridge infrastructures. Full article

Designing thermal–fluid devices that reduce peak temperature while limiting pressure loss is challenging because high-fidelity (HF) Navier–Stokes–convection simulations make direct HF-driven topology optimization computationally expensive. This study presents a two-dimensional, steady, laminar multifidelity topology design framework for thermal–fluid devices operating in a low-to-moderate Reynolds number regime. A computationally efficient low-fidelity (LF) Darcy–convection model is used for topology optimization, where SEMDOT decouples geometric smoothness from the analysis field to produce CAD-ready boundaries. The LF optimization minimizes a P-norm aggregated temperature subject to a prescribed volume fraction constraint; the inlet–outlet pressure difference and the P-norm parameter are varied to generate a diverse candidate set. All candidates are then evaluated using a steady incompressible HF Navier–Stokes–convection model in COMSOL 6.3 under a consistent operating condition (fixed flow; pressure drop reported as an output). In representative single- and multi-channel case studies, SEMDOT designs reduce the HF peak temperature (e.g., ~337 K to ~323 K) while also reducing the pressure drop (e.g., ~18.7 Pa to ~12.6 Pa) relative to conventional straight-channel layouts under the same operating point. Compared with a conventional RAMP-based pipeline under the tested settings, the proposed approach yields a more favorable Pareto distribution (normalized hypervolume 1.000 vs. 0.923). 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

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

Autonomous underwater vehicle (AUV) motion planning in complex three-dimensional ocean environments remains challenging due to the simultaneous requirements of obstacle avoidance, dynamic feasibility, and energy efficiency. Current approaches often decouple these factors or exhibit high computational overhead, limiting applicability in real-time or large-scale missions. This work proposes a hierarchical trajectory planning framework designed to address these coupled constraints in an integrated manner. The framework consists of two stages: (i) a current-biased sampling-based planner (CB-RRT*) is introduced to incorporate ocean current information into the path generation process. By leveraging flow field distributions, the planner improves path geometric continuity and reduces steering variations compared with benchmark algorithms; (ii) spatial–temporal alternating optimization is performed within underwater safe corridors, where Bézier curve parameterization is utilized to jointly optimize spatial shapes and temporal profiles, producing dynamically feasible and energy-efficient trajectories. Simulation results in dense obstacle fields, heterogeneous flow environments, and large-scale maps demonstrate that the proposed method reduces the maximum steering angle by up to 63% in downstream scenarios, achieving a mean maximum turning angle of 0.06 rad after optimization. The framework consistently attains the lowest energy consumption across all tests while maintaining an average computation time of 0.68 s in typical environments. These results confirm the framework’s suitability for practical AUV applications, providing a computationally efficient solution for generating safe, kinematically feasible, and energy-efficient trajectories in real-world ocean settings. 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

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

Coherent structures play a pivotal role in wall-bounded turbulence, serving as primary carriers of momentum, energy, and scalar quantities across the flow. This study examines coherent structures, specifically streamwise streaks and intense Reynolds stress regions (Q structures), within a novel DNS dataset capturing a stepped transition from pure Poiseuille flow to pure Couette flow at $R{e}_{\tau }\approx 250$, based on the stationary wall. Structures are identified using a percolation algorithm to ensure well-defined boundaries, followed by three-dimensional clustering in Cartesian coordinates. They are further classified as wall-attached or wall-detached based on their proximity to the domain walls. Intense Reynolds stress structures are categorized into quadrants according to the signs of their averaged velocity components. The statistical properties of these structures—encompassing geometric characteristics, energy content, and spatial distribution—are thoroughly analyzed. Particular emphasis is placed on how these properties evolve across the transition from Poiseuille to Couette flow. The results reveal that increasing mean shear in Couette-like cases significantly influences the energy content and spatial distribution of the structures while their geometric characteristics remain relatively consistent across the dataset. This spatial distribution is closely linked to the large-scale structures of the streamwise velocity component in Couette flow, confirming that these structures are genuine physical features rather than artificial artifacts of the flow. Full article

This study presents a numerical investigation of turbulent flow and heat transfer in corrugated channels, focusing on the effects of key geometric parameters on thermal–hydraulic performance. The corrugation height-to-channel height ratio (C/H), the length ratio (L₁/L₂), and the expansion angle (θ) were systematically varied, and simulations were performed for Reynolds numbers between 4 × 10³ and 1 × 10⁴ using water as the working fluid and SST k–ω turbulence model. Response Surface Methodology (RSM) was applied to develop predictive models for the Nusselt number (Nu), friction factor (f), and thermal performance index (η). The results indicate that C/H is the dominant geometric parameter controlling both heat transfer and flow resistance. Increasing C/H from 0.10 to 1.00 results in a reduction in Nu of approximately 20–22%, while the friction factor decreases by about 40–45% over the investigated Reynolds number range, revealing a clear thermal–hydraulic trade-off. In contrast, variations in L₁/L₂ (0.5–6.0) and θ (5–30°) have a relatively weak influence, typically causing changes in Nu and f below 5–7%. The thermal performance index remains consistently above unity for all configurations and varies within a narrow range (η ≈ 1.00–1.16). The maximum thermal enhancement of approximately 10–15% is achieved at lower C/H values, particularly at low Reynolds numbers, whereas higher C/H values favor reduced pressure losses. Overall, the findings quantitatively demonstrate that corrugation height governs the thermal–hydraulic behavior of corrugated channels, while L₁/L₂ and θ provide design flexibility with minimal performance penalty. Full article

This study presents a unified parametric optimization framework for the thermal design of microchannel spreaders used in high-power processor cooling. The fin geometry is expressed in a shape-agnostic parametric form defined by fin thickness, top and bottom gap widths, and channel height, without prescribing a fixed cross-section. This approach accommodates practical fin profiles ranging from rectangular to tapered and V-shaped, allowing continuous geometric optimization within manufacturability and hydraulic limits. A coupled analytical–numerical model integrates conduction through the spreader base, interfacial resistance across the thermal interface material (TIM), and convection within the coolant channels while enforcing a pressure-drop constraint. The optimization uses a deterministic continuation method with smooth sigmoid mappings and penalty functions to maintain constraint satisfaction and stable convergence across the design space. The total thermal resistance ($R_{\mathrm{tot}}$) is minimized over spreader conductivities $k_s=400–2200$ W m⁻¹ K⁻¹ (copper to CVD diamond), inlet fluid velocities $U_{\mathrm{in}}=0.5–5.5$ m s⁻¹, maximum pressure drops of 10–50 kPa, and fluid pass counts $N_p\in \{1,2,3\}$. The resulting maps of optimized fin dimensions as functions of $k_s$ provide continuous design charts that clarify how material conductivity, flow rate, and pass configuration collectively determine the geometry, minimizing total thermal resistance, thereby reducing chip temperature rise for a given heat load. Full article

This study introduces a digital fabrication process for producing recyclable, closed-loop wax formwork for architectural concrete applications with visually rich surface articulation while drastically reducing formwork milling time. As such, this paper presents (a) a circular large-scale production method for wax blocks via a single casting process; (b) four machine-time-optimized surface articulation strategies through CNC toolpath-driven design; (c) the investigation of different coating systems to improve architectural concrete surface quality and to ease demolding; and (d) the integration of robotic concrete shotcreting using a low-CO₂ fine-grain concrete. For the first time, wax formwork technology, characterized by its waste-free approach, has been combined with robotic shotcreting in a digital and automated workflow to fabricate fiber-reinforced, geometrically complex thin-shell concrete elements with distinct surface articulations. To evaluate the process, a series of four thin-shell concrete elements was produced, employing four distinct parametric toolpath-driven designs: linear surface articulation, crossed surface articulation, topology-adapted curve flow surface articulation, and robotic drill topology-adapted surface articulation. Results revealed a possible reduction in milling time of between 77% and 94% compared to traditional milling methods. The optimized toolpaths and design-driven milling strategies achieved a high degree of visual richness, showcasing the potential of this integrated approach for the production of high-quality architectural concrete elements. Full article

In this study, a novel and efficient solid-state additive manufacturing technique, friction rolling additive manufacturing (FRAM), was employed to fabricate an aluminum alloy ring component, significantly reducing process complexity and mitigating solidification defects typical of melt-based techniques. However, previous studies on FRAM have primarily focused on the microstructural characteristics and mechanical properties of flat components, with limited attention paid to ring-shaped components. Owing to the unique geometric constraints imposed during the forming process, ring components exhibit markedly different microstructural evolution and defect formation mechanisms compared with flat counterparts, and these mechanisms remain insufficiently and systematically understood. To address this knowledge gap, the coupled Eulerian–Lagrangian (CEL) method was introduced for the first time to numerically simulate the temperature distribution and residual stress evolution during the FRAM process of ring-shaped components. In addition, tracer particles were incorporated into the simulations to analyze the material flow behavior, thereby systematically elucidating the forming behavior and microstructural evolution characteristics under geometric constraint conditions. Moreover, scanning electron microscopy (SEM) and electron backscatter diffraction (EBSD) were employed to systematically characterize the microstructural evolution and defect morphology. The CEL numerical simulations exhibited good consistency with the experimental observations, demonstrating the reliability and accuracy of the simulation method. The results showed that the peak temperatures were primarily concentrated at the advancing side of the rotation tool, and the temperature on the outer diameter side of the ring was consistently higher than that on the inner diameter side. The lack of shoulder friction on the inner side led to an increased heat dissipation rate, thereby resulting in higher residual stress compared to other regions. The particle analysis revealed that, due to ring geometry, material flow varied across radial regions, resulting in distinct microstructures. Further EBSD analysis revealed that, after the rotating tool passed, the material first developed a preferential orientation with {111} planes parallel to the shear direction, and with more layers, dynamic recrystallization produced an equiaxed grain structure. This study provides a theoretical basis and process reference for the application of the FRAM technique in the manufacturing of large ring components. Full article

Biophysics-based, patient-specific modeling remains challenging for clinical translation, particularly for cerebrospinal fluid (CSF) flow where anatomical detail and computational cost are tightly coupled. We present a computational framework for steady net CSF redistribution in an MRI-derived cranial CSF domain reconstructed from T₂-weighted imaging, including the ventricular system, cranial subarachnoid space, and periarterial pathways, to the extent resolvable by clinical MRI. Cranial CSF spaces were segmented in 3D Slicer and a steady Darcy formulation with prescribed CSF production/absorption was solved in COMSOL Multiphysics^®. Geometrical and flow descriptors were quantified using region-based projection operations. We assessed discretization cost–accuracy trade-offs by comparing first- and second-order finite elements. First-order elements produced a 1.4% difference in transmantle pressure and a <10% difference in element-wise mass-weighted velocity metric for 90% of elements, while reducing computation time by 75% (20 to 5 min) and peak memory usage five-fold (150 to 30 GB). This proof-of-concept framework provides a computationally tractable baseline for studying steady net CSF pathway redistribution and sensitivity to boundary assumptions, and may support future patient-specific investigations in pathological conditions such as subarachnoid hemorrhage, hydrocephalus and brain tumors. Full article

With the increasing energy density of lithium-ion batteries, the heat dissipation performance of air-cooled battery energy storage cabinets has become a critical determinant of both system performance and service life. This performance depends strongly on the geometry of the airflow channels and their influence on the internal flow distribution. In this study, the internal flow field of a battery energy storage cabinet was analyzed, and the airflow-channel geometry was optimized using the BOBYQA algorithm. The results indicate that the risk of thermal runaway is largely associated with inadequate airflow design, which leads to localized heat accumulation. Geometric optimization of the airflow channels reduced the maximum hotspot temperature from 72.9 °C to 57.6 °C. The hotspots were concentrated at the tops of the battery modules. Modifications to the channel geometry increased the airflow velocity and improved its directionality in these regions, thereby reducing both the hotspot temperature and the extent of the affected area. Moreover, slightly increasing the inlet pressure while reducing the outlet pressure produced a more uniform temperature distribution across the tops of the battery modules. Full article