Search Results (415)

This study presents a numerical investigation into the hydrodynamic and biomechanical performance of bone-repair scaffolds based on Triply Periodic Minimal Surfaces (TPMSs). Focusing on Gyroid and Diamond architectures, scaffolds with uniform (40–70%) and functionally graded porosities were developed. Computational Fluid Dynamics (CFD) simulations were employed to evaluate permeability, pressure drop, and Wall Shear Stress (WSS) distributions. Results indicate distinct topological advantages: the Gyroid structure demonstrates superior permeability and uniform WSS distribution due to its isotropic fluid channels, whereas the Diamond structure maintains better flow velocity stability. Crucially, the introduction of a porosity gradient (40–60%) successfully mitigates localized pressure surges and optimizes the bioactive WSS window for cell differentiation. Notably, increasing porosity to 70% in Gyroid scaffolds yielded a 277% enhancement in permeability. These findings establish a theoretical basis for designing functionally graded TPMS scaffolds that balance fluid transport efficiency with a favorable cellular microenvironment. Full article

Ferrofluid sealing is an important non-contact sealing technology for ultra-high vacuum (UHV) equipment, but its reliability is affected by more than pressure-bearing capacity alone. This review shows that carrier-liquid evaporation, material outgassing, thermal degradation, magnetic-field distortion, and liquid-ring instability are the main factors limiting UHV ferrofluid seals. Multiphysics simulation and parametric optimization remain the most mature tools for analyzing magnetic-field distribution, pressure resistance, temperature rise, and structural deformation. Data-driven condition identification improves failure monitoring, whereas physics-informed neural networks, topology optimization, and multi-objective optimization are still emerging methods for low-sample prediction and collaborative design. Future studies should focus on low-vapor-pressure ferrofluids, bake-out compatibility, thermal management, lifetime prediction, and integrated model–data design frameworks. Full article

Tesla valves have emerged as promising passive flow-regulation devices for sustainable water systems because they provide directional flow control without moving parts, external energy input, or complex maintenance requirements. This review systematically examines the fundamental mechanisms, structural evolution, and engineering applications of Tesla valves in water-related systems. The underlying rectification behavior is analyzed from the perspectives of flow separation, recirculation, jet interaction, vortex evolution, and mechanism switching under varying hydraulic conditions. Recent advances in geometric optimization, multistage configurations, three-dimensional architectures, topology optimization, and data-driven design approaches are summarized to illustrate the transition from classical Tesla geometries to next-generation passive flow-control structures. Current applications in microfluidic systems, water-quality monitoring, thermo-hydraulic devices, pressure-regulation networks, and hydraulic safety enhancement are critically reviewed. The analysis indicates that Tesla-valve performance is governed by coupled interactions among geometry, flow regime, fluid properties, and operating conditions, while multifunctional designs increasingly integrate flow regulation, mixing enhancement, heat transfer, and pressure management. Finally, key challenges related to performance standardization, realistic operating conditions, manufacturability, and system-level integration are discussed. Tesla valves are expected to play an increasingly important role in intelligent and energy-efficient water infrastructure, supporting the development of next-generation sustainable water and fluid-management systems. Full article

The optimal deployment of pressure monitoring sensors in water distribution networks is crucial for leak detection, network calibration, and system diagnostics. Water utilities face increasing pressure to reduce non-revenue water losses while continuing to improve service quality under budget constraints, thus making the strategic deployment of sensors a critical priority. However, traditional optimization approaches come with various disadvantages including high computational complexity, limited scalability, or dependence on uncertain preliminary parameter estimates. This paper addresses these shortcomings by proposing an innovative integrated framework that balances topological and hydraulic considerations, and applying a flexible metric blending approach to enable robust sensor positioning across networks that differ in scales and topologies. The methodology has been validated through three case studies: a theoretical reference grid, an urban district network, and a large-scale multisource irrigation system. The results prove the methodology to be consistently effective in identifying optimal sensor configurations across all test cases. Full article

Within the subway systems of megacities, individual passenger transfer experiences have long been marginalized due to an overemphasis on macro-level, systemic, and functional performance, positioning low transfer efficiency as a pervasive bottleneck in enhancing the overall network efficacy. Adopting an individual passenger perspective, this study takes the Changping Line of the Beijing Subway as an empirical case. By using walking speed to evaluate transfer efficiency and through field survey, behavioral experiment, and quantitative model analysis, this paper reveals the key built environment factors influencing transfer efficiency and their underlying impact mechanisms and also provides empirical evidence for the synergistic optimization of transfer efficiency and the built environment in megacity subway systems. The findings indicate that the built environment impacts transfer efficiency in macro-non-linear and micro-linear ways, specifically manifesting across six specific mechanisms: the geographic location mechanism, the pressure mechanism of high-density development, the spatial exclusivity mechanism of regional transport hubs, the topological penalty mechanism of transfer paths, the bottleneck constraint mechanism of node facilities, and the compensatory mechanism of information guidance. Furthermore, as a medium affecting transfer efficiency, the shaping of the built environment is essentially determined by the city’s subway planning and construction institutions, the external technical conditions of the particular stations, and localized tactical governance to manage the dynamic daily traffic mobility. Based on these findings, this study suggests that improving the transfer efficiency of megacity metro systems like the Changping Line should implement systemic strategies from four aspects: tailored TOD at the macro-spatial planning phase, the micro-spatial integration of indoor and outdoor built environments during the station design phase, differentiated collaborative governance to alleviate station-external intermodal transfer competitions during the operation phase, and digitally empowered transfer guidance to proactively manage transfer demand across three scenarios. Full article

Accurate multistep-ahead prediction of natural gas pipeline-network states is essential for intelligent dispatching, yet such networks contain physically heterogeneous components (gas sources, pipelines, compressors, valves), and historical states and future dispatching commands are decoupled in both temporal scale and physical semantics. This causes conventional data-driven models to suffer from semantic entanglement and cumulative error during long-horizon forecasting. This study proposes a deep spatiotemporal surrogate model with three coordinated designs: (i) type-specific feature encoding combined with global latent-graph mapping and a shared graph convolutional network (GCN) to disentangle heterogeneous-equipment attributes and represent network-wide topological coupling; (ii) a residual-gated temporal coupling mechanism that adaptively fuses historical operating inertia with future external disturbances; and (iii) a temporal-gradient multi-objective loss with a 12-step autoregressive rolling strategy over a 6 h horizon to suppress cumulative divergence. On 85,248 samples built from field monitoring data and commercial mechanistic simulations, the model attains median relative errors of 1.15% for nodal pressure and 2.10% for pipeline flow, capturing macroscopic pressure decay and high-frequency transient flow induced by valve and compressor switching without noticeable delay, providing an efficient tool for online simulation, real-time warning, and decision support in complex natural gas pipeline networks. Full article

Meeting the rigorous performance standards of modern electrified transit necessitates the deployment of high-performance outer rotor PMSMs with elevated power-to-volume ratios. However, their unique internal heat source topology inherently restricts heat dissipation. This limitation risks permanent magnet demagnetization and winding insulation failure. To address these thermal bottlenecks, this paper proposes internal bio-inspired cooling channels. These channels feature micro-scale V-shaped ribs. This design targets a 60 kW outer rotor PMSM. The motor uses a fractional-slot concentrated winding. The analytical procedure commences with the formulation of a transient 2D numerical model utilizing the Time-Stepping Finite Element approach (TS-FEM). It is coupled with the Bertotti model to compute electromagnetic losses. This approach accurately determines losses under high-frequency rated conditions. Results reveal that stator iron loss constitutes the dominant heat source. It accounts for 76.4 percent of the total electromagnetic loss. Furthermore, these losses show severe spatial concentration at the stator teeth. Subsequently, a three-dimensional fluid-solid coupled CFD model is developed. This model evaluates the proposed internal cooling channels. The design integrates bio-inspired vein networks and V-shaped ribs. These internal ribs disrupt the near-wall thermal boundary layer. This disruption enhances the local convective heat transfer. Comparative multiphysics analyses indicate improved hydraulic and thermal performance of the bio-inspired design under the same numerical boundary conditions. The bio-inspired channel achieves a more uniform static pressure distribution and reduces severe fluid stagnation zones. In the numerical model, the maximum stator and permanent magnet temperatures are reduced to 48 °C and 42 °C, respectively. This work provides a numerical design reference for thermal management in high-performance electric aviation. Full article

High-fidelity computational fluid dynamics (CFD) remains computationally expensive for steady aerodynamic prediction under multi-condition and variable-geometry configurations, which limits rapid design iteration. To address this issue, this study proposes a data-driven surrogate framework for aircraft surface flow-field prediction on irregular meshes. The framework combines a geometry-unification strategy for variable rudder-deflection configurations with KM-GAT, a hybrid neural architecture that integrates graph attention and KAN-based nonlinear feature transformation. Geometry unification maps the surface flow fields associated with different rudder-deflection states onto a common zero-deflection reference template, thereby establishing consistent mesh correspondence and fixed prediction locations across samples while retaining the rudder angle as an operating-condition variable. The KM-GAT model further combines topology-aware message passing with localized nonlinear refinement, while the Huber loss is adopted to improve training robustness for CFD-derived data. Experiments on the F-22 research model show that the proposed framework achieves lower prediction errors and more concentrated error distributions than baseline MLP and GNN-based models. Qualitative comparisons further indicate that KM-GAT better preserves localized high-gradient structures, including pressure transitions and vortex-dominated regions. These results suggest that the proposed framework provides an effective surrogate modeling strategy for variable-geometry aerodynamic flow field prediction. Full article

Rising demand for high-performance battery thermal management systems (BTMSs) has rendered single-mode cooling insufficient for advanced lithium-ion batteries (LIBs) in new energy vehicles (NEVs), particularly under high discharge rates. This study proposes a synergistic hybrid BTMS integrating composite phase-change material (CPCM)–aluminum foam with liquid cooling to enhance thermal regulation of cylindrical battery modules under 5 C discharge conditions. Multiple liquid-cooled plate (LCP) configurations, including serpentine, straight, and leaf-shaped designs, together with different flow channel topologies (FCTs), were systematically investigated and optimized. The effects of coolant flow speed (CFS) and ambient temperature were also analyzed. Results indicate that the optimized leaf-shaped LCP with FCT #2 delivers superior performance, limiting the maximum temperature to 309.98 K, reducing temperature difference by 7.6%, and decreasing pressure drop by 88.79% compared to the serpentine configuration. Increasing CFS improves heat dissipation and delays PCM melting, although it raises pressure losses. Furthermore, the proposed system maintains a cell-to-cell temperature difference below 0.51 K, indicating excellent thermal uniformity. Compared to a CPCM-only system, the hybrid BTMS reduces peak temperature by 8.81 K under elevated ambient conditions (309.15 K), demonstrating strong potential for reliable and efficient thermal management in demanding operating environments. Full article

Ventilation network feature graphs (Q-H graphs) are a key visualisation tool for mine ventilation systems, and their automated generation reduces to a combinatorial optimisation problem over independent-path permutations. Existing methods, however, exhibit three limitations: a single-dimensional evaluation criterion, inadequate nodal pressure-energy assignment, and unstable convergence in factorial-scale search spaces. This paper proposes an adaptive NSGA-II (A-NSGA-II) framework with coordinated enhancements at the evaluation, modelling, and algorithmic levels. A three-objective system that minimises split-block count, topological-spatial discrepancy, and layout fragmentation is established, together with an aggregate evaluation score (AES) for engineering decision-making; nodal pressure energies are reconstructed via the longest path on a directed acyclic graph; and topology-aware initialisation, Lagrange three-point interpolated adaptive operators, and periodic memetic local search are integrated within NSGA-II. Experiments on two mine ventilation networks (75 and 112 branches) over 30 independent trials show that A-NSGA-II consistently outperforms four benchmarks (NSGA-II, MOEA/D, SPEA2, and MOSA) in terms of split-block count, AES, and hypervolume; statistical tests confirm significant, large-effect HV advantages on the 112-branch network, while the 75-branch network shows a 56.6–71.5% reduction in HV standard deviation. Full article

We present a material distribution topology optimization (TO) framework that directly generates capacity-specific radial trims for severe-service control valves. The method uses an out-of-plane resistance modified two-dimensional turbulence model and objective functions that maximize directional change to create tortuous pressure-staging geometries at predefined channel depths. Four trims targeting non-dimensional capacities ($C_V$) of 0.672, 0.96 (two objectives), and 1.248 were optimized, MSLA-printed, and tested in a globe valve using IEC 60534 procedures. The measured capacities ranged from −13.7% to +4.8% of the targets for a fully 2D optimization process, dropping to a maximum of 7.8% when coupled with a hybrid 3D tuning step. Acoustic detection indicated incipient cavitation at a pressure drop ratios greater than $0.87$ for the most highly staged design and $0.73$ for the highest capacity design, which is consistent with our simulations of the flow field before fabrication. These results demonstrate that TO can deliver fit-to-service, capacity-tuned trims with excellent cavitation suppression, reducing reliance on large parametric design libraries. Full article

This paper presents a system-on-chip field-programmable gate array (SoC FPGA) acceleration architecture for graph-neural-network- and deep-reinforcement-learning (GNN–DRL)-based routing inference in low-Earth-orbit (LEO) satellite networks. Because LEO satellites move at high orbital speeds, the network topology changes continuously, and routing decisions must track the current link state rather than rely only on static rules. GNN-based DRL routing can represent the graph structure of the network when selecting paths, but its message-passing and readout stages are computationally expensive for resource-constrained onboard platforms. To address this limitation, the trained GNN routing model is ported to an SoC FPGA and implemented with a collaborative processing-system (PS) and programmable-logic (PL) architecture. The PS handles candidate-path generation, environment setup, path selection, and network-state updates, whereas the PL executes the computationally dominant message-passing neural network (MPNN) and readout layers. Post-training INT8 quantization, nonlinear-function approximation, vector-level parallelization, and a parallel multiply–accumulate structure are applied to reduce memory pressure and execution time. Experiments on a ZCU104 board using a PYNQ-controlled PS–PL implementation and an NSFNET-based routing environment show that the proposed PS–PL structure reduces the evaluation time from 94.08 s to 12.63 s compared with the PS-only implementation while maintaining an evaluation score close to that of the original model. Full article

With the rising integration of high-performance GPUs, localized hotspots induced by discrete heat sources present severe thermal challenges. Traditional single-inlet–single-outlet liquid cold plates can scarcely meet the heat dissipation requirements of inhomogeneous high heat fluxes. This study systematically investigates the effects of nine multiple-inlet–multiple-outlet (MIMO) configurations, ranging from single-inlet–single-outlet to three-inlet–three-outlet, on cold plate hydrothermal performance. An innovative stepwise optimization strategy, topology optimization (TO)-driven channel layout combined with fin-enhancement (FE)-based fine regulation, is proposed and verified to precisely regulate surface temperature distribution of discrete heat sources. The results show that the three-inlet–three-outlet configuration C-3 exhibits the optimal comprehensive performance among the nine configurations. Compared with the worst configuration A-2, C-3 reduces the pressure drop by 58.37% to only 147.18 Pa and yields the highest PEC, striking the optimum trade-off between heat transfer enhancement and fluid flow resistance. Through multi-inlet flow distribution and multi-outlet heat extraction, C-3 accurately suppresses heat accumulation in high heat flux regions, limiting the maximum temperature to merely 29.82 °C and drastically narrowing the substrate temperature difference from 8.69 °C to 2.12 °C. In comparison with the traditional cold plate (TCP), the optimized cold plate (OCP) realizes a 17.42% increase in performance evaluation criterion (PEC). Furthermore, the fin-enhanced optimized cold plate (FEOCP) reduces the temperature standard deviation by 54.15% relative to TCP, significantly enhancing temperature uniformity with only an additional pressure drop penalty of 5.43%. This study reveals the regulation mechanism of MIMO configurations on the flow field distribution of liquid cold plates and verifies the effectiveness of the TO-FE optimization framework, thus providing highly valuable engineering solutions for the high-efficiency, uniform-temperature and low-resistance heat dissipation of high-power electronic devices. Full article

Lattice-based heat sinks have attracted increasing attention as volumetric thermal management architectures for forced-convection cooling of high-power electronic systems. In contrast to conventional plate-fin, pin-fin, and straight-channel configurations, lattice geometries promote three-dimensional flow–solid interaction through interconnected ligament networks that modify boundary-layer development, wake formation, and internal heat-spreading pathways. This review synthesizes recent experimental and numerical studies to examine the thermo-fluid mechanisms governing lattice performance, with emphasis on the coupled influence of porosity, ligament dimensions, topology, orientation, and channel confinement on heat-transfer enhancement and hydraulic resistance. The analysis indicates that while lattice structures can increase average Nusselt number and improve temperature uniformity, these gains are intrinsically linked to pressure-drop penalties associated with flow tortuosity and form drag, resulting in regime-dependent thermal-hydraulic behavior. Apparent discrepancies reported across the literature are frequently attributable to differences in geometric definition, Reynolds-number normalization, and boundary-condition specification rather than to inconsistencies in physical mechanisms. By consolidating geometric scaling, performance metrics, manufacturing considerations, and system-level constraints, this review clarifies the conditions under which lattice heat sinks may provide net benefit relative to conventional cooling technologies and identifies key research directions required to support application-relevant design and evaluation. Full article

High sound pressure level (SPL) acoustic sensing requires miniaturized microphones that can operate under large acoustic loading while maintaining mechanical linearity, sufficient sensing response, and broadband audio frequency behavior. This work targets high-SPL operation and numerically investigates a graphene piezoresistive MEMS microphone based on a membrane-supported beam–island diaphragm. The proposed structure retains a continuous membrane for acoustic load bearing, while the upper beam–island topology redirects deformation-induced strain toward beam root regions where graphene piezoresistors are placed. This design is intended to increase the local strain available for piezoresistive readout without simply relying on larger global diaphragm deflection. Finite-element analysis was used to optimize the diaphragm geometry and evaluate strain enhancement, pressure response linearity, modal behavior, and harmonic response. Under the 170 dB SPL reference condition, the optimized structure increases the peak structural strain from 47.83 με in a thickness-equivalent solid diaphragm to 562.53 με, achieving an approximately 11.8-fold enhancement in local sensing strain while maintaining a highly linear pressure response (R2 > 0.9999). Additionally, the results also show that the sensor exhibits a high first natural frequency of 64.07 kHz and a small response variation of approximately 0.94 dB within the 0–20 kHz target frequency range, indicating excellent dynamic stability and high-fidelity signal transduction characteristics. To connect the structural response with piezoresistive readout, first-order electromechanical output estimation was further performed using representative graphene gauge factors, quarter-bridge readout assumptions, contact resistance correction, and Johnson-noise-limited signal-to-noise ratio estimation. A ±5% geometric tolerance check further indicates that the membrane side length is the most fabrication-sensitive parameter, while the selected design remains generally robust except for reduced linearity margin under positive membrane side-length deviation. These results demonstrate the potential of the proposed graphene-based MEMS microphone for high-SPL broadband acoustic sensing applications in harsh and high-intensity acoustic environments. Full article