Search Results (780)

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

The Changbai Mountain area, the largest Cenozoic intraplate volcanic field in eastern China, features abundant high-temperature hot springs and high geothermal potential. However, the genesis and aggregation patterns of its geothermal systems remain poorly understood. This study recalculates crustal and residual deep/mantle heat- flow components along a representative profile and synthesizes published geological, geophysical, geochemical, and geothermal evidence to characterize the main geothermal system elements, including caprock, reservoirs, water source, and migration pathways. Controlling factors are examined from three dimensions: deep dynamics, magmatic heat source, and fault characteristics. Results reveal a “Cold crust–Hot mantle” thermal structure. The heat-flow calculation indicates that crustal radiogenic heat contributes approximately 40% of the surface heat flow, implying a dominant deep heat contribution. The available evidence suggests the presence of potential hydrothermal reservoirs in carbonate and clastic rocks, possible HDR targets in deeper metamorphic rocks, and locally effective basaltic sealing units. Fault systems and meteoric recharge likely control fluid circulation. Geothermal systems are controlled by mantle upwelling and lithospheric thinning due to western Pacific Plate subduction, multi-source heat coupling, effective caprock sealing, and fault-controlled water–heat conduction. These results provide a conceptual framework for future geothermal exploration and testing. This study elucidates the aggregation patterns and genetic mechanisms, providing a theoretical basis for exploration and development. Full article

Introduction: River connectivity is a fundamental component of lotic ecosystems, frequently disrupted by anthropogenic barriers. Fishways are critical mitigation tools for restoring this connectivity; however, their temporal use by fish is often assumed to be restricted to specific seasonal windows (mainly spawning season). Understanding the fine-scale temporal dynamics of fishway use by native species such as the common barbel (Luciobarbus bocagei) is essential for effective, long-term river management and the conservation of Iberian freshwater biodiversity. Objective: This study aims to evaluate the daily and seasonal patterns of use of pool-type fishways by L. bocagei, in order to improve the determination of their temporal period of use and provide data-driven recommendations for their management. Methodology: The study was conducted in the middle reach of the Duero River (Burgos province). Over a multi-year period, more than 1000 individual barbels were captured, tagged with Passive Integrated Transponder (PIT) tags, and released. Movements were continuously monitored across two pool-type fishways. Telemetry detection data were analyzed to evaluate daily rhythms and seasonal use, as well as to estimate the start and end dates of the movement periods. Results: Our analysis revealed a highly marked daily pattern in fishway use, showing a clear increase during daylight hours, with more pronounced peaks at dawn and dusk. This daily activity dynamically adjusts throughout the year in response to the solar cycle. Furthermore, we successfully determined that the seasonal activity extends from March to November, a pattern that remained consistent throughout the different study years. Crucially, the telemetry data empirically demonstrated that L. bocagei utilizes fishways for a substantial portion of the year, indicating relevant non-reproductive movements. Conclusions: The extended temporal use of these fishways indicates their potential ecological functions throughout much of the year. This must be reflected in management and maintenance protocols that ensure sufficient flow conditions, cleaning, and active vigilance during most of the year. Additionally, this predictability reinforces the value of fishways as strategic biological monitoring stations for endemic populations. Full article

Plot combine harvesters are specialized machines used in breeding trials, germplasm evaluation, and small-batch seed harvesting. Compared with conventional field combine harvesters, they have higher requirements for sample independence, grain integrity, seed purity, low residual grain, rapid plot switching, and plot-level data reliability. However, existing studies remain relatively fragmented, and many studies mainly focus on individual components, whereas analyses of whole-machine coordination, residual-grain control, crop adaptability, and data integration remain insufficient. This paper presents a structured review of the research progress in plot combine harvesters from an agricultural-engineering perspective, covering representative international and domestic models, headers, threshing and separation systems, cleaning systems, residual-seed removal devices, simulation methods, intelligent monitoring, and seed-quality sensing. Existing evidence indicates that plot combine harvesters are developing toward whole-machine low-residue design, coordinated threshing–cleaning–conveying optimization, standardized evaluation methods, sample identification, data traceability, and long-term field validation under continuous multi-plot harvesting conditions. Key challenges include coordinating small-batch intermittent material flow, controlling residual grain during frequent plot switching, balancing threshing completeness with seed protection, improving adaptability to different crops and breeding materials, and validating intelligent sensing technologies under field conditions. This paper provides an engineering reference for improving the mechanization, precision, and intelligence of breeding-trial harvesting equipment. Full article

As core structural components for deep-sea oil and gas exploitation, deep-sea risers are continuously subjected to wind, wave, and current loads, which readily induce vortex-induced vibration (VIV) and further trigger structural fatigue damage. Furthermore, the progressive exploitation of deepwater and ultra-deepwater oil and gas resources has exacerbated the complexity and risk of riser VIV, rendering it a critical engineering problem that urgently requires effective solutions. This paper presents a comprehensive review of numerical studies on deep-sea riser VIV, systematically elaborating the fundamental principles, research advances, and application scenarios of three mainstream numerical approaches: semi-empirical models, computational fluid dynamics (CFD) models, and computational structural dynamics (CSD) models. The respective accuracy advantages and inherent limitations of each numerical method are thoroughly analyzed. Additionally, this review focuses on key research hotspots and challenging issues, including VIV responses of flexible risers, dynamic fluid–structure boundary coupling, internal–external flow coupling effects, wake interference of multi-riser systems, efficient VIV prediction, and vibration suppression optimization. The current technical bottlenecks in existing research are clarified. This study aims to provide a systematic theoretical framework and methodological reference for subsequent numerical investigations and engineering applications of riser VIV, and offer technical support for the optimal structural design and safety risk prevention of deep-sea riser systems. Full article

As the core actuation component of intelligent precision spraying systems, the variable-rate nozzle is essential for achieving on-demand agricultural spraying; improving the use efficiency of water, fertilizers and pesticides; and reducing environmental pollution. This paper systematically reviews the development of agricultural variable-rate nozzles, from early mechanical profiling structures to modern intelligent control technologies based on Pulse Width Modulation (PWM). First, the existing variable-rate nozzles are classified into three major categories: electromagnetic-integrated type, centrifugal type, and variable-diameter type. A comparative analysis is conducted from three dimensions of working principle, performance characteristics and application scenarios, to delineate the respective advantages and limitations of each nozzle category. Second, the paper examines key technological advances in three areas: high-frequency solenoid valves, PWM control, and pressure and flow stabilization. It identifies the nonlinear response of solenoid valves, flow distortion under low duty cycles, and water hammer pressure fluctuation induced by high-speed switching as the three core technical bottlenecks at the current stage. Subsequently, the latest achievements and typical methodologies of variable-rate nozzles in structural design, simulation and experimental analysis are systematically reviewed, and their application performance in scenarios including field crops, orchards, protected agriculture and beyond are summarized. Finally, the remaining open issues in this field are put forward. It is suggested that future research should focus on key breakthroughs in the development of corrosion and wear-resistant high-frequency solenoid valves, the formation mechanism and suppression methods of pressure fluctuation, as well as adaptive algorithms based on machine learning or Model Predictive Control (MPC), to promote the leapfrog development of agricultural variable-rate nozzle technology from single variable control to multi-factor coupling optimization. All references cited in this paper are from articles published after the year 2000. Among them, the literature published in the last decade accounts for 86.6%, and literature published in the last five years accounts for 58.9%. Full article

This study presents the preliminary development of a clustered hybrid propulsion module, and its experimental validation from static motor characterization to dynamic 1-D vertical drop tests to assess the feasibility of a hybrid propulsion system for soft-landing applications. The research progresses from preliminary design of core components (such as fuel, oxidizer supply system, engine configuration), to the performance verification of the clustering module. First, the trade-off between high regression rates and mechanical integrity was evaluated for paraffin-based fuels. However, high-density polyethylene (HDPE) was utilized as the baseline to ensure predictable combustion behavior. Second, cold flow tests of the designed multi-port manifold demonstrated a highly uniform oxidizer distribution, validating the geometric design with a maximum spatial pressure deviation of 2.44% across the four engines. Third, static fire tests confirmed robust dynamic control capabilities, successfully throttling the average chamber pressure from 100% (7.00 bar) down to 43% (3.01 bar) and back to 100% (7.01 bar) with a transient response time of approximately 0.6 s. Finally, the 1-D vertical drop test validated the operational readiness of the system; the open-loop thrust modulation successfully counteracted the module’s dynamic weight, achieving a terminal descent velocity of 1.46 m/s, which strictly satisfies planetary soft-landing safety criteria. These results demonstrate the feasibility and performance of clustered hybrid propulsion systems for planetary exploration, extending to surface launch technology for sample return missions from the Moon and Mars, and precision booster recovery for small launch vehicles. Full article

With the expansion of modern protected agriculture, the amount of post-harvest tomato biomass has increased sharply. Conventional unmanaged disposal practices disrupt carbon flows and cause substantial environmental emissions. Tomato plant residues (TPRs), which are rich in lignocellulose and selected high-value secondary metabolites, have considerable potential as feedstocks for green industrial materials. However, their complex biophysical properties, high physiological moisture content, and recalcitrant cell-wall barriers hinder large-scale processing. This review systematically examines the mechanisms and process architectures for converting TPRs into macromolecular products. First, it analyzes cross-scale anatomical heterogeneity and dynamic rheological properties of TPRs, defining their physicochemical boundaries as industrial precursors. Second, it summarizes the development of physical field-coupled equipment, ranging from anti-tangling harvest-shredding to die-roller densification. Furthermore, it examines the core mechanisms of multi-field-coupled pretreatment technologies, including steam explosion, deep eutectic solvents (DES), and mechanochemistry, in deconstructing vascular skeletons and reducing multiphase mass-transfer resistance. Finally, this review discusses reconstruction pathways for TPR-derived components in advanced polymer materials, including biodegradable nanocellulose films, bio-based composites, aerogels, and lignin-based polyurethane networks. Overall, it links microscopic reaction kinetics with macroscopic equipment engineering, proposes a closed-loop material conversion system from in-field volume reduction to cascaded biorefinery, and provides an engineering framework for future multi-machine intelligent collaboration and continuous production across the industrial chain. Full article

Hydrogen oxy-combustion with high CO₂ dilution is a key component of supercritical CO₂ (sCO₂) power cycles, such as the Allam cycle, enabling high-efficiency, near-zero-emission power generation with integrated carbon capture. However, combustion behavior under high-CO₂ conditions remains insufficiently characterized, particularly with respect to mixing and flame stability. In this study, hydrogen combustion in an O₂–CO₂ environment was investigated experimentally and numerically using a custom-designed multi-hole burner. The experiments were conducted in a 1-bar combustion chamber, while the inlet pressures of the reactants were varied between 10 and 50 bar to isolate the effect of injection conditions. Numerical simulations were performed to analyze flow, mixing, and flame structure. The results show that increasing inlet pressure leads to a more compact and localized flame, despite reduced velocity levels in the combustor due to increased reactant density. Higher inlet pressures result in increased peak temperatures but lower mean combustor temperatures, indicating more intense but spatially confined heat release. The flow field remains structurally similar across cases, while reduced radial spreading and longer residence times influence combustion behavior. Stable flame operation was achieved over a wide range of conditions, demonstrating the feasibility of hydrogen oxy-combustion under high CO₂ dilution. The combined experimental and numerical analysis provides insight into the interplay between injection conditions, mixing, and reaction rates in highly CO₂-diluted hydrogen combustion. The obtained results support the development of compact and stable direct-fired combustors for next-generation supercritical CO₂ power cycles and hydrogen-based low-emission energy systems. Full article

Tourism destinations consume vast quantities of energy, water, food, and materials, yet these resource flows remain largely invisible in destination planning practice. The aim of this paper is to develop a conceptual framework that reconceptualises tourism destinations as industrial ecosystems and makes their material and energy flows visible, quantifiable, and amenable to destination-scale planning. Existing frameworks prioritise governance and demand management, leaving the material dimension of sustainability unaddressed. To this end, the paper proposes a multi-scale resource-flow framework grounded in industrial ecology. This is a conceptual framework paper: it develops analytical architecture for destination-scale resource accounting rather than reporting empirical measurements. The framework organises four analytical components—actors, flows, structural configurations, and feedback mechanisms—across macro, meso, and micro scales. Three planning capabilities are advanced: supply-chain-complete environmental accounting, resource hotspot detection, and policy design along the full causal chain from structural arrangement to environmental outcome. Material flow analysis, life cycle assessment, and industrial symbiosis mapping are presented as operational tools, illustrated through reference to high-intensity coastal tourism systems. Industrial symbiosis is positioned as a structural mechanism through which by-product valorisation reduces destination-level resource throughput. The study contributes a bridging framework between governance-oriented tourism planning and the material accounting rigour of industrial ecology, distinguishing it from circular economy models that supply a design principle but no material accounting, from urban metabolism approaches that assume temporally stable flows, and from regenerative development that is values-based rather than quantitative. The framework offers a foundation for more integrated and resource-efficient destination sustainability planning. Full article

Mechanical Wear particle recognition is an important approach for equipment health monitoring and fault early warning. However, flow-field disturbances and high-speed particle motion in high-speed fluid environments can lead to image degradation, non-stationary electrostatic signals, and insufficient reliability of single-source recognition methods. Therefore, this study proposes a wear particle recognition method based on multi-source information fusion for high-speed fluid environments. The method establishes a multi-scale electrostatic sensing model to characterize the coupling relationship among particle material properties, motion states, and electrostatic response characteristics. Empirical mode decomposition and independent component analysis are combined for adaptive electrostatic signal denoising, and a Transformer network is used to extract multi-domain features. Meanwhile, an ECA-CNN model with an efficient channel attention mechanism is introduced to enhance the feature representation of degraded particle images. On this basis, a meta-learning-based sample-adaptive decision fusion framework is developed to achieve dynamic and complementary fusion of electrostatic and visual information. The experimental results demonstrate that the proposed method exhibits excellent recognition accuracy and robustness in the tested high-speed fluid environment of 10 m/s, achieving a fusion recognition accuracy of 96.0%, which is significantly superior to single-source recognition methods. Ablation experiments further show that removing the global scaling factor, guidance loss, interpolation loss, and category-specific weight generator decreases the average recognition accuracy by 0.7%, 1.2%, 0.4%, and 1.8%, respectively, confirming the contribution of each key module to fusion recognition performance. These findings provide a new technical approach for the online intelligent recognition of wear particles under high-speed fluid conditions and offer theoretical support and methodological guidance for condition monitoring, health assessment, and intelligent operation and maintenance of large-scale equipment. Full article

Accurate traffic flow prediction is fundamental to Intelligent Transportation Systems (ITSs), critical for transportation management and logistics. Despite advances in spatio-temporal prediction methods, existing approaches suffer from two key limitations: (i) multi-scale fusion methods inadequately capture hierarchical constraints between cross-scale features, and (ii) models rely on single spatio-temporal views, neglecting multi-source relationship complementarity. To address these issues, we propose ST-MAFNet, a spatio-temporal multi-scale adaptive fusion network comprising three key components, specifically, a Cross-Scale Hierarchical Anchoring strategy (CSHA) that anchors short-term predictions with multi-scale temporal patterns to mitigate noise; a Dual Spatial Perception Module (DSPM) that learns node heterogeneity and dynamic correlations through node embeddings and adaptive graph attention; and a Spatio-Temporal Adaptive Fusion Module (STAFM) that captures time-varying connectivity by integrating multi-scale temporal features with multi-source spatial relationships. Experiments on four real-world datasets demonstrate that ST-MAFNet is particularly effective for short-term traffic forecasting. Compared with the best previously reported MAE results, ST-MAFNet reduces MAE by 2.95%, 1.43%, 1.25%, and 0.37% on PEMS03, PEMS04, PEMS07, and PEMS08, respectively, and achieves the best or second-best performance on most evaluation metrics. Full article

Mass transfer is central to food processing but remains difficult to quantify because food materials are heterogeneous, multiphase, porous, biologically structured, and dynamically changing. Under these conditions, experiments alone cannot fully capture the spatiotemporal complexity of transport behavior, making modeling and simulation essential for mechanism interpretation, process prediction, and engineering optimization. Existing reviews mainly address specific operations or numerical methods, with limited synthesis of governing equations, simulation workflows, application implementation, and practical applicability. This review examines food mass transfer by linking coupled momentum, heat, and mass transfer laws with governing equation selection, simulation workflow, and representative food processing applications. Governing formulations for Fickian diffusion, conservation-based transport, heat–mass coupling, multicomponent transfer, Darcy-type porous-medium flow, and related model extensions are summarized, together with their assumptions, geometric applicability, and dimensionless criteria. A unified simulation workflow is then organized, covering transport type identification, governing equation and physical model selection, geometric representation, parameter determination, initial and boundary condition specifications, numerical method and simulation tool selection, numerical implementation, validation, and transferability assessment. Representative applications are discussed for drying, heat–mass coupled processes, multicomponent transfer, transport in porous foods, and redistribution in multi-ingredient or multilayer foods. Overall, future progress requires more integrated, structure-aware, experimentally validated, transferable, and application-oriented simulation frameworks. Full article

Local scour and vortex-induced vibrations around cylindrical structures in curved channels pose significant risks to the safety and stability of critical hydraulic infrastructure, such as bridge piers. To address these engineering challenges and elucidate the underlying flow mechanisms, this study conducts numerical simulations of flow past two side-by-side circular cylinders of equal diameter in a curved channel under subcritical conditions at Re = 3900, using the Realizable kε turbulence model. Spectral Proper Orthogonal Decomposition (SPOD) is introduced to quantitatively characterize the energy distribution and dominant coherent structures. Taking the spacing ratio L/D and the placement angle α as key design parameters, the flow field characteristics, modal energy distribution, and coherent structure evolution are systematically investigated for two side-by-side cylinders in three-dimensional straight and curved channels. The numerical results show that, in the straight channel, as L/D increases from 2 to 4, the flow field evolves from strong coupled interference to weak interaction. The vortex shedding frequency structure evolves from a single dominant frequency to a multi-frequency distribution with rich harmonic components, indicating a transition in wake dynamics from energy concentration to multimodal dispersion, accompanied by a significant improvement in flow stability. Under curved channel conditions, the results reveal an asymmetric flow field caused by pronounced energy concentration on the inner side of the channel. SPOD analysis further indicates that as the placement angle α increases from 30° to 90°, the modal energy distribution changes from concentrated to dispersed, the frequency spectrum broadens with enhanced harmonic components, and flow instability gradually intensifies. Overall, the spacing ratio L/D mainly governs the wake-interference pattern, whereas the placement angle α regulates the frequency structure and energy distribution. Among all the cases investigated, relatively favorable flow stability is achieved at L/D = 4 and α = 30°. The SPOD-derived modal energy distributions show that the streamwise fluctuation length of the dominant-mode energy is approximately 0.25 m at α = 30°, compared with 0.5 m at α = 90°, with the energy bandwidth nearly doubling. The combined CFD-SPOD approach effectively captures energy evolution and coherent structure characteristics of complex flows across spatial, temporal, and spectral dimensions. This enables a shift from conventional flow-field description to frequency-based mechanism analysis and provides a theoretical basis for structural layout optimization and scour protection in hydraulic engineering. Full article

Laser-induced breakdown spectroscopy (LIBS) offers rapid, in situ, and multi-element detection, and therefore shows strong potential for quality monitoring of metal powders in additive manufacturing. However, direct LIBS analysis of flowing metal powders is often affected by particle splashing, unstable laser–particle coupling, and plasma fluctuations, which reduce signal repeatability and detection reliability. To address these issues, this study developed an integrated measurement and classification framework for identifying cross-contamination in additive-manufacturing metal powders. A stable powder particle stream was generated through vibratory feeding and particle-flow focusing, while a hollow quartz tube with a side opening was introduced to provide cylindrical spatial confinement, thereby improving the stability of laser–particle interaction and enabling in situ spectral acquisition without pellet preparation. TC4 powder was used as the base material and AlSi10Mg powder as the contaminant, and samples with contamination levels of 0, 0.5, 1, 2, and 5 wt.% were prepared. Two independent batches of single-shot LIBS spectra were collected. To reduce the influence of strong spectral fluctuations, outlier spectra were removed using full-spectrum total-intensity quantile filtering, followed by asymmetric least-squares baseline correction and standard normal variate transformation. PCA combined with multiple machine-learning models was then applied for contamination identification. The results showed that LIBS spectra at different contamination levels exhibited distinguishable distributions in principal-component space, and the spectral differences between clean and contaminated powders became more pronounced with increasing contamination level. In binary classification, several models achieved high classification accuracy at medium and high contamination levels, while PCA-SVM-RBF showed the best performance at low concentrations. In five-class cross-validation, the 5 wt.% class exhibited the clearest decision boundary, whereas confusion remained among low and adjacent contamination levels, indicating that contamination-induced spectral responses followed a more continuous transition. These results demonstrate that the proposed spatially confined particle-flow LIBS framework combined with machine-learning classification can effectively achieve rapid identification of cross-contamination in additive-manufacturing metal powders and provides a feasible technical route for online powder quality monitoring. Full article