PowerChina (China)

Aims: This study aims to conduct a comprehensive static analysis and lightweight multi-objective optimization of the spiral sorting device (auger) used in coal mine roadway concrete pavers. The primary objective is to fundamentally enhance the economic efficiency, structural reliability, and operational stability of the equipment under the harsh, confined, and demanding underground environments typical of modern coal extraction facilities. Study Design: A rigorous numerical computational design combining advanced Finite Element Analysis (FEA) and statistical Response Surface Methodology (RSM) was deployed to systematically evaluate and optimize the mechanical structure. Methodology: Initially, a rigorous mesh independence verification was discussed to ensure the absolute accuracy and computational efficiency of the numerical solutions. Subsequently, a detailed static analysis was performed by fixing both ends of the spiral shaft to simulate bearing constraints, applying a substantial surface load of 15000 N on the active faces of the spiral blades to simulate concrete resistance, and subjecting the spiral shaft to a driving torque of 573 N·m. Building upon the baseline static analysis results, a central composite design (CCD) was employed to construct a accurate mathematical response surface model. The spiral shaft diameter and blade thickness were selected as independent design variables, while the total structural mass, maximum total deformation, and maximum equivalent stress were established as the targeted objective functions. Finally, a Multi-Objective Genetic Algorithm (MOGA) was utilized to navigate the complex design space for global optimal searching based on Pareto efficiency. Results: The initial comprehensive static analysis indicated that the maximum equivalent stress of the spiral sorting device was intrinsically located at the root of the blade, peaking at 126.46 MPa. Concurrently, the maximum deformation was recorded at 1.2891 mm at the outermost blade tip. Both baseline values fully met the rigorous safety design requirements. Detailed sensitivity analysis subsequently revealed that the spiral shaft diameter possessed the most significant dominant impact on both total mass and structural deformation, whereas the blade thickness exerted a more pronounced, localized influence on the stress distribution at the blade roots. Through extensive MOGA optimization iterations, the optimal geometric parameters were mathematically determined and engineering-rounded to a shaft diameter of 44 mm and a blade thickness of 4 mm. Consequently, the mass of the newly optimized device was remarkably reduced from an initial 28.411 kg down to 23.056 kg. Simultaneously, the maximum stress increased to 196.56 MPa and the maximum deformation shifted to 2.4121 mm, both of which dynamically remain strictly and safely within the allowable physical limits of the selected material. Conclusion: The optimized design strategy successfully and significantly reduced the unnecessary dead mass of the spiral sorting device, achieving the coveted lightweight construction without compromising the essential structural strength and operational stiffness. This in-depth research provides a solid theoretical foundation, robust numerical validation, and a practical engineering reference for the future development, upgrading, and manufacturing of intelligent paving equipment operating under severely constrained underground conditions.

To achieve accurate prediction of tunnel boring machine (TBM) penetration rate (PR) under complex geological conditions, this study proposes an interpretable machine learning (ML) framework optimized using Bayesian optimization (BO). A dataset comprising 411 samples collected from two TBM projects was established, incorporating key geological parameters (uniaxial compressive strength, elastic modulus, and Poisson’s ratio) and operational parameters (thrust force and rotation speed). Seven ML models, including decision tree, Gaussian process regression, k-nearest neighbors, random forest (RF), support vector regression, extreme gradient boosting, and light gradient boosting machine, were systematically optimized using BO under five-fold cross-validation. The results demonstrate that ensemble learning models exhibit superior predictive performance. Among them, the RF model achieved the highest accuracy on the testing set with an R 2 of 0.9582, followed by the light gradient boosting machine with an R 2 of 0.9565. SHapley Additive exPlanations (SHAP) analysis indicates that uniaxial compressive strength is the dominant factor controlling PR, while thrust force act as the primary controllable parameters. The proposed framework effectively integrates high predictive accuracy with strong interpretability, providing a reliable methodological basis for intelligent TBM performance prediction and construction parameter optimization.

Understanding the patterns and mechanisms of DOC export from catchments is key to interpreting and quantifying land-to-ocean lateral carbon transport process. The DOC export is highly dynamic and storm-driven, especially in mountainous headwater catchments. Yet traditional low-frequency monitoring and modelling often misses rapid DOC changes and fail to identify potential shifts on DOC export pattern and process, limiting a deeper understanding of short-term export mechanisms. We have conducted high-frequency measurements combined with process-based modeling in a mountainous flash flood headwater catchment in China, to reveal how hydrological processes control DOC mobilization and export under varying storm conditions. Based on high-frequency measurements and hysteresis analysis, we have found a three-phase concentration-discharge (C-Q) relationship of DOC and a shift on DOC export pattern from transport-limited to source-limited across extreme storms. The studied catchment streamflow and DOC dynamics were successful reproduced by the process-based INCA-C model at hourly steps, further supporting the quantification of different flow pathways’ contributions on DOC output. The results showed that more DOC were exported by subsurface flows from shallow organic soil with greater peaks and shorter time-to-peaks at higher storm intensities. DOC is primarily sourced from subsurface runoff from the mineral layer (73 %–77 %) during moderate events, whereas it is primarily sourced from subsurface runoff from the organic layer (61 %–79 %) during extreme events. The two contrasting contributions suggest that hydrological pathway controls and DOC dynamic patterns can shift owing to runoff generation influenced by storm intensity. Our research revealed mechanisms on shifted DOC export regimes at a typical flash flood catchment with increasing storm intensity and changing flow-path contributions. The findings highlight high-frequency measurements and modellings for insights into hydrological controls on DOC export process.

Tropical estuarine hydrodynamic processes are governed by complex interactions between tides, monsoons, and fluvial runoff. To obtain long-term (≥30 years) hydrodynamic conditions of the Mekong River Estuary, this study established a Finite Volume Coastal Ocean Model (FVCOM) coupled with validated Weather Research and Forecast (WRF) wind forcing for a 32-year (1988–2019) high-resolution simulation. Validation against in situ observations confirms the model’s robustness. Temporal–spatial patterns of water level and current were analyzed, and extreme parameters for 1–100 year return periods were derived via the Pearson-III probability distribution. Results indicate the study area is a mesotidal environment (tidal range = 3.58 m) dominated by SSE-NNW reciprocating tidal currents. Relative to Vietnam’s national elevation datum, 100-year return period extreme high/low water levels are 2.15 m and −2.03 m, with a maximum storm surge setup of 2.09 m. The 100-year return period maximum current velocity reaches 4.58 m/s (A21 station), and Mekong River runoff exerts a negligible influence (<5% velocity change). This study provides high-precision baseline data for offshore wind farm engineering and disaster risk assessment, offering a methodological reference for tropical estuarine hydrodynamic simulations.