The heat transfer (HT) characteristics of cylindrical biomass particles (CBPs) in fluidized beds (FBs) are important for their drying, direct combustion, and thermochemical transformation. To provide a deeper insight into the complex mechanisms behind the HT behaviors involving CBPs, this study developed a cylindrical particle HT model within the framework of computational fluid dynamics coupled with the discrete element method (CFD-DEM) in which the CBPs were characterized by the super-ellipsoid model, which has the unique merit of striking a balance between computational accuracy and efficiency. The newly developed heat transfer model considers particle–particle (P-P), particle–wall (P-W), and fluid–particle (F-P). Its accuracy was verified by comparing the numerical results with the experimental infrared thermography measurements in terms of the temperature evolution of the cylindrical particles. The effects of the gas velocity, inlet temperature, and thermal conductivity of particles on the HT behaviors of the CBPs were investigated comprehensively. The results demonstrated the following: (1) Gas velocity can improve the uniformity of bed temperature distribution and shorten the fluctuation process of bed temperature uniformity. (2) A 26.8% increase in inlet temperature leads to a 13.4% increase in the proportion of particles with an orientation in the range of 60–90°. (3) The thermal conductivity of particles has no obvious influence on the bed temperature, convective HT rate, or orientation of particles.

Amid the global energy transition, geothermal energy, as a clean, stable, and renewable energy source, serves as a core direction for energy structure optimization. The development of medium-deep geothermal reservoirs is dominated by thermo–hydro–mechanical (THM) multi-physics coupling effects, yet the quantitative regulation laws of their operational parameters remain unclear. In this study, a numerical model for geothermal extraction considering THM multi-physics coupling was established. Using the single-factor variable method, simulations were conducted within the set parameter ranges of injection–production pressure difference, well spacing, and injection temperature. The spatiotemporal evolution characteristics of the temperature field, the dynamic temperature–pressure responses at the midpoint of injection–production wells and production wells, and efficiency indicators, such as instantaneous heat extraction power and cumulative heat extraction, were analyzed and quantified. The results show that a larger pressure difference accelerates the expansion of the cold zone in the reservoir, which improves short-term heat extraction efficiency but increases the risk of long-term thermal depletion; a smaller well spacing leads to higher initial heat production power but results in lower long-term cumulative heat extraction due to rapid heat consumption; within the normal temperature range of 16–24 °C, the injection temperature has a negligible impact on heat extraction efficiency. This study clarifies the regulatory laws of operational parameters and provides theoretical support for well pattern design and injection–production process optimization in medium-deep geothermal development.

Accurate prediction of tunnel water inflow in water-rich fault zones is important for groundwater control design and construction risk prevention. In this study, a per-linear-meter tunnel water inflow database containing 425 valid samples was established through orthogonal numerical simulations based on a three-dimensional steady-state seepage model with a grouting ring. The input variables included four hydraulic and grouting parameters and two excavation-position descriptors, namely the excavation-position distance and excavation-position category, thereby reflecting both the water-blocking effect of grouting reinforcement and the spatial variation in water inflow as the excavation face approached the fault zone. Considering that the samples were generated from 25 orthogonal simulation cases at different excavation positions, grouped validation was adopted to reduce information leakage at the simulation-case level. Four baseline machine learning models, including SVM, RF, XGBoost, and CatBoost, were evaluated using ten repeated grouped hold-out validations. CatBoost achieved the best overall baseline generalization performance, with an average test R² of 0.6209 ± 0.0405, MAE of 0.1084 ± 0.0079, and RMSE of 0.1555 ± 0.0085. CatBoost was therefore selected for further hyperparameter optimization. Subsequently, random search, Bayesian optimization, the Osprey Optimization Algorithm, and the Grey Wolf Optimizer were compared under the same search space and computational budget. Hyperparameter optimization was conducted only within the training set using grouped cross-validation, and the independent grouped test set was used only for final evaluation. The results showed that the unoptimized CatBoost model achieved the best overall balance between prediction accuracy, stability, and computational efficiency. Although RS-CatBoost slightly improved MAE and MAPE among the optimized models, none of the optimization strategies consistently outperformed the unoptimized CatBoost baseline, indicating that the choice of hyperparameter optimization algorithm played a secondary role under the current dataset and grouped-validation framework. The proposed framework is intended as a preliminary modeling reference under controlled numerical simulation conditions, and its practical engineering reliability requires further validation using field monitoring data or independent benchmark cases.

The transport of particulate materials through flexible tubes, where tube deformation and movement are significant, is quite common in industries but lacks fundamental understanding, especially at the particle level. To gain fundamental particle-level insights, this study employs a coupled DEM-FEM approach, modeling particle flows with DEM and flexible tube with FEM. The DEM-FEM model is used to systematically investigate the impact of key particle properties on the transport dynamics. Specifically, the effects of particle density, particle shape (both spherical and non-spherical particles are included), particle feed rate, and initial particle velocity on both particle behavior (particle velocity and shear impact energy) and tube response (vibration displacement behaviors of the tube) are analyzed in detail. The DEM-FEM simulation results demonstrate that increasing the particle density, sphericity, feed rate and initial velocity can lead to more significant vibration displacements of the flexible tube and higher shear impact energy. However, their effects on the particle velocity are rather complex. For example, the variation trends of particle velocity differ as particle density increases during the downward particle flow along the tube. Moreover, taken as a whole, the particle density and particle shape exert the quite limited influence on the particle velocity. These findings should provide valuable insights for optimizing operations of particle transport processes in flexible tubes.