Abstract The tunneling parameters of tunnel boring machines (TBMs) exhibit distinct spatiotemporal characteristics under different rock mass conditions, particularly their non-Euclidean spatial properties. This paper developed a real-time rock mass identification model based on the spatial-temporal graph convolutional network (STGCN) to simultaneously capture the spatiotemporal characteristics of tunneling parameters. TBM tunneling data and geological data were collected from an 8.41-km-long tunneling section of the YinChaoJiLiao (YCJL) project in China. By matching the data based on tunnel mileage, 2,367 valid samples were obtained. A downsampling strategy was applied to reduce data redundancy and computational cost while maintaining model performance. The optimized model achieved an accuracy of 93.04% and an F 1 -score of 89.75%, and comparative experiments verified its superior performance compared with the mainstream model in this task. A statistical analysis of the adjacency matrix derived from the graph convolutional network (GCN) module was conducted and the influence of input time length on model performance was analyzed to interpret the spatial and temporal characteristics of the tunneling parameters, respectively. Furthermore, the model was validated on a extension section of the same tunnel, confirming its accuracy and efficiency for practical application. The results indicate that the STGCN model can simultaneously capture the complex spatiotemporal characteristics of TBM data and provide accurate real-time rock mass identification results, showing great potential for engineering application in intelligent tunneling.
The integration of micro-nano hybrid architectures serves as a critical strategy for augmenting the thermal efficacy of ultra-thin heat pipes. In this work, a novel composite heat pipe is fabricated by synergistically combining silicon-based microchannel structures with anodic aluminum oxide (AAO) membranes. The liquidvapor interface dynamics and thermal performance were systematically investigated under horizontal, positive gravity, and reverse gravity directions. Results show that gravitational assistance enhances capillary-driven flow with increasing inclination angles, accelerating liquid reflux and intensifying interfacial fluctuations in the evaporator. At 90 degrees inclination angle, gravity significantly improves capillary liquid supply capacity, resulting in excellent heat transfer performance. Although HFE-7100 enables rapid startup, anhydrous ethanol demonstrates superior maximum heat load capacity. A higher filling ratio generally improves effective thermal conductivity and temperature uniformity by sustaining a stable liquid film. As the filling ratio rises, the liquid content within the ultra-thin heat pipe increases, accelerating evaporation and condensation rates and significantly lowering the evaporator section temperature. At a filling ratio of 1.7 and a 90 degrees inclination, the maximum effective thermal conductivity reached 1193 W/(m center dot K), highlighting the synergistic effect of optimal liquid charging and gravitational assistance on heat transfer enhancement.
To address temperature-control challenges in HVAC systems subject to model uncertainty and unknown external disturbances, a predefined-time adaptive control strategy using a fuzzy-neural-network-based disturbance observer is proposed. A state-space model capturing the coupled dynamics of indoor zone temperature and cooling coil temperature is first established, based on which the temperature tracking problem is recast as a predefined-time state convergence problem. A fuzzy neural network (FNN) is employed to identify the unknown nonlinear system dynamics online, and a predefined-time adaptive disturbance observer (PTADO) is constructed to provide feedforward compensation for the lumped disturbance without requiring prior knowledge of the disturbance bound. Building upon the PTADO, a predefined-time adaptive tracking controller (PTAC) is further developed, which guarantees that the closed-loop system converges within a user-assignable settling time regulated by a single design parameter. Comparative simulations against PI and finite-time sliding mode control demonstrate that the proposed scheme achieves faster convergence, higher steady-state accuracy, and chattering-free control signals under time-varying internal heat gain disturbances.
Building on our previous work in [1], this paper establishes the local well-posedness of the Schr & ouml;dinger-KdV system in H-3/16 & times; H-3/4 for the resonant case. Combining this result with those in [1], we obtain local well-posedness for the parameter range max{-3/4, s1-3} <= s2 <= min{4s1, s1 + 2}, and this range is sharp in the sense of the contraction mapping argument.
The reliability of dam-break scour prediction remains sensitive to the calibration of evolving bed resistance and to uncertainty in the associated model parameters. This study develops a probabilistic inversion framework that integrates a two-phase Smoothed Particle Hydrodynamics (SPH) model with Bayesian inference implemented through Markov chain Monte Carlo (MCMC) sampling for uncertainty-aware parameter inference in transient water-soil interaction. The framework captures the progressive loss of bed resistance through a time-dependent degradation of the internal friction angle. It is evaluated against the classical Louvain dam-break experiment using two spatial parameterization strategies: a vertically layered representation and a longitudinally partitioned representation. Bayesian updating improves the reconstruction of the observed transient soil-water interface relative to the deterministic baseline. Posterior inference and multi-chain convergence diagnostics reveal parameter-dependent practical identifiability, with the initial internal friction angle more clearly constrained than the degradation coefficients over the short observation window. A within-experiment temporal holdout and complexity-adjusted model comparison indicate that the longitudinally partitioned model better represents the spatially non-uniform scour response in this benchmark, although its improved morphological fidelity does not ensure reliable estimation of every regional parameter. Overall, the proposed framework provides a tractable route from deterministic calibration toward uncertainty-aware inference in SPH-based scour simulations and a physically interpretable basis for evaluating evolving bed response.
