Search Results (3,007)

Rock mechanical parameters can provide fundamental data for the numerical simulation of hydraulic fracturing, aiding in the construction of hydraulic fracturing models. Due to the laminated nature of shale, constructing a hydraulic fracturing model requires obtaining the rock mechanical parameters of each lamina and the bedding planes. However, acquiring the mechanical parameters of individual shale laminas through physical experiments demands that, after rock mechanics testing, cracks propagate along the centre of the laminae without connecting additional bedding planes, which imposes extremely high requirements on shale samples. Current research on the rock mechanics of the Minfeng subsag shale is relatively limited. Therefore, to obtain the rock mechanical parameters of each lamina and the bedding planes in the Minfeng subsag shale, a numerical simulation approach can be employed. The model, built using PFC2D, is based on prior X-ray diffraction (XRD) analysis, conventional thin-section observation, scanning electron microscopy (SEM), Brazilian splitting tests, and triaxial compression tests. It replicates the processes of the Brazilian splitting and triaxial compression experiments, assigning initial parameters to different bedding planes based on lithology. A trial-and-error method is then used to adjust the parameters until the simulated curves match the physical experimental curves, with errors within 10%. The model parameters for each lamina at this stage are then applied to single-lithology Brazilian splitting, biaxial compression, and three-point bending models for simulation, ultimately obtaining the tensile strength, uniaxial compressive strength, Poisson’s ratio, Young’s modulus, brittleness index, and Mode I fracture toughness for each lamina. Simulation results show that the Minfeng subsag shale exhibits strong heterogeneity, with all obtained rock mechanical parameters spanning a wide range. Calculated brittleness indices for each lamina mostly fall within the “good” and “medium” ranges, with carbonate laminae generally demonstrating better brittleness than felsic laminae. Fracture toughness also clearly divides into two ranges: mixed carbonate shale laminae have overall higher fracture toughness than mixed felsic laminae. Full article

The precessing vortex core (PVC) is a major source of low-frequency harmful pressure pulsations that constrain the stable operating range of Francis turbines under part-load regimes. This study presents an experimental demonstration of active frequency control for the PVC in an aerodynamic turbine model (at Reynolds number 1.5 × 10⁴), employing a resonant forcing strategy grounded in linear stability theory. Low-energy air injection with a momentum flux coefficient in the range of approximately 0.06% to 1.56% was applied via rotating actuators positioned within the flow region of highest receptivity. The core finding is the observation of frequency, where the PVC’s natural precession frequency synchronizes with that of the rotating actuator. A comparative analysis of actuator geometry revealed that a single-jet configuration achieves a significantly greater frequency shift, up to 22%, and a wider lock-in range than a dual-jet actuator (8% shift). This enhanced performance is attributed to the higher momentum flux density and more spatially coherent forcing generated by the single jet, which couples more effectively with the global instability mode. The results validate the successful adaptation of a highly efficient, physics-based control paradigm from reacting flows to hydraulic machinery, offering a promising approach to mitigate vortex-induced vibrations and expanding turbine operational flexibility. Full article

This study investigated the dynamic behavior of AA7075 plates joined by Friction Stir Welding (FSW), focusing on the influence of key process parameters, rotation, and traverse speeds, on the resulting dynamic characteristics. Experimental Modal Analysis (EMA) was performed under free boundary conditions to determine resonance frequencies, mode shapes, and damping ratios, revealing that an increase in traverse speed consistently led to a decrease in natural frequencies across most modes, thereby indicating reduced joint stiffness attributed to insufficient heat input. Furthermore, localized weld defects caused significant damping variations, particularly in low-order modes. To complement the experimental findings and enable simultaneous, multi-output prediction of these coupled dynamic parameters, a Co-Active Neuro-Fuzzy Inference System (CANFIS) model was developed. The CANFIS architecture utilized spindle speed and feed rate as inputs to predict natural frequency and damping ratio for multiple vibration modes as tightly coupled outputs. The trained model demonstrated strong agreement and high predictive accuracy against the EMA experimental data, with convergence analysis confirming its stable learning and excellent generalization capability. The successful integration of EMA and CANFIS establishes a robust hybrid framework for both physical interpretation and intelligent, coupled prediction of the dynamic behavior of FSW-welded AA7075 plates. Full article

During the high-intensity mining of shallow-buried thick coal seams, the formation of a water-conducting fracture zone within the overburden is a primary cause of damage to the groundwater system. To address the challenge of balancing efficiency and cost in traditional water-retaining mining methods, this study proposes and validates a trapezoidal strip filling mining technology based on the “span reduction effect”. By developing a mechanical model of a four-sided simply supported thin plate representing the key layer, the fundamental mechanism of the filling body was elucidated. This mechanism involves the active adjustment of the support boundary, which effectively reduces the force span of the key layer. Furthermore, leveraging the fourth-power relationship (w ∝ a⁴) between deflection and span, the bending deformation of the overburden rock is exponentially mitigated. This study employs a four-tiered integrated verification system comprising theoretical modeling, physical simulation, numerical simulation, and engineering field testing: First, theoretical calculations indicate that reducing the effective span of the key layer by 40% can decrease its maximum deflection by 87%. Second, large-scale physical similarity simulations predict that implementing this filling method can significantly control the height of the water-conducting fracture zone, reducing it from 94 m under the collapse method to 58 m, which corresponds to a 45.5% reduction in surface settlement. Third, FLAC^3D numerical simulations further elucidated the mechanical mechanism by which the backfill system transforms stress distribution from “coal pillar-dominated bearing capacity” to “synergistic bearing capacity of backfill and coal pillars”. Shear failure in the critical layer was suppressed, and the development height of the plastic zone was restricted to approximately 54 m, showing high consistency with physical simulation results. Finally, actual measurements of water injection through the inverted hole underground provide direct evidence: The heights of the water-conducting fracture zones in the filling working face and the collapse working face are 59 m and 93 m, respectively, reflecting a reduction of 36.6%. Based on the consistency between measured and simulated results, the numerical model employed in this study has been effectively validated. Research indicates that employing trapezoidal strip filling technology based on principal stress dynamics regulation can effectively promote a shift in the failure mode of the overlying critical layer from “fracture–conduction” to “bending–subsidence”. This mechanism provides a clear mechanical explanation and predictable design basis for the green mining of shallow coal seams. Full article

This phantom study presents a thorough characterization of the physical image quality of a clinical whole-body photon-counting computed tomography (PCCT) scanner. Multiple quality metrics—noise, noise power spectrum (NPS), task transfer function (TTF), and detectability index (d′)—were analyzed across a range of reconstruction algorithms (filtered back projection, FBP, and Quantum Iterative Reconstruction, QIR, with strength levels Q1–Q4), and varying reconstruction kernels (Br40/Br60/Br76/Br98). Both standard (STD, 0.4 mm slice thickness) and high-resolution (HR, 0.2 mm slice thickness) reconstruction modes were assessed. QIR significantly reduced image noise (60–95%) compared to FBP, particularly with sharper kernels. Spatial resolution improved with increasing QIR strength level for smoother kernels and was further enhanced using HR mode with sharp kernels. HR mode exhibited better noise performance than STD with sharper reconstructions, due to the small pixel effect. While STD mode showed higher d′ values for larger objects, HR mode outperformed it for smaller objects and sharper kernels. Compared to a conventional energy-integrating computed tomography system, the PCCT scanner showed superior d′ values under similar settings. Overall, this study highlights the complex interplay between acquisition and reconstruction parameters on image quality, confirms the potential of PCCT technology, and underscores the need for further clinical validation. Full article

Exoskeleton knee-assistance (EKA) systems are wearable robotic technologies designed to rehabilitate and improve impaired mobility of the lower limbs. Clinical exercises are conducted on disabled patients based on physically demanding tasks which are prescribed by expert physicians. In order to carry out good tracking of the desired tasks, efficient controllers must be designed. In this study, a novel control framework is introduced to improve the robustness characteristics and tracking precision of EKA systems. The control approach integrates a super-twisting sliding mode controller (STSMC) with a nonlinear disturbance observer (NDO) to ensure robust and precise tracking of the knee joint trajectory. An evaluation of the proposed system is conducted through numerical simulations under the influence of external disturbances. The findings reveal considerable enhancements to trajectory tracking accuracy and disturbance rejection when compared to conventional STSMCs and sliding mode perturbation observer (SMPO)-based STSMCs. Full article

The cutterhead torque of a full-face tunnel boring machine (TBM) is a pivotal parameter that characterises the rock-machine interaction. Its dynamic prediction is of considerable significance to achieve intelligent regulation of the boring parameters and enhance the construction efficiency and safety. In order to achieve high-precision time series prediction of cutterhead torque under complex geological conditions, this study proposes an intelligent prediction method (VBGAP) that integrates signal decomposition mechanism and physical constraints. At the data preprocessing level, a multi-step data cleaning process is designed. This process comprises the following steps: the processing of invalid values, the detection of outliers, and normalisation. The non-smooth torque time-series signal is decomposed by variational mode decomposition (VMD) into narrow-band sub-signals that serve as a data-driven, frequency-specific input for subsequent modelling, and a hybrid deep learning model based on Bi-GRU and self-attention mechanism is built for each sub-signal. Finally, the prediction results of each component are linearly superimposed to achieve signal reconstruction. Concurrently, a novel modal energy conservation loss function is proposed, with the objective of effectively constraining the information entropy decay in the decomposition-reconstruction process. The validity of the proposed method is supported by empirical evidence from a real tunnel project dataset in Northeast China, which demonstrates an average accuracy of over 90% in a multi-step prediction task with a time step of 30 s. This suggests that the proposed method exhibits superior adaptability and prediction accuracy in comparison to existing mainstream deep learning models. The findings of the research provide novel concepts and methodologies for the intelligent regulation of TBM boring parameters. Full article

We show how a source-aware fluid closure framework for turbulent transport performs well on the confinement timescale in magnetically confined plasmas. A central result is that whether a source is resonant with the turbulence determines which fluid moments must be retained. Using a nonlinear current formulation, we show that resonance broadening—the dominant kinetic nonlinearity—cancels linear resonances and thereby justifies a quasilinear fluid closure already on the turbulence timescale. We derive a practical negative-energy criterion and identify parameter regimes satisfied by ion-temperature-gradient (ITG) modes (slab and toroidal), with parallel ion compressibility and magnetic curvature controlling the sign. The framework clarifies when velocity-space dynamics must be retained in the kinetic Fokker–Planck equation (for example, for fast-particle instabilities at frequencies about 10² higher than drift-wave frequencies). The present study provides additional support for our model by predicting transport that increases with radius and by showing—consistent with nonlinear kinetic simulations—that the diamagnetic flow dominates the Reynolds stress. Altogether, the results obtained provide a consistent, reduced-cost path to fluid closures that retain the essential kinetic physics while remaining tractable on confinement timescales. Full article

With the development of aerospace technology, hypersonic flight vehicles are evolving towards larger size, lighter weight, and higher performance. Their cross-domain maneuverability and extreme flight environment led to the rigid–flexible coupling effect and became the core bottleneck restricting performance improvement, seriously affecting flight stability and control accuracy. This paper systematically reviews the research status in the field of control for high-speed rigid–flexible coupling aircraft and conducts a review focusing on two core aspects: dynamic modeling and control strategies. In terms of modeling, the modeling framework based on the average shafting, the nondeformed aircraft fixed-coordinate system, and the transient coordinate system is summarized. In addition, the dedicated modeling methods for key issues, such as elastic mode coupling and liquid sloshing in the fuel tank, are also presented. The research progress and challenges of multi-physical field (thermal–structure–control, fluid–structure–control) coupling modeling are analyzed. In terms of control strategies, the development and application of linear control, nonlinear control (robust control, sliding mode variable structure control), and intelligent control (model predictive control, neural network control, prescribed performance control) are elaborated. Meanwhile, it is pointed out that the current research has limitations, such as insufficient characterization of multi-physical field coupling, neglect of the closed-loop coupling characteristics of elastic vibration, and lack of adaptability to special working conditions. Finally, the relevant research directions are prospected according to the priority of “near-term engineering requirements–long-term frontier exploration”, providing Refs. for the breakthrough of the rigid–flexible coupling control technology of the new-generation high-speed aircraft. Full article

Validation of ICON model configurations optimized over a limited domain is essential before accepting new semi-empirical parameters that influence the behavior of subgrid-scale schemes. Because such parameters can modify the dynamics of a numerical weather prediction (NWP) model in highly nonlinear ways, we analyze one season of forecasts (December 2022, January and February 2023) generated with the NWP ICON-LAM through the lens of machine learning–based diagnostics as a complement to traditional evaluation metrics. The goal is to extract physically interpretable information on the model behavior induced by the optimized parameters. This work represents the first part of a wider study exploring machine learning tools for model validation, focusing on two specific approaches: Empirical Orthogonal Functions (EOFs), which are widely used in meteorology and climate science, and autoencoders, which are increasingly adopted for their nonlinear feature extraction capability. In this first part, EOF analysis is used as the primary tool to decompose weather fields from observed reanalysis and forecast datasets. Hourly 2-m temperature forecasts for winter 2022–2023 from multiple regional ICON configurations are compared against downscaled ERA5 data and in situ observations from ground station. EOF analyses revealed that the optimized configurations demonstrate a high skill in predicting surface temperature. From the signal error decomposition, the fourth EOF mode is effective particularly during night-time hours, and contributes to enhancing the performance of ICON. Analyses based on autoencoders will be presented in a companion paper (Part II). Full article

Baroclinic wave resonance, particularly Rossby waves, has attracted great interest in ocean and atmospheric physics since the 1970s. Research on Rossby wave resonance covers a wide variety of phenomena that can be unified when focusing on quasi-stationary Rossby waves traveling at the interface of two stratified fluids. This assumes a clear differentiation of the pycnocline, where the density varies strongly vertically. In the atmosphere, such stationary Rossby waves are observable at the tropopause, at the interface between the polar jet and the ascending air column at the meeting of the polar and Ferrel cell circulation, or between the subtropical jet and the descending air column at the meeting of the Ferrel and Hadley cell circulation. The movement of these air columns varies according to the declination of the sun. In oceans, quasi-stationary Rossby waves are observable in the tropics, at mid-latitudes, and around the subtropical gyres (i.e., the gyral Rossby waves GRWs) due to the buoyant properties of warm waters originating from tropical oceans, transported to high latitudes by western boundary currents. The thermocline oscillation results from solar irradiance variations induced by the sun’s declination, as well as solar and orbital cycles. It is governed by the forced, linear, inviscid shallow water equations on the β-plane (or β-cone for GRWs), namely the momentum, continuity, and potential vorticity equations. The coupling of multi-frequency wave systems occurs in exchange zones. The quasi-stationary Rossby waves and the associated zonal/polar and meridional/radial geostrophic currents modify the geostrophy of the basin. Here, it is shown that the ubiquity of resonant forcing in (sub)harmonic modes of Rossby waves in stratified media results from two properties: (1) the natural period of Rossby wave systems tunes to the forcing period, (2) the restoring forces between the different multi-frequency Rossby waves assimilated to inertial Caldirola–Kanai (CK) oscillators are all the stronger when the imbalance between the Coriolis force and the horizontal pressure gradients in the exchange zones is significant. According to the CK equations, this resonance mode ensures the sustainability of the wave systems despite the variability of the forcing periods. The resonant forcing of quasi-stationary Rossby waves is at the origin of climate variations, as well-known as El Niño, glacial–interglacial cycles or extreme events generated by cold drops or, conversely, heat waves. This approach attempts to provide some new avenues for addressing climate and weather issues. Full article

Urban rail transit networks in developing countries are rapidly expanding, entering a networked operational phase where accurate passenger flow forecasting is crucial for optimizing vehicle scheduling, resource allocation, and transportation efficiency. In the short term, accurate real-time forecasting enables the dynamic adjustment of train headways and crew deployment, reducing average passenger waiting times during peak hours and alleviating platform overcrowding; in the long term, reliable trend predictions support strategic planning, including capacity expansion, station retrofitting, and energy management. This paper proposes a novel hybrid forecasting model, EEMD-TiDE, that combines improved Ensemble Empirical Mode Decomposition (EEMD) with a Time Series Dense Encoder (TiDE) to enhance prediction accuracy. The EEMD algorithm effectively overcomes mode mixing issues in traditional EMD by incorporating white noise perturbations, decomposing raw passenger flow data into physically meaningful Intrinsic Mode Functions (IMFs). At the same time, the TiDE model, a linear encoder–decoder architecture, efficiently handles multi-scale features and covariates without the computational overhead of self-attention mechanisms. Experimental results using Xi’an Metro passenger flow data (2017–2019) demonstrate that EEMD-TiDE significantly outperforms baseline models. This study provides a robust solution for urban rail transit passenger flow forecasting, supporting sustainable urban development. Full article

This work presents, to the best of our knowledge, the first experimental report of an erbium/ytterbium double-clad ring fiber laser based on nonlinear polarization rotation (NPR) operating in a self-starting Q-switched high-order harmonic mode locking noise-like pulse (QHML-NLP) regime. The NPR mechanism relies on an arrangement composed of a beam splitter cube, a half-wave retarder, and a quarter-wave retarder. Through specific adjustments of the wave retarders and pump power, the laser cavity engages the QHML-NLP regime, where mode-locked burst-like pulses containing a significant number of NLPs are modulated by a giant Q-switched envelope. The laser system emits at the 132nd-order harmonic mode locking (HML) frequency, representing the highest order achieved to date in the framework of QHML-NLP. Additional features include a broadband optical spectrum with dual-wavelength emission at 1568.4 nm and 1605.9 nm, and maximum energies of 2.37 µJ for the Q-switched envelope and 200 nJ for the mode-locked burst-like pulse. These detailed experimental results reveal remarkable aspects in the NLP dynamics, contributing to a deeper understanding of their physical mechanisms and highlighting their potential as novel laser sources for micromachining and nonlinear optics. Full article

In the operation mode arrangement of bulk power systems, unreasonable reactive power injection data at nodes tend to result in power flow calculation non-convergence. Owing to the extremely high dimension of the variable space and the heterogeneous impacts of different variables on power flow convergence, it is imperative to accurately identify the key variables inducing non-convergence and provide physical justifications. For this purpose, this paper proposes a data-driven key variable identification and adjustment method: firstly, based on the blocking cut-set theory and the characteristic that the active unbalanced power ΔP of intermediate power flow exhibits opposite signs at the sending and receiving ends of the cut-set, a blocking cut-set identification method leveraging the characteristics of the active unbalanced power of intermediate power flow is developed; secondly, relying on the feature that the reactive unbalanced power ΔQ of intermediate power flow is less than zero, a key variable identification method based on the characteristics of the reactive unbalanced power of intermediate power flow is presented; finally, a key variable adjustment method grounded in the numerical value of ΔQ is proposed. The validity of the proposed approach was validated via simulated computations using both the IEEE 39 bus system and a practical bulk power system. Full article

This study addresses a key limitation in meso-scale discrete element modeling (DEM) of ultra-high performance concrete (UHPC). Most existing DEM frameworks rely on extensive macroscopic calibration and do not provide a clear, transferable pathway to derive contact law parameters from measurable micro-scale properties, limiting reproducibility and physical interpretability. To bridge this gap, we develop and validate a micro-indentation-informed, poromechanics-consistent calibration framework that links UHPC phase-level micromechanical measurements to a flat-joint DEM contact model for predicting uniaxial compression, direct tension, and flexural response. Elastic moduli and Poisson’s ratios of the constituent phases are obtained from micro-indentation and homogenization relations, while cohesion (c) and friction angle (α) are inferred through a statistical treatment of the indentation modulus and hardness distributions. The tensile strength limit (σₜ) is identified by matching the simulated flexural stress–strain peak and post-peak trends using a parametric set of (c, α, σₜ) combinations. The resulting DEM model reproduces the measured UHPC responses with strong agreement, capturing (i) compressive stress–strain response, (ii) flexural stress–strain response, and (iii) tensile stress–strain response, while also recovering the experimentally observed failure modes and damage localization patterns. These results demonstrate that physically grounded micro-scale measurements can be systematically upscaled to meso-scale DEM parameters, providing a more efficient and interpretable route for simulating UHPC and other porous cementitious composites from indentation-based inputs. Full article