Hui Liu

CFD numerical simulations are conducted to investigate wind effects on super-large, low-inclination floating PV arrays, with emphasis on the influence of streamwise pitch and tilt angle on the mean wind pressure coefficients and the sheltering effect. Following validation of the CFD model against wind-tunnel measurements, the validated CFD numerical simulation method is applied to these arrays under 0° and 180° wind directions to examine the effects of streamwise pitch and tilt angle and the corresponding variation patterns. Results show that due to the sheltering effect, mean wind pressure coefficients decrease with increasing row number and eventually stabilize; compared with the 0° wind direction, mean wind pressure coefficients of all rows are higher under the 180° wind direction, indicating the array sustains greater wind loads. Moreover, except for the first row, the coefficients for the other rows increase with increasing streamwise pitch. Further flow-field analysis reveals that, under low tilt angles, both the coefficients and the sheltering effect are far more sensitive to tilt-angle changes, whereas the streamwise pitch modulates the coefficients by influencing the sheltering effect between adjacent PV panels. After clarifying the applicable scope of the numerical simulation model and computational method, empirical formulas for the first-row mean wind pressure coefficient and the average wind load reduction coefficient both accounting for the influence of streamwise pitch of the solar panel array are proposed, with recommended parameter values derived via fitting analysis. Compared with conventional methods, the holistic wind load calculation method based on the average reduction coefficient is concise, intuitive and broadly applicable, which provides a reasonable and efficient approach for accurate wind load prediction of large-scale PV arrays.

To account for specimen-to-specimen variability in the fatigue crack growth behavior of U75V rail steel, this study develops a probabilistic remaining-life prediction framework that integrates equivalent crack-tip shielding evolution, parameter uncertainty, and early crack growth monitoring data. Using 95th-percentile quantile regression on CT specimen data, a minimum-shielding master curve is constructed to decouple intrinsic fatigue resistance from geometry- and wake-dependent shielding via an equivalent driving-force ratio. A log-normalized shielding evolution model captures diverse shielding evolutions, while a bivariate normal distribution preserves their strong negative correlation (r=-0.923). Integrating limited early monitoring data through Bayesian updating reduces the remaining useful life prediction (RUL) mean absolute percentage error (MAPE) from 32.64% (conventional nominal Paris model) to 10.63%. The resulting life estimates provide a quantitative basis for rail structural health monitoring and damage-tolerant design.

Vortex-induced vibration (VIV) is the main cause of fatigue failure in steel catenary risers (SCRs). This study developed a fluid–structure interaction (FSI) model, combining Reynolds-Averaged Navier–Stokes (RANS)-based computational fluid dynamics (CFD) with the Newmark-β algorithm, to simulate VIV responses under ocean currents and platform heave motion. First, the FSI model analyzed SCR behaviors under steady currents, then was adapted to oscillatory flow mimicking heave motion. A finite element model (FEM) was built, using the simulated VIV response as displacement boundary conditions to compute the equivalent stress time history along the riser. Finally, Miner’s rule was applied to quantify fatigue damage in three scenarios: current-only, heave-only, and the combined action of both factors. The results indicate that, in the South China Sea’s 10-year return period sea state, the SCR experiences a broad vortex-induced resonance interval under ocean current loads, with a maximum vibration amplitude of 0.7D. At the associated resonant height, platform heave motion triggers near-complete lock-in of the SCR’s VIV. The peak fatigue damage induced by ocean currents alone, platform heave motion alone, and their combined action all concentrates at the riser touchdown point (TDP). Over the 600 s VIV response duration, fatigue damage from platform heave motion alone constitutes 8.48% of that caused by ocean currents alone, while the combined action results in fatigue damage 1.847 times that of ocean currents alone. Thus, the combined action significantly amplifies both the magnitude and spatial non-uniformity of VIV-induced fatigue damage in SCRs.

This study investigated the fluctuating wind pressure distribution characteristics on the geometrically complex spatial structure of the Chengdu Future Science and Technology City Exhibition Center through an integrated approach combining wind tunnel testing with numerical simulations across multiple wind directions. The numerical simulation methodology was rigorously validated against wind tunnel experimental data prior to implementation. Utilizing the Consistent Discretizing Random Flow Generation (CDRFG) method for turbulent inflow boundary conditions and the delayed detached eddy simulation (DDES) approach for complex flow field resolution, the research systematically analyzed multiple key parameters: mean and fluctuating wind pressure coefficients, time‐domain correlation characteristics, surface vorticity distribution, and higher order statistical parameters including skewness, kurtosis, and peak factors. The findings demonstrate distinct spatial patterns in wind pressure correlation, with windward surfaces exhibiting strong coherence whereas leeward and side surfaces show significantly weaker correlation (typically 0.1–0.4, occasionally negative) due to vortex shedding and flow separation phenomena. Notably, the measured peak factors consistently exceeded the conventional 3.5 threshold, reaching maximum values of 6.11, indicating enhanced turbulence intensity that generates more extreme pressure distributions. Vorticity analysis reveals marked directional dependence, with a uniform distribution on windward surfaces contrasting with intense, intermittent shedding on side and leeward surfaces. The non‐Gaussian characteristics of pressure distributions are further confirmed by significant skewness (up to 1.11) and kurtosis (up to 7.12) values. These results collectively demonstrate the highly irregular nature of wind load distributions on complex‐shaped structures, emphasizing the necessity of accounting for these adverse effects in wind‐resistant design practices.

Owing to welding process‐related factors and environmental influences, stress concentration is prone to occur in the welded regions of grid structure joints, rendering these joints the most damage‐susceptible locations within the entire structural system. In this paper, an intelligent damage diagnosis framework that integrates deep learning techniques with joint stress field analysis is proposed to enable damage diagnosis of structural joints. A quasidistributed fiber Bragg grating (FBG) sensor network deployed at the joints facilitated the construction of a dual‐dimensional damage index, characterized by high sensitivity to damage location and severity. The proposed index enables precise identification of joint damage by capturing the abrupt changes in the local stress field caused by joint failure. By leveraging the spatial distribution patterns of the damage index, a convolutional neural network (CNN) model incorporating the squeeze‐and‐excitation (SE) attention mechanism was developed, with Bayesian optimization algorithms employed for hyperparameter tuning to perform simultaneous localization and quantification of joint damage. Moreover, the model exhibits robust noise tolerance, retaining 93.8% identification accuracy at a signal‐to‐noise ratio (SNR) above 10 dB without requiring preprocessing denoising. To validate the feasibility of the proposed SE‐CNN model, a spatial grid structure model with adjustable damage joints and controllable damage levels was designed and fabricated, and identification tests were conducted under various joint damage conditions. The damage diagnosis accuracy for joint damage in the spatial grid structure reached 97.2% using the proposed method with damage indices derived from stress data measured by quasidistributed FBG sensors. Therefore, the SE‐CNN model established based on the characteristics of the damage index proves to be an effective method for joint damage diagnosis in spatial grid structures.

This study integrates experimental and numerical methods to investigate the fatigue crack propagation behavior of U75V, a high‐carbon vanadium microalloyed steel commonly used in Chinese railway applications. Room‐temperature crack propagation tests are conducted on 8 CT specimens, where the a ‐ N curves of welded and base materials are achieved for obtaining the Paris formula parameters C and m . Experimental results show that the crack propagation rate of base material is , and it is for the welded one. Additionally, numerical simulations are performed by the ABAQUS/FRANC3D software to characterize the wheel–rail interaction, where the rail stress field and the subcrack propagation are achieved. It indicates that the base rail exhibits a maximum stress of 501 MPa, while the welded material reaches 488 MPa, with both peaks occurring 3.5 mm below the rail surface. Under the same cyclic load, the maximum initial stress intensity factor of the welded material is lower than that of the base material, and the crack propagation rate of the welding material is faster, while the crack propagation angle of the base material is larger. This study provides accurate experiment parameters for U75V rails and also proposes a numerical method for fatigue life assessment potentially applicable in similar steel structures.

The 60 kg/m U75V rail serves as the predominant rail type within China’s high-speed rail network. This study comprehensively evaluates the fatigue behavior of U75V rails through experimental investigations encompassing monotonic tensile testing, high-cycle fatigue characterization, and fatigue crack propagation analysis. All specimens were extracted from standardized 60 kg/m high-speed rail sections to ensure material consistency. Firstly, monotonic tensile tests were conducted to determine the fundamental mechanical properties of the U75V rail. Secondly, uniaxial tension–compression fatigue tests were conducted to establish the S-N and P-S-N relationships of the U75V rail. Lastly, fatigue crack propagation analysis was carried out on three compact tension specimens under three incremental loading forces. Monotonic tensile test results demonstrated full compliance of the material’s basic mechanical properties with Chinese national standards. Fatigue crack propagation results indicated that the crack growth rate of the U75V rail was not only related to the stress-intensity range ∆K but was also correlated with the loading force range ∆F due to a typical crack tip shielding effect, i.e., plasticity-induced crack closure effect. The derived fatigue performance parameters and crack growth mechanism provide essential inputs for predictive fatigue life modeling of high-speed rail infrastructure and development of refined finite element models for fatigue analysis.