Search Results (177)

This paper aims to design and optimize a new type of Darrieus H-Rotor VAWT, specifically adapted to the conditions of urban and industrial environments, as a contribution to energy transition and the search for new sustainable solutions for decentralized electricity generation. It is designed to be a strong and efficient wind turbine capable of providing a nominal power of 2 kW at low and moderate wind speed. The methodology used is based on the analysis of wind resources (wind rose, Weibull distribution) and aerodynamic modelling using QBlade. The structural analysis with CATIA showed that the configuration of the third case (with 4 mm blade thickness and four supports of 2 mm) was an excellent compromise, with a reduced mass (13 kg) and a controlled maximum stress (0.854 MPa), far below the elastic limit of aluminum. From an aerodynamic point of view, CFD simulations in ANSYS Fluent 2023 R1 (k-ω SST model in transient regime) allowed us to visualize flow fields, pressure distribution and torque evolution. The results obtained showed a power coefficient (Cp) very close to 0.4, validating the configuration. This proves the chosen configuration to be effective. In this work, a suitable wind blade has been optimized with a strong and aerodynamically efficient design for operation in urban areas. Full article

Permeability models are essential for quantitatively describing coal permeability evolution and predicting gas migration during deep mining. Deep coal subjected to mining disturbance commonly exhibits pronounced nonlinear changes in permeability, limiting the applicability of conventional models. In this study, coal is idealized as a dual-component medium comprising the matrix and fractures, and deformation of both components induced by mining-related stress changes and gas adsorption is incorporated into the model. The conventional Weibull statistical damage variable is generalized to a fractional-order form using the Caputo derivative, yielding a Mittag–Leffler-type damage evolution law. By coupling this formulation with matrix–fracture deformation and an exponential damage–permeability term, a fractional-order damage-based permeability model is established to describe the complete evolution from elastic deformation through pre-peak damage to post-peak failure. The model parameters are calibrated separately using published datasets for protective-seam mining, top-coal caving, no-pillar mining, and a full-process loading case. The calibrated model yields coefficient of determination (R²) values of 0.9374, 0.9625, 0.9875, and 0.9980, respectively. The identified fractional order is λ = 1 for the three mining-disturbance datasets, whereas the full-process dataset yields λ = 0.7734. For the full-process dataset, the fractional-order model reduces root mean square error (RMSE) and mean absolute error (MAE) by approximately 31.4% and 34.7%, respectively, compared with its integer-order counterpart. Sensitivity analysis shows that λ, p, ε_d, and γ play distinct roles in permeability evolution. At an axial strain of 0.8%, increasing ε_d from 0.721% to 1.121% decreases k/k₀ from 2.6919 to 1.3433, whereas increasing γ from 0 to 2.543 increases k/k₀ from 1.0003 to 3.0334, indicating that ε_d and γ strongly affect the strain level and magnitude of permeability enhancement, respectively. The proposed model provides an effective tool for characterizing the nonlinear permeability evolution of deep coal under mining disturbance. Full article

This study investigates how offshore wind farm layout geometry influences farm-level performance using a computationally efficient Jensen–Park wake model combined with directionally resolved Weibull wind-speed statistics. A fixed-capacity 1.8 GW case study, consisting of 120 V236-15.0 MW turbines, is used to examine the effects of grid aspect ratio, inter-turbine spacing, cumulative row skew, and global layout rotation on wake losses, annual energy production (AEP), and capacity factor under representative offshore screening assumptions. Structured layouts with identical turbine count and installed capacity are compared with a regular baseline grid to isolate geometric effects within a consistent modelling framework. For the nominal offshore Jensen wake-expansion coefficient, k = 0.04, the highest sampled AEP is obtained for the 5 × 24 configuration, which produces 8930.69 GWh yr⁻¹ and a capacity factor of 56.64%. The regular baseline produces 7397.50 GWh yr⁻¹ and a capacity factor of 46.91%, corresponding to a 20.73% AEP increase for the highest sampled layout. However, the performance differences among Layouts D–F are small, indicating a high-performing layout plateau rather than a clearly separated optimum. The contribution of this paper is therefore not a new wake model, optimisation algorithm, or general offshore design rule. Instead, this study provides an auditable screening workflow that documents modelling assumptions, parameter bounds, coordinate transformations, convergence checks, sensitivity analyses, and spatial-efficiency indicators for one turbine model, one turbine count, one synthetic wind rose, and a limited set of structured row–column layouts. Full article

Fiber-reinforced self-compacting concrete (FR-SCC) incorporating granite powder (GP), an expansive agent (EA), steel fibers (SFs), and polypropylene fibers (PPFs) was investigated for potential pre-cast tunnel-segment applications. Sixteen mixtures, covering GP replacement ratios of 0–18%, EA dosages of 0–8% by binder mass, and SF and PPF volume fractions of 0–0.75% and 0–0.15%, were tested in uniaxial compression on 100 mm × 100 mm × 300 mm prisms with acoustic emission (AE) monitoring. Within the tested range, 12% GP and 8% EA gave the most favorable binder composition. XRD and SEM analyses indicated that GP acted predominantly as an inert filler with no detectable portlandite consumption, while the expansive agent was associated with additional ettringite formation. At this composition, hybrid SF/PPFs increased the post-peak energy by a factor of 7.66 relative to the fiber-free mixture, mainly improving the post-peak rather than the pre-peak behavior. Among the Carreira–Chu, GB 50010, and modified Weibull formulations, the GB 50010 piecewise model best reproduced the full stress–strain curves and was used as the primary constitutive model. Two-variable regressions were established to separate the apparent effects of the SF and PPF volume fractions on the ascending- and descending-branch shape parameters, and a ductility-calibrated expression was developed for the descending-branch parameter. The Pearson coefficient between the descending-branch parameter and the AE characteristic strain was −0.904, while that between the AE characteristic strain and the macroscopic residual strain was +0.983. These results link constitutive modeling, AE damage evolution, and macroscopic post-peak ductility for FR-SCC within the tested range of mix proportions. Full article

Natural fiber-reinforced polymer composites are attractive for lightweight and sustainable engineering applications; however, property scatter remains a major barrier to reliable design. Mean tensile properties alone are insufficient when material selection depends on repeatability and lower-tail performance. This study presents a statistical variability and lower-tail reliability assessment of flax, jute, and carbon fiber composite laminates using 590 open-access tensile test records from a published natural-fiber composite dataset. Flax and jute were selected as representative bast-fiber systems covering a range of woven, unidirectional, and short-fiber architectures; carbon fiber was included as a synthetic-fiber reference system. Three mechanically important properties were analyzed: the recalculated tensile modulus, tensile strength, and axial failure strain. Normal, lognormal, and two-parameter Weibull distributions were screened for each material–property combination using the Akaike information criterion (AIC); empirical fifth percentiles (P5) and bootstrap 95% confidence intervals (CI) were computed as lower-tail descriptors. The results show that Carbon-0 has the highest lower-tail modulus and strength, with empirical fifth percentiles of 104.95 GPa and 989.64 MPa, respectively. Among the natural fiber systems, Flax-0 and Flax-VE-0 provided the highest lower-tail strengths, whereas Flax-Twill and Flax-CP showed the highest lower-tail failure strains. The lowest tensile strength coefficient of variation was observed for Flax-90 (2.41%), followed by Flax-Twill (3.43%), Flax-0 (4.50%), Jute-Satin (4.83%), and Jute-Plain (4.92%). A balanced reliability ranking that combined lower-tail property ranks and coefficient of variation ranks identified Flax-0, Flax-VE-0, Flax-Twill, Flax-CP, and Jute-Satin as the most favorable natural-fiber systems. The lower coefficient of variation values observed in aligned and satin-weave architectures relative to short-fiber and plain-weave systems reflect the role of fiber orientation uniformity in moderating property scatter at the laminate scale. This study provides a reproducible statistical framework based on lower-tail performance descriptors for comparative screening purposes, not on formal design allowables for distinguishing high mean performance from reliable minimum-level performance in natural fiber composite laminates. Full article

In a digital watermarking technology system, robustness, imperceptibility and payload capacity are the three core performance indicators that restrict one another. How to achieve the optimal balance between the three is still the key scientific challenge to be solved in this field. This paper proposes a digital watermarking algorithm based on the magnitude coefficient of Non-Subsampled Shearlet Transform Fast and Accurate Polar Harmonic Fourier Moments (NSST-FAPHFMs) and the Weibull–Power Cauchy (W-PC) statistical model. The algorithm consists of two stages: watermark embedding and detection. In the embedding phase, the original image is first decomposed by NSST multi-scale decomposition, and the high-frequency subbands are divided into non-overlapping blocks and partitioned. High-energy coefficient blocks are extracted to obtain NSST-FAPHFM magnitude coefficient features, which serve as robust carriers for watermark embedding. In the detection phase, the W-PC distribution is used to accurately statistically model the above magnitude coefficients to characterize their heavy-tailed characteristics and strong correlation structure. Maximum likelihood estimation (MLE) is employed to estimate the model parameters, and a blind watermark detection mechanism is further constructed by integrating the W-PC model with the Local Optimal Detector (LOD) under the Neyman–Pearson (N-P) criterion. Experimental results show that the proposed algorithm has good imperceptibility, and the area under the receiver operating characteristic curve (AUROC) can reach 0.9991 without attack. The algorithm maintains strong robustness against various attacks and can effectively realize the joint optimization of the three core performance indicators of the watermarking system. Full article

Carbonation in CO₂-storage solid-waste backfill materials couples CO₂ transport, mineral reaction, and strength evolution, making carbonation-front prediction and transport–reaction inference important for evaluating sequestration performance. This study proposes an evidence-ranked, physics-guided inverse-learning framework for carbonation-front prediction, auxiliary strength reconstruction, PDE-residual assessment, and practical-identifiability analysis. The framework represents carbonation using group-conditioned latent fields of effective CO₂ concentration and remaining reactive capacity, maps latent carbonation degree to measured depth through a differentiable front operator, and reconstructs unconfined compressive strength through a supervised auxiliary head. Empirical front laws and reaction–diffusion physics-informed neural-network variants were evaluated using held-out ranking, repeated stratified splits, residual-weight sweeps, front-operator threshold and smoothing-coefficient sensitivity checks, profile-likelihood and Fisher-information diagnostics, and controlled synthetic tests. Results show that the grouped Weibull front law achieved the best short-range carbonation-depth interpolation, while the retained constant-diffusion PINN was used as a diagnostic formulation within the physics-guided family to improve auxiliary strength reconstruction and to evaluate residual consistency, front-threshold selection, parameter sharing, and inverse-parameter behavior rather than to replace the empirical depth regressor. Increasing the PDE-residual weight substantially reduced residual magnitudes, but profile-likelihood and Fisher-information diagnostics indicated strong parameter trade-offs; the fitted diffusion, reaction, depletion, and diffusion–decay quantities are therefore interpreted as effective, observation-conditional parameters rather than unique material constants. The proposed framework provides a prediction-first and attribution-aware approach for analyzing carbonation evolution in solid-waste backfill materials and supports coordinated assessment of front advancement, strength response, and transport–reaction behavior, while explicitly delimiting the generalization and physical interpretation that can be supported by sparse literature-derived observations. Full article

Accurate characterization of wind-speed distributions is essential for preliminary wind-resource assessment, vertical wind-profile evaluation, and energy-yield estimation. This study presents a CFD-assisted reconstruction and validation framework that integrates two-parameter Weibull statistics with class-conditioned OpenFOAM v13 simulations to reconstruct wind-speed distributions at different measurement heights. Hourly wind-speed records measured at 10 m and 30 m at the Sakarya–Esentepe station during the period of 2009–2010 were used. The 2009 dataset was employed to estimate the Weibull shape and scale parameters by maximum likelihood estimation, while the 2010 dataset was reserved for independent validation. To ensure methodological consistency between statistical wind characterization and steady CFD modeling, the fitted Weibull distribution was discretized into representative wind-speed classes. For each class, a steady Reynolds-averaged Navier–Stokes simulation was performed in OpenFOAM under neutral atmospheric boundary-layer assumptions using the standard k–ε turbulence model, a logarithmic inlet velocity profile, and rough-wall boundary treatment. The class-wise CFD velocity responses extracted at 10 m and 30 m were then weighted by the corresponding Weibull class probabilities to reconstruct height-specific wind-speed probability distributions. The reconstructed distributions showed good agreement with the measured and fitted Weibull references. The RMSE values obtained by CFD for measurements at heights of 10 m and 30 m on the measurement mast were 0.45 m s⁻¹ and 0.52 m s⁻¹, respectively, and the Pearson correlation coefficients were 0.97 and 0.96, respectively; these values indicate that the CFD analyses are reliable. For the Lilliefors-adjusted Kolmogorov–Smirnov statistics, there is no value higher than 0.06. The differences between the reference and CFD-reconstructed AEP estimates were +0.40% at 10 m and −1.97% at 30 m. These findings indicate that the proposed Weibull–OpenFOAM framework provides a reproducible engineering approach for CFD-assisted wind-speed distribution reconstruction and height-specific consistency assessment. However, the method should be interpreted as a class-conditioned reconstruction framework rather than a stand-alone transient atmospheric wind prediction model. Full article

Fractured coal in the residual-strength stage is a primary medium for gas migration and drainage in deep mining areas. To investigate the hydro–mechanical seepage response of post-peak fractured coal under constant-pressure-difference conditions, triaxial CO₂ seepage tests were conducted on coal specimens collected from the Xinyuan Coal Mine. A Weibull-based damage constitutive model was established to characterize the confining-pressure-induced hysteresis in the damage-evolution path. The flow-rate evolution and Reynolds number analysis indicated that gas flow remained within the linear Darcy regime. A controlled-variable analysis was used to examine the competing effects governing permeability evolution. Mechanical compaction induced an exponential decrease in permeability, whereas the decrease in permeability with increasing pore pressure was interpreted, within the proposed model framework, as the combined effect of possible adsorption-induced matrix swelling and weakened gas slippage. To address the limitations of conventional constant-slip-factor models, a pressure-dependent slip modulation coefficient was introduced into a composite permeability equation incorporating effective stress, adsorption-related deformation, and dynamic gas slippage. Global nonlinear fitting yielded R² = 0.97 and an RMSE of 0.1909, with the residuals generally distributed around zero, supporting the fitting reliability of the model within the investigated stress–pressure range. Response-surface analysis identified mechanical compaction as the dominant controlling mechanism, while adsorption-related deformation and gas slippage acted as secondary correction mechanisms. The proposed framework provides a quantitative basis for distinguishing the mechanical and fluid-related effects governing permeability evolution in post-peak fractured coal. Full article

Understanding the structural characteristics of Pinus taiwanensis plantations in climatically transitional regions is essential for developing science-based management strategies under global change. This study investigated 23 plots in Huangbai Mountain Forest Farm, Henan Province, China, classified into low-, medium-, and high-density stands (n = 9, 9, and 5, respectively). Diameter distributions were fitted using six probability functions, and four spatial structure parameters—mixing degree (Mc), size ratio (U), uniform angle index (W), and forest layer index (S)—were quantified. In addition, five comprehensive spatial structure indices—average superiority coefficient index (SPV), spatial structure comprehensive index (Q), stand spatial structure distance index (FSI), Comprehensive Distance Evaluation (CDEV), and Comprehensive Assessment of Proximity Vector (CAPV)—were constructed using a combined analytic hierarchy process and entropy weight method. Given the unbalanced sample sizes, non-parametric Kruskal–Wallis tests were employed for comparisons, and bootstrap resampling (1000 iterations) was performed to assess the reliability of mean estimates. The results showed that both the Gamma and Weibull distributions were equally suitable for describing diameter distribution under different stand densities, as their AIC differences were below 2 for all density classes. Correlation analysis indicated that the relative importance of spatial parameters followed the order S > U > Mc > W. Medium-density stands exhibited the most optimal spatial structure, whereas low-density stands showed the poorest performance. These findings suggest that both overly dense and sparse stands negatively affect spatial organization. Appropriate management practices, such as thinning or enrichment planting, are recommended to optimize stand structure and enhance ecological resilience. Full article

Accurate performance prediction for large offshore wind turbines requires a principled treatment of uncertainty in both the wind resource and the rotor design parameters. In the present work, we develop a surrogate-based, multi-level uncertainty quantification (UQ) framework coupling a physics-based Blade Element Momentum (BEM) solver with a spectral Polynomial Chaos Expansion (PCE) surrogate that replaces the expensive Monte Carlo loop and apply it to the IEA 15 MW offshore reference wind turbine. The framework is completed by Sobol variance-based global sensitivity analysis. The contribution is methodological rather than algorithmic: although each individual ingredient (PCE, Sobol, BEM, and Jensen) is well established, their joint deployment in a single, internally consistent, end-to-end probabilistic workflow that simultaneously delivers (i) aerodynamic–structural UQ with analytical Sobol ranking, (ii) a like-for-like cross-comparison of three reference turbines, (iii) a quantitative leading-edge icing degradation study, and (iv) a farm-level wake-steering optimization on the same IEA 15 MW reference rotor yields a unified probabilistic envelope from which manufacturing tolerances, cold-climate investment thresholds, and farm-layout/control trade-offs can be read off consistently. Five input parameters are treated as random variables: hub-height wind speed (Weibull, k = 2.2, c = 9.8 m/s), air density, blade chord length, twist angle, and rotor speed. A degree-4 sparse PCE is built by non-intrusive spectral projection using N = 5000 Sobol quasi-random realizations, which allows the Sobol indices to be recovered analytically from the expansion coefficients at essentially no extra cost. Three parallel engineering studies complement the core UQ analysis: (A) a head-to-head comparison of the NREL 5 MW, DTU 10 MW, and IEA 15 MW reference turbines; (B) a quantitative assessment of leading-edge ice accretion at four severity levels; and (C) a Jensen-based wake optimization for a 25-turbine offshore array with static wake steering. The main results are as follows: the turbine reaches C_p,max = 0.480 at λ_opt = 8.51, and an annual energy production (AEP) of 71,261 MWh/year (PCE: 70,840 ± 2,140 MWh/year, 95% CI). Wind speed emerges as the dominant driver of C_p variance (S₁ = 0.412), followed by blade twist (0.198) and chord (0.143). Severe icing (30 kg/m) reduces C_p by 18.2% and increases the blade-root Damage Equivalent Load (DEL) by 18.5%. For the array, the optimal spacing (s_x = 8D, s_y = 6D) gives a farm efficiency of 89.6% and 1296 GWh/year, and a 15° wake-steering offset adds a further +3.2% to farm AEP. Compared with plain Monte Carlo, the sparse PCE delivers the same statistics with about 36% fewer model evaluations and a relative error below 0.8%. Full article

Ultra-high-performance concrete (UHPC) has attracted extensive attention because of its superior mechanical performance and durability. However, many existing tensile constitutive models are still obtained mainly by fitting macroscopic stress–strain curves, and the coupling among tensile damage development, steel-fiber parameters, and structural-scale response has not been sufficiently clarified. In this work, an acoustic-emission-informed tensile damage model was established for UHPC. Direct tensile tests were carried out on UHPC specimens containing steel fibers with aspect ratios of 43, 65, and 100 and volume fractions ranging from 0.5% to 3.0%, while acoustic emission signals were collected during loading. The normalized cumulative AE count was adopted as a damage indicator, and its evolution with tensile strain was described using a Weibull-type function. A fiber factor combining fiber volume fraction and aspect ratio was further incorporated into the damage constitutive equation. The proposed relationship was checked against 14 independent tensile datasets reported in the literature. After correction, the mean relative error of the predicted model parameter was reduced to 2.6%, with a standard deviation of 4.1%, and the fitted stress–strain curves all achieved R² values above 0.85. The constitutive model was then implemented in ABAQUS for the simulation of reinforced UHPC beams. By introducing a member-level reduction coefficient of μ = 0.84, the numerical load–deflection curve showed improved agreement with the experimental beam response. The coefficient is empirical and is applicable only to the beam configuration investigated here unless further validation is performed. Overall, the proposed model provides a damage-based link among AE monitoring, steel-fiber reinforcement parameters, and member-scale numerical analysis. Full article

This study investigates the influence of temperature on the bending properties, fatigue life, and breakdown voltage of glass fiber/epoxy composites (EPGF). The three-point bending tests were conducted at room temperature (RT) and 60 °C, and the bending fatigue tests were carried out under three displacement amplitudes (0.80, 0.75, 0.70). At the same time, fatigue life prediction was conducted using the Weibull distribution fitting, microscopic structure analysis by scanning electron microscopy (SEM), and breakdown voltage tests in accordance with the GB/T1408-2006 standard. The results show that at 60 °C, the ultimate bending strength and flexural modulus of EPGF decreased by 52.67% and 65.45%, respectively. At high displacement amplitudes (S = 0.80, 0.75), 60 °C leads to a sharp rise in data dispersion with the coefficient of variation (CV) surging by 1.56 and 2.32 times separately. S and temperature exert a significant synergistic degradation effect on fatigue life, and the two-parameter Weibull distribution (R² > 0.85) can well characterize the fatigue life of EPGF. In terms of dielectric properties, 60 °C reduces the initial breakdown voltage of EPGF by 4.23% (p < 0.05). Fatigue damage causes a continuous drop in breakdown voltage. At RT with 80% damage, the reduction rate increases from 16.28% to 26.95% as S rises, showing a synergistic characteristic between amplitude and fatigue damage. Moreover, 60 °C only affects the initial breakdown voltage and has no significant effect on the fatigue-induced decrease in breakdown voltage. SEM observations indicate that 60 °C induces matrix cracking, fiber curling and interfacial debonding in EPGF. This study provides key experimental data and theoretical support for the fatigue life prediction and insulation performance evaluation of EPGF under different temperature fatigue conditions. Full article

The modernization of global energy infrastructure within the Industry 5.0 framework requires the use of high-strength steels and reliable joining technologies to ensure safe, sustainable pipeline transport. This study focuses on the analysis of heterogeneous welded joints formed between high-strength alloy steel (34KhN2MA/EN 34CrNiMo6) and an austenitic welded seam (ER 307). While austenitic welds mitigate the risk of cold cracking, they introduce significant structural and mechanical heterogeneity. To address this, the research proposes and validates a material homogeneity criterion (MHC) derived from the LM-hardness methodology. By analyzing the statistical dispersion of macrohardness (HRC) through indicators such as the Weibull homogeneity coefficient (m) and the coefficient of variation (ν), the study establishes a quantitative approach to assess material degradation and structural uniformity across key weld zones. Results demonstrate that macrohardness profiling effectively distinguishes between structurally heterogeneous regions near the weld axis characterized by low homogeneity coefficients (m = 4.04 < 10, A_m = 0.742 < 0.878), elevated variability (ν = 29.68% > 11.6%), and high technological damageability (D = 0.92 > 0.81, j_D = 11.87 > 4.38) with pronounced step-like variation in macrohardness (HRC ∈ [12.6; 47]), on the one hand, and stabilized homogeneous zones in the base material, where m = 24.89 > 10, A_m = 0.947 > 0.878, ν = 4.39% < 11.6%, D = 0.52 ⟶ 0, j_D = 1.09 ⟶ 0, and characteristic range of HRC = 47–55, on the other hand. This methodology provides a robust, quasi-non-destructive tool for enhancing predictive maintenance, digital twins, and the overall integrity management of “smart” pipeline systems. Full article

Climate change poses a significant challenge to food security, as it alters crop productivity, distribution patterns, and the overall food supply. This study modelled the emergence of Amaranthus hybridus L. in bean (Phaseolus vulgaris L.) and maize (Zea mays L.) production systems in the Brazilian state of Minas Gerais, in the cities of Coimbra, Paracatu, São João del-Rei, and Uberaba, under the Coupled Model Intercomparison Project Phase 6 (CMIP6) SSP1-2.6 and SSP5-8.5 scenarios. Using Hydrothermal Time (HTT), computational modelling, and nonlinear Weibull regression, weed emergence was simulated under current and future climate scenarios for 2050 and 2070. Although biological triggers such as temperature and base water potential remain constant, higher average temperatures accelerate HTT accumulation. Thus, this results in earlier and more intense emergence flows. The highest and lowest cumulative emergence were observed in Uberaba and Paracatu, respectively. The SSP5-8.5 scenario projects high emergence windows for 2070. This reduces the time available for management interventions. The root-mean-square error (RMSE) associated with the coefficient of determination (R²) of the models validates HTT as an essential tool in computational agriculture. The integration of these models into decision-support systems is essential to mitigating productivity losses and it will increase control efficiency amid future climate uncertainties. Full article