Search Results (145)

The offshore implementation of vertical-axis wind turbines (VAWTs) presents a promising new paradigm for advancing marine wind energy utilization, owing to their omnidirectional wind acceptance, compact structural design, and potential for lower maintenance costs. However, VAWTs still face major aerodynamic challenges, particularly due to the pitching motion, where the angle of attack varies cyclically with the blade azimuth. This leads to strong unsteady effects and susceptibility to dynamic stalls, which significantly degrade aerodynamic performance. To address these unresolved issues, this study conducts a comprehensive investigation into the dynamic stall behavior and wake vortex evolution induced by Darrieus-type pitching motion (DPM). Quasi-three-dimensional CFD simulations are performed to explore how variations in blade geometry influence aerodynamic responses under unsteady DPM conditions. To efficiently analyze geometric sensitivity, a surrogate model based on a radial basis function neural network is constructed, enabling fast aerodynamic predictions. Sensitivity analysis identifies the curvature near the maximum thickness and the deflection angle of the trailing edge as the most influential geometric parameters affecting lift and stall behavior, while the blade thickness is shown to strongly impact the moment coefficient. These insights emphasize the pivotal role of blade shape optimization in enhancing aerodynamic performance under inherently unsteady VAWT operating conditions. Full article

The geometry of the railway track affects rail wear significantly. If the rail wear can be predicted and considered during the alignment design phase, the problems it causes can be mitigated at the source by optimizing the values and combinations of railway track geometry parameters. However, the relationship between railway track geometry and rail wear remains unclear. It is hard to acquire rail wear data for different alignments with varying geometric parameters during the alignment design phase. This study develops a PSO-ANN model to establish the mapping relationship between railway track geometry and rail wear, enabling prediction of rail wear based on track geometry parameters. The model achieves prediction accuracies of 96.70% for inner rail wear and 98.13% for outer rail wear. Compared with the conventional ANN model, the PSO-ANN model reduces the prediction errors by 22.54% for inner rail wear and 55.69% for outer rail wear. Sobol sensitivity analysis is conducted to analyze the influence of the track geometry parameters on rail wear, revealing that inner rail wear is mainly affected by curve radius, transition curve length, and superelevation, while outer rail wear is predominantly influenced by curve radius. Full article

Aiming at the practical difficulties (e.g., high cost of full-scale tests) in testing the rolling resistance of aircraft wheels on unpaved runways, this study establishes a theoretical calculation formula for wheel rolling resistance based on the Bekker model, following an analysis of the development and application of wheel–soil interaction models. Global sensitivity analysis using the Sobol’ method was performed on the theoretical formula to derive a simplified calculation model. Aircraft load simulation tests under 80 kN, 100 kN, and 120 kN loading conditions were conducted using a specialized loading vehicle to determine parameters for the simplified prediction model. The resistance values obtained from this model were then applied to calculate aircraft takeoff roll distance. The accuracy of resistance estimation was verified by comparing the calculated results with takeoff distances reported in relevant literature. This research provides a novel approach for estimating wheel rolling resistance of transport aircraft on unpaved runways and offers valuable reference for determining the required length of unpaved runways. Full article

Brucellosis is a zoonotic disease with significant public health implications. Understanding the risks of consuming unpasteurized (raw) milk is critical for effective control measures. A quantitative risk assessment was conducted to estimate Brucella abortus contamination in milk from unregulated sources in Punjab, India, where 70% of milk is sold unpasteurized. Samples from lactating cattle and buffalo (N = 261) in ten villages were tested using the Rose Bengal plate test and indirect IgG ELISA. Modelled risk pathways estimated B. abortus shedding probabilities and colony-forming unit (CFU) concentrations in milk, with Sobol sensitivity analysis identifying influential parameters. Buffalo had a higher estimated shedding prevalence (0.04, 95% PI: 0.02–0.07) than cattle (6.3 × 10⁻³, 95% PI: 2.5 × 10⁻³–13.2 × 10⁻³). Mean contamination levels were 2843 CFU/100 mL (95% PI: 0–32,693 CFU/100 mL) for cattle, 17,963 CFU/100 mL (95% PI: 612–67,121 CFU/100 mL) for buffalo, and 7587 CFU/100 mL (95% PI: 82–39,038 CFU/100 mL) combined. High-shedding animals were the most influential factor (total effect sensitivity index of 0.86 [95% CI: 0.63–0.74]). Removing high-shedding animals reduced risk considerably for people who might drink raw milk once (absolute risk reduction of up to 54% in buffalo milk), but once-per-month consumption is still likely high risk. Effective risk mitigation requires a One Health approach, strengthening both public and animal health interventions, because animal health strategies alone will fail if milk from high-shedding animals reaches the unregulated milk market. Full article

Carbon capture, utilization, and storage (CCUS) is an emerging technology with significant potential for large-scale emissions reduction. To reduce the overall system costs of CCUS, this study first establishes a comprehensive economic cost model for the entire CCUS process. Subsequently, a Monte Carlo-based Sobol’ global sensitivity analysis method is proposed to calculate both first-order and total-order sensitivity indices, followed by qualitative and quantitative analyses of parameter sensitivity. Additionally, convergence analyses of the results and their engineering applicability are examined. The findings reveal that the total-order sensitivity indices for electricity price, flue gas inlet flow rate, pipeline diameter, pipeline material price, pipeline inlet pressure, and injection pressure are 0.6578, 0.3857, 0.5585, 0.3823, 0.2205, and 0.1949, respectively, which are significantly higher than those of the other parameters. This indicates that these parameters have a dominant impact on energy consumption costs through the processes of capture and compression, pipeline transportation, and storage injection. These results provide a basis for selecting decision variables when optimizing the entire CCUS process. Full article

Due to their higher strength-to-weight ratio and ability to operate in harsh environments, the usage of fiber-reinforced cylindrical shells experienced a significant increase in the past decades. The key novelty of this study lies in implementing dual analytical approaches to address the complex failure mechanisms and stress distributions in composites. Two distinct theoretical solutions were investigated, membrane theory and Mindlin–Reissner theory, for failure prediction in filament-wound structures, while uniquely providing a platform for easy comparison of theoretical approaches. Experimental data from different setups, materials, and winding angles were collected in the literature and compared using the developed online MECH-Gcomp software. Failure analysis was also carried out by applying five different failure criteria well-established for composite materials. The results from the Mindlin–Reissner theory showed 46.9% deviation and those for the membrane theory 36.2% deviation, considering more than 120 cases. Sobol sensitivity analysis identified pressure (P), transverse tensile strength, winding angle, and radius as the most influential parameters regarding the index of failure of composite cylinders. Full article

Many important practical problems connected to energy efficiency in buildings, ecology, metallurgy, the development of wireless communication systems, the optimization of radar technology, quantum computing, pharmacology, and seismology are described by large-scale mathematical models that are typically represented by systems of partial differential equations. Such systems often involve numerous input parameters. It is crucial to understand how susceptible the solutions are to uncontrolled variations or uncertainties within these input parameters. This knowledge helps in identifying critical factors that significantly influence the model’s outcomes and can guide efforts to improve the accuracy and reliability of predictions. Sensitivity analysis (SA) is a method used efficiently to assess the sensitivity of the output results from large-scale mathematical models to uncertainties in their input data. By performing SA, we can better manage risks associated with uncertain inputs and make more informed decisions based on the model’s outputs. In recent years, researchers have developed advanced algorithms based on the analysis of variance (ANOVA) technique for computing numerical sensitivity indicators. These methods have also incorporated computationally efficient Monte Carlo integration techniques. This paper presents a comprehensive theoretical and experimental investigation of Monte Carlo algorithms based on “symmetrized shaking” of Sobol’s quasi-random sequences. The theoretical proof demonstrates that these algorithms exhibit an optimal rate of convergence for functions with continuous and bounded first derivatives and for functions with continuous and bounded second derivatives, respectively, both in terms of probability and mean square error. For the purposes of numerical study, these approaches were successfully applied to a particular problem. A specialized software tool for the global sensitivity analysis of an air pollution mathematical model was developed. Sensitivity analyses were conducted regarding some important air pollutant levels, calculated using a large-scale mathematical model describing the long-distance transport of air pollutants—the Unified Danish Eulerian Model (UNI-DEM). The sensitivity of the model was explored focusing on two distinct categories of key input parameters: chemical reaction rates and input emissions. To validate the theoretical findings and study the applicability of the algorithms across diverse problem classes, extensive numerical experiments were conducted to calculate the main sensitivity indicators—Sobol’ global sensitivity indices. Various numerical integration algorithms were employed to meet this goal—Monte Carlo, quasi-Monte Carlo (QMC), scrambled quasi-Monte Carlo methods based on Sobol’s sequences, and a sensitivity analysis approach implemented in the SIMLAB software for sensitivity analysis. During the study, an essential task arose that is small in value sensitivity measures. It required numerical integration approaches with higher accuracy to ensure reliable predictions based on a specific mathematical model, defining a vital role for small sensitivity measures. Both the analysis and numerical results highlight the advantages of one of the proposed approaches in terms of accuracy and efficiency, particularly for relatively small sensitivity indices. Full article

The accurate prediction of moisture content is crucial for controlling the drying process of agricultural products. While existing studies on drying models often rely on laboratory-scale experiments with limited data, real-time and high-frequency data collection under industrial conditions remains underexplored. This study collected and constructed a multi-dimensional dataset using an industrial-grade data acquisition system specifically designed for heat pump low-temperature circulating dryers. An artificial neural network (ANN) prediction model for moisture content during the rice drying process was developed. To prevent model overfitting, K-fold cross-validation was utilized. The model’s performance was evaluated using the mean squared error (MSE) and the coefficient of determination (R²), which also helped determine the preliminary structure of the ANN model. Bayesian regularization (trainbr) was then employed to train the network. Furthermore, optimization was conducted using neural network weights (RI) analysis and Sobol variance contribution analysis of the input variables to simplify the model structure and improve predictive performance. The experimental results showed that optimizing the model through RI sensitivity analysis simplified its topology to a 5-14-1 structure. The optimized model exhibited not only simplicity but also high prediction accuracy, achieving R² values of 0.969 and 0.966 for the training and testing sets, respectively, with MSEs of 5.6 × 10⁻³ and 6.3 × 10⁻³. Additionally, the residual errors followed a normal distribution, indicating that the predictions were reliable and realistic. Statistical tests such as t-tests, F-tests, and Kolmogorov–Smirnov tests revealed no significant differences between the predicted and actual values of rice moisture content, confirming the high consistency between them. Full article

The hybrid laser inertial navigation system (INS) is increasingly vital for high precision under high-dynamic, long-duration conditions, especially in complex aircraft environments. Key components like the base, platform, and inner/outer frames significantly impact system accuracy and stability through thseir static and dynamic characteristics. This study focuses on minimizing deviations in the INS body coordinate system caused by elastic deformation under high overload by developing a mechanical simulation model of a rotational modulation structure and a structural model of the outer frame assembly. A reliability analysis model is established, considering both functional and structural strength failures. To address uncertainties from manufacturing, technical conditions, material selection, and assembly errors, a global sensitivity analysis based on Bayesian inference evaluates parameter contributions to functional failure probability, using a sample size of N1 = 5 × 10⁵. Additionally, uncertainty analysis via Sobol sequence sampling (N2 = 10,000) examines the impact of mean design parameter variations on failure probability for ZL107 and SiCp/Al aluminum matrix composite frames. Experimental verification concludes the study. The results indicate that the SiCp/Al composite material demonstrates superior mechanical performance compared to traditional materials such as the ZL107 aluminum alloy. The uncertainties in the inner frame thickness, inner frame material strength, and outer frame thickness have the most significant impact on the probability of functional failure in the hybrid INS, with sensitivity indices of ${\delta }_6^{P\left\{F\right\}}$ = 0.01657, ${\delta }_2^{P\left\{F\right\}}$ = 0.00873, and ${\delta }_4^{P\left\{F\right\}}$ = 0.00818, respectively. The mechanical properties of the outer frame structure made from SiCp/Al are significantly enhanced, with failure probabilities across three failure modes markedly lower than those of the ZL107 frame, indicating high reliability. In an impact test conducted on the product, the laser gyroscope worked normally, the hybrid laser system function was normal, and the platform angular velocity change corresponding to each impact direction was less than 12 ″/s. The maximum angle change of the inner and outer frames was 0.107°, indicating that the system structure can withstand large overloads and multiple levels of mechanical environments and has good environmental adaptability and reliability. This analytical approach provides a valuable method for reliability evaluation and design of new hybrid INS structures. More importantly, it provides insights into the influence of design parameter uncertainties on navigation accuracy, offering critical support for the advancement of inertial technology. Full article

In this paper, we present a kinetic–metabolic model describing adeno-associated virus (AAV) production via HEK293 cells that encompasses the main metabolic pathways, namely, glycolysis, tricarboxylic acid cycle (TCA), pyruvate fates, the pentose phosphate pathway, anaplerotic reaction, amino acid metabolism, nucleotides synthesis, biomass synthesis, and the metabolic pathways of protein synthesis of the AAV (capsid and Rep proteins). For the modeling, Michaelis–Menten kinetics was assumed to define the metabolic model. A dataset from bioreactor cultures containing metabolite profiles of adeno-associated virus 6 (AAV6) production via triple transient transfection in a low-cell-density culture, including the concentration profiles of glutamine, glutamic acid, glucose, lactate, and ammonium, was utilized for fitting and computing the model parameters. The model that resulted from the adjusted parameters defined the experimental data well. Subsequently, a Sobol-based global sensitivity analysis procedure was applied to determine the most sensitive parameters in the final model. Full article

Different gas hydrate types, such as methane hydrate and carbon dioxide hydrate, exhibit distinct geomechanical responses and hydrate morphologies in gas-hydrate-bearing sediments (GHBSs). However, most constitutive models for GHBSs focus on methane-hydrate-bearing sediments (MHBSs), while largely overlooking carbon-dioxide-hydrate-bearing sediments (CHBSs). This paper proposes a modified Mohr–Coulomb (M-C) model for GHBSs that incorporates the geomechanical effects of both MHBSs and CHBSs. The model integrates diverse hydrate morphologies—cementing, load-bearing, and pore-filling—into hydrate saturation and incorporates an effective confining pressure. Its validity was demonstrated through simulations of reported triaxial compression tests for both MHBSs and CHBSs. Moreover, a variance-based sensitivity analysis using Sobol’s method evaluated the effects of hydrate-related soil properties on the geomechanical behavior of GHBSs. The results indicate that the shear modulus influences the yield axial strain of the CHBSs and could be up to 1.15 times more than that of the MHBSs. Similarly, the bulk modulus showed an approximate 5% increase in its impact on the yield volumetric strain of the CHBSs compared with the MHBSs. These findings provide a unified framework for modeling GHBSs and have implications for CO₂-injection-induced methane production from deep sediments, advancing the understanding and simulation of GHBS geomechanical behavior. Full article

Non-point source pollution (NPSP) originates from domestic agricultural pollutants and deforestation. Agricultural NPSP discharges into rivers and oceans through precipitation and soil runoff. Awareness and research regarding NPSP and its harmful effects on human health and the environment are increasing. The Diffuse Pollution Estimation with Remote Sensing (DPeRS) model, a distributed NPSP model proposed by Chinese researchers, seeks to predict agricultural NPSP and includes modules estimating nitrogen and phosphorus balance, vegetation coverage, dissolved pollution, and absorbed pollution. By applying the DPeRS model, the present work aims to predict the distribution of all nitrogen and phosphorus pollutants in Henan Province, China in 2021. We used statistical yearbook, remotely sensed, and hydrological data as input. To facilitate uncertainty characterization in pollution predictions, we performed sensitivity analysis, which identified the model input variables that contributed most to uncertainty in model output. Specifically, we used ArcGIS for processing data for nitrogen and phosphorus balance equations, an ENVI 5.3 software system for deriving vegetation cover, and the RUSLE soil erosion model for predicting absorption pollution. Dissolved pollution was estimated using a unified approach to estimating agricultural runoff, urban runoff, rural resident, and livestock pollutants. Absorbed pollution was estimated by considering the soil erosion model and precipitation. Moreover, Sobol’s method was applied for sensitivity analysis. We found that regardless of the accumulation of nitrogen or phosphorus, indicators of the dissolved pollution of Zhoukou were relatively high. Sensitivity analysis of the models for estimating dissolved pollution and absorbed pollution revealed that the top four influential variables for dissolved pollution were standard runoff coefficient ${\mathsf{\epsilon }}_0$, natural factor correction coefficient ${\mathrm{N}}_{\mathrm{i}}$, the newly produced TN pollutants per area ${\mathrm{Q}}_{\mathrm{i}\mathrm{N}}$, and runoff coefficient $\mathsf{\epsilon }$. For absorbed pollution, influential variables were rainfall erosion factor R, water and soil conservation factor P, slope degree factor S, and slope length factor L. The total discharges of Henan Province were 9546.4649 t, 1061.8940 t, 6031.4577 t, and 3587.6113 t for TN, TP, ${\mathrm{N}\mathrm{H}}_4^{+}$-N, and COD, respectively, in 2021. This paper provides a valuable reference for understanding the status of NPSP in Henan province. The DPeRS approach presented in this paper provides strong support for policymakers in the field of environmental management in China. This study confirmed that the DPeRS model can be feasibly applied to larger areas for NPSP prediction enhanced with sensitivity analysis due to its fast computation and reliance on accessible and simple data sources. Full article

The ongoing changes in climate and the rapid pace of urbanization are contributing to an alarming increase in the prevalence of urban flooding, which is having a profound impact on the quality of life for residents and the smooth functioning of urban areas. The 1D–2D coupled model is an effective tool for simulating the process of urban flooding, thereby providing a scientific basis for urban planning, flood prevention, and mitigation strategies. The values of numerous parameters within the model not only influence the computational efficiency but also influence the precision of the simulation outcomes. It is of particular significance to ascertain the sensitivity of model parameters. In this study, a 1D–2D coupled model of urban flooding was constructed, and a parameter sensitivity analysis was conducted using the modified Morris method and the Sobol method in two ways, with the amount of waterlogging as the target. The findings indicate that the minimum infiltration rate is the most sensitive parameter in the local sensitivity analysis, whereas the Manning coefficient of the permeable surface area is the most sensitive in the global sensitivity analysis. The research outcomes can facilitate the optimization of the model parameters and enhance the precision and dependability of the model predictions, thereby providing more accurate data support for urban flooding early warning and emergency response. Full article

The axial-flow pump system has been widely applied to coastal drainage pump stations, but the hydraulic performance optimization based on the contraction angles of the inlet passage has not been studied. This paper combined the computational fluid dynamics (CFD) method, machine learning (ML) algorithms and genetic algorithm (GA) to find the optimal contraction angles of the inlet passage. The 125 sets of comprehensive objective function were obtained by the CFD method. Three contraction angles and comprehensive objective function values were regressed by three ML algorithms. After hyperparameter optimization, the Gaussian process regression (GPR) model had the highest R² = 0.958 in the test set and had the strongest generalization ability among the three models. The impact degree of the three contraction angles on the objective function of the GPR model was investigated by the Sobol sensitivity analysis method; the results indicated that the order of impact degree from high to low was ${\theta }_3>{\theta }_2>{\theta }_1$. The optimal objective function values of the GPR model and corresponding contraction angles were searched through GA; the maximum objective function value was 0.963 and corresponding contraction angles were ${\theta }_1=13.34°$, ${\theta }_2=28.36°$ and ${\theta }_3=3.64°$, respectively. The results of this study can provide reference for the optimization of inlet passages in coastal drainage pump systems. Full article

The mechanisms and interactive effects of multiple operating parameters of tower cranes on load swing are not yet clear, which leads to the exacerbation of load swing during the lifting process due to improper control parameter settings. To address this issue, this paper establishes an electromechanical rigid-flexible coupling (EMRFC) model for tower cranes to accurately simulate the characteristics of load swing caused by flexible transmission and electromechanical nonlinear coupling. Furthermore, the Sobol sensitivity method is used to screen out the dominant and interactive operating parameters affecting load swing, and to reveal the patterns of their impact on load swing. The results show that the stiffness of the flexible transmission system has a significant impact on the load swing, which cannot be neglected in modeling and analysis. Among the dominant operating parameters, the lifting height has the greatest effect on load swing. Lifting height, luffing speed, and slewing speed show significant interactions on load swing, and the interactions make a significant difference to the load swing in different operating phases. Finally, this paper gives the reasonable interval of operation parameters of a hoisting operation under the composite working condition, which provides a scientific basis and theoretical guidance for intelligent control of tower crane operation. Full article