Search Results (9)

As the development of floating offshore wind turbines (FOWTs) progresses from offshore to deeper sea, the demands on mooring systems to ensure the safety of the structure have become increasingly stringent, leading to a concomitant rise in costs. A parameter optimization method for the mooring system of FOWTs is proposed, with the mooring line length and anchor radial spacing as the optimization variables, and the minimization of surge, yaw, and nacelle acceleration as the objectives. A series of mooring system configuration samples are generated by the fully analytical factorial design method, and the open source program OpenFAST is employed to simulate the global responses in the time domain. To enhance the efficiency of the optimization process, a multi-objective evolutionary algorithm, Non-dominated Sorting Genetic Algorithm II (NSGA-II), is utilized to find the Pareto-optimal solutions, alongside a Kriging model, which serves as a surrogate model for the FOWTs. This approach was applied to an IEC 15MW FOWT to demonstrate the optimization procedure. The results indicate that the integration of the genetic algorithm and the surrogate model achieved rapid convergence and high accuracy. Through this optimization process, the longitudinal motion response of FOWTs is reduced by a maximum of 6.46%, the yaw motion by 2.87%, and the nacelle acceleration by 11.55%. Full article

The operational parameters of plant protection unmanned aerial vehicles (UAVs) significantly impact spraying effectiveness, but the underlying mechanism remains unclear. This paper conducted a full factorial experiment with varying flight speeds, heights, and nozzle flow rates to collect parameter space data. Using the Kriging surrogate model, we characterized this parameter space and subsequently optimized the average deposition rate and coefficient of variation by employing a variable crossover (mutation) probability multi-objective genetic algorithm. In the obtained Pareto front, the average sedimentation rate is no less than 46%, with a maximum of 56.08%, and the CV coefficient is no more than 13.91%, with a minimum of only 8.42%. These optimized parameters enhance both the average deposition rate and spraying uniformity compared to experimental data. By employing these optimized parameters in practical applications, a balance between the maximum average deposition rate and minimum coefficient of variation can be achieved during UAV spraying, thereby reducing pesticide usage, promoting sustainable agriculture, and mitigating instances of missed spraying and re-spraying. Full article

In order to further extend the service life of calcium sulfonate complex–polyurea grease (CSCPG) while ensuring its tribological performance, this article starts with the production of raw materials and the preparation process of the grease and explores the factors that significantly affect the tribological performance and service life of CSCPG based on unreplicated saturated factorial design (USFD). The Kriging prediction model is used along with the optimization objectives of friction coefficient and service life, and nondominated sorting genetic algorithm II (NSGA-II) was used for a multi-objective optimization solution. The tribological and service life tests were conducted before and after optimization. The results show that the viscosity of the base oil and the content of the nano-solid friction reducers have a significant impact on the tribological properties of CSCPG. The content of polyurea thickeners and antioxidants, as well as the thickening reaction temperature, have a significant impact on the service life of CSCPG. When the friction coefficient and service life are optimized as objectives and are compared to the initial group, the friction coefficient of CSCPG could be reduced by 5.3%, and the service life could be extended by 3.8%. The Kriging prediction model based on USFD has high accuracy and can be used to guide the preparation and performance optimization of CSCPG. Full article

In the realm of van design, researchers have been diligently working to enhance rollover crashworthiness while concurrently achieving lightweight body structures. Unlike front and side impacts, rollover crashworthiness is impacted by a greater number of structural dimensions and material parameters. As such, this paper implements an unreplicated saturated factorial design to conduct factor screening for vehicle rollover crashworthiness. This approach effectively and accurately resolves the screening challenges that arise from large numbers of factors, and eliminates dependence on traditional design experience. Consequently, it shortens the design cycle and reduces development costs. In addition, this paper establishes four Kriging approximate models that describe the specific energy absorption and total mass of the key body structure, the displacement of the roof, and the maximum angular velocity of the body’s center of mass. To address the multi-objective optimization problem of improving rollover crashworthiness while reducing mass, this paper combines the particle swarm optimization algorithm with the artificial immune algorithm. This hybrid algorithm converges rapidly, and the Pareto solution set exhibits superior uniformity and diversity. Finally, the shortest distance method is employed to identify the optimal design scheme that can enhance the rollover crashworthiness of vans and reduce the mass of body parts. Full article

As far as the knowledge of the seabed is concerned, both for safe navigation and for scientific research, 3D models, particularly digital bathymetric models (DBMs), are nowadays of fundamental importance. This work aimed to evaluate the quality of DBMs according to the interpolation methods applied to obtain grid format 3D surfaces from scattered sample points. Other complementary factors affecting DBM vertical accuracy, such as seabed morphological complexity and surveyed points sampling density, were also analyzed by using a factorial ANOVA experimental design. The experiments were performed on a multibeam dataset provided by the Italian Navy Hydrographic Institute (IIM) with an original resolution of 1 m × 1 m grid spacing, covering a surface of 0.24 km². Six different sectors comprising different seabed morphologies were investigated. Eight sampling densities were randomly extracted from every sector, each with four repetitions. Finally, four different interpolation methods were tested, including: radial basis multiquadric function (RBMF), ordinary kriging (OK), universal kriging (UK) and Gaussian Markov random fields (GMRF). The results demonstrated that both RBMF and OK produced very accurate DBM in areas characterized by low levels of seabed ruggedness at sampling densities of only 0.0128 points/m² (equivalent grid spacing of 8.84 m). In contrast, a higher density of 0.1024 points/m² (3.13 m grid spacing) was required to produce accurate DBM in areas with more complex seabed topography. On the other hand, UK and GMRF were strongly influenced by morphology and sampling density, yielding higher vertical random errors and more prone to slightly overestimate seabed depths. In addition, sampling density and morphology were the factors that most influenced the vertical accuracy of the interpolated DBM. In this sense, the highly statistically significant influence of the interaction between sampling density and morphology on the vertical accuracy of the interpolated DBM confirms the need to perform a preliminary analysis of seabed morphological complexity in order to increase, if necessary, the number of surveyed points in cases of complex morphologies. Full article

To evaluate the dynamic characteristics at all positions of the main spindle of a machine tool, an experimental point was selected using a full factorial design, and a vibration test was conducted. Based on the measurement position, the resonant frequency was distributed from approximately 236 to 242 Hz. The approximation model was evaluated based on its resonant frequencies and dynamic stiffness using regression and interpolation methods. The accuracy of the resonant frequency demonstrated by the kriging method was approximately 89%, whereas the highest accuracy of the dynamic stiffness demonstrated by the polynomial regression method was 81%. To further verify the approximation model, its dynamic characteristics were measured and verified at additional experimental points. The maximum errors yielded by the model, in terms of the resonant frequency and dynamic stiffness, were 1.6% and 7.1%, respectively. Full article

Natural and anthropogenic activities affect soil heavy metal pollution at different spatial scales. Quantifying the spatial variability of soil pollution and its driving forces at different scales is essential for pollution mitigation opportunities. This study applied a multivariate factorial kriging technique to investigate the spatial variability of soil heavy metal pollution and its relationship with environmental factors at multiple scales in a highly urbanized area of Guangzhou, South China. We collected 318 topsoil samples and used five types of environmental factors for the attribution analysis. By factorial kriging, we decomposed the total variance of soil pollution into a nugget effect, a short-range (3 km) variance and a long-range (12 km) variance. The distribution of patches with a high soil pollution level was scattered in the eastern and northwestern parts of the study domain at a short-range scale, while they were more clustered at a long-range scale. The correlations between the soil pollution and environmental factors were either enhanced or counteracted across the three distinct scales. The predictors of soil heavy metal pollution changed from the soil physiochemical properties to anthropogenic dominated factors with the studied scale increase. Our study results suggest that the soil physiochemical properties were a good proxy to soil pollution across the scales. Improving the soil physiochemical properties such as increasing the soil organic matter is essentially effective across scales while restoring vegetation around pollutant sources as a nature-based solution at a large scale would be beneficial for alleviating local soil pollution. Full article

Soil is an important natural resource. The excessive amount of heavy metals in soil can harm and threaten human health. Therefore, monitoring of soil heavy metal content is urgent. Monitoring soil heavy metals by traditional methods requires many human and material resources. Remote sensing has shown advantages in the field of monitoring heavy metals. Based on 971 heavy metal samples and Sentinel-2 multi-spectral images in Tai Lake, China, we analyzed the correlation between six heavy metals (Cd, Hg, As, Pb, Cu, Zn) and spectral factors, and selected As and Hg as the input factors of inversion model. The correlation coefficient of the best model of As was 0.53 (p < 0.01), and of Hg was 0.318 (p < 0.01). We used the methods of partial least squares regression (PLSR) and back propagation neural network (BPNN) to establish inversion models with different combinations of spectral factors by using 649 measured samples. In addition, 322 measured samples were used for accuracy evaluation. Compared with the PLSR model, the BP neural network builds the model with higher accuracy, and B1-B4 combined with LnB1-LnB4 builds the model with the highest accuracy. The accuracy of the best model was verified, with an average error of 19% for As and 45% for Hg. Analyzing the spatial distribution of heavy metals by using the interpolation method of Kriging and IDW. The overall distribution trend of the two interpolations is similar. The concentration of As elements tends to increase from north to south, and the relatively high value of Hg elements is distributed in the east and west of the study area. The factories in the study area are distributed along rivers and lakes, which is consistent with the spatial distribution of heavy metal enrichment areas. The relatively high-value areas of heavy metal elements are related to the distribution of metal products factories, refractory porcelain factories, tile factories, factories and mining enterprises, etc., indicating that factory pollution is the main reason for the enrichment of heavy metals. Full article

The environment of the urban fringe is complex and frangible. With the acceleration of industrialization and urbanization, the urban fringe has become the primary space for urban expansion, and the intense human activities create a high risk of potentially toxic element (PTE) pollution in the soil. In this study, 138 surface soil samples were collected from a region undergoing rapid urbanization and construction—Weinan, China. Concentrations of As, Pb, Cr, Cu, and Ni (Inductively Coupled Plasma Mass Spectrometry, ICP-MS) and Hg (Atomic Fluorescence Spectrometry, AFS) were measured. The Kriging interpolation method was used to create a visualization of the spatial distribution characteristics and to analyze the pollution sources of PTEs in the soil. The pollution status of PTEs in the soil was evaluated using the national environmental quality standards for soils in different types of land use. The results show that the content range of As fluctuated a small amount and the coefficient of variation is small and mainly comes from natural soil formation. The content of Cr, Cu, and Ni around the automobile repair factory, the prefabrication factory, and the building material factory increased due to the deposition of wear particles in the soil. A total of 13.99% of the land in the study area had Hg pollution, which was mainly distributed on category 1 development land and farmland. Chemical plants were the main pollution sources. The study area should strictly control the industrial pollution emissions, regulate the agricultural production, adjust the land use planning, and reduce the impact of pollution on human beings. Furthermore, we make targeted remediation suggestions for each specific land use type. These results are of theoretical significance, will be of practical value for the control of PTEs in soil, and will provide ecological environmental protection in the urban fringe throughout the urbanization process. Full article