When the XBeach model is used to simulate beach profiles, the selection of four sensitive parameters—facua, gammax, eps, and gamma—is crucial. Among these, the two key parameters, facua and gamma, are particularly sensitive. However, the XBeach model does not specify the exact choice of these four key parameters, offering only a broad range for each one. In this paper, we investigate the applicability of tuning these four parameters within the XBeach model. We employ Generalized Likelihood Uncertainty Estimation (GLUE) to optimize the model settings. The Brier Skill Score (<i>BSS</i>) for each parameter combination is calculated to quantify the likelihood probability distribution of each parameter. The optimal parameter set (facua = 0.20, gamma = 0.50) was ultimately determined. Here, the facua parameter represents the degree of influence of wave skewness and asymmetry on the direction of sediment transport, while the gamma parameter represents the equivalent random wave in the wave dissipation model and is used to calculate the probability of wave breaking. Six profiles of the southern beach on Chudao Island are selected to validate the results, establishing the XBeach model based on profile measurement data before and after Typhoon “Lekima”. The results indicate that after parameter optimization, the simulation accuracy of XBeach is significantly improved, with the <i>BSS</i> increasing from 0.3 and 0.17 to 0.68 and 0.79 in P1 and P6 profiles, respectively. This paper provides a recommended range for parameter values for future research.
Huinan Kang, Yunsen Hu, Sakdirat Kaewunruen
et al.
Geometric and mechanical analyses were performed on 82 selenium-rich eggs, which underwent hydrostatic testing as 2 raw eggs, 60 steamed eggs, and 20 emptied eggshells. By analyzing the geometric and mechanical properties of the egg, we can draw inspiration from its structural design to create a pressure shell capable of effectively withstanding the immense water pressure in deep-sea environments. The major axis, minor axis, egg-shape coefficient, weight, thickness, volume, superficial area, and ultimate compressive strength were measured, and their correlations were analyzed. The thickness, egg-shape coefficient, and ultimate compressive strength were normally distributed, and many parameters were strongly correlated. Moreover, finite element analysis was conducted to evaluate the compressive resistance of egg-like pressure shells made from different materials, including metal, ceramic, resin, and selenium-enriched eggshell materials. The performance ratio of the ceramic shells was 2.6 times higher than that of eggshells, and eggshells outperformed metal and resin shells by factors of 2.14 and 4.49, respectively. The eggshells had excellent compression resistance. These findings offer novel insights into the design and optimization of egg-like pressure shells.
The performance of Organic Solar Cells (OSCs) is intrinsically linked to the molecular, electronic, and structural properties of donor and acceptor materials. This study employs various machine learning techniques, namely the Generalized Regression Neural Network (GRNN), Support Vector Machine (SVM), and Tree Boost, to predict key performance metrics of OSCs, including power conversion efficiency (PCE), short-circuit current density (JSC), open-circuit voltage (VOC), and fill factor (FF). The models are trained and evaluated using an experimentally reported dataset compiled by Sahu et al. Correlation analysis demonstrates that material characteristics such as polarizability, bandgap, dipole moment, and charge transfer are statistically associated with OSC performance. The predictive performance of the GRNN model is compared with that of the SVM and Tree Boost models, showing consistently lower prediction errors within the considered dataset. In addition, sensitivity analysis is performed to assess the relative importance of the predictor variables and to examine the influence of kernel functions on GRNN performance. The results indicate that machine learning models, particularly GRNN, can serve as effective data-driven tools for predicting the performance of organic solar cells and for supporting computational screening studies.
Marine diesel engines work in an environment with multiple excitation sources. Effective feature extraction and fault diagnosis of diesel engine vibration signals have become a hot research topic. Time-domain synchronous averaging (TSA) can effectively handle vibration signals. However, the key phase signal required for TSA is difficult to obtain. During signal processing, it can result in the loss of information on fault features. In addition, frequency multiplication signal waveforms are mixed. To address this problem, a multi-scale time-domain averaging decomposition (MTAD) method is proposed and combined with signal-to-image conversion and a convolutional neural network (CNN), to perform fault diagnosis on a marine diesel engine. Firstly, the vibration signals are decomposed by MTAD. The MTAD method does not require the acquisition of the key phase signal and can effectively overcome signal aliasing. Secondly, the decomposed signal components are converted into 2-D images by signal-to-image conversion. Finally, the 2-D images are input into the CNN for adaptive feature extraction and fault diagnosis. Through experiments, it is verified that the proposed method has certain noise immunity and superiority in marine diesel engine fault diagnosis.
Kyeong-Tae Lee, Garance Perrois, Hyun-Sung Yang
et al.
This study was carried out to determine the levels of resistance and resilience of kelp forests to large-scale physical disturbances. Our study site, Seongsan, Jeju Island, was impacted by super typhoon ‘Hinnamnor’. Before the typhoon, Seongsan had shown high ecosystem stability. Our results indicated that the ecological stability of a kelp forest facing a severe typhoon is strongly linked to the prevailing environmental conditions. Although typhoon impact resulted in a significant loss of brown macroalgae canopy, <i>Ecklonia cava</i> remained dominant within the kelp forest community. Resistance and resilience levels strongly depended on water temperature and movement and presence of turf-forming algae. Hence, hydrodynamic and biological factors strongly influence the overall stability of a kelp forest. We also report the first occurrences of a scleractinian coral species (i.e., <i>Montipora millepora</i>) at Seongsan, which became visible after canopy loss following the typhoon. Our findings provide valuable ecological information about the benthic community of kelp-dominated ecosystems and are essential to mitigate the impacts of expected climate change-driven rises in seawater temperature and the frequency of super typhoons.
Sung Youn Boo, Steffen Allan Shelley, Seung-Ho Shin
et al.
There has been growing interest recently in hybrid installations integrating the offshore wind farm and aquaculture farm as co-existence while optimizing ocean space use. The offshore marine farms beyond coastal or sheltered areas will require mooring to ensure the station-keeping of the farm system during the storms. In the present work, a sub-surface longline farm is installed in a fixed offshore wind farm at a distance from the wind foundations. The farm is designed to cultivate oysters in multi-compartment bags attached to the longlines vertically. The farm with a cultivating area of 200 m × 200 m is supported by the various farm lines made of polypropylene and buoys that is moored with catenary mooring arrangements. Drag coefficients of a full-scale oyster bag in wave and current are determined using the results of wave basin tests. A lumped model is developed and validated with a complete model for a partial farm. The lumped model is used to simulate the coupled responses of the whole farm in the site extreme waves and currents of a 50-year return period. The strength and fatigue designs of the mooring and farm lines are evaluated against the industry standards and confirmed to comply with the design requirements.
The natural dispersion of oil depends on the oil types, wave-mixing energy, and the temperature and salinity of water. Laboratory experiments were conducted to investigate the effects of these factors on oil dispersion. The results demonstrated that the increase in temperature significantly enhanced the oil dispersion efficiency, particularly for low-viscosity oils. At 30 °C, the dispersion efficiency is 2 times higher than that at 15 °C, while salinity has no significant effect on dispersion efficiency. Nonlinear fitting results revealed an exponential increase in dispersion efficiency with the energy dissipation rate. Furthermore, partial correlation analysis was employed to examine the effects of oil density, viscosity, and surface tension on dispersion efficiency. The results indicated a high correlation between density, viscosity, and dispersion efficiency (<i>r</i> = −0.801, <i>r</i> = −0.812), whereas the correlation coefficient of surface tension was low (<i>r</i> = −0.286). Based on these findings, linear and nonlinear regression models were established between dispersion efficiency and density and viscosity, enabling a rough estimation of oil spill dispersion efficiency under low sea state conditions.
Control of the disturbed displacement of adjacent tunnel during excavation is a significant issue for design and construction. Based on the multi-objective optimization method, the multi-type monitoring data in the excavation of the excavation are integrated, the key soil parameters are inverted and identified, and the time effect of the tunnel displacement is quantified and corrected. A dynamic multi-objective optimization method with adaptive infill criterion (DMO-AIC) is proposed to improve the updating efficiency of dynamic surrogate models. The proposed method takes into account the computational redundancy of dynamic surrogate models in engineering optimization, and designs an adaptive point-adding discrimination strategy, which can autonomously identify invalid updates of surrogate models on the optimization path. The results show that the proposed DMO-AIC significantly reduces the invocations of the black-box model during optimization while ensuring the good search performance and the convergence speed of the algorithm. The improved computational efficiency of DMO-AIC is helpful for the application of dynamic surrogate models in engineering optimization. The results of the virtual numerical example show that DMO-AIC can predict and update multiple model responses during excavation, such as wall deflections and tunnel displacements. The engineering practice of Shanghai Bund 596 excavation indicates that the time effect is properly updated, and the staged vertical displacements of the adjacent tunnel are accurately predicted.
Engineering (General). Civil engineering (General), Chemical engineering
Maritime search and rescue (SAR) plays a very important role in emergency waterway traffic situations, which is supposed to trigger severe personal casualties and property loss in maritime traffic accidents. The study aims to exploit an optimal allocation strategy with limited SAR resources deployed at navigation-constrained coastal islands. The study formulates the problem of SAR resource allocation in coastal areas into a non-linear optimization model. We explore the optimal solution for the SAR resource allocation problem under constraints of different ship and aircraft base station settings with the help of an enhanced particle swarm optimization (EPSO) model. Experimental results suggest that the proposed EPSO model can reasonably allocate the maritime rescue resources with a large coverage area and low time cost. The particle swarm optimization and genetic algorithm are further implemented for the purpose of model performance comparison. The research findings can help maritime traffic regulation departments to make more reasonable decisions for establishing SAR base stations.
Attention is increasingly being paid to the influence of hinterland cities on port competitiveness, but in-depth research is lacking on the formation conditions and mechanism of hinterland cities’ influence on port competitiveness. Based on the technology–organization–environment (TOE) framework and the characteristics of Chinese government organizational behavior, in this study, we used fuzzy-set qualitative comparative analysis (fsQCA) to conduct a condition configuration analysis of 21 coastal ports and their hinterland cities in China. The findings showed the following: (1) The technology, organization, and environment conditions of hinterland cities cannot provide the necessary conditions for high or low port competitiveness alone: different combinations of these conditions have produced three high and four low port competitiveness configurations. (2) The three configurations of high port competitiveness are the organization–environment, economy–balance, and finance–balance types. Adequate government financial supply, high tertiary industry proportion, good economic development, and market openness are the core conditions required for achieving high port competitiveness. (3) The four configurations of low port competitiveness are finance–facilities–environment, capability–finance–environment, technology–finance–economy, and capability–industry–economy restrictions. Here, low-level innovation capability, inadequate government financial supply, and low tertiary industry proportion are the core conditions leading to low port competitiveness. We revealed the concurrent synergistic effect of the three conditions of technology, organization, and environment in hinterland cities and demonstrated the causal complexity and asymmetry of the impact of hinterland cities on port competitiveness. Our conclusions provide empirical evidence that will aid hinterland cities in formulating differentiated port competitiveness promotion policies according to their own conditions and endowments.
LI Han, ZHANG Botao, WANG Junjie, SUN Yunda, GONG Shengjie
This paper uses a single fiber bragg grating (FBG) sensor to implement an experiment to measure vibration and temperature signals at the same time, and proposes a MATLAB-based decoupling method to separate vibration and temperature signals. The experimental results show that under the condition of single signal measurement, the static temperature measurement error of the FBG sensor is within ±0.4 ℃ and the relative error of the dynamic measurement of the main frequency of vibration is 0.5%. The FBG sensor measures the composite signal of vibration and temperature. The relative error of the main vibration frequency obtained by the decoupling method proposed in this experiment is 0.65%, the relative error of the vibration amplitude is 7.14%, and the temperature signal error is within ±3.3 ℃.
Engineering (General). Civil engineering (General), Chemical engineering