Abstract The subsea suspended manifold designed to replace the traditional foundation structure with the buoys is a new generation subsea production system that can be suspended at a certain height from the seafloor and rapidly recycled by its own buoyancy. Due to complex environmental conditions, its hydrodynamic performance in the splash zone is extremely important for the safety of the whole installation process. In this paper, the mathematical model for the dynamic analysis of the seawater ingress process of the single-layer pre-set horizontal cabin is proposed based on the different center of gravity positions of the buoy. Meanwhile, the theoretical analysis of fiber cable is divided into infinite differential units by the discretization method, and the formulae of the horizontal displacement of the subsea suspended manifold are presented. In addition, the simulations are carried out to verify the rules of the dynamic responses on the subsea suspended manifold system with the consideration of the environmental conditions in the South China Sea. Comparing with the calculated value of the mathematical model of the cabin water ingress, the error of the simulation result by use of FLUENT is about 5.47%. Furthermore, the wave height is greater than the current impact on the lowering manifold system and the azimuth angle of the installation vessel is aligned with the direction of the environmental load.
With the continuous improvement of infrastructure, some high-speed railway lines will inevitably cross the goaf ground, and there is less research on the safety of high-speed rail construction in goaf ground. To make a reasonable and accurate safety evaluation of the high-speed railway construction in the mine goaf ground, machine learning combined with numerical simulation is used to evaluate the safety depth of goaf under the impact of high-speed railway load. An optimal algorithm is selected among BP, RBF, and PSO-RBF neural networks based on the accuracy of the predicted height of a caving fracture zone. Numerical models for simulating high-speed railway founded on goaf are set up using the commercial software package FLAC3D, where factors such as subgrade height, train speed, and axle load are investigated in terms of train load disturbance depth and the extent of the caving fracture zone. The results indicate that the PSO-RBF neural network has an error of 2.76% in predicting the height of the caving fracture zone; the depth of train load disturbance is linearly related to the axle weight and roadbed height but is a sinusoidal function of the train speed. Based on the numerical simulation results, a formula for calculating the depth of train load disturbance is proposed, which provides a certain reference value for the construction of high-speed railways in the goaf ground.
Abstract The transmitted laser mode of Geosciences Laser Altimeter System (GLAS) is a significant factor in determining the received pulse waveforms, which are used for inversing target information. The inversion algorithms in the scientific literature are based on the assumption that the transmitted laser is circular Gaussian. The practical laser pattern of GLAS is not circularly symmetric, but elliptical Gaussian. The received pulse shape will contain a bias, which would cause an error in the inversion information. In this paper, we describe new theoretical models about received pulse signal and inversion errors of range, surface slope and roughness. We present the results of waveforms shape and inversion errors for three representative terrains with different surface slope and roughness. The results show that the maximal inversion errors of range, surface slope, and roughness will reach 24.25 cm, 8.82° and 4.58 m, respectively, which cannot be negligible. Therefore, the inversion information should be reevaluated and amended depending on the type of terrain.
Landslide inventory incompleteness (LII) may significantly affect the model performance in landslide susceptibility mapping (LSM). However, traditional methods, including heuristic, statistical and deterministic models, cannot address LII issue. In this work, we introduce a novel hybrid LEO-MAHP model, blending landslide frequency, empirical adjustments, optimization functions, and multi-participated analytic hierarchy process to address it by taking Badong County as the study area. This hybrid model mitigates the drawbacks of data-heavy statistical approaches and subjective heuristic models by incorporating LII into weight determination. The findings show that the LEO-MAHP model demonstrates superior performance (AUROC = 0.809 and 0.805) over conventional statistical (AUROC = 0.714 and 0.770) and heuristic models (AUROC = 0.738 and 0.741) across different LII levels. We further discuss alternative LII solutions, proposing an updated landslide management strategy that accounts for climate change and human activities. Our findings underscore the necessity of evaluating LII before applying statistical or machine learning methods in LSM.
Developing a strategy for the resource utilization of spent zeolite catalysts is essential for addressing the environmental hazards of spent catalysts.
During the era of global warming and highly urbanized development, extreme and high impact weather as well as air pollution incidents influence everyday life and might even cause the incalculable loss of life and property. Although with the vast development of numerical simulation of atmosphere, there still exists substantial forecast biases objectively. To predict extreme weather, severe air pollution, and abrupt climate change accurately, the numerical atmospheric model requires not only to simulate meteorology and atmospheric compositions and their impacts simultaneously involving many sophisticated physical and chemical processes but also at high spatiotemporal resolution. Global atmospheric simulation of meteorology and atmospheric compositions simultaneously at spatial resolutions of a few kilometers remains challenging due to its intensive computational and input/output (I/O) requirement. Through multi-dimension-parallelism structuring, aggressive and finer-grained optimizing, manual vectorizing, and parallelized I/O fragmenting, an integrated Atmospheric Model Across Scales (iAMAS) was established on the new Sunway supercomputer platform to significantly increase the computational efficiency and reduce the I/O cost. The global 3-km atmospheric simulation for meteorology with online integrated aerosol feedbacks with iAMAS was scaled to 39,000,000 processor cores and achieved the speed of 0.82 simulation day per hour (SDPH) with routine I/O, which enables us to perform 5-day global weather forecast at 3-km horizontal resolution with online natural aerosol impacts. The results demonstrate the promising future that the increasing of spatial resolution to a few kilometers with online integrated aerosol impacts may significantly improve the global weather forecast.
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