Grid Division Research Articles

CFD modeling of gas–solid flow in a two‐stage jetting fluidized bed with an overflow standpipe

AbstractThe two‐fluid model–kinetic theory of granular flow (TFM–KTGF) model and the sub‐grid‐scale (SGS) model are used to conduct a three‐dimensional numerical simulation study on the hydrodynamic characteristics of a two‐stage fluidized bed with an integrated overflow pipe. Using various drag models, the gas–solid flows, bed expansions, pressure drop distributions, and equilibrium conditions in the two‐stage bed are compared, and the conditions for the formation of a stable overflow in the two‐stage bed are thoroughly investigated. In addition, during the simulation of the secondary distribution in the bed, the porous media model simulates the momentum loss caused by the redistribution plate to avoid the direct simulation of the redistribution plate, which requires extensive grid division work and calculation costs. This work can provide technical direction for the industrialization of one‐step coal to multistage fluidized bed natural gas technology.

MATLAB Combined with APDL Simulation in Reference to Y-shaped Steel Turnout Pipe

This paper takes a symmetric Y-shaped steel bifurcation pipe as an example, uses ANSYS finite element analysis software to carry out parametric modeling, grid division and load application, combined with MATLAB software to carry out operation processing, and finally analyzes the results and draws a conclusion.

Neutron and photon calculations of beam shaping assembly for BNCT based on Monte Carlo-discrete ordinates coupled method

The radiation field parameters at the beam shaping assembly (BSA) exit port are the key to boron neutron capture therapy (BNCT) treatment. However, obtaining calculation results with high efficiency and accuracy using only Monte Carlo (MC) method or discrete ordinates (SN) method is difficult. Based on NECP-MCX code and Marvin code, this paper implemented the Monte Carlo-discrete ordinates coupled method and applied this method to the calculation of the radiation field at the BSA exit port. The effect of mesh grid division, the order of quadrature set, and the bias of quadrature set on the results of the coupled calculation was studied. It was concluded that the order and bias of the quadrature set have not improved the coupled calculation results. It was found that the data library used in the SN calculation is the main cause. Therefore, the Monte Carlo-discrete ordinates coupled method can be quickly and accurately applied to the calculation of the radiation field at the BSA exit port by dividing the energy and coordinate intervals rationally and using a more suitable cross section library.

Optimized multi-point hemispherical grid model with adaptive grid division based on the prior information of multipath error

The multi-point hemispherical grid model (MHGM) utilizes residual of double-differenced observations to extract precise multipath error information. It models the entire network of multipath error effects across different stations to achieve effective error correction. However, because all the parameters are estimated collectively using the least squares method, the increased number of grid point parameters can significantly consume memory, CPU, and other computing resources required for modeling. In response to the computational resource consumption challenge associated with fixed-resolution MHGM in multi-station applications, a space domain adaptive grid division method is proposed to optimize the modeling of multipath errors. This approach utilizes prior distribution information of multipath errors to optimize the grid structure. It reduces the number of grids in areas where multipath errors exhibit minimal changes, and provides detailed parameterization for areas with significant variations. Experimental results demonstrate the effectiveness of this method in significantly reducing the number of estimated parameters using MHGM. In statistical analysis of double-differenced phase observation residuals with fixed ambiguities, as the number of estimated parameters in the MHGM decreases to only 24.6 % of the fixed-resolution approach, memory usage during parameter estimation remains a mere 6 % of that required in the fixed-resolution approach. This highlights its potential value in mitigating multipath errors when modeling GNSS large-scale network data.

Two-dimensional electromagnetic scattering analysis based on the boundary element method

An effective formula for the shape-sensitivity analysis of electromagnetic scattering is presented in this paper. First, based on the boundary element method, a new electromagnetic scattering formula is derived by combining the traditional electromagnetic scattering formula with the non-uniform rational B-spline (NURBS) curve, and the geometric model is represented by NURBS, which ensures the geometric accuracy, avoids the heavy grid division in the optimization process, and realizes the fast calculation of high-fidelity numerical solutions. Second, by deducing the sensitivity variables, the electromagnetic scattering equation of shape optimization is obtained, which can provide reliable data references for shape optimization. Finally, the effectiveness and accuracy of the algorithm are demonstrated by an example, and the sensitivity data of some examples are given.

GTDIM: Grid-based Two-stage Dynamic Incentive Mechanism for Mobile Crowd Sensing

Mobile Crowd Sensing (MCS) technology, as an emerging data collection paradigm, offers distinct advantages, particularly in applications like smart city management. However, existing researches inadequately address the comprehensive solution to the problem of reliable task allocation according to the requirements such as task budget, sensory data quality, and real-time data collection, especially under varying participant engagement in MCS systems. To bridge this gap, we propose the Grid-based Two-stage Dynamic Incentive Mechanism (GTDIM). In the first stage, the Candidate Participant Set (CPS) establishment phase, participants receive compensation for collecting sensory data when a sufficient number are available. When participants are insufficient, additional rewards inspired by the grid division of sensing areas are progressively offered to attract more participants. In the subsequent stage, utilizing the established CPS, participants are selected through a greedy algorithm based on the newly devised Participant Matching Index (PMI), which integrates various participant features. Extensive simulation results reveal the impact of PMI on participant selection. Numerical findings conclusively demonstrate GTDIM’s superior performance over baseline incentive mechanisms in terms of task assignment ratio, participant payment, and especially when dealing with larger sensing tasks.

Optimized Grid Partitioning and Scheduling in Multi-Energy Systems Using a Hybrid Decision-Making Approach

This paper presents a thorough review of our state-of-the-art technique for enhancing dynamic grid partitioning and scheduling in multi-energy source systems. We use a hybrid approach to T-spherical fuzzy sets, combining the alternative ranking order method accounting for the two-step normalization (AROMAN) method for alternating ranking order to enable two-step normalisation with the method based on removal effects of criteria (MEREC) for eliminating criteria effects. This enables us to obtain the highest level of accuracy from our findings. To ascertain the relative importance of these criteria, we use MEREC to perform a rigorous examination of the influence that each evaluation criterion has on the outcomes of the decision-making process. In addition, we use AROMAN to provide a strong foundation for assessing potential solutions by accounting for spherical fuzzy sets to account for any ambiguity. We illustrate how our approach successfully considers several factors, such as social acceptability, technical feasibility, environmental sustainability, and economic feasibility, through the analysis of an extensive case study. Our approach provides decision-makers (DMs) with a rigorous and rational framework for assessing and choosing the best grid division and scheduling options. This is done in an effort to support the administration and design of resilient and sustainable multi-energy systems. This research contributes to the growing body of knowledge in this area by offering insights that help to direct policy, planning, and investment decisions in the shift towards more sustainable energy infrastructures. Moreover, it adds to the growing body of information on multi-criteria decision-making (MCDM) in energy system optimization.

Rasterized Data Image Processing (RDIP) Techniques for Photovoltaic (PV) Data Cleaning and Application in Power Prediction

Photovoltaic (PV) power generation has attracted widespread interest as a clean and sustainable energy source, with increasing global attention given to renewable energy. However, the operation and monitoring of PV power generation systems often result in large amounts of data containing missing values, outliers, and noise, posing challenges for data analysis and application. Therefore, PV data cleaning plays a crucial role in ensuring data quality, enhancing data availability and reliability. This study proposes a PV data cleaning method based on Rasterized Data Image Processing (RDIP) technology, which integrates rasterization and image processing techniques to select optimal contours and extract essential data. To validate the effectiveness of our method, we conducted comparative experiments using three data cleaning methods, including our RDIP algorithm, the Pearson correlation coefficient interpolation method, and cubic spline interpolation method. Subsequently, the cleaned datasets from these methods were utilized for power prediction using two linear regression models and two neural network models. The experimental results demonstrated that data cleaned using the RDIP algorithm improved the short-term forecast accuracy by approximately 1.0% and 3.7%, respectively, compared to the other two methods, indicating the feasibility and effectiveness of the RDIP approach. However, it is worth noting that the RDIP technique has limitations due to its reliance on integer parameters for grid division, potentially leading to coarse grid divisions. Future research efforts could focus on optimizing the selection of binarization thresholds to achieve better cleaning results and exploring other potential applications of RDIP in PV data analysis.

Stability analysis of layered circular rock tunnels considering anchor reinforcement based on an enhanced mesh-free method

The impact of geotechnical structure and support construction on the stability of a layered circular rock tunnel was investigated based on an improved mesh-free method. The Smoothed Particle Hydrodynamics (SPH) was improved by incorporating a total bridge broken strategy into the kernel function and was validated through analysis of the mechanical behavior in circular opening problem and uniaxial compression tests. The Particle Domain Search (PDS) method is utilized to create crack pathways and implement anchor reinforcement in specific areas without the requirement for grid division. An SPH model of the Muzhailing tunnel was established to investigate the stability of the surrounding rock considering different anchor lengths and spacing. The result indicated that the enhanced SPH method provides a feasible approach for the rapid assessment of tunnel convergence displacement and damage coefficients. Furthermore, this method was validated through laboratory experiments and analytical solutions. For layered tunnels with other different dip angles, this approach offers a general optimization strategy for anchor reinforcing surrounding rock. These findings suggest that the enhanced mesh-free method, owing to its characteristics of efficient parameter calibration and computation, can be further expanded to the rapid assessment of the stability of layered slopes and foundations considering anchor reinforcement.

Research on the annual average thermal power of heliostat field in tower solar energy

The concentrating mirror field is a concentrating and heat collecting subsystem in the tower solar thermal power station, and its optical performance directly affects the solar energy utilization efficiency of the power station. In this paper, the coordinate system of heliostat field and single heliostat is established, and the grid division technology is introduced to describe its reflection effect on sunlight and its occlusion. In this paper, the shadow occlusion loss that affects the optical efficiency is divided into three parts, including incident light occlusion loss, reflected light occlusion loss and absorption tower shadow occlusion loss. For any time, the output power of the heliostat near the solar direction is small, while the output power of the heliostat far away from the solar direction is large. Compared with other influencing factors, the cosine loss has the greatest influence on the heliostat field, and the center of the absorption tower installed in the circular heliostat field is obtained. The size of the heliostat is a square with a side length of 6m, the installation height is 4m, and the annual average optical efficiency is 0.6216 when the position of the heliostat is determined. The annual average output thermal power is 36.3958 MW, and the annual average output thermal power per unit mirror area is 0.5794 .

Evolution and influencing factors of coastal resilience in the East China Sea

The coastal zone serves as a crucial hub for economic and population concentration. Amid the context of high-intensity development and global climate change, uncertain risks from diverse sources—including extreme weather events (i.e., high temperatures, typhoons, and excessive precipitation), natural disasters (i.e., floods, tsunamis, landslides, and mudslides), and societal disruptions (i.e., economic crises and viral diseases)—are escalating rapidly. Enhancing coastal resilience to minimize these risk impacts is progressively becoming a mandatory requirement for the sustainable development of coastal zones. However, existing research primarily focuses on distinct disasters, the ecological environment, or specific socio-economic aspects, thereby lacking a comprehensive theoretical framework and thorough analysis of the factors influencing coastal resilience. Here, we construct a theoretical framework centered on the unique traits and processes of coastal resilience, analyze the spatiotemporal evolution characteristics of coastal resilience from a grid and administrative division standpoint, and utilize geographic detectors to determine the factors influencing coastal resilience while considering the modifiable areal unit problem (MAUP). Our findings indicate that: (1) Coastal resilience in the East China Sea (ECS) initially declined but then increased, transitioning from a lower to a medium level. Barring the pressure index, other dimensional indices had an upward trend; (2) Continuous improvements were observed in coastal resilience across different land uses. Forests, waters, and oceans demonstrated higher resilience levels than other lands, with construction land resilience developing swiftly. The effect of changes in land use types on coastal resilience showed a rapid initial increase and subsequent decrease; (3) The change pattern of coastal resilience in the ECS is mainly unchanged and slightly increased. Areas with the most drastic changes were concentrated in Shanghai, northern Zhejiang, and central Fujian, with the main change patterns continuously rising; (4) The primary factors influencing coastal resilience in the ECS included gross domestic product and infrastructure construction level. Advanced industrial structure, technological and educational prowess, and effective government management are important determinants of coastal resilience development. The significance of human factors continues to grow. Our findings offer valuable insights for optimizing national spatial planning of coastal zones, responding to internal and external impacts, achieving resilient coastal zones, and implementing a comprehensive sustainable management approach.

Complex fracture description and quasi-elastic energy development mathematical model for shale oil reservoirs

Reservoir numerical simulation is an important tool and method for the reasonable and efficient development of shale reservoirs. Accurate description of three-dimensional fractures in shale reservoir development is a necessary and sufficient condition to improve the accuracy and robustness of shale reservoir numerical simulation. This paper achieves precise characterization of complex fracture shapes and oil, gas and water flow by establishing an embedded discrete fracture model based on a non-structural network, which has advantages in the fine characterization of complex morphological fractures in the reservoir and the grid division of the reservoir. In the large matrix solution method, the Newton-Raphson method is used to linearize the nonlinear equations, the Jacobian matrix is ​​constructed, the ILU method is used for preprocessing, the conjugate gradient method is used to solve the linear equations, and the shale oil quasi-elasticity is established A fully implicit solution method for mathematical models of energy development.

Influence of Pile Spacing on the Compressive Bearing Performance of CEP Groups

Pile spacing is an important factor affecting the bearing capacity of concrete expansion pile (CEP) groups. In this study, a pile group was simulated and analyzed using ANSYS software R19.0. The influence of pile spacing on the bearing capacity of the pile group under a vertical load was determined using three sets of four-, six-, and nine-pile models with different pile spacings. The grid division of the pile soil model adopts a mapping method, using the contact types of rigid and flexible bodies and applying surface loads to the model piles step-by-step. After vertical pressure was applied to the model pile, in-depth analysis was conducted on the displacement cloud map, pile top displacement, and other data. The different stress conditions of corner, edge, and center piles in each model group were compared and analyzed, revealing the relationship between the stress mechanism and failure law of the soil around the pile and the pile spacing. It was found that the soil displacement range of edge piles is slightly larger than that of corner piles. This phenomenon gradually decreases with increasing pile spacing. When the pile spacing increases to four times the cantilever diameter, the difference in soil displacement at different pile positions is small, and the pile spacing has little effect on the compressive bearing capacity of the pile group. Thus, it is reasonable to control the pile spacing at three to four times the cantilever diameter. In the nine-pile model, when the load is loaded to the 20-step level, the displacement value of the central pile is −72.278 mm, while the displacement values of the edge pile and corner pile are −69.012 mm and −66.806 mm. It is shown that increasing the pile spacing can effectively reduce the pile group effect and improve the bearing capacity of the pile foundation. At present, CEP pile groups are gradually being applied in practical engineering, but research on the influence of pile spacing on the compressive bearing performance of CEP pile groups is still at a very early stage. This article reinforces the influence of pile spacing on the compressive bearing performance of CEP pile groups. It provides theoretical support for its application in practical engineering.

Mask-Homo: Pseudo Plane Mask-Guided Unsupervised Multi-Homography Estimation

Homography estimation is a fundamental problem in computer vision. Previous works mainly focus on estimating either a single homography, or multiple homographies based on mesh grid division of the image. In practical scenarios, single homography is inadequate and often leads to a compromised result for multiple planes; while mesh grid multi-homography damages the plane distribution of the scene, and does not fully address the restriction to use homography. In this work, we propose a novel semantics guided multi-homography estimation framework, Mask-Homo, to provide an explicit solution to the multi-plane depth disparity problem. First, a pseudo plane mask generation module is designed to obtain multiple correlated regions that follow the plane distribution of the scene. Then, multiple local homography transformations, each of which aligns a correlated region precisely, are predicted and corresponding warped images are fused to obtain the final result. Furthermore, a new metric, Mask-PSNR, is proposed for more comprehensive evaluation of alignment. Extensive experiments are conducted to verify the effectiveness of the proposed method. Our code is available at https://github.com/SAITPublic/MaskHomo.

Research on charging demands of commercial electric vehicles based on Voronoi diagram and spatial econometrics model: An empirical study in Chongqing China

In order to address the mismatch between the current charging demand of electric vehicles and the layout of charging stations, this paper collects multi-source heterogeneous data that may affect charging demand, and explores their spatiotemporal distributions in depth. Then, this paper proposes a Voronoi polygon spatial partitioning method based on charging demand clustering and two spatial econometric models, spatial lag model (SLM) and spatial error model (SEM), to quantitatively analyze the influences of various elements on charging demands. Empirical research in Chongqing, China reveals that the spatiotemporal distribution of charging demand is uneven, with peak areas and periods for charging, and the division of Voronoi polygon can better reflect the spatial heterogeneity of charging demands than regular grid division. The positive factors include the low SOC, parking lot density, population density, and road network density, especially the low SOC and parking lot density, and the negative items contain consumption related Point Of Interest (POI), tourism related POI, and transportation hub related POI. Among them, population density and road network density, as static data, have similar influences on charging demands at different intervals, while the impact of other factors on charging demand varies with periods, with obvious peak-valley characteristics. There is spatial dependence and lag effect between charging demands within polygons, causing SLM outperforms SEM. The research results are beneficial for the rational siting and optimization of charging stations.

Experimental study on quantitative characterization of cavitation internal structure based on the distribution of void fraction in pressure relief valve

Accurately quantify the complex structure of cavitation in pressure relief valve by experiment remains a challenging task. In this research, a quantitative method for assessing the vapor volume fraction in cavitation images by void fraction experimentally is proposed, this method is less affected by environmental interference and could achieve rapid and accurate characterization of cavitation. The results show that void fraction effectively represents changes in vapor volume fraction under different conditions. The influence of the grid division numbers on the vapor volume fraction is investigated, the optimal number of grid division is 93 in X direction, with a misjudgment number of 130 and a misjudgment rate of 1.62 %. The effects of grid numbers on vapor volume fraction under different cavitation image pixels is also discussed, the optimal number of grid division is 112 in flow direction, with a misjudgment number of 144 and a misjudgment rate of 1.21 %. Through processing the vapor volume fraction image using the Proper Orthogonal Decomposition (POD) method under different cavitation numbers, the results show that the cavitation number has an important influence on the proportion of re-entrant jet area and cavitation collapse area, when the cavitation number decreases to 0.047, the proportion of re-entrant jet area and cavitation collapse area achieve the maximum that 6.41 % and 8.68 %, respectively. Such quantitative analysis of the cavitation degrees under various working conditions can contribute to a better understanding of the impact of cavitation on valve dynamics mechanisms.

Anisotropic media hybrid grid tomographic inversion method based on angle domain imaging gathers

The grid tomography velocity inversion method prevails as the most efficacious technique for acquiring background velocity requisite for migration. Originating from imaging gathers, this method not only inverses velocity but also subsequently ascertains the anisotropic parameters field. Traditional approaches utilize a parameter field discretized in a rectangular grid for both ray tracing and parameter inversion. However, the rectangular grids may not provide optimal accuracy for structures with pronounced lateral parameter variations. In contrast, triangular grid discretization allows for grid division along steep slopes, offering sparsity and flexibility when juxtaposed with conventional rectangular grid methods. This paper introduces an optimized triangular grid travel time parameter tomography inversion method, formulated on the parameter sensitivity to travel time and employing angle gathers to derive travel time residuals. Analytical modeling and real data processing substantiate that the triangular grid ray tracing method ameliorates the ill-conditioned nature of the tomography equation and enhances the accuracy of the inversion parameter field effectively.

Improved matrix model of sequence grid partition based on vector space sampling

In information technology and data science predictive analysis work, the goal is to infer guess values as accurately as possible. A more densely spaced grid of points should be set as a tool for operating measurement models. It has a wide range of applications in psychology, education and other social science research fields. Although the grid seems to meet the requirements of further improving the accuracy of reasoning and guessing from a certain point of view, it also means complex calculations. When multiple false values fall between another grid point, the traditional Data Oriented Architecture fails. To guarantee the accuracy and efficiency of the calculation, it is necessary to solve the problem of excessive noise in the prediction of samples in meta-learning to solve the uncertainty caused by the noise database data or the assumption of the matrix model. This paper proposes a grid partition movable inference least squares method, that is, the sparse Bayesian least squares method based on the grid minimum matrix. The method is used for solving the problem that the prediction noise of the sample in the meta-learning is too large, to solve the uncertainty brought by the generated noise database data or the array matrix model assumption. According to the Symplectic Bayesian learning of the sequence matrix model, the basic parameters for processing the input sample database data are determined. The sparse Bayesian algorithm solution is used on the grid, combined with the off-grid inference guess of the sequence matrix model and the grid division. We set a coarse grid of points around the grid of false values. The rough point grid division is set, and the grid division around the false value is divided in detail. Then select more appropriate meta-learning parameters to clean up data. In the experiment analysis, we take the small sample study as the scene and carry on the concrete analysis to the experiment. The test proof in the fitting degree of the algorithm adopted in the paper surpasses the Support Vector Machine (SVM) and the k-Nearest Neighbor (KNN) algorithm by 3.5% and 6.4% respectively. The convergence effect surpasses the contrast plan above 10 and has a bigger superiority in the inference factor thrust. It proves that the optimization method in this paper has a strong promotion effect for the application of data forecasting systems in various industries, and has theoretical value as well as practical significance.

Parallel algorithm for multi-viewpoint viewshed analysis on the GPU grounded in target cluster segmentation

ABSTRACT Viewshed analysis is a significant method for GIS spatial analysis. The needed computational resources rise sharply with the increase in the number of viewpoints, making viewshed analysis inefficient in multi-viewpoint scenes. Building on the shadow map algorithm, a parallel algorithm for multi-viewpoint viewshed analysis based on target cluster segmentation for large-scale, multi-viewpoint complex three-dimensional scenes was proposed. Target cluster segmentation was achieved using uniform grid division and K-means spatial clustering, and visibility was determined by comparing the depths. The customized graphics processing unit (GPU) rendering pipeline was adopted to execute the algorithm efficiently and in parallel. The experimental results indicated that the efficiency of the proposed algorithm improves by 6.252%, 8.280%, and 9.047% on average for viewshed analysis with 150, 300, and 600 viewpoints, respectively, compared to the algorithm based solely on shadow map. It is also able to eliminate the majority of shadow acne, thus clearly improving accuracy. The algorithm is compatible with the terrain and features while fully utilizing the parallel computational capability of the GPU and avoiding the interpolation of the digital elevation model (DEM). It significantly improves the efficiency of analysis in large-scale and multi-viewpoint scenes.

Fast Magnetization Vector Inversion Method with Undulating Observation Surface in Spherical Coordinate for Revealing Lunar Weak Magnetic Anomaly Feature

The three-dimensional magnetic vector structure (magnetization intensity and direction) of the planet can be effectively used to analyze the characteristics of its formation and operation. However, the quick acquisition of a large region of the magnetic vector structure of the planet with bigger observation surfaces undulation is hard and indispensable. We firstly proposed a fast magnetization vector inversion method for the inversion of a magnetic anomaly with the undulating observation surfaces in the spherical coordinate system, which first transforms the data to a plane when the data are distributed on a surface. Then, it uses a block-Toeplitz-Toeplitz-block (BTTB)-FFT to achieve fast inversion with the constraint that the magnetization intensities of the grids between the transformed observation surfaces and the terrain are zero. In addition, Gramian constraint term is used to reduce the ambiguity of the magnetic vector inversion. The theoretical model tests show that the proposed method can effectively improve the computational efficiency by 23 times in the 60 × 60 × 10 grid division compared to the conventional inversion method, and the accuracy of the two computation methods is comparable. The root-mean-square error of the magnetization intensity is only 0.017, and the angle error is within 1°. The magnetization vector structure shows that the largest crater diameter does not exceed 340 km in the Mare Australe region, the amplitude of the magnetic anomaly is much higher than the current meteorite impact simulation results, and the depth of the magnetic source is less than 10 km, which cannot be explained by the impact simulation experiments. In addition, the magnetization directions of adjacent sources differ by 122° (or 238°), and the high-frequency dynamics of the Moon as well as the short-lived dynamics may be responsible for this phenomenon. The magnetization directions of the three adjacent sources in the Mare Crisium region are close to each other and differ in depth with different cooling times, making it difficult to record the transient fields produced by meteorite impacts. In addition to the above characteristics, the magnetization direction of the magnetic sources in both regions is uniformly distributed without reflecting the dispersion of the magnetization direction of the meteorite impact magnetic field. Therefore, it can be inferred that the magnetic anomalies in these two regions are related to the generator hypothesis.