Hierarchical Grid Research Articles

HierGP: Hierarchical Grid Partitioning for Scalable Geospatial Data Analytics

Application domains such as environmental health science, climate science, and geosciences—where the relationship between humans and the environment is studied—are constantly evolving and require innovative approaches in geospatial data analysis. Recent technological advancements have led to the proliferation of high-granularity geospatial data, enabling such domains but posing major challenges in managing vast datasets that have high spatiotemporal similarities. We introduce the Hierarchical Grid Partitioning (HierGP) framework to address this issue. Unlike conventional discrete global grid systems, HierGP dynamically adapts to the data’s inherent characteristics. At the core of our framework is the Map Point Reduction (MPR) algorithm, designed to aggregate and then collapse data points based on user-defined similarity criteria. This effectively reduces data volume while preserving essential information. The reduction process is particularly effective in handling environmental data from extensive geographical regions. We structure the data into a multilevel hierarchy from which a reduced representative dataset can be extracted. We compare the performance of HierGP against several state-of-the-art geospatial indexing algorithms and demonstrate that HierGP outperforms the existing approaches in terms of runtime, memory footprint, and scalability. We illustrate the benefits of the HierGP approach using two representative applications: analysis of over 289 million location samples from a registry of participants and efficient extraction of environmental data from large polygons. While the application demonstration in this work has focused on environmental health, the methodology of the HierGP framework can be extended to explore diverse geospatial analytics domains.

MuSic-UDF: Learning Multi-Scale dynamic grid representation for high-fidelity surface reconstruction from point clouds

Surface reconstruction for point clouds is a central task in 3D modeling. Recently, the attractive approaches solve this problem by learning neural implicit representations, e.g., unsigned distance functions (UDFs), from point clouds, which have achieved good performance. However, the existing UDF-based methods still struggle to recover the local geometrical details. One of the difficulties arises from the used inflexible representations, which is hard to capture the local high-fidelity geometry details. In this paper, we propose a novel neural implicit representation, named MuSic-UDF, which leverages Multi-Scale dynamic grids for high-fidelity and flexible surface reconstruction from raw point clouds with arbitrary typologies. Specifically, we initialize a hierarchical voxel grid where each grid point stores a learnable 3D coordinate. Then, we optimize these grids such that different levels of geometry structures can be captured adaptively. To further explore the geometry details, we introduce a frequency encoding strategy to hierarchically encode these coordinates. MuSic-UDF does not require any supervisions like ground truth distance values or point normals. We conduct comprehensive experiments under widely-used benchmarks, where the results demonstrate the superior performance of our proposed method compared to the state-of-the-art methods.

Layered Porous Ring-Like Carbon Network Protected FeNi Metal Atomic Pairs for Bifunctional Oxygen Electrocatalysis and Rechargeable Zn-Air Batteries.

Bimetallic atom catalysts exhibit ultra-high oxygen electrocatalytic activity by harnessing mutual promotion and synergistic effects between adjacent metal active centers, surpassing the performance of single metal atomic catalysts. Herein, FeNi atom pairs protected by hierarchical porous annular carbon grids (P-FeNi-NPC) are introduced using a mediator-assisted MOFs-derived strategy. The introduction of the multi-block copolymer P123 ensures the uniform confinement and dispersion of metal ions, followed by thermal decomposition to form a "planetary-ring-like" carbon framework that anchors the bimetallic atomic pairs in the active region. The homogeneous distribution of adjacent Fe-N4 and Ni-N4 active sites significantly enhances catalytic activity and stability. Leveraging unique electronic and geometric structures, the resulting P-FeNi-NPC catalyst demonstrates exceptional ORR and OER activities with an ΔE value of 0.705 (E1/2 = 0.845V, Ej = 10 = 1.55V). Theoretical calculations unveil that FeNi bimetallic sites loaded on nitrogen-doped carbon frameworks with specific curvature effectively modulate the energy of d-band centers, thus balancing the free energy of oxygen-containing intermediates. This study presents a novel and versatile approach for synthesizing advanced bifunctional catalysts, poised to drive the future development of Zn-air batteries.

A hierarchical urban power grid disaster response method based on automatic voice warning technology

This article proposes a hierarchical and graded urban power grid disaster response method based on automatic voice warning technology. This method combines automatic speech technology and hierarchical classification methods to achieve fast and accurate information transmission and response. This article first introduces the background and significance of this method, and then introduces the current research status in China. Next, this article elaborates in detail on the experimental dataset and preprocessing, CNN and RNN algorithm model design, experimental results and analysis, and the implementation of speech warning using TTS speech synthesis technology. Finally, this article summarizes the main work and contributions of this article, and points out future research directions.

A hierarchical model with hexagon grids for multi-objective route planning in large-scale off-road environments

Off-road route planning plays a vital role across various domains, including civil transportation, earthquake relief and civil production. Off-road route planning requires the consideration of a diverse range of environmental constraints, which is a considerable challenge compared with conventional route planning along existing networks. We aimed to present a multi-objective route planning method for off-road environments. Specifically, we designed a heuristic function incorporating multiple characteristics to generate reliable route planning solutions based on Pareto optimality. We also constructed a hierarchical hexagonal grid model by merging similar grid cells and reconstructing adjacency relationships with other grid cells to achieve high efficiency. The proposed method was compared with conventional route planning methods using single- and multi-objective approaches in traditional and hierarchical hexagonal grid models. The proposed method can effectively plan off-road routes, improve optimal route distance and trafficability, and provide more efficient route solutions. It reduced the planning time by 97.1% compared with multi-objective route planning based on a traditional hexagonal grid model.

Analyzing temporal and spatial forest carbon storage using Google Plus Code: a case study of Zijin Mountain National Forest Park, China.

It is always a challenging job to compare forest resources as there is not a standardized spatial unit with location information. Google Plus Code, the newest alphanumeric geocoding system, uses 20 specifically selected letters and numbers to assign a unique global ID to every cell at different levels of a hierarchical grid system which is established based on latitude and longitude. It can be used as a standardized, unique global geospatial unit to segment, locate, quantitate, evaluate, and compare natural resources with area, boundary, and location information embedded. For this proof-of-concept case study, forest inventory data from 1987, 2002, and 2019 for the Zijin Mountain National Forest Park in Jiangsu Province, China was analyzed based on Google Plus Code grid/cell. This enabled the quantification of carbon storage at each cell allowing for the comparison of estimated carbon storage at same or different locations over time. This methodology is used to quantify the impacts of changing forest conditions and forest management activities on carbon storage with high spatial accuracy through the 32-year study period. Furthermore, this technique could be used for providing technical support and validation of carbon credit quantification and management.

Numerical simulation of rime ice accretion on airfoil using rigid sphere model

A 2D/3D numerical model for rime ice shape prediction is developed by combining the rigid sphere model (RSM) and computational fluid dynamics (CFD), and its characteristics are investigated. In the RSM, the ice accretion is modeled based on first principles with little physical modeling. The ice particle on an airfoil is described as a rigid sphere, and the ice shape is represented as a collection of rigid spheres. We use a non-body-fitted hierarchical Cartesian grid-based CFD solver so that complex ice shape represented by RSM can be handled. The tree data of the hierarchical Cartesian grid is also used for fast search of droplet positions. In addition, multiple ice particles are modeled as one large particle to reduce the number of trajectory calculations. The sensitivity of several parameters in the calculation is studied in 2D. Subsequently, 3D ice accretion analysis on NACA0012 airfoil using the RSM is performed for the first time. The simulation results are compared with those of the wind-tunnel experiments conducted at NASA. Although the RSM is a simple ice accretion model, reliable results are obtained in both 2D and 3D calculations.

In situ analysis of plastic flow near interfaces and free surfaces

Spatio–temporal analysis of large strain plastic flow at or near interfaces and free surfaces is important for understanding practical problems in the cutting and sliding of metals. In this context, the use of direct in situ imaging, coupled with digital image correlation (DIC), has gained popularity in the past couple of decades since it does not require a priori assumptions about the nature of the deformation field. Moreover, the application of DIC to dynamically evolving interfaces remains challenging. Common techniques such as hierarchical grid refinement or post analysis interpolation are either spatially restrictive or can lead to significant data loss. In this work, we present an alternative experimental method -termed ensemble averaged DIC- that circumvents both these limitations by resorting to ensemble averaging of deformation fields over a number of related, yet independent, unstructured grids. The resulting fields are accurate to second order and are benchmarked against standard 1D and 2D test cases, before being applied to two plastic flow fields arising in deformation processing—frictional sliding and orthogonal machining. We benchmark our scheme against commercially available packages to demonstrate its enhanced ability to resolve plastic flow near interface and free surface. The scheme is shown to accurately estimate residual surface strains on the cut/processed material surface without any a priori information about the flow field.

도시 기능지역 추출을 위한 Geo Data Cube 모델 기반 계층적 격자 분할 기법

도시 기능지역 추출을 위한 Geo Data Cube 모델 기반 계층적 격자 분할 기법

Synergizing 3D-printed structure and sodiophilic interface enables highly efficient sodium metal anodes

Sodium (Na) metal batteries have gained increasing attention more recently, owing to their high energy densities and cost efficiencies, but are severely handicapped by the unsatisfactory Coulombic efficiency (CE) and cycling stability stemming from dendrite growth on Na anodes. In this study, we developed a strategy of direct ink writing (DIW) 3D printing combined with electroless deposition to construct a hierarchical Cu grid coated with a dense nanoscale Ag interfacial layer as the host material for Na plating. The sodiophilic Ag interface contributes to a fall in the Na nucleation energy, hence enabling uniform Na deposition on each 3D-printed filament. The constructed 3D-printed structure can effectively moderate the electric-field distribution and lower the local current density for relieving Na inhomogeneous growth, as confirmed by finite element simulation and Na plating/stripping morphology evolution results. In particular, the unique 3D structure also promotes the lateral growth of Na, thus the volume change of Na metal was accommodated to stabilize the solid electrolyte interphase (SEI). As a result, the CE of the half-cell can reach 99.9 % at the current density of 1 mA/cm2 after 300 cycles and the full-cell exhibits outstanding electrochemical performance (capacity retention of 91.0 % after 500 cycles at 2 C).

S_IDS: An efficient skyline query algorithm over incomplete data streams

The efficient processing of mass stream data has attracted wide attention in the database field. The skyline query on the sensor data stream can monitor multiple targets in real time, to avoid abnormal events such as fire and explosion, which is very useful in the practical application of sensor data monitoring. However, real-world stream data may often contain incomplete data attributes due to faulty sensing devices or imperfect data collection techniques. Skyline queries over incomplete data streams may lead to a lack of transitivity and loop domination issues. To solve the problem of the skyline query over incomplete data streams, firstly, this paper uses differential dependency rule (DD) to fill the missing attribute values of data in the incomplete data stream. Then, the hierarchical grid index (HGrid) is introduced into the field of skyline query to improve pruning efficiency. In the process of skyline calculation, this paper only keeps as few calculation results as possible for the data that may affect the result to avoid a large number of repeated calculations. Thus, S_IDS (Skyline query algorithm over Incomplete Data Stream) is proposed to query skyline results with high confidence from the incomplete data stream. Finally, by comparing with the most advanced skyline query algorithms over incomplete data streams, the correctness and efficiency of the proposed S_IDS algorithm are proved.

Nanotwining induced by tensile fatigue and dynamic impact of laser powder bed fusion additively manufactured CoCrFeNi high-entropy alloy

The laser powder bed fusion (L-PBF) additively manufactured CoCrFeNi high-entropy alloy (HEA), with face-centered cubic (FCC) crystal structure, demonstrates better comprehensive mechanical properties in the building direction (BD). Loading quasi-static, dynamic fatigue, and dynamic separated Hopkinson press bar (SHPB) impact stress conditions along the BD of the L-PBF processed HEA exhibit intriguing microstructural evolution characteristics. The L-PBF generates hierarchical dislocation grids containing numerous cell substructures within the HEA FCC grains, impeding dislocation motion during deformation and improving the strength. When subjected to dynamic fatigue loading, the dislocation grids restrict the mean free path of dislocations and thus trigger the activation of abundant stacking faults. Hence, numerous nanotwins form near the end of the fatigue life. Multiple twinning systems can also be activated under dynamic high-speed impact loading. Especially at a low temperature of 77 K, the stacking fault energy of the CoCrFeNi HEA decreases, resulting in increased activation of nanotwins, exhibiting exceptional toughness and resistance to dynamic loads. Additional twin boundaries also impede dislocation movement for the strain hardening. These findings hold valuable implications for the study of additively manufactured HEA parts working in extreme environments.

Hexagon-based adaptive hierarchies for efficient point-in-spherical-polygon tests on GPUs

Point-in-spherical-polygon tests are a fundamental problem in computational geometry. For such tests, efficiency is much required for many global information processing matters, especially their real-time processing. Though many efforts have been made, it is still challenging due to the constraints from the non-Euclidean space of the sphere. Recently, a method is proposed to construct an Adaptive Hexagonal Hierarchical Grid (AHHG) to manage spherical polygon edges, by which the non-Euclidean space constraint of the sphere can be well handled to improve point-in-spherical-polygon tests. However, this method is very expensive in the construction of AHHGs, which prevents its use in practice. In this paper, we present novel measures to divide the task of AHHG construction into several subtasks to solve the problems that arise in the parallel construction of incongruent adaptive hierarchies, so we can exploit the parallel computing potential of GPUs for acceleration. We also develop measures to adaptively optimize the hierarchical levels of an AHHG for high efficiency. Experimental results show that we can answer 1,000,000 query points against dynamically varying spherical polygons in over 100,000 edges in real time on a personnel computer, where AHHGs for spherical polygons are individually constructed. This is much superior to the existing methods.

A parallel geometric multigrid method for adaptive topology optimization

This work presents an efficient parallel geometric multigrid (GMG) implementation for preconditioning Krylov subspace methods solving differential equations using non-conforming meshes for discretization. The approach does not constrain such meshes to the typical multiscale grids used by Cartesian hierarchical grid methods, such as octree-based approaches. It calculates the restriction and interpolation operators for grid transferring between the non-conforming hierarchical meshes of the cycle scheme. Using non-Cartesian grids in topology optimization, we reduce the mesh size discretizing only the design domain and keeping the geometry of boundaries in the final design. We validate the GMG method operating on non-conforming meshes using an adaptive density-based topology optimization method, which coarsens the finite elements dynamically following a weak material estimation criterion. The GMG method requires the generation of the hierarchical non-conforming meshes dynamically from the one used by the adaptive topology optimization to analyze to the one coarsening all the mesh elements until the coarsest level of the mesh hierarchy. We evaluate the performance of the adaptive topology optimization using the GMG preconditioner operating on non-conforming meshes using topology optimization on a fine-conforming mesh as the reference. We also test the strong and weak scaling of the parallel GMG preconditioner with two three-dimensional topology optimization problems using adaptivity, showing the computational advantages of the proposed method.

An Enhanced Earthquake Risk Analysis using H3 Spatial Indexing

Risk assessment is the first step in disaster risk reduction and crucial for types of disasters with potentially significant impacts, such as earthquakes. Besides the direct impact of very devastating shocks, earthquakes can also trigger cascading events that are no less damaging such as landslides, liquefaction, and tsunamis. This study aims to apply the H3 spatial indexing system to improve earthquake risk assessment using Sorong City as a case study, and compare the result of H3-based risk assessment with the normative risk assessment in Indonesia as defined by relevant Perka BNPB (Badan Nasional Penanggulangan Bencana/National Disaster Management Authority) 2/2012. H3 is an open-source geospatial indexing system developed by Uber, which utilizes a hierarchical hexagonal grid for indexing, calculations, and visualization. The hexagonal cells in the grid have equidistant neighbors and possess the property of expanding neighboring rings, approximating circles and optimizing space filling. The H3 spatial index finds extensive applications in earthquake hazard, vulnerability, capacity, and risk assessment systems. The value of each parameter is entered into each hexagon so that in the end each hexagon has its risk index value. The study shows that the use of H3 in earthquake risk assessment can provide a more detailed level of analysis and indirectly does not depend on regional administrative boundaries. Technically, this advantage provides a more efficient calculation method and is also easier to modify partially both spatially and in terms of the parameters used. Based on the performance evaluation results on the spatial join aspect, H3 provides much faster performance compared to traditional indexing systems. Hence, the H3 spatial index will be very useful to meet the need for an earthquake risk assessment on a larger scale with a higher level of resolution.

Numerical simulation of thixotropic–viscoelastic models for structured fluids in hierarchical grids

In the present work, we implement thixotropic–viscoelastic models in the HiGFlow system, which is a recently developed Computational Fluid Dynamics software that is able to simulate Newtonian, Generalized-Newtonian and viscoelastic flows using finite differences in tree-based grids. The system uses a moving least squares meshless interpolation technique, allowing to use more complex mesh configurations, still keeping the overall order of accuracy. We provide here a brief introduction to each of the implemented models (the Bautista–Manero–Puig, the Modified-Bautista, the NM_τp and NM_T models), where we describe their parameters, their strengths and their limitations. Unlike previous numerical works focused on this kind of fluids, non-zero Reynolds number numerical simulations of these models are carried out here in HiGFlow using different geometries like 2D channels, planar contraction 4:1 and 4:1:4 and our results are compared with those predicted by RheoTool, an open-source toolbox based on OpenFOAM. Although the transient numerical solutions differ, we observe that there is an excellent agreement in the steady-state profiles obtained in both software. A discussion of our results and their physical interpretation, a comparison with previously published results in the literature and a deep analysis of the corner vortex behaviour in the contraction geometries are also presented. Moreover, we show that our methodology offers flexibility and stability in the numerical simulations of thixotropic–viscoelastic flows for structured fluids, which allows the users to accurately simulate rheological behaviour of interest.

Accelerating Gas Escape in Anion Exchange Membrane Water Electrolysis by Gas Diffusion Layers with Hierarchical Grid Gradients.

At high current densities, gas bubble escape is the critical factor affecting the mass transport and performance of the electrolyzer. For tight assembly water electrolysis technologies, the gas diffusion layer (GDL) between the catalyst layer (CL) and the flow field plate plays a critical role in gas bubble removal. Herein, we demonstrate that the electrolyzer's mass transport and performance can be significantly improved by simply manipulating the structure of the GDL. Combined with 3D printing technology, ordered nickel GDLs with straight-through pores and adjustable grid sizes are systematically studied. Using an in situ high-speed camera, the gas bubble releasing size and resident time have been observed and analyzed upon the change of the GDL architecture. The results show that a suitable grid size of the GDL can significantly accelerate mass transport by reducing the gas bubble size and the bubble resident time. An adhesive force measurement has further revealed the underlying mechanism. We then proposed and fabricated a novel hierarchical GDL, reaching a current density of 2 A/cm2 at a cell voltage of 1.95 V and 80 °C, one of the highest single-cell performances in pure-water-fed anion exchange membrane water electrolysis (AEMWE).

Accelerating Gas Escape in Anion Exchange Membrane Water Electrolysis by Gas Diffusion Layers with Hierarchical Grid Gradients

AbstractAt high current densities, gas bubble escape is the critical factor affecting the mass transport and performance of the electrolyzer. For tight assembly water electrolysis technologies, the gas diffusion layer (GDL) between the catalyst layer (CL) and the flow field plate plays a critical role in gas bubble removal. Herein, we demonstrate that the electrolyzer's mass transport and performance can be significantly improved by simply manipulating the structure of the GDL. Combined with 3D printing technology, ordered nickel GDLs with straight‐through pores and adjustable grid sizes are systematically studied. Using an in situ high‐speed camera, the gas bubble releasing size and resident time have been observed and analyzed upon the change of the GDL architecture. The results show that a suitable grid size of the GDL can significantly accelerate mass transport by reducing the gas bubble size and the bubble resident time. An adhesive force measurement has further revealed the underlying mechanism. We then proposed and fabricated a novel hierarchical GDL, reaching a current density of 2 A/cm2 at a cell voltage of 1.95 V and 80 °C, one of the highest single‐cell performances in pure‐water‐fed anion exchange membrane water electrolysis (AEMWE).

High-order immersed boundary method for inviscid flows applied to flux reconstruction method on a hierarchical Cartesian grid

We propose novel high-order immersed boundary methods for simulating inviscid flows on hierarchical Cartesian grids using the flux reconstruction (FR) method. A high-order distribution of physical quantities derived from the FR method is utilized to interpolate flow variables to maintain the same order near the wall as the interior domain. In particular, the wall’s curvature effect is investigated in depth. We validate the accuracy through basic 2-D and 3-D simulations of inviscid flows. Consequently, the same accuracy as the order of the FR method (up to the fourth order) is achieved, including in the near-wall region.

3D bioprinting of a gelatin-alginate hydrogel for tissue-engineered hair follicle regeneration.

Hair follicle (HF) regeneration remains challenging, principally due to the absence of a platform that can successfully generate the microenvironmental cues of hair neogenesis. Here, we demonstrate a 3D bioprinting technique based on a gelatin/alginate hydrogel (GAH) to construct a multilayer composite scaffold simulating the HF microenvironment in vivo. Fibroblasts (FBs), human umbilical vein endothelial cells (HUVECs), dermal papilla cells (DPCs), and epidermal cells (EPCs) were encapsulated in GAH (prepared from a mixture of gelatin and alginate) and respectively 3D-bioprinted into the different layers of a composite scaffold. The bioprinted scaffold with epidermis- and dermis-like structure was subsequently transplanted into full-thickness wounds in nude mice. The multilayer scaffold demonstrated suitable cytocompatibility and increased the proliferation ability of DPCs (1.2-fold; P<0.05). It also facilitated the formation of self-aggregating DPC spheroids and restored DPC genes associated with hair induction (ALP, β-catenin, and α-SMA). The dermal and epidermal cells self-assembled successfully into immature HFs in vitro. HFs were regenerated in the appropriate orientation in vivo, which can mainly be attributed to the hierarchical grid structure of the scaffold and the dot bioprinting of DPCs. Our 3D printed scaffolds provide a suitable microenvironment for DPCs to regenerate entire HFs and could make a significant contribution in the medical management of hair loss. This method may also have broader applications in skin tissue (and appendage) engineering. STATEMENT OF SIGNIFICANCE: Hair loss remains a challenging clinical problem that influences quality of life. Three-dimensional (3D) bioprinting has become a useful tool for the fabrication of tissue constructs for transplantation and other biomedical applications. In this study, we used a 3D bioprinting technique based on a gelatin/alginate hydrogel to construct a multi-layer composite scaffold with cuticular and corium layers to simulate the microenvironment of dermal papilla cells (DPCs) in the human body. This new approach permits the controllable formation of self-aggregating spheroids of DPCs in a physiologically relevant extracellular matrix and the initiation of epidermal-mesenchymal interactions, which results in HF formation in vivo. The ability to regenerate entire HFs should have a significant impact on the medical management of hair loss.