Regular Grid Research Articles

A Heat Method for Generalized Signed Distance

We introduce a method for approximating the signed distance function (SDF) of geometry corrupted by holes, noise, or self-intersections. The method implicitly defines a completed version of the shape, rather than explicitly repairing the given input. Our starting point is a modified version of the heat method for geodesic distance, which diffuses normal vectors rather than a scalar distribution. This formulation provides robustness akin to generalized winding numbers (GWN) , but provides distance function rather than just an inside/outside classification. Our formulation also offers several features not common to classic distance algorithms, such as the ability to simultaneously fit multiple level sets, a notion of distance for geometry that does not topologically bound any region, and the ability to mix and match signed and unsigned distance. The method can be applied in any dimension and to any spatial discretization, including triangle meshes, tet meshes, point clouds, polygonal meshes, voxelized surfaces, and regular grids. We evaluate the method on several challenging examples, implementing normal offsets and other morphological operations directly on imperfect curve and surface data. In many cases we also obtain an inside/outside classification dramatically more robust than the one obtained provided by GWN.

A Fault Direction Criterion Based on Post-Fault Positive-Sequence Information for Inverter Interfaced Distributed Generators Multi-Point Grid-Connected System

In response to the poor reliability in identifying fault direction in distribution networks with Inverter Interfaced Distributed Generators (IIDGs), considering the control strategy of low-voltage ride-through, a fault direction criterion based on post-fault positive-sequence steady-state components is proposed. Firstly, the output steady-state characteristics of IIDGs considering the low-voltage ride-through capability are analyzed during grid failure, and the applicability of existing directional elements in a distribution network with IIDGs connected dispersively is demonstrated. Subsequently, for the typical structure of an active distribution grid operating under flexible modes, the positive-sequence voltage and current are examined in various fault scenarios, and a reliable direction criterion is suggested based on the difference in post-fault positive-sequence impedance angles on different sides of the lines that are suitable whether on the grid side or the IIDG side. Lastly, the reliability of the proposed direction criterion is verified by simulation and the results indicate that the fault direction can be correctly determined, whereas phase-to-phase and three-phase short circuit faults occur in different scenarios, independent of the penetration and grid-connected positions of IIDGs, fault location, and transition resistance. It is suitable for fault direction discrimination of an IIDGs multi-point grid-connected system under a flexible operation mode.

A High-Precision Sub-Grid Parameterization Scheme for Clear-Sky Direct Solar Radiation in Complex Terrain—Part I: A High-Precision Fast Terrain Occlusion Algorithm

In atmospheric modeling, sub-grid parameterization is an important method for studying the topographic effects of solar radiation using high-resolution Digital Elevation Model (DEM) data. For reducing the amount of computation, some approximate methods that can lead to errors are used in existing sub-grid parameterization schemes for clear-sky direct solar radiation (SPS-CSDSR). The lack of a high-precision fast terrain occlusion algorithm (HPFTOA) remains one of the biggest constraints in this field. This study proposed an HPFTOA. It mainly uses two kinds of acceleration algorithms. One method is to use a dynamic, lossless, and fast occlusion search radius. Another way is to use the rectangular grid for calculations within the accuracy of DEM data to avoid coordinate projection conversions. The test results indicate that the HPFTOA can carry out large-scale computation based on DEM data with a resolution of 90 m. Because it rarely uses approximation algorithms and considers the curvature of the Earth, SPS-CSDSR can achieve unprecedented precision. The HPFTOA can also be used in the fields of mountain solar energy assessment, remote sensing, and telemetry, including terrain-obscuring the probe. As computer performance improves and algorithms and execution code are optimized, the application prospects will be very broad.

A novel magnetoelastic biosensor consisting of carbon quantum dots/nitrocellulose membranes and NiFe2O4/ polylactic acid based on 3D printing for α2-macroglobulin detection

α2-macroglobulin (α2-M) have crucial clinical significance as a potent biomarker for diabetes nephropathy (DN). There is an increasing demand for rapid detection of α2-M. Herein, a magnetoelastic (ME) biosensor with a layered composite structure is developed for α2-M detection. Based on 3D printing, the basal layer of the biosensor is prepared as a grid structure fabricated by polylactic acid (PLA) doped with NiFe2O4. The conductivity of the biosensor was improved significantly due to the addition of multi-walled carbon nanotubes (MWCNTs). The biological site for capturing α2-M antigen was provided by the carbon quantum dots (CDs) coupled with anti-α-2M (anti-α2-M@CDs) in the nitrocellulose filter (NC) membrane. Meanwhile, the distribution of antibodies on the biosensor surface can be observed more directly due to the fluorescence characteristics of CDs. The biosensor in this work can realize multi-pattern recognition of fluorescent signal and electromagnetic signal. The results show that the limit of detection (LOD) was 0.506 ng/mL in the linear range from 10 ng/mL to 100 µg/mL and the linear equation of fitting curve is: y = 0.21 x - 0.15. The ME biosensors with a simple preparation method have advantages of high sensitivity, good stability and low LOD, showing the great potential for α2-M detection.

A memristive circuit for self-organized network topology formation based on guided axon growth

Circuit implementations of neuronal networks so far have been focusing on synaptic weight changes as network growth principles. Besides these weight changes, however, it is also useful to incorporate additional network growth principles such as guided axon growth and pruning. These allow for dynamical signal delays and a higher degree of self-organization, and can thus lead to novel circuit design principles. In this work we develop an ideal, bio-inspired electrical circuit mimicking growth and pruning controlled by guidance cues. The circuit is based on memristively coupled neuronal oscillators. As coupling element, we use memsensors consisting of a general sensor, two gradient sensors, and two memristors. The oscillators and memsensors are arranged in a grid structure, where oscillators and memsensors realize nodes and edges, respectively. This allows for arbitrary 2D growth scenarios with axon growth controlled by guidance cues. Simulation results show that the circuit successfully mimics a biological example in which two neurons initially grow towards two target neurons, where undesired connections are pruned later on.

Communication method of distribution network geographic information system

A communication method and system between the distribution network planning system and the grid geographic information system. After the change of the grid structure, the grid equipment change information is generated with the grid equipment as the unit. The production control region information structure is used to encapsulate the grid equipment change information, obtain the equipment change bus message, and send the equipment change bus message to the production control region service bus, This enables the distribution network planning system to receive equipment change bus messages from the production control area service bus, so that the distribution network planning system can directly communicate with the grid geographic information system.

Liquid Metal Grid Patterned Thin Film Devices Toward Absorption-Dominant and Strain-Tunable Electromagnetic Interference Shielding

Multiple internal reflection-based absorption-dominant stretchable electromagnetic shielding thin film by incorporating liquid metal grid structure is developed.The device demonstrates high electromagnetic shielding effectiveness (SE) (SET of up to 75 dB) with low reflectance (SER of 1.5 dB at the resonant frequency).The shielding properties of the device can be tuned by adjusting the liquid metal patterned grid spaces upon strain.

Computing irregular hypar-based quad-mesh patterns for segmented timber shells

Hyperbolic paraboloids, “hypars,” are special types of ruled surfaces. Their geometric properties provide them with loadbearing and stabilizing capacities, as well as distinct esthetic qualities. These attributes become evident in numerous applications in buildings, in many of which concrete or timber is used for the construction of the hypars. Hypars could also be relevant in the context of circular construction and design for disassembly, and the upcycling of construction waste. Due to the geometric simplicity of straight lines, which generate ruled surfaces, hypar-based structures can be designed and built with relatively simple means. They can consist of self-similar or even identical elements, which could facilitate their reuse.Compared to other types of ruled surfaces, such as conoids, hypars have the advantage of being doubly ruled, meaning that structural grids of straight elements can be formed. This paper investigates another interesting property, which is the possibility of creating flat-quad meshes by diagonally connecting the intersection points of the generatrices. This property has been previously described by other scholars, some of which explored its applicability for glass-clad steel grid shells. In this research, we focus on its potential for segmented timber shells that can serve as stand-alone structures, or as modular and reusable building parts, such as façade or roof components. The reusability of such modular units could be achieved by using reversible joints between them.More specifically, our research investigates the design space of construction systems based on such components via computational design and optimization algorithms, such as the memory limited Broyden–Fletcher–Goldfarb–Shanno (LBFGS) algorithm with automatic computation of the gradient, within the Julia programming environment. By applying principles and methods of differential geometry, we study hypars with irregular tilings, enabling the integration of panels with diverse proportions, shapes and sizes, as they can occur in wood production waste. By reducing construction waste, the work aims at reducing the negative environmental impact of the building construction sector. Moreover, irregular tilings could enable a more customized design of acoustic qualities and offer visual variety in segmented hypar based timber structures.The here presented studies show that the proposed optimization method provides a good fit of many tiles to rhombi, particularly when the steepness is not too large. We also show that optimizing towards rectangles provides better results. Overall, the results support the initial assumption that irregular rulings could be a means of adapting to both homogeneous and diverse material stocks.

Optimizing pseudo-spiral sampling for abdominal DCE MRI using a digital anthropomorphic phantom.

For reliable DCE MRI parameter estimation, k-space undersampling is essential to meet resolution, coverage, and signal-to-noise requirements. Pseudo-spiral (PS) sampling achieves this by sampling k-space on a Cartesian grid following a spiral trajectory. The goal was to optimize PS k-space sampling patterns for abdomin al DCE MRI. The optimal PS k-space sampling pattern was determined using an anthropomorphic digital phantom. Contrast agent inflow was simulated in the liver, spleen, pancreas, and pancreatic ductal adenocarcinoma (PDAC). A total of 704 variable sampling and reconstruction approaches were created using three algorithms using different parametrizations to control sampling density, halfscan and compressed sensing regularization. The sampling patterns were evaluated based on image quality scores and the accuracy and precision of the DCE pharmacokinetic parameters. The best and worst strategies were assessed in vivo in five healthy volunteers without contrast agent administration. The best strategy was tested in a DCE scan of a PDAC patient. The best PS reconstruction was found to be PS-diffuse based, with quadratic distribution of readouts on a spiral, without random shuffling, halfscan factor of 0.8, and total variation regularization of 0.05 in the spatial and temporal domains. The best scoring strategy showed sharper images with less prominent artifacts in healthy volunteers compared to the worst strategy. Our suggested DCE sampling strategy also showed high quality DCE images in the PDAC patient. Using an anthropomorphic digital phantom, we identified an optimal PS sampling strategy for abdominal DCE MRI, and demonstrated feasibility in a PDAC patient.

On determining structural wall layout during the adaptive reuse process of historic masonry buildings

Defining modifications to a building’s layout during the adaptive reuse of historic masonry buildings can be challenging since the existing structural grid constrains the transformations to be performed. This paper proposes a novel methodology for exploring modifications to a structural wall layout during the adaptive reuse process of historic buildings. The primary objective is to redefine structural wall arrangements to accommodate new openings and/or cuts in existing structural elements, offering designers flexibility in spatial arrangement modifications. Considering that existing buildings are subjected to damage, an evolutionary approach is used to define which wall segments can be removed from the existing layout, according to a multi-objective function that targets minimizing the presence of damaged elements and maximizing the presence of undamaged elements in the design, in addition to minimizing the building’s eccentricity. The problem considers structural constraints based on the compressive capacity and minimum length of shear components. A computational strategy based on Genetic Algorithm is conceptualized as a tool for deriving potential layout solutions that adhere to structural requirements while facilitating the incorporation of new openings. By systematically evolving potential solutions over multiple generations, the algorithm navigates the design space to pinpoint areas within the original shear layout where material can be removed while respecting the given structural constraints. The methodology’s validity is demonstrated through a case study situated in a UNESCO World Heritage Site, providing evidence of the practical application and success of the genetic algorithm approach in real-world scenarios. The findings presented in this paper contribute to the convergence of structural engineering and architectural preservation, offering a systematic and efficient means of determining structural wall layouts for the adaptive reuse of historic masonry buildings. Integrating computational techniques into the adaptive reuse process holds transformative potential, establishing a robust framework for sustainable urban development that honors and enhances the historical fabric of our built environment.

Comparison of Transcranial Magnetic Stimulation Dosimetry between Structured and Unstructured Grids Using Different Solvers.

In recent years, the interest in transcranial magnetic stimulation (TMS) has surged, necessitating deeper understanding, development, and use of low-frequency (LF) numerical dosimetry for TMS studies. While various ad hoc dosimetric models exist, commercial software tools like SimNIBS v4.0 and Sim4Life v7.2.4 are preferred for their user-friendliness and versatility. SimNIBS utilizes unstructured tetrahedral mesh models, while Sim4Life employs voxel-based models on a structured grid, both evaluating induced electric fields using the finite element method (FEM) with different numerical solvers. Past studies primarily focused on uniform exposures and voxelized models, lacking realism. Our study compares these LF solvers across simplified and realistic anatomical models to assess their accuracy in evaluating induced electric fields. We examined three scenarios: a single-shell sphere, a sphere with an orthogonal slab, and a MRI-derived head model. The comparison revealed small discrepancies in induced electric fields, mainly in regions of low field intensity. Overall, the differences were contained (below 2% for spherical models and below 12% for the head model), showcasing the potential of computational tools in advancing exposure assessment required for TMS protocols in different bio-medical applications.

Enhancement layout optimisation of grid structures with stability constraints

Grid structures have found widespread use in civil and mechanical engineering owing to their ability of covering long spans with an outstanding strength-to-weight ratio. However, their shallow structural depth and slender members make them susceptible to buckling failures. Therefore, it is crucial to enhance the stability of grid structures. An under-explored way to achieve this is by optimally configuring enhancement materials. This paper addresses the challenges of identifying the optimal enhancement layouts for grid structures by solving a density-based topology optimisation problem. The problem formulation is based on the SIMP approach, minimising compliance while satisfying constraints on material volume and structural stability. The stability constraint is expressed using buckling load factors obtained from a linear eigenvalue analysis. Two types of enhancement schemes, involving in-plane and out-of-plane elements, are developed for the single-layer base grid structures. The efficiency of the proposed method is evaluated by performing enhancement layout optimisation for a flat and three curved square grid structures, subject to a uniformly distributed vertical load and a half-span load. The optimisation results shed light on the impact of in-plane and out-of-plane enhancements on the stability of grid structures, providing insights on optimally configuring enhancement materials to achieve desired stability. In addition, comparative analysis between the case studies indicates that for curved structures, a higher curvature normally leads to greater achievable stability. In contrast, the flat grid structure exhibits the broadest range of customisable stability due to its primarily bending-dominant load-carrying mechanism. Furthermore, the outcomes of geometric non-linear analysis confirm the load-carrying capacity of the optimal structures, with non-linear critical loads typically showing a positive correlation with linear buckling loads.

TCFormer: Visual Recognition via Token Clustering Transformer.

Transformers are widely used in computer vision areas and have achieved remarkable success. Most state-of-the-art approaches split images into regular grids and represent each grid region with a vision token. However, fixed token distribution disregards the semantic meaning of different image regions, resulting in sub-optimal performance. To address this issue, we propose the Token Clustering Transformer (TCFormer), which generates dynamic vision tokens based on semantic meaning. Our dynamic tokens possess two crucial characteristics: (1) Representing image regions with similar semantic meanings using the same vision token, even if those regions are not adjacent, and (2) concentrating on regions with valuable details and represent them using fine tokens. Through extensive experimentation across various applications, including image classification, human pose estimation, semantic segmentation, and object detection, we demonstrate the effectiveness of our TCFormer. The code and models for this work are available at https://github.com/zengwang430521/TCFormer.

Simulate the elastic wavefields in media with an irregular surface topography based on staggered grid finite difference

Abstract Wave equation forward modeling is a useful method to study the propagation regulation of seismic wavefields. Finite difference (FD) is one of the most extensively employed numerical approaches for computing wavefields in earthquake and exploration seismology. However, the FD approach relying on regular grids often struggles to calculate wavefields in regions featuring surface topographies. The elastic wave equation can more accurately describe the propagation of seismic wavefields in elastic media compared to the acoustic wave equation. We introduce a new FD scheme to calculate the elastic wavefields in an isotropic model with a surface topography. The novel approach can use a conventional staggered grid FD (SGFD) approach based on regular grids. A new elastic model with a horizontal surface is first obtained from the nearby surface's elastic properties and the undulating terrain elevation. We subsequently employ a topography-related strategy to eliminate the effects of surface topographies on the seismic wavefields in models with irregular surface topographies. The merits of our proposed scheme lie in its ability to stable numerically compute wavefields in models with irregular surface topographies without altering the conventional SGFD relying on regular grids. To validate the effectiveness and practicality of our method, we utilize elastic models featuring complex surface topographies. Numerical experiments demonstrate that our approach efficiently calculates elastic wavefields in isotropic media with irregular topographies based on conventional SGFD.

A three-dimensional immersed boundary method for accurate simulation of acoustic wavefields with complex surface topography

Abstract Irregular topography of the free surface significantly affects seismic wavefield modelling, especially when employing finite-difference methods on rectangular grids. These methods represent the free surface as discrete points, resulting in a boundary that resembles a ‘staircase’. This approximation inaccurately represents surface topography, introducing errors in surface reflection traveltimes and generating artificial diffractions in wavefield simulation. We introduce a stable three-dimensional immersed boundary method (3DIBM) employing Cartesian coordinates to address these challenges. The 3DIBM enables the simulation of acoustic waves in media with complex topography through standard finite difference, extending the two-dimensional immersed boundary approach to compute spatial coordinates for ghost and mirror points in a three-dimensional space. Wavefield values at these points are obtained by three-dimensional spatial iterative symmetric interpolation, specifically through the Kaiser-windowed sinc method. By implicitly implementing the free surface boundary condition in three dimensions, this method effectively reduces artificial diffractions and enhances the accuracy of reflection traveltime. The effectiveness and accuracy of 3DIBM are validated through numerical tests and pre-stack depth migration imaging with simulated data, demonstrating its superiority as a modelling engine for migration imaging and waveform inversion in three-dimensional land seismic analysis.

Dynamic path planning of autonomous bulldozers using activity-value-optimised bio-inspired neural networks and adaptive cell decomposition

Autonomous bulldozers encounter critical challenges in dynamic path planning in complex construction environments such as dynamic obstacles and narrow passages. Bio-inspired neural networks (BINN) are commonly employed for real-time path planning in dynamic environments. However, the algorithm is not feasible enough to calculate the positive input of neighbour cells, thus resulting in unreasonable distribution of activity values in the obstacle-dense regions. Additionally, the use of regular grids results in excessive computation. These drawbacks cause the algorithm to plan irrational paths and reduce computational efficiency. This study proposed an activity-value-optimised BINN and adaptive cell decomposition for the dynamic path planning of autonomous bulldozers. First, an adaptive cell decomposition method was proposed to model a highly intricate and constantly evolving environment, resulting in a reduction in the computational workload of the BINN. Second, the calculation of the activity value is optimised by enhancing the shunt equation of the BINN, which considers both obstacles and grid levels. This improvement enabled more reasonable distribution results. The smoothness of dynamic path planning is refined by incorporating the turning radius, which can be more effectively integrated with local path planning. Compared with the original BINN algorithm and other baseline algorithms, a practical large-scale hydropower project application demonstrated that the proposed method can effectively reduce the number of computational iterations, enrich the travel direction, and improve path quality while ensuring dynamic obstacle avoidance.

Unified method for predicting the fatigue life of pipe–sphere joints in grid structures

Pipe–sphere joints (PSJs), which are commonly used in grid structures, are susceptible to fatigue failure under cyclic loading caused by suspended cranes. This paper presents a unified method for predicting the fatigue life of PSJs, including the weld toes on both the pipe and sphere surfaces, based on equivalent structural stress. A decomposition and recombination fitting (DRF) method was proposed to determine the optimal functional form of stress concentration factors (SCFs). A nonlinear regression analysis was conducted on the calculated results of 61 common PSJs to obtain a semi-analytical expression for the structural stress of the weld toes. Using this unified method, the fatigue life of the weld toes on both the pipe and sphere surfaces was estimated. The results indicated that the logarithmic ratios between the predicted fatigue life and experimental results were typically 0.93–1.08 for weld toes on pipe surfaces and 0.97–1.13 for weld toes on spherical surfaces, confirming the accuracy of the method. This unified method is applicable to predict the fatigue life of PSJs of various sizes and involves concise mathematical calculations independent of finite element analysis, thereby facilitating engineering applications. The DRF method can be used as a reference for fitting SCFs to specific structures. Furthermore, this prediction method enables the identification of the failure modes in PSJs. As the size of the steel pipe increased, the fatigue failure gradually shifted from the pipe surface to the sphere surface.

Privacy Preserving Human Mobility Generation Using Grid-Based Data and Graph Autoencoders

This paper proposes a one-to-one trajectory synthetization method with stable long-term individual mobility behavior based on a generalizable area embedding. Previous methods concentrate on producing highly detailed data on short-term and restricted areas for, e.g., autonomous driving scenarios. Another possibility consists of city-wide and beyond scales that can be used to predict general traffic flows. The now-presented approach takes the tracked mobility behavior of individuals and creates coherent synthetic mobility data. These generated data reflect the person’s long-term mobility behavior, guaranteeing location persistency and sound embedding within the point-of-interest structure of the observed area. After an analysis and clustering step of the original data, the area is distributed into a geospatial grid structure (H3 is used here). The neighborhood relationships between the grids are interpreted as a graph. A feed-forward autoencoder and a graph encoding–decoding network generate a latent space representation of the area. The original clustered data are associated with their respective H3 grids. With a greedy algorithm approach and concerning privacy strategies, new combinations of grids are generated as top-level patterns for individual mobility behavior. Based on the original data, concrete locations within the new grids are found and connected to ways. The goal is to generate a dataset that shows equivalence in aggregated characteristics and distances in comparison with the original data. The described method is applied to a sample of 120 from a study with 1000 participants whose mobility data were generated in the city of Munich in Germany. The results show the applicability of the approach in generating synthetic data, enabling further research on individual mobility behavior and patterns. The result comprises a sharable dataset on the same abstraction level as the input data, which can be beneficial for different applications, particularly for machine learning.

Validating Volumetric Measurements Using Helium-Filled Soap Bubbles in a Turbulent Boundary Layer And Wake Of An Axisymmetric Body Of Revolution

This study focuses on the validation of a volumetric measurement technique to characterize the turbulent boundary layers (TBL) and wake of a slender axisymmetric body. Specifically, Lagrangian Particle Tracking (LPT) was employed to analyse a TBL subjected to an adverse pressure gradient (APG) and transverse curvature at the tail of an axisymmetric body, with a Reynolds number based on model length of 3.95×10 6 . For the LPT method, a seeding rake with 100 nozzles was used to disperse helium-filled soap bubbles (HFSB), which served as tracers. These tracers were illuminated by high-powered LED arrays, allowing their motion to be captured by three high-speed cameras at acquisition rates up to 20 kHz. A key aspect of this research was using shadowgraphy to measure the diameter of the bubbles exiting the nozzles and obtaining their statistical variations across the various nozzles in the seeding rake. The study identified that the diameter of bubbles averaged 0.45 mm, indicating a state of near-neutral buoyancy. The LPT results were then obtained using the shake-the-box (STB) tracking alogirthm, while ensemble bin-averaging of the scattered particle tracks was used to obtain turbulence statistics on a regular grid. To validate the LPT results, comparisons were made with time-resolved 2D Particle Image Velocimetry (TR-PIV) and highly resolved hot-wire anemometry. This comparative analysis revealed good agreement of the mean profiles across the methodologies, highlighting the robustness and accuracy of the LPT technique. While the turbulence levels in the outer layer were well captured by the LPT, some discrepancies were observed near the wall in both PIV and LPT data, indicating limitations in capturing small-scale phenomena. The implications of these findings, particularly in understanding the complex 3D flow effects of such TBLs and the resulting wake flow field are discussed.

Interplay of Size, Deformability, and Device Layout on Cell Transport in Microfluidics.

Microfluidics have been widely used for cell sorting and capture. In this work, numerical simulations of cell transport in microfluidic devices were studied considering cell sizes, deformability, and five different device designs. 
Among these five designs, deterministic lateral displacement device (DLD) and hyperuniform device (HU) performed better in promoting cell-micropost collision due to the continuously shifted micropost positions as compared with regular grid, staggered, and hexagonal layout designs. However, the grid and the hexagonal layouts showed best in differentiating cells by their size dependent velocity due to the size exclusion effect for cell transport in clear and straight paths in the flow direction. A systematic study of the velocity differentiation under different dimensionless groups was performed showing that the velocity difference is dominated by the micropost separation distance perpendicular to the direction of flow. Microfluidic experiments also confirmed the velocity differentiation results. The study can provide guiding principles for microfluidic design.