Mesh Creation Research Articles

CFD modelling and analysis of a radial ORC turbine with backswept rotor blades

Abstract Global electricity demand has steadily risen over the past five decades due to increased industrialization and improved accessibility worldwide. Addressing this escalating demand requires expanding electricity production, achievable through adopting renewable energy sources or enhancing production efficiency. Organic Rankine Cycle (ORC) power systems offer a promising solution for efficient electricity generation by utilizing organic fluids with lower critical parameters. Unlike conventional steam power plants, ORC systems operate at lower temperatures, making them adaptable to various applications. The efficiency of ORC power systems heavily relies on the design and performance of their turbines. This study focuses on modelling and analyzing the ORC turbine using Computational Fluid Dynamics (CFD) techniques, specifically employing a radial-inflow design with backswept rotor blades. Geometric parameters were derived from a simplified zero-dimensional (0-D) turbine model developed in MATLAB. CFD modelling was conducted using ANSYS software, integrating the BladeGen module for turbine geometry generation and the TurboGrid toolbox for mesh creation. Comparative analysis with the 0-D design method validated the accuracy of the CFD turbine modelling and the achievement of desired operational parameters. This research underscores the potential of CFD techniques in optimizing ORC turbine design thus allowing to achieve efficient electricity generation, contributing to the advancement of sustainable energy technologies.

Modal analysis of taipei 101 building

In this paper, a multi-stage modeling and simulation approach incorporating different deformation conditions is used to analyze the structure of the Taipei 101 building. The modeling process mainly involves the formation of an initial simplified model based on the basic geometry, the determination of an accurate model based on the actual dimensions, and finally the introduction of a hollow structure design to mimic the density and mass distribution of the real building. After the model is established, mesh creation and mesh independence study are carried out. The simulation is carried out using Ansys software, which mainly performs the process of selecting boundary conditions, determining the equivalent density, dividing the mesh for modal analysis, and obtaining the modal vibration pattern as well as the corresponding intrinsic frequency. By analyzing the intrinsic frequencies and vibration modes, the structure of Taipei 101 Building is prone to resonance under certain circumstances, and this study provides some significance to the geology of local seismic activities.

Image2Flow: A proof-of-concept hybrid image and graph convolutional neural network for rapid patient-specific pulmonary artery segmentation and CFD flow field calculation from 3D cardiac MRI data.

Computational fluid dynamics (CFD) can be used for non-invasive evaluation of hemodynamics. However, its routine use is limited by labor-intensive manual segmentation, CFD mesh creation, and time-consuming simulation. This study aims to train a deep learning model to both generate patient-specific volume-meshes of the pulmonary artery from 3D cardiac MRI data and directly estimate CFD flow fields. This proof-of-concept study used 135 3D cardiac MRIs from both a public and private dataset. The pulmonary arteries in the MRIs were manually segmented and converted into volume-meshes. CFD simulations were performed on ground truth meshes and interpolated onto point-point correspondent meshes to create the ground truth dataset. The dataset was split 110/10/15 for training, validation, and testing. Image2Flow, a hybrid image and graph convolutional neural network, was trained to transform a pulmonary artery template to patient-specific anatomy and CFD values, taking a specific inlet velocity as an additional input. Image2Flow was evaluated in terms of segmentation, and the accuracy of predicted CFD was assessed using node-wise comparisons. In addition, the ability of Image2Flow to respond to increasing inlet velocities was also evaluated. Image2Flow achieved excellent segmentation accuracy with a median Dice score of 0.91 (IQR: 0.86-0.92). The median node-wise normalized absolute error for pressure and velocity magnitude was 11.75% (IQR: 9.60-15.30%) and 9.90% (IQR: 8.47-11.90), respectively. Image2Flow also showed an expected response to increased inlet velocities with increasing pressure and velocity values. This proof-of-concept study has shown that it is possible to simultaneously perform patient-specific volume-mesh based segmentation and pressure and flow field estimation using Image2Flow. Image2Flow completes segmentation and CFD in ~330ms, which is ~5000 times faster than manual methods, making it more feasible in a clinical environment.

Preparation of surgical meshes using self-regulating technology based on reaction-diffusion processes

While reaction-diffusion processes are utilized in multiple scientific fields, these phenomena have seen limited practical application in the polymer industry. Although self-regulating processes driven by parallel reaction and diffusion can lead to patterned structures, most polymeric products with repeating subunits are still prepared by methods that require complex and expensive instrumentation. A notable, high-added-value example is surgical mesh, which is often manufactured by weaving or knitting. In our present work, we demonstrate how the polymer and the biomedical industry can benefit from the pattern-forming capabilities of reaction-diffusion. We would like to propose a self-regulating method that facilitates the creation of surgical meshes from biocompatible polymers. Since the control of the process assumes a thorough understanding of the underlying phenomena, the theoretical background, as well as a mathematical model that can accurately describe the empirical data, is also introduced and explained. Our method offers the benefits of conventional techniques while introducing additional advantages not attainable with them. Most importantly, the method proposed in this paper enables the rapid creation of meshes with an average pore size that can be adjusted easily and tailored to fit the intended area of application.Graphical abstract

Beyond conventional: An integrated aerostructural optimization approach for innovative tailplane configurations

This research paper presents a comprehensive study focused on optimizing innovative tailplane configurations for transport jet aircraft. This study aims to enhance the aerodynamics of the aircraft's rear end by introducing a novel tail arrangement. The ultimate goal is to reduce the size of the horizontal empennage, which will have a positive impact on aircraft fuel efficiency. To fulfill its objectives, this work develops a systematic approach, integrating advanced methodologies such as the Design of Experiments and Response Surface Models for both aerodynamics and structural disciplines. By levering response surfaces methodology, the aerostructural optimization has been performed towards a size reduction of the horizontal tail. Results indicate a potential 9% reduction in the tailplane reference area, which could result in enhanced aerodynamic performance and weight saving. This target would only be achievable if the innovative design includes a Leading Edge Extension device to maintain the stability and handling characteristics of a conventional reference aircraft. The impact of the innovative optimized tail arrangement developed in this study at the aircraft level resulted in a 1% reduction in block fuel for a mission range of approximately 3,400 nautical miles for a 180-seat capacity jet aircraft (similar to A320neo). Furthermore, from an industrial perspective, this paper also demonstrates how an automated workflow assists with CAD modeling, mesh creation, simulation execution, and surrogate models to accelerate complex multidisciplinary optimization processes, thereby reducing the time and costs associated with the development of a new aircraft configuration.

A stream-aligned mixed polyhedral meshing strategy for integrated surface-subsurface hydrological models

Mesh refinement near river corridors is commonly deployed to resolve topographic details important for accurately representing riverine flow dynamics in watershed-scale spatially distributed integrated hydrology models. However, this increased accuracy comes with higher computational costs due to larger mesh and smaller time steps resulting from small mesh cells. We present a novel stream-aligned mixed-polygonal meshing approach that resolves river-corridor topography efficiently by placing long quadrilateral cells along the mapped center lines of the rivers and standard Triangulated Irregular Network (TIN) mesh in the rest of the domain. Our integrated hydrology simulations using mixed-polyhedral mesh generated from the mixed-polygonal approach closely match those using a highly refined extruded-TIN mesh but with a 96.43% reduction in the number of mesh cells and a 99.75% reduction in CPU hours. Conversely, coarser extruded-TIN meshes, due to poor water conveyance relative to the refined mesh simulations, yielded lower peak flows, longer recession curves, and higher inundation fractions.The creation of this mixed-polyhedral mesh is implemented within the Watershed Workflow tool, a Python-based workflow library for watershed simulation. We also implemented a layered process of hydrologic conditioning specific to the cells in the river corridor, which are explicitly meshed within the mixed-polyhedral mesh. The hydrologic conditioning enforces monotonic topographic gradients, removes spurious obstructions, and allows for burning-in depths of the river cells based on ancillary information. This approach paves the way for efficiently modeling narrow engineered channels and specifying river-specific hydrodynamic details and biogeochemical processes.

Automation of the meshing process of geological data

This work proposes a novel meshing technique that is able to extract surfaces from processed seismic data and integrate surfaces that were constructed using other extraction techniques. Contrary to other existing methods, the process is fully automated and does not require any user intervention. The proposed system includes an approach for closing the gaps that arise from the different techniques used for surface extraction. The developed process is able to handle non-manifold domains that result from multiple surface intersections. Surface and volume meshing that comply with user specified mesh control techniques are implemented to ensure the desired mesh quality. The integrated procedures provide a unique facility to handle geotechnical models and accelerate the generation of quality meshes for geophysics modelling. The developed procedure enables the creation of meshes for complex reservoir models to be reduced from weeks to a few hours. Various industrial examples are shown to demonstrate the practicable use of the developed approach to handle real life data.

Implementation of a Real Estate Management System using Photogrammetry Method Allowing 3d Navigation of Buildings

The objective of this study is to evaluate how the use of virtual representations of buildings, created through photogrammetry, can improve the efficiency and effectiveness of the real estate management system. In this regard, investigations have been conducted with stakeholders in the real estate sector, such as real estate agencies and tenants, to gather valuable information on real estate management. For illustration purposes, we have chosen to model the NASPW gymnasium. We have implemented a real estate management approach based on the photogrammetry method. To achieve our objective, we describe each step of the process, from the preparation of the photogrammetric mission (planning and simulating the mission) to data acquisition, carried out using a Phantom 4 Pro drone for aerial images and a Nikon D300 digital camera for interior shots. The data processing phase is also crucial, and it was done using the Agisoft Metashape software. We provide detailed explanations of the different steps in this process, which include image quality estimation, image alignment, georeferencing, dense point cloud generation, 3D mesh creation, and orthophoto generation.

AI and augmented reality for 3D Indian dance pose reconstruction cultural revival

This paper delves into the specialized domain of human action recognition, focusing on the Identification of Indian classical dance poses, specifically Bharatanatyam. Within the dance context, a “Karana” embodies a synchronized and harmonious movement encompassing body, hands, and feet, as defined by the Natyashastra. The essence of Karana lies in the amalgamation of nritta hasta (hand movements), sthaana (body postures), and chaari (leg movements). Although numerous, Natyashastra codifies 108 karanas, showcased in the intricate stone carvings adorning the Nataraj temples of Chidambaram, where Lord Shiva’s association with these movements is depicted. Automating pose identification in Bharatanatyam poses challenges due to the vast array of variations, encompassing hand and body postures, mudras (hand gestures), facial expressions, and head gestures. To simplify this intricate task, this research employs image processing and automation techniques. The proposed methodology comprises four stages: acquisition and pre-processing of images involving skeletonization and Data Augmentation techniques, feature extraction from images, classification of dance poses using a deep learning network-based convolution neural network model (InceptionResNetV2), and visualization of 3D models through mesh creation from point clouds. The use of advanced technologies, such as the MediaPipe library for body key point detection and deep learning networks, streamlines the identification process. Data augmentation, a pivotal step, expands small datasets, enhancing the model’s accuracy. The convolution neural network model showcased its effectiveness in accurately recognizing intricate dance movements, paving the way for streamlined analysis and interpretation. This innovative approach not only simplifies the identification of Bharatanatyam poses but also sets a precedent for enhancing accessibility and efficiency for practitioners and researchers in the Indian classical dance.

A complex model decomposition algorithm based on 3D frame fields and features

PurposeHexahedral meshing is one of the most important steps in performing an accurate simulation using the finite element analysis (FEA). However, the current hexahedral meshing method in the industry is a nonautomatic and inefficient method, i.e. manually decomposing the model into suitable blocks and obtaining the hexahedral mesh from these blocks by mapping or sweeping algorithms. The purpose of this paper is to propose an almost automatic decomposition algorithm based on the 3D frame field and model features to replace the traditional time-consuming and laborious manual decomposition method.Design/methodology/approachThe proposed algorithm is based on the 3D frame field and features, where features are used to construct feature-cutting surfaces and the 3D frame field is used to construct singular-cutting surfaces. The feature-cutting surfaces constructed from concave features first reduce the complexity of the model and decompose it into some coarse blocks. Then, an improved 3D frame field algorithm is performed on these coarse blocks to extract the singular structure and construct singular-cutting surfaces to further decompose the coarse blocks. In most modeling examples, the proposed algorithm uses both types of cutting surfaces to decompose models fully automatically. In a few examples with special requirements for hexahedral meshes, the algorithm requires manual input of some user-defined cutting surfaces and constructs different singular-cutting surfaces to ensure the effectiveness of the decomposition.FindingsBenefiting from the feature decomposition and the 3D frame field algorithm, the output blocks of the proposed algorithm have no inner singular structure and are suitable for the mapping or sweeping algorithm. The introduction of internal constraints makes 3D frame field generation more robust in this paper, and it can automatically correct some invalid 3–5 singular structures. In a few examples with special requirements, the proposed algorithm successfully generates valid blocks even though the singular structure of the model is modified by user-defined cutting surfaces.Originality/valueThe proposed algorithm takes the advantage of feature decomposition and the 3D frame field to generate suitable blocks for a mapping or sweeping algorithm, which saves a lot of simulation time and requires less experience. The user-defined cutting surfaces enable the creation of special hexahedral meshes, which was difficult with previous algorithms. An improved 3D frame field generation method is proposed to correct some invalid singular structures and improve the robustness of the previous methods.

Three‐dimensional mapping and immersive human–robot interfacing utilize Kinect‐style depth cameras and virtual reality for agricultural mobile robots

AbstractNavigation and autonomous operation in farmland or greenhouse environments present significant challenges for agricultural mobile robots on account of unstructured scene conditions and complex task requirements. The immersive interfaces based on human–robot interaction have been extensively developed, which bridge the high‐level decision of human beings and the precise sensing and high motion ability of robots. In this study, we propose a human–robot interaction framework based on three‐dimensional mapping and virtual reality (VR) visualization. On the construction client, dense three‐dimensional point‐cloud maps are created based on the simultaneous localization and mapping real‐time appearance‐based mapping algorithm with a Kinect‐style depth camera. Subsequently, point‐cloud filtering and mesh creation methods are introduced for postprocessing the map models. A data communication network is employed to transmit the optimized models to the VR‐based exploration client. Utilizing VR visualization, the operator can gain an intuitive and comprehensive understanding of the environments to be explored. The three‐dimensional agricultural scenes are mapped into VR models through the system, which can combine the physical worlds with the VR spaces in a better manner. Compared with the video streaming‐based method for three‐dimensional mapping, the map models with colors and textures are entirely displayed in the VR headset, thus providing robust and global human–robot interaction interfaces and enhancing the immersive exploration for human beings to a considerable extent. Experimental results indicate that the three‐dimensional map models exhibit relatively sufficient construction integrity and yield favorable optimization effects. Additionally, according to a user questionnaire survey, immersive VR‐based interfaces can be more effectively utilized for the three‐dimensional mapping of mobile robots. Research evidence and results demonstrate that our proposed system framework offers positive assistance and support for enhancing human–robot interaction and executing specific robotic tasks in unstructured field agricultural environments. Code implementation and related data sets are shared at the link https://github.com/WangDongBUAA/Mapping_Interfacing.

Investigation the thermal performance of Nano-Enhanced Phase Change Material (NEPCM) in an enclosure

Phase change materials (PCMs) play a vital role in thermal energy storage applications, as they provide efficient and dependable heat storage and release capabilities. This study provides a comprehensive evaluation of the thermal performance of Nano-Enhanced Phase Change Material (NEPCM) in an enclosure, with a focus on the effect of temperature variation and the role of nanoparticles. Variable temperature regimes significantly influence the physical and thermal properties of the NEPCM, as revealed by numerical simulations. The enthalpy-porosity approach for the phase change process and the solution of the Navier-Stokes equations for fluid flow serve as the primary foundations for the numerical model that will be covered in this study. With only a single phase of user input, ANSYS facilitates the creation of three-dimensional models and solid geometry meshes. Notably, an increase in the temperature of the heat-supplying wall resulted in substantial changes to the NEPCM, which we have thoroughly investigated and quantified. It has been demonstrated that the incorporation of various nanoparticles (Al2O3, CuO, and ZnO) into paraffin enhances the heating process, thereby enhancing the thermal conductivity, heat transfer coefficient, and surface tension of the NEPCM. The three nanoparticles decrease the mass fraction of paraffin in the range of 0.00 to 0.960 at the same concentration and testing temperature. In addition, increasing nanoparticle concentration under higher temperature regimes resulted in a faster and more uniform nanoparticle synthesis, significantly enhancing the NEPCM's thermal performance. Particularly, as the temperature increased, the NEPCM near the wall melted more rapidly. The study reveals the crucial role that temperature plays in modulating the behavior and performance of NEPCM, thereby providing valuable insights for thermal management systems. These results demonstrate the significance of temperature control for maximizing the potential of NEPCM in a variety of applications. As the temperature rises, one side becomes covered in the blue color (solid), while in the standard case, the melting curve shows a little red region (melting) at the other side that starts to increase when the nanomaterial's are added.

Application of SFEM Method to Analyse Crack Parameters of Ultra High Molecular Weight Polyethylene Material

The application of numerical methods, today occupies a much needed place for modeling, and for finding solutions to any problem related to fatigue and damage to materials. This article deals numerically with the evolution of the stress intensity factor and the integral of the contour J in I mode, of an initial rectilinear crack of languor a=0.7, 1.4, 2.1, 2.8 and 3.5mm, with different ratio, a/w=0.1, 0.2, 0.3, 0.4 and 0.5mm. By the stretching finite element method (SFEM) of the (UHMWPE) Ultra High Molecular Weight Polyethylene material. On the other hand, the creation of the parametric mesh based on the computer language (FORTRAN) and to create a model of the square crack front with 5 contours of size L=1mm. In addition, the simulation was done by Abaqus 16.3.1 code. The maximal circumferential stress criterion MCSC were applied. Other materials like Alu20- 17 and Steel XC65-90 were used to make the comparison. Crack parameters, such as stress intensity factors KI, KII and J-contour integral were evaluated. The results obtained in our work have justified that there is proportionality between the three materials.

Designing Silicon Interposers for 2.5/3DIC Heterogeneous Integration - Meeting Foundry and OSAT Requirements

End market demand for higher performance, higher bandwidth and higher throughput processing such as AI, HPC, VR, 5G and even autonomous vehicles is driving the trend of heterogenous integration of ASICs, chiplets and memory, including HBM, onto advanced high-performance substrates using 2.5 and 3D IC assembly techniques that are manufactured using silicon or silicon-like processes. There are multiple suppliers of such advanced high-performance substrates ranging from the one stop mega foundries to multiple OSATs of different sizes. It’s fair to say that all of them have common design requirements or constraints that must pass a rigorous verification process of the manufacturing mask files (GDSII or OASIS). Such GDSII or OASIS files are post processed or exported from the physical design tools making the need to create the mask files with 100% fidelity an additional challenge to the design team. In this paper we will cover the major steps needed to design and implement an advanced silicon-based interposer that addresses common requirements of foundries and OSATs. We will talk about what each step is, why its required, any challenges that exist in addressing the step and actual design examples for each step. The major steps we will cover include: * What information you need from your manufacturer? * Definition of connectivity & floorplanning of the interposer * Defining the interposer materials stackup & vias for routing * Breakout or fanout routing from chiplet or IC pads * Power and ground mesh creation * Routing of interposer connectivity * Generation of interposer manufacturing data (GDS/OASIS) * Verification of the design to meet foundry/OSATs rules * Handoff to foundry/OSAT At the end of this paper attendees will understand what the major workflow steps for interposer design are and why. What general information they will need to obtain from their manufacturer in order to start design and how to use it. They will learn what each design step entails with clear examples which will enable creation of workflows to successfully handoff their designs to their chosen foundry or OSAT manufacturer.

Efficient finite element modelling of helical strand cables utilising periodicity

This paper proposes a repeated unit cell (RUC) FE modelling strategy for multilayered helical strand cables subject to tensile loading and bending with an initial tensile load. The approach utilises the axial periodicity in helical structures to reduce computational domain to two cable periods. A novel mesh creation method allowing the exact geometry of helical strands to be discretised into solid elements while maintaining flat end surfaces is proposed. Periodic boundary conditions (PBC) allowing for geometric and material nonlinearity are used to implement cyclic loads to study hysteresis in single and multilayered cables. Comparisons made to existing experimental and analytical results validate the models ability to capture hysteresis due to plasticity and geometric effects such as the stick–slip transition in bending. Examination of the cross-sectional stress distribution shows plasticity initiates at contact points at loads which are 25% of the load at which the cable tensile response deviates from linearity.

A Robust Grid‐Based Meshing Algorithm for Embedding Self‐Intersecting Surfaces

AbstractThe creation of a volumetric mesh representing the interior of an input polygonal mesh is a common requirement in graphics and computational mechanics applications. Most mesh creation techniques assume that the input surface is not self‐intersecting. However, due to numerical and/or user error, input surfaces are commonly self‐intersecting to some degree. The removal of self‐intersection is a burdensome task that complicates workflow and generally slows down the process of creating simulation‐ready digital assets. We present a method for the creation of a volumetric embedding hexahedron mesh from a self‐intersecting input triangle mesh. Our method is designed for efficiency by minimizing use of computationally expensive exact/adaptive precision arithmetic. Although our approach allows for nearly no limit on the degree of self‐intersection in the input surface, our focus is on efficiency in the most common case: many minimal self‐intersections. The embedding hexahedron mesh is created from a uniform background grid and consists of hexahedron elements that are geometrical copies of grid cells. Multiple copies of a single grid cell are used to resolve regions of self‐intersection/overlap. Lastly, we develop a novel topology‐aware embedding mesh coarsening technique to allow for user‐specified mesh resolution as well as a topology‐aware tetrahedralization of the hexahedron mesh.

Fast 3D Reconstruction of UAV Images Based on Neural Radiance Field

Traditional methods for 3D reconstruction of unmanned aerial vehicle (UAV) images often rely on classical multi-view 3D reconstruction techniques. This classical approach involves a sequential process encompassing feature extraction, matching, depth fusion, point cloud integration, and mesh creation. However, these steps, particularly those that feature extraction and matching, are intricate and time-consuming. Furthermore, as the number of steps increases, a corresponding amplification of cumulative error occurs, leading to its continual augmentation. Additionally, these methods typically utilize explicit representation, which can result in issues such as model discontinuity and missing data during the reconstruction process. To effectively address the challenges associated with heightened temporal expenditures, the absence of key elements, and the fragmented models inherent in three-dimensional reconstruction using Unmanned Aerial Vehicle (UAV) imagery, an alternative approach is introduced—the neural radiance field. This novel method leverages neural networks to intricately fit spatial information within the scene, thereby streamlining the reconstruction steps and rectifying model deficiencies. The neural radiance field method employs a fully connected neural network to meticulously model object surfaces and directly generate the 3D object model. This methodology simplifies the intricacies found in conventional 3D reconstruction processes. Implicitly encapsulating scene characteristics, the neural radiance field allows for iterative refinement of neural network parameters via the utilization of volume rendering techniques. Experimental results substantiate the efficacy of this approach, demonstrating its ability to complete scene reconstruction within a mere 5 min timeframe, thereby reducing reconstruction time by 90% while markedly enhancing reconstruction quality.

Contributions of deep learning to automated numerical modelling of the interaction of electric fields and cartilage tissue based on 3D images.

Electric fields find use in tissue engineering but also in sensor applications besides the broad classical application range. Accurate numerical models of electrical stimulation devices can pave the way for effective therapies in cartilage regeneration. To this end, the dielectric properties of the electrically stimulated tissue have to be known. However, knowledge of the dielectric properties is scarce. Electric field-based methods such as impedance spectroscopy enable determining the dielectric properties of tissue samples. To develop a detailed understanding of the interaction of the employed electric fields and the tissue, fine-grained numerical models based on tissue-specific 3D geometries are considered. A crucial ingredient in this approach is the automated generation of numerical models from biomedical images. In this work, we explore classical and artificial intelligence methods for volumetric image segmentation to generate model geometries. We find that deep learning, in particular the StarDist algorithm, permits fast and automatic model geometry and discretisation generation once a sufficient amount of training data is available. Our results suggest that already a small number of 3D images (23 images) is sufficient to achieve 80% accuracy on the test data. The proposed method enables the creation of high-quality meshes without the need for computer-aided design geometry post-processing. Particularly, the computational time for the geometrical model creation was reduced by half. Uncertainty quantification as well as a direct comparison between the deep learning and the classical approach reveal that the numerical results mainly depend on the cell volume. This result motivates further research into impedance sensors for tissue characterisation. The presented approach can significantly improve the accuracy and computational speed of image-based models of electrical stimulation for tissue engineering applications.

Auto Mesh generation algorithm for the convex domain with the triangular elements

Mesh creation is one of the primary tasks in implementing the Finite Element Method (FEM) to solve two- and three-dimensional boundary value problems. However, the whole procedure becomes tedious and problematic when higher-order finite elements are employed to construct mesh and prepare corresponding element data. In this study, we strive to develop a versatile algorithm for discretizing the two-dimensional domain using linear, quadratic, and cubic triangular finite elements. The algorithm is developed based on n vertices (actual or more in number) that constitute the boundary of the domain, along with a computed (n+1)-th point such as the centroid. The algorithm, using this input, generates an initial mesh consisting of n triangular elements. Then, in every subsequent step, the algorithm increases the number of triangles in the mesh fourfold compared to the previous step. The algorithm we present here can employ (a) straight-sided (linear, quadratic, and cubic) triangular elements to generate the mesh for domains with polygonal boundaries and (b) both straight-sided and curved (quadratic and cubic) triangular elements with two straight sides and one curved side to generate meshes for domains with curved boundaries. Thus, the algorithm generates the desired meshes if the vertices are specified once at the initial step. Additionally, the inclusion of the mathematical expression of the curved boundary enables the algorithm to generate the fine mesh for the curved domain utilizing higher-order curved and straight-sided triangular elements. We also present a computer code in MATLAB incorporating the algorithm to create the mesh, prepare the element's data, and determine the element's connectivity.
 GANIT J. Bangladesh Math. Soc. 43.1 (2023) 017- 035

Automating Scan to BIM Operations Using Grasshopper

Remote sensing technologies such as laser scanning and photogrammetry have advanced significantly in the field-to-BIM workflow in recent years, becoming key instruments for modeling as-built frameworks. They can be utilized to collect dense 3D measured data on the condition of a building, and the derived point cloud can be processed to generate the as-built BIM. It provides building information to report as-built conditions and serves as a skill set for data on problem-solving issues in civil engineering. This research presents an efficient and automated workflow for modifying and evaluating point cloud data, focusing on supporting scan-to-BIM operations. The workflow utilizes cubic voxel mesh creation and voxel subsampling techniques to ensure precise representation of scanned data. The validation of this reconstruction methodology using Grasshopper and Volvox demonstrates its potential to reduce manual labor and analysis typically required in conventional scan-to-BIM methodologies. The presented workflow simplifies the critical task of acquiring building profiles, an essential BIM result, and streamlines the overall process. The integration of the Volvox plugin has further augmented the capabilities of Grasshopper and Rhino, providing users with intuitive tools for manipulating point clouds. Automating certain operations through the presented workflow has significant potential to enhance the efficiency and accuracy of the scan-to-BIM methodologies. These findings have implications in architecture and design, demonstrating how technology can be leveraged to unlock new possibilities and streamline critical processes.