3D Geometry Research Articles

A high-order fully Lagrangian particle level-set method for dynamic surfaces

We present a fully Lagrangian particle level-set method based on high-order polynomial regression. This enables meshfree simulations of dynamic surfaces, relaxing the need for particle-mesh interpolation. Instead, we perform level-set redistancing directly on irregularly distributed particles by polynomial regression in a Newton-Lagrange basis on a set of unisolvent nodes. We demonstrate that the resulting particle closest-point (PCP) redistancing achieves high-order accuracy for 2D and 3D geometries discretized on irregular particle distributions and has better robustness against particle distortion than regression in a monomial basis. Further, we show convergence in classic level-set benchmark cases involving ill-conditioned particle distributions, and we present an example application to multi-phase flow problems involving oscillating and dividing droplets.

Probing cold gas with Mg ii and Ly α radiative transfer

ABSTRACT The Mg ii resonance doublet at 2796 Å and 2803 Å is an increasingly important tool to study cold, $T \sim 10^{4}\,$ K, gas – an observational-driven development requiring theoretical support. We develop a new Monte Carlo radiative transfer code to systematically study the joined Mg ii and Ly α escape through homogeneous and ‘clumpy’ multiphase gas with dust in arbitrary three-dimensional geometries. Our main findings are (i) the Mg ii spectrum differs from Ly α due to the large difference in column densities, even though the atomic physics of the two lines are similar. (ii) The Mg ii escape fraction is generally higher than that of Ly α because of lower dust optical depths and path lengths – but large variations due to differences in dust models and the clumpiness of the cold medium exist. (iii) Clumpy media possess a ‘critical covering factor’ above which Mg ii radiative transfer matches a homogeneous medium. The critical covering factors for Mg ii and Ly α differ, allowing constraints on the cold gas structure. (iv) The Mg ii doublet ratio $R_{\rm MgII}$ varies for strong outflows/inflows ($\gtrsim 700 \,\mathrm{km\, s}^{-1}$), in particular, $R_{\rm MgII}\lt 1$ being an unambiguous tracer for powerful galactic winds. (v) Scattering of stellar continuum photons can decrease $R_{\rm MgII}$ from two to one, allowing constraints on the scattering medium. Notably, we introduce a novel probe of the cold gas column density – the halo doublet ratio – which we show to be a powerful indicator of ionizing photon escape. We discuss our results in the context of interpreting and modelling observations as well as their implications for other resonant doublets.

High-speed single-frame polarimeter for thermal radiation to measure the 3D geometry of hot metal surfaces

The 3D geometry of the interaction zone in laser material processing is of major importance as it defines the absorption of the laser beam and may influence the hydrodynamics of the process. With the aim of measuring this geometry, which typically changes with frequencies in the order of 10 kHz, a single-frame polarimeter with acquisition rates of up to 75 kHz is presented in this work. It simultaneously records four images of the thermal process emission, through four linear polarizers with different orientations. The formulae required for the reconstruction of the 3D geometry from these images are derived and validated on an example of a heated steel sphere. The reconstructed geometry was found to be in good agreement with the examined sphere. An experimental example is also given of the application of this technology to geometry measurement of a highly dynamic laser cutting front at a framerate of 75 kHz.

Depth Prior-Guided 3D Voxel Feature Fusion for 3D Semantic Estimation from Monocular Videos

Existing 3D semantic scene reconstruction methods utilize the same set of features extracted from deep learning networks for both 3D semantic estimation and geometry reconstruction, ignoring the differing requirements of semantic segmentation and geometry construction tasks. Additionally, current methods allocate 2D image features to all voxels along camera rays during the back-projection process, without accounting for empty or occluded voxels. To address these issues, we propose separating the features for 3D semantic estimation from those for 3D mesh reconstruction. We use a pretrained vision transformer network for image feature extraction and depth priors estimated by a pretrained multi-view stereo-network to guide the allocation of image features within 3D voxels during the back-projection process. The back-projected image features are aggregated within each 3D voxel via averaging, creating coherent voxel features. The resulting 3D feature volume, composed of unified voxel feature vectors, is fed into a 3D CNN with a semantic classification head to produce a 3D semantic volume. This volume can be combined with existing 3D mesh reconstruction networks to produce a 3D semantic mesh. Experimental results on real-world datasets demonstrate that the proposed method significantly increases 3D semantic estimation accuracy.

Greedy Kernel Methods for Approximating Breakthrough Curves for Reactive Flow from 3D Porous Geometry Data

Greedy Kernel Methods for Approximating Breakthrough Curves for Reactive Flow from 3D Porous Geometry Data

Text-to-building: experiments with AI-generated 3D geometry for building design and structure generation

The paper seeks to investigate novel potentials for building design and structure generation that arise at the intersection of computational design and AI-generated 3D geometries. Although the use of AI technologies is exponentially increasing inside the architectural discipline, the design of spatial building configurations using AI-generated 3D geometries is still limited in its applications and represents an ongoing field of investigation in advanced architectural research. In this regard, several questions still need to be answered: how can we design new building typologies from AI-generated 3D geometries? And how can we use these typologies to shape both the real and the virtual world?The paper proposes a new approach to architectural design where artificial intelligence is used as the starting point for design exploration, while computational design procedures are employed to convert AI-generated 3D geometries into building elements – such as columns, beams, horizontal and vertical surfaces. The paper starts with a general overview of the current use of artificial intelligence inside the architectural discipline, and then it moves towards the explanation of specific AI generative models for 3D geometry reconstruction and representation. Subsequently, the proposed working pipeline is analysed in more detail – from the creation of 3D geometries using generative AI models to the conversion of such geometries into building elements that can be further designed and optimised using computational design tools and methods. The results shown in the paper are achieved using Shap-E as the main AI model, though the proposed pipeline can be implemented with multiple AI models. The paper ends by showing some of the generated results, finally adding some considerations to the relationship between human and artificial creativity inside the architectural discipline.The work presented in the paper suggests that the use of computational design tools and methods combined with the tectonics of the latent space opens new opportunities for topological and typological explorations. In a time where traditional architectural typologies are moving towards stagnation due to their inability to satisfy new human needs and ways of living, exploring AI-based working pipelines related to architectural design allows the definition of new design solutions for the generation of new architectural spaces. In doing so, the serendipitous aspect of AI biases is used as an auxiliary force to inform design decisions, promoting the discovery of a new inbuilt dynamism between human and artificial creativity. In a time where AI is everywhere, understanding the measure of such dynamism represents a key aspect for the future of the architectural discipline.

Gossamer: Scaling Image Processing and Reconstruction to Whole Brains.

Neuronal reconstruction-a process that transforms image volumes into 3D geometries and skeletons of cells- bottlenecks the study of brain function, connectomics and pathology. Domain scientists need exact and complete segmentations to study subtle topological differences. Existing methods are diskbound, dense-access, coupled, single-threaded, algorithmically unscalable and require manual cropping of small windows and proofreading of skeletons due to low topological accuracy. Designing a data-intensive parallel solution suited to a neurons' shape, topology and far-ranging connectivity is particularly challenging due to I/O and load-balance, yet by abstracting these vision tasks into strategically ordered specializations of search, we progressively lower memory by 4 orders of magnitude. This enables 1 mouse brain to be fully processed in-memory on a single server, at 67× the scale with 870× less memory while having 78% higher automated yield than APP2, the previous state of the art in performant reconstruction.

3D airway geometry analysis of factors in airway navigation failure for lung nodules

BackgroundThis study aimed to quantitatively reveal contributing factors to airway navigation failure during radial probe endobronchial ultrasound (R-EBUS) by using geometric analysis in a three-dimensional (3D) space and to investigate the clinical feasibility of prediction models for airway navigation failure.MethodsWe retrospectively reviewed patients who underwent R-EBUS between January 2017 and December 2018. Geometric quantification was analyzed using in-house software built with open-source python libraries including the Vascular Modeling Toolkit (http://www.vmtk.org), simple insight toolkit (https://sitk.org), and sci-kit image (https://scikit-image.org). We used a machine learning-based approach to explore the utility of these significant factors.ResultsOf the 491 patients who were eligible for analysis (mean age, 65 years +/- 11 [standard deviation]; 274 men), the target lesion was reached in 434 and was not reached in 57. Twenty-seven patients in the failure group were matched with 27 patients in the success group based on propensity scores. Bifurcation angle at the target branch, the least diameter of the last section, and the curvature of the last section are the most significant and stable factors for airway navigation failure. The support vector machine can predict airway navigation failure with an average area under the curve of 0.803.ConclusionsGeometric analysis in 3D space revealed that a large bifurcation angle and a narrow and tortuous structure of the closest bronchus from the lesion are associated with airway navigation failure during R-EBUS. The models developed using quantitative computer tomography scan imaging show the potential to predict airway navigation failure.

Area of origin estimation from multiple arbitrarily oriented surfaces using marker-guided structure from motion

Bloodstain pattern analysis plays a crucial role in forensic investigations. Projected patterns can offer valuable insights into the dynamics of crime scenes. In this paper, we propose and validate a novel approach that extends existing software, HemoVision, to analyze impact patterns that are distributed across multiple arbitrarily oriented surfaces. The proposed method integrates HemoVision’s marker-based system with structure from motion (SfM) techniques to reconstruct the three-dimensional geometry of impact patterns using only two-dimensional photographs. Controlled experiments were used to validate the proposed approach, demonstrating robustness in reconstruction accuracy with median translation errors below 3 mm and median angular errors below 0.2°, irrespective of imaging device or image resolution. Comparing the estimated areas origin to their known ground truth, the proposed method achieved an average total error of 8.12 cm, with the primary source of error being the vertical dimension. Despite this, the overall error remains well within the ranges of error reported in prior work. This study demonstrates that HemoVision can be used to analyze complex impact patterns using only two-dimensional photographs, providing forensic experts with an efficient and accessible tool for investigating intricate crime scenes involving multi-surface impact patterns.

Operando Colorations from Real-Time Growth of 3D-Printed Nanoarchitectures.

While artificial 3D nanostructures can generate precise and flexible coloration, their real-time color changes during 3D nanoprinting remain unexplored owingto the inherent challenges of in situ transient measurements and observations. In this study, a 3D-printing system which supports the operando observation/measurement of the color dynamics of subwavelength metallic nanoarchitectures fabricated in real time is developed and evaluated. During 3D printing, the dimensions and geometries of the 3D nanostructures grow over time, producing a large library of optical spectra associated with real-time color changes. Only a timer is needed to define the expected colors from a single 3D print run. Fin-like nanostructures are used to toggle colors based on the polarization effect and produce color gradients. Based on structural coloration, nanoarchitectures are designed and printed to animate desired color patterns. Moreover, the resulting color dynamics can also serve as an operando identifier for real-time structural information during 3D nanoprinting. A single print run enables the efficient creation of a comprehensive library of desired colorations owing to the flexibility in time-dependent controllability and 3D geometries at the subwavelength scale. 3D nanoprinted plasmonic structures exhibiting time-varying colorations (4D printing of colors) uniquely redefines the coloring stategy,offering considerable potential for numerousapplications.

Analysis and Optimization of Fluid Flow on the Surface of the Prototype Body of Electrical Vehicle Ganesha Diffabilities Using Solidworks Software

Purpose: The aerodynamic aspect is an aspect that takes into account a force caused by the fluid flow shown by the Coefficient of Drag value. This will affect the optimization of performance and energy consumption used. This study aims to modify the standard design of Electrical Ganesha Diffabilities so that a design with a low Coefficient of Drag value considers ergonomic parameters and user comfort to follow the community of people with disabilities. Theoretical Framework: The main theoretical foundation is based on the laws of aerodynamics, which analyze the movement of air around solid objects and its effect on vehicle performance, with a specific emphasis on minimizing drag. The research employs computational fluid dynamics (CFD), a branch of fluid flows. Models and simulations in Solidworks offer a digital environment for improving design aspects to increase aerodynamic efficiency. This theoretical approach focuses on two main objectives: reducing the drag coefficient (CD), which measures resistance caused by shape and surface characteristics, and applying these findings to achieve practical energy savings and enhance vehicle operation specifically designed for individuals with disabilities. The integration of aerodynamic theory and practical application is essential for solving the specific transportation requirements emphasized in the introduction. This alignment with legislation and social objectives aims to provide improved support for the impaired community. Design/Methodology/Approach: This is a numerical method with an R2D2 research model (Reflective, Recursive, Design, and Development) using the 2020 version of Solidworks Software. Solidworks software is known for software that has speed and accuracy in analyzing. Findings: The results of the fluid flow analysis simulation showed that the design of the modified Electrical Ganesha diffabilities vehicle had a Coefficient of Drag value of 0.353 or 35% lower than the standard vehicle value of 0.547. Through expert assessment, the modified design of Electrical Ganesha Diffabilities has received an assessment of 98% with excellent qualifications. Research, Practical & Social implications: The research contributes to the topic of aerodynamic for disability-friendly vehicles, providing a basis for future academic investigations into accessible transportation options. Essentially, the results can immediately impact the development of electric cars that are both more energy-efficient and performance-optimized for those with impairments. This would lead to lower running expenses and improved user-friendliness. From a social perspective, this research emphasizes a dedication to inclusiveness and accessibility, which has the potential to enhance the ability of impaired persons to move around and ultimately enhance their overall well-being. Additionally, it increases awareness among policymakers and manufactures of the significance of incorporating aerodynamic efficiency into the design of assistive devices, hence fostering wider social acceptance and support for this advancement. Originality/Value: The uniqueness and significance of this work stem from its innovative strategy of combining aerodynamic principles with the development of electric cars particularly tailored for those with disabilities-a relatively unexplored field in transportation research. This research examines the E-GADIS vehicle and fills the gap in the current literature on accessible transportation options.

Optimized quantization parameter selection for video-based point cloud compression

Point clouds are sets of points used to visualize three-dimensional (3D) objects. Point clouds can be static or dynamic. Each point is characterized by its 3D geometry coordinates and attributes such as color. High-quality visualizations often require millions of points, resulting in large storage and transmission costs, especially for dynamic point clouds. To address this problem, the moving picture experts group has recently developed a compression standard for dynamic point clouds called video-based point cloud compression (V-PCC). The standard generates two-dimensional videos from the geometry and color information of the point cloud sequence. Each video is then compressed with a video coder, which converts each frame into frequency coefficients and quantizes them using a quantization parameter (QP). Traditionally, the QPs are severely constrained. For example, in the low-delay configuration of the V-PCC reference software, the quantization parameter values of all the frames in a group of pictures are set to be equal. We show that the rate-distortion performance can be improved by relaxing this constraint and treating the QP selection problem as a multi-variable constrained combinatorial optimization problem, where the variables are the QPs. To solve the optimization problem, we propose a variant of the differential evolution (DE) algorithm. Differential evolution is an evolutionary algorithm that has been successfully applied to various optimization problems. In DE, an initial population of randomly generated candidate solutions is iteratively improved. At each iteration, mutants are generated from the population. Crossover between a mutant and a parent produces offspring. If the performance of the offspring is better than that of the parent, the offspring replaces the parent. While DE was initially introduced for continuous unconstrained optimization problems, we adapt it for our constrained combinatorial optimization problem. Also, unlike standard DE, we apply individual mutation to each variable. Furthermore, we use a variable crossover rate to balance exploration and exploitation. Experimental results for the low-delay configuration of the V-PCC reference software show that our method can reduce the average bitrate by up to 43% compared to a method that uses the same QP values for all frames and selects them according to an interior point method.

Evaluation of the effects of temperature and centrifugation time on elimination of uncured resin from 3D-printed dental aligners

The study investigated the effects of temperature and centrifugation time on the efficacy of removing uncured resin from 3D-printed clear aligners. Using a photo-polymerizable polyurethane resin (Tera Harz TC-85, Graphy Inc., Seoul, Korea), aligners were printed and subjected to cleaning processes using isopropyl alcohol (IPA) or centrifugation (g-force 27.95g) at room temperature (RT, 23 °C) and high temperature (HT, 55 °C) for 2, 4, and 6 min. The control group received no treatment (NT). Cleaning efficiency was assessed through rheological analysis, weight measurement, transparency evaluation, SEM imaging, 3D geometry evaluation, stress relaxation, and cell viability tests. Results showed increased temperature and longer centrifugation times significantly reduced aligner viscosity, weight (P < 0.05), and transmittance. IPA-cleaned aligners exhibited significantly lower transparency and rougher surfaces in SEM images. All groups met ISO biocompatibility standards in cytotoxicity tests. The NT group had higher root mean square (RMS) values, indicating greater deviation from the original design. Stress relaxation tests revealed over 95% recovery in all groups after 60 min. The findings suggest that a 2-min HT centrifugation process effectively removes uncured resin without significantly impacting the aligners’ physical and optical properties, making it a clinically viable option.

DENNIS: a design and analysis tool for dynamic material x-ray diffraction experiments

We present DENNIS (Diffraction Experiment desigN and aNalysiS): a graphical software tool useful for the design and analysis of dynamic x-ray diffraction experiments, such as those performed on the Z Pulsed Power Facility, Thor Pulsed Power Generator, and Dynamic Compression Sector (DCS) of the Advanced Photon Source. DENNIS provides rapid powder and single-crystal diffraction pattern predictions and powder diffraction pattern image integration in three-dimensional geometries. Additional features include crystallographic information file reading, image processing, and synthetic diffraction pattern image generation. We overview the software's capabilities, detail the prediction and integration methodologies, and provide example implementations on Z and DCS experiments.

Competition between bead boundary fusion and crystallization kinetics in material extrusion-based additive manufacturing

Assembling polymer beads into well-defined 3D geometries through a layer by layer deposition process, such as material extrusion additive manufacturing, remains challenging due to the complex interplay between the several kinetics involved (flow, inter-beads diffusion/fusion, cooling, solidification, etc). Here, we explore the influence of physical parameters like bead cross section geometry, partial bead fusion (also called bead coalescence or bead welding) and crystallization kinetics on the 3D printing of isotactic polypropylene as a model semi-crystalline polymer. New methods for the characterization of printed stacks cross sections, interlayer cohesion and viscoelastic coalescence are introduced. Our results demonstrate how printing conditions, through input parameters like layer height and nozzle temperature, can greatly affect interlayer cohesion in relation to polymer interdiffusion at the interface. We show that the relevant timescale for fusion is mainly driven by the polymer’s rheology, and that fusion is many-fold faster under Hertzian compression, i.e. due to the effect of contact pressure. Quantitative description of the influence of the extrusion nozzle and build platform temperatures on the crystallization time of printed beads highlights how the processability window is defined by the competition between fusion and crystallization. Our approach therefore provides a framework for the optimization of process parameters based on the physical processes involved during the production run of semi-crystalline polymers.

Aerosol jet 3D printing of gold micropillars and their behavior under compressive loads

Three-dimensional (3D) gold microarchitectures such as micropillars, microwires, and microlattices are used in applications such as implantable biosensors, microelectronic circuits, and catalytic devices. Additive Manufacturing (AM) is being explored to create such structures as lithography is more suitable to fabricate 2D or planar architectures. In this work, we use Aerosol Jet (AJ) nanoprinting, a jetting-based AM technique, to demonstrate fabrication of gold micropillars via stacking of nanoparticles and sintering them at temperatures ranging from 300°C to 900°C. We first demonstrate that AJ printed 2D planar films and 3D micropillars exhibit a different grain size distribution, even if they are printed and sintered under identical conditions. This unusual observation indicates that specific AM technique and structures are important in determining their grain structure, which affects their mechanical behavior. The AJ printed 3D gold micropillars are fabricated with aspect ratios up to 10:1 and sintered from lower to higher temperatures, to yield porosities ranging from 15 % to 2 %, and average grain sizes from 25 nm to 1.7 µm, respectively. Micropillars sintered at lower temperatures exhibit a brittle behavior with higher yield strength despite having a higher porosity but with smaller grain sizes, and vice versa. These results indicate that the 3D geometry of AJ printed architectures dictates the grain size evolution, and hence the mechanical properties. Further, the grain size dominates over porosity in determining the micropillar deformation. These results provide important design guidelines for 3D printed microarchitected structures fabricated via jetting-based AM techniques.

Non-Contact Acoustic Emission Monitoring of Corrosion in Mooring Chain Links

Mooring chains are key parts of floating energy production units, such as floating wind turbines, photovoltaic islands, and floating production-storage-offloading units (FPSOs). These structures are predominantly subject to corrosion and fatigue. Detailed integrity assessment of mooring chains can be challenging due to difficult access, complex geometry, surface conditions (often with the presence of corrosion pits), and weather conditions. Additionally, the presence of marine growth on the surface of the chain links often requires the need of surface cleaning to obtain a detailed assessment of the structural integrity. This paper highlights an experimental investigation of monitoring of corrosion-induced damage in mooring chain links by means of non-contact Acoustic Emission (AE) technique. Accelerated corrosion experiments were performed on a large-scale mooring chain sample recovered from the field. A dedicated experimental set-up was designed to apply accelerated corrosion on two chain links submerged in natural sea-water. An immersion tank, i.e. an instrumented 1000x1000x1200mm3 water tank, was used to submerge the chain links in natural sea-water. The corrosion process was accelerated by means of anodic polarization. Ultrasound signals were continuously measured using two arrays of underwater AE transducers (in the frequency range between 50-450 kHz) placed on two perpendicular planes at a fixed distance from the chain links. Corrosion-induced AE sources were localized on the 3D geometry of the tested links. The variation and evolution of AE parameters extracted from the measured signals were analyzed as a function of testing time. The results of the accelerated corrosion experiment suggest that corrosion-induced ultrasound signals can be detected and monitored by means of non-contact AE. The results of this study may be used for characterization of corrosion-induced AE signals in mooring chain links.

Generic low-atmosphere signatures of swirled-anemone jets

Context. Solar jets are collimated plasma flows moving along magnetic field lines and are accelerated at low altitude following magnetic reconnection. Several of them originate from anemone-shaped low-lying arcades, and the most impulsive ones tend to be relatively wider and display untwisting motions. Aims. We aim to establish typical behaviours and observational signatures in the low atmosphere that can occur in response to the coronal development of such impulsive jets. Methods. We analysed an observed solar jet associated with a circular flare ribbon using high-resolution observations from SST coordinated with IRIS and SDO. We related specifically identified features with those developing in a generic 3D line-tied numerical simulation of reconnection-driven jets performed with the ARMS code. Results. We identified three features in the SST observations: the formation of a hook along the circular ribbon, the gradual widening of the jet through the apparent displacement of its kinked edge towards (and not away) from the presumed reconnection site, and the falling back of some of the jet plasma towards a footpoint offset from that of the jet itself. The 3D numerical simulation naturally accounts for these features, which were not imposed a priori. Our analyses allowed us to interpret them in the context of the 3D geometry of the asymmetric swirled-anemone loops and their sequences of reconnection with ambient coronal loops. Conclusions. Given the relatively simple conditions in which the observed jet occurred, together with the generic nature of the simulation that comprised minimum assumptions, we predict that the specific features that we identified and interpreted are probably typical of every impulsive jet.

Three-Dimensional Modeling of Anion Exchange Membrane Electrolysis: A Two-Phase Flow Approach

Anion exchange membrane water electrolyzers (AEMWEs) are attracting growing interest as a green hydrogen production technology. Unlike proton exchange membrane (PEM) systems, AEMWEs operate in an alkaline environment, allowing one to use less expensive, non-noble materials as catalysts for the reactions and non-fluorinated anion exchange polymer membranes. However, the performance and stability of AEMWEs strongly depend on the alkaline electrolyte concentration. In this work, a three-dimensional multi-physics model considering two-phase flow effects is applied to understand the impact of KOH electrolyte concentration and its flow rate on AEMWE performance, as well as on the current and gas volume fraction distributions. The numerical results were compared to experimental data published in the literature. For current densities above 1 A/cm2, a strongly non-uniform H2 and O2 gas volume distribution could be evidenced by the 3D simulations. Increasing the KOH electrolyte flow rate from 10 to 100 mL/min noticeably improves cell performance for current densities above 1 A/cm2. These results show the importance of accounting for the three-dimensional geometry of an AEMWE and two-phase flow effects to accurately describe its operation and performance.

A simulation study of the anti-ARM decoy effectiveness in 3D geometry

A simulation study of the anti-ARM decoy effectiveness in 3D geometry