Detailed Geometry Research Articles

Chiro-plasmon responses of x-shaped titanium nitride (TiN) nanoarrays by numerical simulations

Titanium nitride (TiN) has recently been taken as a potential candidate for plasmonic material, which supports surface plasmon resonances and exhibits excellent thermal stability. In this article, we proposed a novel chiral metamaterial with TiN, which consists of X-shaped TiN nanorods periodically arranged on a glass substrate. Its extinction, circular dichroism (CD) spectra, and g-factors were calculated and regulated by the detailed geometry through numerical simulations using the finite element method to further boost the application of TiN in chiro-plasmonic system. We show that it presents chiral responses both in visible and near infrared (NIR) ranges. Under the optimized geometric parameters and NIR incidence, it predicts ∼4 and 2 fold E-field enhancement and g-factor, respectively, than that of experimental reports of TiN nanohelices. The obtained excellent chiro properties are elucidated well in terms of the obtained superchiral field and charge distributions, whose origin was analyzed by a linear superposition method. Moreover, the influence of dielectric environments is discussed as well. Overall, the findings underscore the potential of TiN as a chiro-plasmonic refractory metamaterial and shed light on the design of alternative chiro-plasmon metamaterials for NIR applications in the future.

Comparison of finite element modeling approaches for Ti6Al4V porous bone implants

Additive technologies have made it possible to make a significant breakthrough in the development and production of personalized porous implants, which have clear advantages over monolithic predecessors. At the same time, engineers are faced with a number of new tasks, one of which is correct and effective numerical modeling of porous implants in the process of solving optimization problems. The study compares approaches to numerical modeling of porous titanium for bone implants, taking into account differences in the mechanical parameters of the material and the geometry of the models. The detailed geometry of the structure of 9 models is considered, for which two variants of material parameters are specified: pure Ti6Al4V alloy and experimental ones obtained for specimens printed from Ti6Al4V powder. As a proposed approach to homogenizing the structure, a continuum model of a cylinder is considered, for which the experimental properties of the specimen’s material are specified. The numerically obtained values of Young's moduli for each approach are compared with those obtained in the experiment. It has been established that when setting the properties of a pure alloy, the calculated Young's modulus exceeds the experimental one by 83–92 %. The closest thing to experiment is detailed modeling of geometry with setting the experimental properties of the material structure, while the calculated Young's modulus does not exceed the experimental one by more than 10 %. However, in this case the calculation takes from 2 to 8 hours. In the case of continuum modeling, the calculated Young's moduli exceed the calculated moduli in detailed modeling by no more than 11 %, while the calculation lasts no more than 1 minute. Based on this, it is proposed to use a continuum approach to modeling implants in the process of calculating the optimization of their shape, as an initial iteration before detailed modeling of the structure. As material properties, it is necessary to use the mechanical parameters of the structures, previously obtained from uniaxial compression experiments. Using the properties of a pure alloy will lead to results that significantly exceed the actual ones when calculating the porous structure.

Global optimization for the design of shell and tube horizontal thermosyphon reboiler

Shell and tube horizontal thermosyphon reboilers (STHTs) find wide applications in the process industry particularly due to their inherent advantages in handling large duties, high viscosity fluids and low fouled susceptibility. However, current design commercial packages suffer from obvious limitations including extensive user manipulations, trial-and-error necessity, increased human factor influence. To overcome these limitations, we develop in this work a rigorous MINLP model for STHT sizing, by achieving automated selection of detailed exchanger geometries with optimal overall heat transfer coefficient. The corrected minimum cross-flow area in shell-side is incorporated to accommodate full cross-flow patterns in tube bundle, thus handling popular service of X shell without baffle construction. A dependable convective boiling heat transfer model is proposed to obtain the shell-side heat transfer coefficient, validated with Aspen EDR. The critical variable, heat flux, which affects nucleate boiling heat transfer performance, is optimized through a simultaneous equation-solving process using a global solver, reducing excessive unit overdesign. Pressure balance between STHT and distillation column is fully considered by a proposed optimization model incorporating pressure drop and pipe sizing. By employing global optimization techniques and automating the design process, this innovative approach minimizes manual adjustments and provides more accurate and cost-effective designs.

PointAttN: You Only Need Attention for Point Cloud Completion

Point cloud completion referring to completing 3D shapes from partial 3D point clouds is a fundamental problem for 3D point cloud analysis tasks. Benefiting from the development of deep neural networks, researches on point cloud completion have made great progress in recent years. However, the explicit local region partition like kNNs involved in existing methods makes them sensitive to the density distribution of point clouds. Moreover, it serves limited receptive fields that prevent capturing features from long-range context information. To solve the problems, we leverage the cross-attention and self-attention mechanisms to design novel neural network for point cloud completion with implicit local region partition. Two basic units Geometric Details Perception (GDP) and Self-Feature Augment (SFA) are proposed to establish the structural relationships directly among points in a simple yet effective way via attention mechanism. Then based on GDP and SFA, we construct a new framework with popular encoder-decoder architecture for point cloud completion. The proposed framework, namely PointAttN, is simple, neat and effective, which can precisely capture the structural information of 3D shapes and predict complete point clouds with detailed geometry. Experimental results demonstrate that our PointAttN outperforms state-of-the-art methods on multiple challenging benchmarks. Code is available at: https://github.com/ohhhyeahhh/PointAttN

PaintHuman: Towards High-Fidelity Text-to-3D Human Texturing via Denoised Score Distillation

Recent advances in zero-shot text-to-3D human generation, which employ the human model prior (e.g., SMPL) or Score Distillation Sampling (SDS) with pre-trained text-to-image diffusion models, have been groundbreaking. However, SDS may provide inaccurate gradient directions under the weak diffusion guidance, as it tends to produce over-smoothed results and generate body textures that are inconsistent with the detailed mesh geometry. Therefore, directly leveraging existing strategies for high-fidelity text-to-3D human texturing is challenging. In this work, we propose a model called PaintHuman to addresses the challenges from two perspectives. We first propose a novel score function, Denoised Score Distillation (DSD), which directly modifies the SDS by introducing negative gradient components to iteratively correct the gradient direction and generate high-quality textures. In addition, we use the depth map as a geometric guide to ensure that the texture is semantically aligned to human mesh surfaces. To guarantee the quality of rendered results, we employ geometry-aware networks to predict surface materials and render realistic human textures. Extensive experiments, benchmarked against state-of-the-art (SoTA) methods, validate the efficacy of our approach.Project page: https://painthuman.github.io/.

Explicitly Perceiving and Preserving the Local Geometric Structures for 3D Point Cloud Attack

Deep learning models for point clouds have shown to be vulnerable to adversarial attacks, which have received increasing attention in various safety-critical applications such as autonomous driving, robotics, and surveillance. Existing 3D attack methods generally employ global distance losses to implicitly constrain the point-wise perturbations for optimization. However, these simple losses are quite difficult to accurately measure and restrict the proper 3D geometry as point clouds are highly structured. Although few recent works try to exploit additional shape-aware surface knowledge to globally constrain the point position, they still fail to preserve the detailed point-to-point geometric dependency in different local regions. To this end, in this paper, we propose a novel Multi-grained Geometry-aware Attack (MGA), which explicitly captures the local topology characteristics in different 3D regions for adversarial constraint. Specifically, we first develop multi-scale spectral local filter banks adapting to different 3D object shapes to explore potential geometric structures in local regions. Considering that objects may contain complex geometries, we then extend each filter bank into multi-layer ones to gradually capture the topology contexts of the same region in a coarse-to-fine manner. Hence, the focused local geometric structures will be highlighted in the coefficients calculated by the filtering process. At last, by restricting these coefficients between benign and adversarial samples, our MGA is able to properly measure and preserve the detailed geometry contexts in the whole 3D object with trivial perturbations. Extensive experiments demonstrate that our attack can achieve superior performance on various 3D classification models, with satisfying adversarial imperceptibility and strong resistance to different defense methods.

A Pre-convolved Representation for Plug-and-Play Neural Illumination Fields

Recent advances in implicit neural representation have demonstrated the ability to recover detailed geometry and material from multi-view images. However, the use of simplified lighting models such as environment maps to represent non-distant illumination, or using a network to fit indirect light modeling without a solid basis, can lead to an undesirable decomposition between lighting and material. To address this, we propose a fully differentiable framework named Neural Illumination Fields (NeIF) that uses radiance fields as a lighting model to handle complex lighting in a physically based way. Together with integral lobe encoding for roughness-adaptive specular lobe and leveraging the pre-convolved background for accurate decomposition, the proposed method represents a significant step towards integrating physically based rendering into the NeRF representation. The experiments demonstrate the superior performance of novel-view rendering compared to previous works, and the capability to re-render objects under arbitrary NeRF-style environments opens up exciting possibilities for bridging the gap between virtual and real-world scenes.

High-Fidelity 3D Head Avatars Reconstruction through Spatially-Varying Expression Conditioned Neural Radiance Field

One crucial aspect of 3D head avatar reconstruction lies in the details of facial expressions. Although recent NeRF-based photo-realistic 3D head avatar methods achieve high-quality avatar rendering, they still encounter challenges retaining intricate facial expression details because they overlook the potential of specific expression variations at different spatial positions when conditioning the radiance field. Motivated by this observation, we introduce a novel Spatially-Varying Expression (SVE) conditioning. The SVE can be obtained by a simple MLP-based generation network, encompassing both spatial positional features and global expression information. Benefiting from rich and diverse information of the SVE at different positions, the proposed SVE-conditioned NeRF can deal with intricate facial expressions and achieve realistic rendering and geometry details of high-fidelity 3D head avatars. Additionally, to further elevate the geometric and rendering quality, we introduce a new coarse-to-fine training strategy, including a geometry initialization strategy at the coarse stage and an adaptive importance sampling strategy at the fine stage. Extensive experiments indicate that our method outperforms other state-of-the-art (SOTA) methods in rendering and geometry quality on mobile phone-collected and public datasets. Code and data can be found at https://github.com/minghanqin/AvatarSVE.

A Detailed Numerical Model for a New Composite Slim-Floor Slab System.

The paper concerns the numerical modelling of a new slim-floor system with innovative steel-concrete composite beams called "hybrid beams". Hybrid beams consist of a high-strength TT inverted cross-section steel profile and a concrete core made of high-performance concrete and are jointed with prestressed hollow core slabs by infill concrete and tie reinforcement. Such systems are gaining popularity since they allow the integration of the main structural members within the ceiling depth, shorten the execution time, and reduce the use of concrete and steel. A three-dimensional finite element model is proposed with all parts of the system taken into account and detailed geometry reproduction. Advanced constitutive models are adopted for steel and concrete. Special attention is paid to the proper characterisation of interfaces. The new approach to calibration of damaged elastic traction-separation constitutive model for cohesive elements is applied to concrete-to-concrete contact zones. The model is validated with outcomes of experimental field tests and analytical calculations. A satisfactory agreement between different assessment methods is obtained. The model can be used in the development phase of a new construction system, for instance, to plan further experimental campaigns or to calibrate simplified design formulas.

Geometry-aware 3D pose transfer using transformer autoencoder

3D pose transfer over unorganized point clouds is a challenging generation task, which transfers a source’s pose to a target shape and keeps the target’s identity. Recent deep models have learned deformations and used the target’s identity as a style to modulate the combined features of two shapes or the aligned vertices of the source shape. However, all operations in these models are point-wise and independent and ignore the geometric information on the surface and structure of the input shapes. This disadvantage severely limits the generation and generalization capabilities. In this study, we propose a geometry-aware method based on a novel transformer autoencoder to solve this problem. An efficient self-attention mechanism, that is, cross-covariance attention, was utilized across our framework to perceive the correlations between points at different distances. Specifically, the transformer encoder extracts the target shape’s local geometry details for identity attributes and the source shape’s global geometry structure for pose information. Our transformer decoder efficiently learns deformations and recovers identity properties by fusing and decoding the extracted features in a geometry attentional manner, which does not require corresponding information or modulation steps. The experiments demonstrated that the geometry-aware method achieved state-of-the-art performance in a 3D pose transfer task. The implementation code and data are available at https://github.com/SEULSH/Geometry-Aware-3D-Pose-Transfer-Using-Transformer-Autoencoder.

Optimization and experimental study of stationary endwall of stator labyrinth cavity in a low-speed research compressor

Controlling the leakage flow of the labyrinth seal cavity is very important for the efficiency and aerodynamic matching design of aero-engine high-pressure compressor. However, due to the complexity of the geometry and internal flow of the labyrinth seal cavity, the design optimization of the labyrinth seal cavity is rarely studied. In this paper, a multi-objective design optimization method was used to optimize the stationary endwall of the shrouded stator labyrinth cavity, and the optimal endwall shape was obtained. Based on the optimal geometry, the 3D printing and aerodynamic testing of experimental parts were carried out, and the aerodynamic performance and flow field details of the optimal geometry were analyzed by unsteady numerical simulation. The experimental results show that for the optimal geometry, it has a little effect on the total pressure ratio, but the efficiency at the design point is increased by 1.4 %. The numerical results show that the optimal cavity shape reduces low-energy fluid, thus reduces the total pressure loss of S3. At the near stall point, the optimal S3 still has a better performance, which means that the optimal shape is not limited to the design point. From the result, an engineering design criterion can be concluded. It is suggested that the labyrinth seal cavity should be designed to match the velocity components of the leakage flow with the mainstream of the stator properly to make the leakage flow go evenly in the blade passage instead of gathering to the suction side.

Ortho-NeRF: generating a true digital orthophoto map using the neural radiance field from unmanned aerial vehicle images

ABSTRACT True Digital Orthophoto Maps (TDOMs) have high geometric accuracy and rich image characteristics, making them essential geographic data for national economic and social development. Complex terrain and artificial structures, automatic distortion elimination and occluded area recovery in TDOM generation pose significant challenges. Hence, the need for further improvements in both mapping accuracy and automation is highlighted. In this paper, we present an approach for generating a TDOM based on a Neural Radiance Field (NeRF) without utilizing prior three-dimensional geometry information called an Ortho Neural Radiance Field (Ortho-NeRF). The Ortho-NeRF divides a large-scale scene into small tiles, implicitly reconstructing each tile by selecting pixels on posed images, and individually generate TDOMs of all tiles using a true-ortho-volume rendering before mosaicking. Additionally, the Ortho-NeRF uses a strategy to skip empty spaces and adaptively set the spatial resolution of a voxel grid, improving the generated TDOM quality with fewer computational resources. Many experiments showed that our approach outperforms ContextCapture, Metashape, Pix4DMapper, and Map2DFusion, especially in challenging areas. Owing to its global consistency and continuous nature, Ortho-NeRF was able to effectively reconstruct the geometry information and details, generating TDOMs without distortion or misalignment. Eight ground control points were randomly selected to evaluate the geometric accuracy of the TDOMs, with an average median error of 0.267 m. The length between two points on a plane was also measured for quantitative evaluation, with a mean absolute error of 0.08 m and a mean relative error of 0.14%. Compared with the NeRF efficiency, that of the Ortho-NeRF increased 104 times in training and about 1000 times in rendering.

MeltpoolGAN: Melt pool prediction from path-level thermal history

In metal laser powder bed fusion, a laser beam rapidly scans through a thin layer of powder. The laser heats up and liquefies the powder forming a melt pool. The melt pool characteristics affect the material property and physical performance of the part. Physical simulations of melt pool dynamics are computationally expensive and are often limited to a short laser scan. Alternatively, Finite Element (FE) simulations for part-scale thermal history are less computationally intensive but do not capture the detailed melt pool temperature profile and geometry in general. Accurate modeling of the high spatial and temporal thermal gradients inside and near the melt pool is essential to many applications of the thermal history including design and optimization of the laser scan path, material microstructure characterization, and deformation prediction.In this work, we developed MeltpoolGAN, the first conditional Generative Adversarial Network (cGAN) to predict the melt pool image based on the simulated thermal history along the laser scan path. The integration of generative deep learning and thermal history simulation achieves melt pool-level accuracy while significantly reducing the physical and computational complexity. The data pair of thermal history and melt pool images for neural network training and validation are obtained through Contact-Aware Path Level (CAPL) thermal simulation and a co-axial melt pool monitoring system developed by NIST, respectively. The predicted melt pool morphology is validated against experimentally acquired melt pool images through geometric characteristics, including the length and width, of the melt pool.

An experimental array setup to study the integration of biosonar and maneuvering flight in bats

Understanding biosonar-based flight control in bats remains a challenge for fundamental bioacoustics and holds promise for engineered flight-control systems, especially for highly maneuverable drones. To accomplish this, detailed data on bat flight is required that captures the variability across flight situations, individuals, and species. Hence, a flight tunnel has been constructed that provides enough space for natural flight maneuvers. Reconstructing the detailed geometry of freely maneuvering bats requires capturing a flying bat from many different angles to ensure freedom from occlusions. Ideally, this would also be done without placing artificial markers on the bat. Hence, a flight tunnel has been instrumented with arrays of 50 high-speed video cameras and 28 ultrasonic microphones that were all integrated within a modular canvas setup. The optical properties of each camera have been determined from multiple images taken of a panel with a calibration grid seen from different orientations. The spatial relationship between the cameras was estimated based on a large set of images containing random points created with an LED. Reconstructing the 3D geometries of the bats from large numbers of high-speed video frames requires an automated method due to the problem's complexity. To this end, deep-learning methods are currently under development.

Refined Coseismic Slip Model and Surface Deformation of the 2021 Maduo Earthquake: Implications for Sensitivity of Rupture Behaviors to Geometric Complexity

Geometric complexities of a fault system have a significant impact on the rupture behavior of the fault. The 2021 Mw7.4 Maduo earthquake occurred on a multi-segmented complex sinistral fault in the interior of the Bayan-Har block in the northern Tibetan Plateau. Here, we integrate centimeter-resolution surface rupture zones and Sentinel-2 optical displacement fields to accurately determine the geometric parameters of the causative fault in detail. An adaptive quadtree down-sampling method for interferograms was employed to enhance the reliability of the coseismic slip model inversion for interferograms. The optimal coseismic slip model indicated a complex non-planar structure with varying strike and dip angles. The largest slip of ~6 m, at a depth of ~7 km, occurred near a 6 km-wide stepover (a geometric complexity area) to the east of the epicenter, which occurred at the transition zone from sub-shear to super-shear rupture suggested by seismological studies. Optical and SAR displacement fields consistently indicated the local minimization of effective normal stress on releasing stepovers, which facilitated rupture through them. Moreover, connecting intermediate structures contributes to maintaining the rupture propagation through wide stepovers and may even facilitate the transition from subshear to supershear. Our study provides more evidence of the reactivation of a branched fault at the western end during the mainshock, which was previously under-appreciated. Furthermore, we found that a strong asymmetry in slip depth, stress drop, and rupture velocity east and west of the epicenter was coupled with variations in geometric and structural characteristics of fault segments along the strike. Our findings highlight the sensitivity of rupture behaviors to small-scale details of fault geometry.

The electron transport properties and thermoelectric performance in graphene–boron–nitride-nanoribbon-based heterojunctions

The electron transport properties and thermoelectric performance in graphene–boron–nitride-nanoribbon-based heterojunctions (GNR-BNN heterojunctions) have been studied by first-principles calculation. It is found that the electronic transport properties and thermoelectric performance properties are highly sensitive to the geometry details of the GNR-BNN heterojunction. Notably, our calculations reveal that the GNR-BNN heterojunctions not only can present obvious negative differential resistance effects, but also can present high thermoelectric figure of merit ZT: the figure-of-merit ZT will be over 1 at room temperature T = 300 K. Moreover, the thermoelectric performance is significantly enhanced by the combination of defects and alternating boron-nitride structures. Therefore, the GNR-BNN heterojunctions will provide the possibilities for designing and fabrication of multifunctional molecular device design.

Large-Eddy Simulation Study of Injector Geometry and Parcel Injection Location on Spray Simulation of the Engine Combustion Network Spray G Injector

Abstract Recent improvements in computing power and numerical techniques have enabled us resolve minute details of spray plume behavior and its wall boundary interactions, and made detailed spray simulations using large-eddy simulation (LES) turbulence models available to many more engineers. However, guidelines for parcel-based spray simulation boundary and initial conditions are still based on results from lower-resolution and Reynolds averaged Navier–Stokes (RANS) simulations. Hence, it is necessary to critically examine those assumptions and compare their impact with results using a RANS turbulence model. Three different parameters, including whether a simulation includes a detailed injector tip geometry or a flat surface, whether parcels are initialized at the counterbore exit, which is more common, or at the nozzle exit, and whether to use an experimentally derived rate of injection or one-way coupling with a separate internal nozzle volume of fluid simulation, were examined with an LES turbulence model. Both local data close to the injector and global penetration results were used to compare simulations. Local data, such as the local liquid volume fraction, showed greater variation between the conditions, which may have an impact on mixing and combustion predictions in engine applications. Spray penetration and other global measures demonstrated limited sensitivity to the boundary conditions/initialization procedure. Results were also compared with prior results that used a RANS turbulence model. RANS simulations had overall smoother responses to the changes, as would be expected, but LES simulations showed similar trends in the effects for the measured variables.

Effect of the Inclusion of the Detailed Geometry of the Reactor Core on the Calculated Antineutrino Flux from a VVER-1000 Reactor

Effect of the Inclusion of the Detailed Geometry of the Reactor Core on the Calculated Antineutrino Flux from a VVER-1000 Reactor

The accessible seismological dataset of a high-density 2D seismic array along Anninghe fault

The scientific goal of the Anninghe seismic array is to investigate the detailed geometry of the Anninghe fault and the velocity structure of the fault zone. This 2D seismic array is composed of 161 stations forming sub-rectangular geometry along the Anninghe fault, which covers 50 km and 150 km in the fault normal and strike directions, respectively, with ∼ 5 km intervals. The data were collected between June 2020 and June 2021, with some level of temporal gaps. Two types of instruments, i.e. QS-05A and SmartSolo, are used in this array. Data quality and examples of seismograms are provided in this paper. After the data protection period ends (expected in June 2024), researchers can request a dataset from the National Earthquake Science Data Center.

Procedure for evaluation of LNG CCS with reference vessel using nonlinear dynamic analysis

This paper presents a procedure for evaluating the structural safety of Liquefied Natural Gas (LNG) Cargo Containment System (CCS) against sloshing load by comparing the results of linear and nonlinear dynamic structural strength analysis with those of verified reference vessels. This comparative procedure indirectly considers (1) limitation of implementing real physical phenomena in model experiments including the Froude scaling method, density ratio, etc., (2) assumptions in statistical analysis for calculating design loads, and (3) complicated effects not implemented in nonlinear dynamic analysis such as phase transition between liquid and gas, hull deflection, and the detailed geometry of the CCS, among others. The uncertainties in sloshing model experiments and statistical analysis can be addressed by introducing the pressure correction factors, which make the linear design loads of the reference vessel satisfy the CCS’s capacity. Finally, the structural nonlinearity, which was not accounted for in the nonlinear dynamic analysis, can be taken into consideration through a comparison of the usage factors from nonlinear dynamic analysis. To apply the proposed structural assessment procedure, the GTT NO96 series were selected and applied to two failure modes. In the application of this procedure, the Triangular Impulse Response Function (TIRF) method was adopted, to efficiently calculate numerous linear dynamic structural strength analyses required for Dynamic Amplification Factor (DAF). Through this application applying larger sloshing loads to the reinforced NO96 CCS of the target vessel compared to the reference vessel, the feasibility of this procedure was shown.