Detailed Shape Research Articles

Study of Cooling Performance of Liquid-Cooled EV Battery Module According to the TIM Compression Ratio

AbstractThis study examines the coolant and heat flows in electric vehicle (EV) battery pack that employs a thermal interface material (TIM). The overall temperature distribution of the battery pack that consists of many battery modules is precomputed based on the cooling circuit design, and the battery module that is most strongly influenced by cooling circuit is selected. To investigate the detailed effects of the TIM’s performance, we measure its thermal conductivity based on its compression ratio and consider the detailed shape of the battery cell module for incorporating the TIM’s thermal conductivity in the battery assembly. The TIM, which functions as part of the cooling system within the confined space of the battery cell module, is included in the CFD analysis to assess the effect of thermal conductivity variation resulting from the compression ratio of the TIM on the cooling performance of the battery pack. Various cases of confined spaces in the battery module are compared to optimize the cooling efficiency of the EV battery pack.

3D human avatar reconstruction with neural fields: A recent survey

3D human avatar reconstruction aims to reconstruct the 3D geometric shape and appearance of the human body from various data inputs, such as images, videos, and depth information, acting as a key component in human-oriented 3D vision in the metaverse. With the progress in neural fields for 3D reconstruction in recent years, significant advancements have been made in this research area for shape accuracy and appearance quality. Meanwhile, substantial efforts on dynamic avatars with the representation of neural fields have exhibited their effect. Although significant improvements have been achieved, challenges still exist in in-the-wild and complex environments, detailed shape recovery, and interactivity in real-world applications. In this survey, we present a comprehensive overview of 3D human avatar reconstruction methods using advanced neural fields. We start by introducing the background of 3D human avatar reconstruction and the mainstream paradigms with neural fields. Subsequently, representative research studies are classified based on their representation and avatar partswith detailed discussion. Moreover, we summarize the commonly used available datasets, evaluation metrics, and results in the research area. In the end, we discuss the open problems and highlight the promising future directions, hoping to inspire novel ideas and promote further research in this area.

New watershed methods for isolating and characterizing discrete objects in 3D data sets

This paper introduces new algorithms for conducting and improving watershed analysis, implemented with the particular goal of improving the ability to measure the shapes of mineral grains to be subsequently be analyzed by mass spectrometry. This application requires a high degree of accuracy and fidelity in terms of both separating all touching grains and preserving their shapes. The algorithms are designed to take advantage of a vector-based programming environment. A new implementation of the Euclidean distance transform utilizes the fact that the distance from any adjacent pair of voxels to the nearest boundary must be within one voxel of each other. In practice, however, this algorithm is outperformed by a smoothed approximate distance transform that is faster to compute and results in less irregular watershed boundaries. A one-pass rainfall-based watershed algorithm is introduced that runs in linear time with the number of segmented voxels, and requires no priority queue. Unlike marker-based watershed algorithms based on the basin-filling approach, the rainfall approach finds watersheds associated with all local maxima in the distance map, even if a marking algorithm is used. A post-watershed smoothing algorithm improves watershed boundaries and eliminates small spurious watersheds. The one-pass watershed and post-watershed smoothing algorithms run in times superior or comparable to basin-fill watershed algorithms implemented in other environments, and offers excellent ability to separate touching objects efficiently while placing watershed boundaries that maximize the preservation of details of particle shape. Further time improvement could come from implementing them in a vector-based environment that allows explicit multi-threading.

Recovering detailed shape of phase response curves from the higher harmonics of singularity response

The phase response curve (PRC) is crucial in elucidating the synchronization characteristics of nonlinear oscillators under the influence of external forces. Oscillatory phenomena in nature often consist of an ensemble of multiple oscillators, which complicates the measurement of individual oscillator PRCs. A prior study introduced a technique for estimating individual PRCs from the singularity response (SR)—the reaction of an ensemble of desynchronized oscillators without couplings. However, the utility of this method is somewhat limited, as it considers only the first harmonic of the PRC. The current study demonstrates that the detailed contour of the PRC can be ascertained by employing the higher harmonics of the SR. Published by the American Physical Society 2024

Derivation of surface models using satellite imagery deep learning architectures with explainable AI

Global mapping of urban morphology and human settlement is an area of great interest, with significant scientific and humanitarian value. Current approaches for high-resolution surface modeling using aerial LiDAR are impressive and useful, but it is impractical for application on a global scale. This is due to technical challenges (efficiency of coverage, etc), as well as cost. These are significant obstacles, particularly in the less wealthy regions of the world, many of which are experiencing the greatest increase in population and physical infrastructure. Advances in satellite imagery and deep learning are now enabling the lower resolution alternative, but one which is more scalable with global application. This paper examines the efficacy of four state-of-the-art deep learning architectures (DeepLabv3+, SegNet, U-Net, and U-Net++) for application to Digital Surface Modeling (DSM), particularly for urban districts. The study considered New York City as the test area, using satellite based Radar backscatter images and visible and near infrared images for surface model development, along with high resolution LiDAR based surface height model of the city. We performed a quantitative evaluation using metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R-Squared, and a qualitative analysis to identify the best performing model. U-Net++ proved to be the most effective, with superior accuracy in capturing key urban features. In addition, we employed Explainable Artificial Intelligence (XAI) techniques, such as Gradient-Weighted Class Activation Mapping (GRAD-CAM) and Saliency Maps, to improve model interpretability, revealing important information about decision-making processes and highlighting critical input features. The results of this study contribute to the advancement of remote sensing techniques for urban analysis, with potential applications. Despite the overall success, the study also identified challenges, such as the loss of detailed building shapes in the predicted DSMs and the influence of optical shadowing and SAR-induced quiescence on model performance.

A decomposition method for form-finding of circular hybrid cable-truss structures considering self-weight

A hybrid cable-truss structure has been previously utilized in the Maracana Stadium. There are two ways to design the initial prestress distribution in the hybrid cable-truss structure, taking into account the functionality of the upper and lower hoop cables. This study proposes a decomposition method for determining the form-finding of circular hybrid cable-truss structures with two different prestress distributions. The hybrid cable-truss structure is decomposed into the inner and outer cable-truss structures. The PBS (partial balance strategy) method is employed to design the prestress and shape of both the inner and outer cable-truss structures with consideration of self-weight. A detailed prestress and shape design analysis of a circular hybrid cable-truss structure with two prestress distributions is conducted, and the precision of the initial prestress design is analyzed. The illustrative example presented herein demonstrates that the proposed method, combined with the PBS method, provides a simple and accurate approach for the form-finding of hybrid cable-truss roof systems subject to self-weight.

PyramidPCD: A novel pyramid network for point cloud denoising

Point cloud denoising, which aims to restore high-quality point clouds from noisy input, is an ingredient in various fields, including 3D mapping, 3D vision, and structured modeling. In this study, we present a feature pyramid network that can effectively remove noise while accurately preserving structural-to-detailed shape characteristics at varying scales. Our method is built upon the key insight that the coarser scale in the feature pyramid naturally contains more primary structures, while the finer scale provides more shape details. This discovery motivates us to progressively upgrade the current-scale feature with coarser-scale features to preserve structures while recovering details from the finer scale. To this end, we present a U-Net-shaped architecture that incorporates structure-aware and detail-preserving units at multiple scales for feature-preserving point cloud denoising. The structure-aware unit can enhance the current-scale feature by applying structural guidance from the coarse level of the feature pyramid, while the detail-preserving unit can learn a more comprehensive representation that incorporates the finer-scale feature in the pyramid. Extensive experiments conducted on publicly available benchmarks demonstrate that the proposed approach achieves state-of-the-art performances and outperforms the competing methods. Our source code and data are available at https://github.com/ForestNobear/PyramidPCD.

Effect of pavement layer temperature on fatigue performance of rib-to-deck weld details in orthotropic steel bridge decks

To investigate the effect of asphalt pavement layer temperature on the fatigue performance of welded details in orthotropic steel bridge deck (OSD), this study focuses on the critical fatigue crack regions of the rib-to-deck weld details. A systematic study was conducted based on the theory of linear elastic fracture mechanics (LEFM) and the finite element model (FEM). In this research, the elastic modulus of the pavement layer at different temperatures, and the initial crack shape, were considered as parameters. The maximum normal stress, stress intensity factor (SIF), and fatigue crack growth (FCG) shape and life of the weld details under the influence of these parameters were analyzed. The analysis results indicate that using a constant temperature of 20°C for the pavement layer will ignore the adverse effects of high summer temperatures. Under the influence of the pavement layer temperature effects, the weld toe of the top deck is the most critical welded detail. The FCG life and final crack shape at the weld toe of top deck are closely related to the effects of pavement layer temperature and the initial crack shape, higher pavement layer temperatures result in shorter fatigue life and larger final crack propagation sizes. Therefore, it is recommended that pavement layer and temperature effects be considered in the FCG analysis of welded details in OSDs.

Fin angles optimization of water-cooled plate-fin heat sink based on anisotropic Darcy–Forchheimer theory

Flat heat sinks are devices for efficient heat dissipation and are important components in manufacturing. For example, in precision glass molding, it is utilized as a cooling plate contributing to improve the quality of the product by reducing residual stress and shape deviation, during the gradual cooling phase. In this study, we attempted to achieve precise temperature design of the cooling plate using variable lattice optimization. Fins allow for controlling the flow direction and achieving efficient heat transfer. Therefore, elliptical cylindrical fins were adopted as lattice unit cells, increasing the design freedom of the flow field. To account for the asymmetric flow field within the unit cell, we introduced an asymmetric tensor for the coefficients of the effective physical properties in the Darcy–Forchheimer equation. The proposed method's effectiveness and validity are discussed through numerical calculations with effective material properties, reproduced detailed shapes, and experimental verification. The optimization aiming to minimize the average temperature yielded a numerical simulation temperature decrease of 24.6 % and an experimental temperature decrease of 14.1 % compared to the benchmark model.

The Impact of Flow-Thermal Characteristics in Ship-Board Solid Oxide Fuel Cells

Hydrogen is increasingly recognized as a clean and reliable energy vector for decarbonization in the future. In the marine sector, marine solid oxide fuel cells (SOFCs) that employ hydrogen as an energy source have already been developed. In this study, a multi-channel plate-anode-loaded SOFC was taken as the research object. A three-dimensional steady-state computational fluid dynamics (CFD) model for anode-supported SOFC was established, which is based on the mass conservation, energy conservation, momentum conservation, electrochemical reactions, and charge transport equations, including detailed geometric shapes, model boundary condition settings, and the numerical methods employed. The polarization curves calculated from the numerical simulation were compared with experimental results from the literature to verify the model’s accuracy. The curved model was applied by enlarging the flow channels or adding blocks. Numerical calculations were employed to obtain the current density, temperature distribution, and component concentration distribution under the operating conditions of the SOFC. Subsequently, the distribution patterns of various physical parameters during the SOFC operation were analyzed. Compared to the classical model, the temperature of the curved model was reduced by 1.3%, and the velocities of the cathode and anode were increased by 4.9% and 5.0%, respectively, with a 2.42% enhancement in performance. The findings of this study provide robust support for research into and the application of marine SOFCs, and offer they insights into how we may achieving “dual carbon” goals.

Aircraft Ducted Heat Exchanger Aerodynamic Shape and Thermal Optimization

Abstract Interest in aircraft electrification and hydrogen fuel cells is driving demand for efficient waste heat management systems. Ultimately, most of the heat must be rejected to the freestream air. Ducted heat exchangers, also called ducted radiators, are the most common and effective way to do this. Engineers manually design ducted heat exchangers by adjusting the duct's shape and heat exchanger's configuration to reduce drag and transfer sufficient heat. This manual approach misses potential performance improvements because engineers cannot simultaneously consider all of the complex interactions between the detailed duct shape, heat exchanger design, and operating conditions. To find these potential gains, we apply gradient-based optimization to a three-dimensional ducted heat exchanger computational fluid dynamics (CFD) model. The optimizer determines the duct shape, heat exchanger size, heater exchanger channel geometry, and coolant flowrate that minimize the ducted heat exchanger's power requirements while rejecting enough heat. Gradient-based optimization enables the use of nearly 100 shape design variables, creating a large design space and allowing fine-tuning of the optimal design. When applied to an arbitrary, poorly performing baseline, our method produces a nuanced and sophisticated ducted heat exchanger design with five times less cruise drag. Employing this method in the design of electric and fuel cell aircraft thermal management could uncover performance not achievable with manual design practices.

Three-Dimensional Dense Reconstruction: A Review of Algorithms and Datasets.

Three-dimensional dense reconstruction involves extracting the full shape and texture details of three-dimensional objects from two-dimensional images. Although 3D reconstruction is a crucial and well-researched area, it remains an unsolved challenge in dynamic or complex environments. This work provides a comprehensive overview of classical 3D dense reconstruction techniques, including those based on geometric and optical models, as well as approaches leveraging deep learning. It also discusses the datasets used for deep learning and evaluates the performance and the strengths and limitations of deep learning methods on these datasets.

Response to "Comment on 'Anomalous reflection from a two-layered marine sediment' " [J. Acoust. Soc. Am. 156, 1524-1527 (2024)

In their Comment, the authors conclude that acoustic glint is not present in the reflection coefficient of a two-layer sediment in which the top layer is an Airy medium. They conclude, not that the original, inverse-square analysis of the glint is incorrect, but rather that the presence of glint is very sensitive to the detailed shape of the sound speed profile in the top layer.

Machine learning and numerical investigation on drag coefficient of arbitrary polygonal particles

AbstractThe drag coefficient, as the most important parameter that characterizes particle dynamics in flows, has been the focus of a large number of investigations. Although good predictability is achieved for simple shapes, it is still challenging to accurately predict drag coefficient of complex‐shaped particles even under moderate Reynolds number (Re). The problem is that the small‐scale shape details of particles can still have considerable impact on the drag coefficient, but these geometrical details cannot be described by single shape factor. To address this challenge, we leverage modern deep‐learning method's ability for pattern recognition, take multiple shape factors as input to better characterize particle‐shape details, and use the drag coefficient as output. To obtain a high‐precision data set, the discrete element method coupled with an improved velocity interpolation scheme of the lattice Boltzmann method is used to simulate and analyze the sedimentation dynamics of polygonal particles. Four different machine‐learning models for predicting the drag coefficient are developed and compared. The results show that our model can well predict the drag coefficient with an average error of less than 5% for particles. These findings suggest that data‐driven models can be an attractive option for the drag‐coefficient prediction for particles with complex shapes.

Modeling of plasma facing component erosion, impurity migration, dust transport and melting processes at JET-ILW

An overview of the modeling approaches, validation methods and recent main results of analysis and modeling activities related to the plasma-surface interaction (PSI) in JET-ILW experiments, including the recent H/D/T campaigns, is presented in this paper. Code applications to JET experiments improve general erosion/migration/retention prediction capabilities as well as various physics extensions, for instance a treatment of dust particles transport and a detailed description of melting and splashing of PFC induced by transient events at JET. 2D plasma edge transport codes like the SOLPS-ITER code as well as PSI codes are key to realistic description of relevant physical processes in power and particle exhaust. Validation of the PSI and edge transport models across JET experiments considering various effects (isotope effects, first wall geometry, including detailed 3D shaping of plasma-facing components, self-sputtering, thermo-forces, physical and chemically assisted physical sputtering formation of W and Be hydrides) is very important for predictive simulations of W and Be erosion and migration in ITER as well as for increasing quantitative credibility of the models. JET also presents a perfect test-bed for the investigation and modeling of melt material dynamics and its splashing and droplet ejection mechanisms. We attribute the second group of processes rather to transient events as for the steady state and, thus, treat those as independent additions outside the interplay with the first group.

Digital toy design: A doll head modeling method based on model matching and texture generation

With the development of technology and the improvement of living standards, consumer demand for toy products has also changed. More people are showing strong interest and demand for digital toy products. However, in the current digital toy design process, extracting the hairstyle features of the doll’s head is still a challenge. Therefore, this study extracts the two-dimensional contour of the target hairstyle and matches it with the template hairstyle in the database. Then, combining hair texture information and hairstyle structural features, fine geometric texture details are generated on the hairstyle mesh surface. Finally, a doll head modeling method based on model matching and texture generation is proposed. The results showed that the hairstyle of the Generative model was almost the same as the real hairstyle. Compared with the modeling methods based on interactive genetic algorithm and digital image, the average F1 value of this method was 0.95, and the mean absolute error was the smallest. The accuracy of the target model modeling was 95.4%, and the area enclosed by the receiver operating characteristic curve and coordinate axis was 0.965. In summary, the doll head modeling method based on model matching and texture generation proposed in this study can generate high-precision and realistic hairstyle models corresponding to the hairstyle. The overall shape and local geometric details of the hairstyle can meet the needs of 3D printing, providing certain reference significance for hairstyle reconstruction.

Automatic topology and capacity generation framework for urban drainage systems with deep learning-based land use segmentation and hydrological characterization

This paper proposed a comprehensive and automated framework that integrates the entire urban drainage design process, from refined land use segmentation, to catchment subdivision and characterization, to network topology generation, and finally to hydraulic estimation of pipeline capacity, based on multiple open-source datasets. The method incorporated deep learning-based semantic segmentation, GIS-based hydrological characterization, topology generation and hydraulic calculation algorithms. The performance and applicability of the method were validated through case studies of two cities. The results showed that the deep learning model with prior knowledge could accurately segment urban land use to obtain the detailed shape and boundary description, and achieved higher detection accuracy for water bodies, buildings, and roads. The stormwater drainage topology and conveyance capacity were automatically generated based on subcatchment division and hydrological characterization under a set of topological parameter conditions. In the optimal Critical Success Index (CSI) scenario, only 8% and 21% of the generated pipelines were inconsistent with the expert-designed networks. Compared to the traditional manual design process, the automated approach can save significant time and resources and reduce the reliance on field surveys and empirical/expert work. At the same time, the tool can be employed for system layout and capacity optimization, providing important support for the automation, design, and rehabilitation of stormwater drainage systems.

In-beam performance of a Resistive Plate Chamber operated with eco-friendly gas mixtures

ALICE (A Large Ion Collider Experiment) studies the Quark-Gluon Plasma (QGP): a deconfined state of nuclear matter obtained in ultra-relativistic heavy-ion collisions. One of the key probes for QGP characterization is the study of quarkonia and open heavy flavor production, of which ALICE exploits the muonic decay. In particular, a set of Resistive Plate Chambers (RPCs), placed in the forward rapidity region of the ALICE detector, is used for muon identification purposes.The correct operation of these detectors is ensured by the choice of the proper gas mixture. Currently they are operated with a mixture of C2H2F4, i-C4H10 and SF6 but, starting from 2017, new EU regulations have enforced a progressive phase-out of C2H2F4 because of its large Global Warming Potential (GWP), which is making it difficult and costly to purchase. Moreover, CERN asked LHC experiments to reduce greenhouse gases emissions, to which RPC operation contributes significantly.A possible candidate for C2H2F4 replacement is the C3H2F4 (diluted with other gases, such as CO2), which has been extensively tested using cosmic muons. Promising gas mixtures have been devised; the next crucial steps are the detailed in-beam characterization of such mixtures as well as the study of their performance under increasing irradiation levels.This contribution will describe the methodology and results of beam tests carried out at the CERN Gamma Irradiation Facility (equipped with a high activity 137Cs source and muon beam) with an ALICE-like RPC prototype, operated with several mixtures with varying proportions of CO2, C3H2F4, i-C4H10 and SF6 . Absorbed currents, efficiencies, prompt charges, cluster sizes, time resolutions and rate capabilities will be presented, both from digitized (for detailed shape and charge analysis) and discriminated (using the same front-end electronics as employed in ALICE) signals.

Cognitive differences in product shape evaluation between real settings and virtual reality: case study of two-wheel electric vehicles

Product shape evaluation is an important part of new product development. In the shape design stage, design schemes are often presented through visual images. The presentation of visual images causes evaluators to form different cognitive experiences and evaluation results. In recent years, virtual reality (VR) technology has been widely used in the field of industrial design, enriching the presentation forms of design scheme images. Although VR technology has shown the potential to improve evaluators’ perception and cognitive experiences in product shape design, research comparing it with traditional methods remains relatively scattered. This study used two-wheel electric vehicles as an example to examine the difference in evaluators’ cognition of product shape in VR and a real setting (RS). First, we established a semantic scale comprising seven pairs of opposite adjectives to evaluate the shape scheme. Second, we built VR and RS evaluation environments using head-mounted displays and paper renderings, respectively. The participants evaluated the vehicle shape design in alternating viewing and underwent semi-structured interviews on cognitive experience. We analyzed the experimental and interview results based on three aspects of product shape cognition. The results demonstrated that volume cognition was significantly more accurate in VR environments. Furthermore, graphic cognition, particularly regarding shape details, differed partially between environments. VR provided a better sense of immersion and more variable viewing angles than RS. Treatment cognition did not exhibit significant differences between environments, as it depended on human experience rather than visualization. These findings suggest that VR tools are more suited for shaping design evaluations early. Selecting suitable visual presentation tools based on evaluators’ cognitive characteristics at different evaluation nodes to display design schemes is a practical, economical, and efficient strategy.

SHOWMe: Robust object-agnostic hand-object 3D reconstruction from RGB video

In this paper, we tackle the problem of detailed hand-object 3D reconstruction from monocular video with unknown objects, for applications where the required accuracy and level of detail is important, e.g. object hand-over in human–robot collaboration, or manipulation and contact point analysis. While the recent literature on this topic is promising, the accuracy and generalization abilities of existing methods are still lacking. This is due to several limitations, such as the assumption of known object class or model for a small number of instances, or over-reliance on off-the-shelf keypoint and structure-from-motion methods for object-relative viewpoint estimation, prone to complete failure with previously unobserved, poorly textured objects or hand-object occlusions. To address previous method shortcomings, we present a 2-stage pipeline superseding state-of-the-art (SotA) performance on several metrics. First, we robustly retrieve viewpoints relying on a learned pairwise camera pose estimator trainable with a low data regime, followed by a globalized Shonan pose averaging. Second, we simultaneously estimate detailed 3D hand-object shapes and refine camera poses using a differential renderer-based optimizer. To better assess the out-of-distribution abilities of existing methods, and to showcase our methodological contributions, we introduce the new SHOWMe benchmark dataset with 96 sequences annotated with poses, millimetric textured 3D shape scans, and parametric hand models, introducing new object and hand diversity. Remarkably, we show that our method is able to reconstruct 100% of these sequences as opposed to SotA Structure-from-Motion (SfM) or hand-keypoint-based pipelines, and obtains reconstructions of equivalent or better precision when existing methods do succeed in providing a result. We hope these contributions lead to further research under harder input assumptions. The dataset can be downloaded at https://download.europe.naverlabs.com/showme.