Geometric Model Research Articles

Multi-grooved channel design in continuous casting mold for enhancing heat transfer efficiency considering pressure drop and flow rate loss

An efficient, speedy, multi-grooved (ESMG) mold was designed and manufactured for optimization to address issues such as low heat transfer rate, slow casting speed, and quality defects in traditional continuous casting molds. The flow resistance mechanism of multi-grooved channels with varying parameters was investigated by considering the ESMG geometric model, the convective heat transfer characteristic variation trends were revealed with different channel designs. Considering constraints of the dimensional chain and supply pressure, variation trends and mechanisms of the pressure drop, flow rate loss, and convective heat transfer coefficient of the ESMG mold were explored using multiple channel variables. Based on the numerical model of the ESMG channel, temperature variation trends in the copper mold were verified by comparison with relevant literature data, supporting the convective-heat-transfer model and variation trends of the ESMG mold. A high-efficiency heat-transfer ESMG assembly that casts U71Mn high-carbon large rectangular billets was fabricated, achieving a closed-loop dimensional chain and replacing traditional molds. Experimental validation on the continuous casting machine (CCM) proved directly that redesigning the ESMG mold cooling channel improved heat transfer efficiency and reduced CO2 emissions. After 504 h on the CCM, the ESMG mold casting speed increased from 1.1 to 1.6 m/min, the heat transfer efficiency was 17.6% higher than that of traditional molds and CO2 emissions were estimated to decrease by 31.2%. The billets produced by the ESMG mold had no quality defects in shape or surface with the original casting conditions, which provided enhanced support for accelerating continuous casting lines.

Efficient identification of cutter axis offset in five-axis ball-end interrupted milling using twin data method free from cutting force model

Although many methods for the identification of cutter axis offset have been proposed, almost all approaches are based on the computation model of cutting force. The mechanical behavior of the cutting tool cannot always be completely described by the existing force model. Once the cutting force is calculated inaccurately, the identification of cutter axis offset certainly is affected. In order to get rid of dependence on cutting force model, this paper presents a twin data driven model for the efficient identification of cutter axis offset in five-axis ball-end interrupted milling. The cutter coupled motion is divided into the two decouple standard movement units at first. The measured feature parameter of the axis offset is then extracted from cutting force signal by the geometric modeling technology. Subsequently, the theoretical and measured critical cutting positions are defined as a pair of twins. The axis offset parameters are identified by minimizing the distance of the twin data using the intelligent optimization algorithm. Lastly, the effectiveness of the proposed method is verified by the numerical examples and cutting experiments performed in five-axis ball-end milling.

The electric transmission stress behaviour investigation while being used for a middle-class road vehicle

Abstract The paper presents the design for an electric transmission used for a middle-class road vehicle. The transmission gears are designed, calculated and virtually developed. A middle-class road vehicle is chosen for virtual implementation. The geometrical models for the gears are designed and developed. The transmission integration into the chosen vehicles is defined. The stress behaviour for the transmission is investigated following different torque performances demands. Different materials for the gears are considered.

Геометрическая модель главного корпуса Ивановской ТЭЦ-2 в ПВК Ansys

Modeling of the heat-air regime in the main body of the TPP in the scientific and technical literature is given insufficient attention. To simulate the heat-air regime in the main building of a thermal power plant, it is necessary to develop a geometric model of the boiler-turbine shop. A mathematical model of the heat-air regime in the main building of the TPP will allow us to develop energy-saving measures and improve the microclimate in the premises of the plant. The study has been carried out using mathematical modeling methods based on the DesignModeler graphic editor in ANSYS software. A geometric model of the boiler-turbine shop of Ivanovo combined heat and power plant-2 (CHPP-2) has been developed. It allows us to develop a mathematical model of heat and mass transfer in the air space of the main building of the plant. The three-dimensional geometric model of Ivanovo CHPP-2 is the first stage of the development of an integrated mathematical model of the heat-air regime of the main building. Its aim is to evaluate measures that reduce consumption of thermal and electrical energy for the in-house needs.

Geometric modelling of corrosion inhibitor pigments in active protective coatings based on SR-nano-CT images

This paper reports on an essential step towards accelerating the development of new, environmentally friendly active protective coatings by structure optimization. The complex microstructure of the pigment particles within a coating have been observed non-destructively in 3D by nano-computed tomography using synchrotron radiation. For the first time, a stochastic geometry model is fitted based on spatial geometric features of the particles observed in the 3D images. The typical cell of a random Gibbs-Laguerre tessellation is used to model the particles' polyhedral shapes as well as the observed joint size and aspect ratio distributions.

Adaptive B-spline curve fitting with minimal control points using an improved sparrow search algorithm for geometric modeling of aero-engine blades

Adaptive B-spline curve fitting with minimal control points using an improved sparrow search algorithm for geometric modeling of aero-engine blades

The flaring activity of blazar AO 0235+164 in 2021

Context. The blazar AO 0235+164, located at redshift z = 0.94, has displayed interesting and repeating flaring activity in the past, with recent episodes in 2008 and 2015. In 2020, the source brightened again, starting a new flaring episode that peaked in 2021. Aims. We study the origin and properties of the 2021 flare in relation to previous studies and the historical behavior of the source, in particular the 2008 and 2015 flaring episodes. Methods.We analyzed the multiwavelength photo-polarimetric evolution of the source. From Very Long Baseline Array images, we derived the kinematic parameters of new components associated with the 2021 flare. We used this information to constrain a model for the spectral energy distribution of the emission during the flaring period. We propose an analytical geometric model to test whether the observed wobbling of the jet is consistent with precession. Results. We report the appearance of two new components that are ejected in a different direction than previously, confirming the wobbling of the jet. We find that the direction of ejection is consistent with that of a precessing jet. Our derived period agrees with the values commonly found in the literature. Modeling of the spectral energy distribution further confirms that the differences between flares can be attributed to geometrical effects.

Retrieval of snow and soil properties for forward radiative transfer modeling of airborne Ku-band SAR to estimate snow water equivalent: the Trail Valley Creek 2018/19 snow experiment

Abstract. Accurate snow information at high spatial and temporal resolution is needed to support climate services, water resource management, and environmental prediction services. However, snow remains the only element of the water cycle without a dedicated Earth observation mission. The snow scientific community has shown that Ku-band radar measurements provide quality snow information with its sensitivity to snow water equivalent and the wet/dry state of snow. With recent developments of tools like the snow micropenetrometer (SMP) to retrieve snow microstructure data in the field and radiative transfer models like the Snow Microwave Radiative Transfer (SMRT) model, it becomes possible to properly characterize the snow and how it translates into radar backscatter measurements. An experiment at Trail Valley Creek (TVC), Northwest Territories, Canada, was conducted during the winter of 2018/19 in order to characterize the impacts of varying snow geophysical properties on Ku-band radar backscatter at a 100 m scale. Airborne Ku-band data were acquired using the University of Massachusetts radar instrument. This study shows that it is possible to calibrate SMP data to retrieve statistical information on snow geophysical properties and properly characterize a representative snowpack at the experiment scale. The tundra snowpack measured during the campaign can be characterize by two layers corresponding to a rounded snow grain layer and a depth hoar layer. Using RADARSAT-2 and TerraSAR-X data, soil background roughness properties were retrieved (msssoil=0.010±0.002), and it was shown that a single value could be used for the entire domain. Microwave snow grain size polydispersity values of 0.74 and 1.11 for rounded and depth hoar snow grains, respectively, were retrieved. Using the geometrical optics surface backscatter model, the retrieved effective soil permittivity increased from C-band (εsoil=2.47) to X-band (εsoil=2.61) and to Ku-band (εsoil=2.77) for the TVC domain. Using the SMRT and the retrieved soil and snow parameterizations, an RMSE of 2.6 dB was obtained between the measured and simulated Ku-band backscatter values when using a global set of parameters for all measured sites. When using a distributed set of soil and snow parameters, the RMSE drops to 0.9 dB. This study thus shows that it is possible to link Ku-band radar backscatter measurements to snow conditions on the ground using a priori knowledge of the snow conditions to retrieve snow water equivalent (SWE) at the 100 m scale.

Clarifying the Impact of Electrode Material Heterogeneity on the Thermal Runaway Characteristics of Lithium‐Ion Batteries

The safety and efficiency of lithium‐ion batteries (LIBs) suggest a promising future for this technology, particularly in the automobile industry. However, thermal runaway—wherein a LIB undergoes an uncontrollable increase in temperature that may result in smoke, fire, or explosion—represents an important and widely studied failure type. Since the electrodes of LIBs are manufactured from porous composite materials, their heterogeneity can significantly influence the effective material characteristics and microscale behaviors of LIBs during operation. Microstructure geometric and electrochemical–thermal models are typically used to investigate these impacts. Herein, a microstructure geometric model is constructed of LIB's electrodes. A virtual multiphysics model is used to simulate the overcharging thermal runaway condition. The model's accuracy is validated through real‐world experiments. The model shows greater accuracy compared to the result from a conventional homogeneous geometric model and better reflects the heterogeneous internal phenomenon. The model is applied to a variable analysis in order to investigate how the varying heterogeneity of the cathode's porosity impacts the cell during overcharging thermal runaway behavior. Our results indicate that decreasing porosity heterogeneity at the cathode may delay thermal runaway, owing to the heterogeneous impact on particle diffusion behaviors and the side reaction rate.

Gamma-ray line emission from the Local Bubble

Deep-sea archives that include intermediate-lived radioactive 60Fe particles suggest the occurrence of several recent supernovae inside the present-day volume of the Local Bubble during the last ~10 Myr. The isotope 60Fe is mainly produced in massive stars and ejected in supernova explosions, which should always result in a sizeable yield of 26Al from the same objects. 60Fe and 26Al decay with lifetimes of 3.82 and 1.05 Myr, and emit γ rays at 1332 and 1809 keV, respectively. These γ rays have been measured as diffuse glow of the Milky Way, and would also be expected from inside the Local Bubble as foreground emission. Based on two scenarios, one employing a geometrical model and the other state-of-the-art hydrodynamics simulations, we estimated the expected fluxes of the 1332 and 1809 keV γ-ray lines, as well as the resulting 511 keV line from positron annihilation due to the 26Al β+ decay. We find fluxes in the range of 10−6–10−5 ph cm−2 s−1 for all three lines with isotropic contributions of 10–50%. We show that these fluxes are within reach for the upcoming COSI-SMEX γ-ray telescope over its nominal satellite mission duration of 2 yr. Given the Local Bubble models considered, we conclude that in the case of 10–20 Myr-old superbubbles, the distributions of 60Fe and26 Al are not co-spatial - an assumption usually made in γ-ray data analyses. In fact, this should be taken into account however when analysing individual nearby targets for their 60Fe to26 Al flux ratio as this gauges the stellar evolution models and the age of the superbubbles. A flux ratio measured for the Local Bubble could further constrain models of 60Fe deposition on Earth and its moon.

An investigation of inflationary and bounce cosmology within the context of f(Q) gravity

In this work, we have examined the metric affine-based geometric model [Formula: see text] to investigate inflationary cosmology and bouncing cosmology. The four bouncing models we have examined are the exponential, matter, symmetric and super-bounce models. Furthermore, in the inflationary situation, we have employed a scalar field to test whether or not kinetic energy is dominated and whether or not all of the bouncing models are consistent with inflationary conditions. We have investigated various energy conditions for various scale factors and verified whether or not these scale factors are consistent with inflationary scenarios within the framework of inflationary cosmology under [Formula: see text]. In the end, we have shown that the bouncing scale factor under the polynomial model ([Formula: see text]) of [Formula: see text] gravity in a single scalar field is compatible with the inflationary scenario.

Nanoparticle Diffusion in Crowded Polymer Nanocomposite Melts.

This study examines nanoparticle diffusion in crowded polymer nanocomposites by diffusing small Al2O3 nanoparticles (NPs) in SiO2-loaded P2VP matrices. Time-of-flight secondary ion mass spectroscopy (ToF-SIMS) measures Al2O3 NP diffusion coefficients within a homogeneous PNC background of larger, immobile SiO2 NPs. By developing a geometric model for the average interparticle distance in a system with two NP sizes, we quantify nanocomposite confinement relative to the Al2O3 NP size with a bound layer. At low SiO2 concentrations, Al2O3 NP diffusion aligns with the neat polymer results. In more crowded nanocomposites with higher SiO2 concentrations where the interparticle distance approaches the size of the mobile Al2O3 NP, the 6.5 nm Al2O3 NPs diffuse faster than predicted by both core-shell and vehicular diffusion models. Relative to our previous studies of NPs diffusing into polymers, these findings demonstrate that the local environment in crowded systems significantly complicates NP diffusion behavior and the bound layer lifetimes.

Camera-in-the-loop based test scenario generation method for pedestrian collision avoidance system

The Pedestrian Collision Avoidance System (PCAS) of Intelligent Vehicle (IV) can be effective in preventing the occurrence of traffic accidents. However, the complicated operation environments introduce great challenges to the camera used by the PCAS. Therefore, the camera based PCAS should be fully tested and evaluated before deployment. The traditional simulation test for the camera based PCAS attempted to use geometric or physical simulation models, which have low reality and are suitable for the primary stage of the PCAS development. Camera-in-the-Loop (CIL) test is one of Hardware-in-the-Loop methods that embeds the real camera hardware into the virtual simulation system to test the camera. CIL can utilize the real hardware response while overcoming the common simulation weakness of fidelity. In this paper, we construct a CIL test platform, and propose the CIL based test scenarios generation and scenario parameter impact evaluation method for PCAS. First, we construct the CIL test platform whose image quality and functional confidence are both validated to prove CIL credibility. Second, the PCAS under the test is analyzed and the corresponding test scenario parameters are designed. In order to accelerate the test scenario generation, a Greedy Based Combination test method (GBC) based on the CIL is proposed. The Chi-square analysis and two-factor of variance analysis verification methods are used to analyze the influence of individual and multiple scenario parameters on the PCAS performance. The experiment results show that the GBC improves the test speed by 12 times compared to the traversal test, and the frequency ratio of each scenario parameter is no more than 3% different from that of the traversal test. Also, GBC has an equivalent ability to find the PCAS collision scenarios parameter combination to the traversal test.

A unified geometric error model applicable to all configurations of three-axis machine tools

A unified geometric error model applicable to all configurations of three-axis machine tools

The shading isophotes: Model and methods for Lambertian planes and a point light

Structure-from-Motion (SfM) and Shape-from-Shading (SfS) are complementary classical approaches to 3D vision. Broadly speaking, SfM exploits geometric primitives from textured surfaces and SfS exploits pixel intensity from the shading image. We propose an approach that exploits virtual geometric primitives extracted from the shading image, namely the level-sets, which we name shading isophotes. Our approach thus combines the strength of geometric reasoning with the rich shading information. We focus on the case of untextured Lambertian planes of unknown albedo lit by an unknown Point Light Source (PLS) of unknown intensity. We derive a comprehensive geometric model showing that the unknown scene parameters are in general all recoverable from a single image of at least two planes. We propose computational methods to detect the isophotes, to reconstruct the scene parameters in closed-form and to refine the results densely using pixel intensity. Our methods thus estimate light source, plane pose and camera pose parameters for untextured planes, which cannot be achieved by the existing approaches. We evaluate our model and methods on synthetic and real images.

Using the polynomial chaos expansion and bias-variance tradeoff to analyse the statistical characteristics of a trimaran cross-deck structure

The tendency to design high-performance ships demonstrates the importance of the trimaran and the cross-deck is of particular interest due to its exclusive structures and wave loads. Limited by the high cost of structural numerical simulation, relevant studies emphasize the effectiveness of a specific design scheme. This paper introduces the polynomial chaos expansion (PCE) and the bias-variance tradeoff to explore the statistical characteristics of the cross-deck's strength. The geometric model of a high-speed trimaran is built and the load case is designed. Samples are obtained using the Latin hypercube sampling technique and the corresponding stress response of the cross-deck is extracted by the finite element method. To overcome the limitation of sample size, the PCE is applied as a surrogate model to compute the statistical indicators of the stresses and the bias-variance tradeoff is introduced to select the hyperparameter of the PCE. The results show that the stresses at the cross-deck's corner are much higher and more sensitive to the plate thickness than those at other locations. Furthermore, the PCE is also compared with the Monte Carlo simulation to demonstrate its advantages, and it is shown that the PCE performs better under the limitation of sample size considering accuracy and convergence. The feasibility and efficiency of the PCE and bias-variance tradeoff in the statistical analysis of trimaran cross-deck structure is proved. The insights and recommendations summarized will be a useful aid for the design and optimization of the trimaran.

Optimization of structural reinforcement assessment for architectural heritage digital twins based on LiDAR and multi-source remote sensing

This study investigates the geometric modelling of architectural heritage digital twins constructed based on multi-source point cloud data and its effectiveness in structural reinforcement assessment. Particular emphasis has been placed on the use of static stiffness rules to identify areas of structural weakness in the geometric models of digital twins and the need for their reinforcement, in order to prevent potential structural problems and to ensure the long-term preservation of the built heritage. Taking Yingxian wooden pagoda as a study case, based on the collection of multi-source point cloud data, the digital twin geometric model is constructed through fine modelling, decoupling of digital models, and geometric transformation. This enhances the true reflection of the column-architrave structure morphology, providing a more accurate model for structural stress analysis. Based on verifying the accuracy of the digital twin geometric model, the instability conditions are identified through static stiffness rules and the deformation values at multiple points are analyzed, enabling precise identification of weak areas in the column-architrave structure. Two types of reinforcement measures are designed and simulated for the structural weak areas identified through the geometric modelling, and the optimal reinforcement scheme is obtained after detailed analysis, according to which specific adjustments and optimization strategies are proposed to enhance the overall stability and durability of the structure. The results showed that the maximum deformation value of 4.65 mm existed in column M2W23, which required reinforcement. Aluminum reinforcement reduced the deformation to 3.5 mm (24.7% reduction), while CFRP fabric reinforcement was more effective, reducing the deformation to 2.8 mm (39.7% reduction), showing high stability. The research results demonstrate the potential application of digital twin technology in architectural heritage preservation and restoration, providing methodological and empirical guidance for heritage preservation research.

A Hierarchical Neural Network for Point Cloud Segmentation and Geometric Primitive Fitting

Automated generation of geometric models from point cloud data holds significant importance in the field of computer vision and has expansive applications, such as shape modeling and object recognition. However, prevalent methods exhibit accuracy issues. In this study, we introduce a novel hierarchical neural network that utilizes recursive PointConv operations on nested subdivisions of point sets. This network effectively extracts features, segments point clouds, and accurately identifies and computes parameters of regular geometric primitives with notable resilience to noise. On fine-grained primitive detection, our approach outperforms Supervised Primitive Fitting Network (SPFN) by 18.5% and Cascaded Primitive Fitting Network (CPFN) by 11.2%. Additionally, our approach consistently maintains low absolute errors in parameter prediction across varying noise levels in the point cloud data. Our experiments validate the robustness of our proposed method and establish its superiority relative to other methodologies in the extant literature.

A deformation procedure using position-based dynamics to optimize the geometric model of woven fabrics

Generating realistic three-dimensional (3D) yarn-level models for fabrics has become a significant research topic due to the complexity of fabric structures and the requirement for high-quality models with no interpenetration between yarns. The generation process of idealized geometric models leads to inaccurate descriptions of yarn contact, specifically the interpenetrations and spurious voids between yarns. A geometric optimization procedure was developed to address the defects in the idealized models, involving a series of geometry-driven operations, such as the shrinking and expansion of yarn volume and the straightening of the yarn centerline, to obtain accurate and consistent fabric models for Finite Element Analysis. During the geometric optimization, a method for yarn deformation based on position-based dynamics (PBD) was applied to simulate the real deformation during yarn interweaving, ensuring no interpenetrations between yarns while preserving yarn volume. The accuracy of the model is validated by comparison with the real fabric images. Although the methods involved in the procedure are all geometric, their deformation results have realistic physical effects. In addition, the procedure can be applied to various woven fabric structures.

Geometric Stiffness and Model Improvement of Rigid Elements for Preloaded Modal Analysis

Geometric Stiffness and Model Improvement of Rigid Elements for Preloaded Modal Analysis