Surface Points Research Articles

Surface temperature field real-time reconstruction of hot forging die based on 1DCNN

Accurate reconstruction of the die surface temperature field during the hot forging process is crucial for ensuring optimal control of die temperature and high-quality metal forgings. In this study, we propose a novel method that combines hot forging process parameters with a deep neural network to reconstruct the temperature field on the die surface in real-time. Firstly, a high-fidelity finite element model of the hot forging process is created and validated with a forging experiment. Secondly, temperature observation points are initially selected based on the simulated temperature distribution of the forging die, and the correlation coefficient method is used to optimize the layout of observation points to improve reconstruction accuracy and efficiency. Subsequently, multiple high-quality simulations with varying process parameters are conducted, generating a comprehensive dataset. The 1DCNN algorithm is then presented to produce a mapping from the input to the surface temperature field. Finally, the effectiveness of the surface temperature field reconstruction method is verified. The results indicate that the overall performance of the proposed 1DCNN algorithm is better than the traditional regression algorithms. In terms of reconstruction accuracy, it achieves a prediction accuracy within an absolute error of 3 °C for the outer surface points and within a relative error of 5 % for the cavity region. Regarding reconstruction efficiency, it requires an approximate execution time of 0.064 s for predicting a new sample. This paper presents a method for real-time reconstruction of the die surface temperature field, leveraging hot forging process parameters and temperature data from observation points. This approach enables reliable online monitoring of transient temperature variations on the hot forging die surface, offering valuable support for operational control and maintenance.

TPO12-graphene: A new two-dimensional metallic carbon with 4–5 ring for Lithium ion battery

For many decades, the stability of two-dimensional carbon structures involving connected 4–5 ring remained speculative. Recently, some of the planar carbon structures with connected 4–5 ring have showcased possible stability. In this work we present a new 4–5 ring carbon structure, belonging to Pmmm space group, having additional octagonal and dodecagonal rings to fill the space. The stability of this structure has been ensured in terms of its dynamical, thermal and mechanical stability. The electronic structure indicates that it is a non-magnetic metallic material. Moreover, the Fermi surface points to distinct electron and hole pockets in the first Brillouin zone. The quantum capacitance of the material suggests its use as anode in asymmetric supercapacitors. Besides its low optical absorption in visible range, its potential as an anode material for lithium-ion batteries is worth exploring. The storage capacity results of 558 mAh/g for the new material may be encouraging for its application as a lithium-ion battery anode material.

Non-contact discharge estimation at a river site by using only the maximum surface flow velocity

The study proposes a novel method of computing river discharge based on the maximum surface velocity recorded using a non-contact-based measurement at a singular water surface point. This location, generally, coincides with the maximum flow depth of the cross-section and accounts for the dip phenomena, where the maximum instream velocity occurs below the water surface. The method is based on information entropy theory developed by Shannon (1948) and applied to river hydraulics. In this study an alternate form of entropy is used to compute discharge as a function of the cross-sectional mean velocity, maximum velocity and shear velocity (Keulegan,1938) by minimizing the error of the state equilibrium constant, ΦM, which is the ratio between the mean and maximum flow velocity, and that estimated using the Keulegan-based relationship. To test the accuracy of the proposed method, the maximum surface flow velocities measured at two gauging stations, each located on two different Italian rivers were studied. The estimated discharges by the proposed method were found to be comparable with the existing non-contact discharge method advocated by Moramarco et al. (2017) and, the traditional velocity-area method, using, e.g., the mean-section approach, based on the following metrics: the Nash-sutcliffe Efficiency (NSE), the coefficient of correlation and the percent bias (PBIAS). The mean velocity error emulates a Gaussian distribution for both the gauging stations and was within 95% and 5% confidance levels. Further, the entropy-based velocity profiles generated by the proposed method at the y-axis are consistent with those of the depth-based velocity profiles observed by the mechanical-current meter, thus, proving the appropriateness of the proposed discharge estimation method.

A novel immersed boundary approach for irregular topography with acoustic wave equations

Irregular terrain has a pronounced effect on the propagation of seismic and acoustic wavefields but is not straightforwardly reconciled with structured finite-difference (FD) methods used to model such phenomena. Accurate wavefield simulation is paramount in subsurface imaging applications such as reverse time migration and full-waveform inversion, requiring suitable topography handling. Methods currently detailed in the literature generally are limited in scope application-wise or nontrivial to apply to real-world geometries. A general immersed boundary treatment capable of imposing a range of boundary conditions in a relatively equation-agnostic manner has been developed, alongside a framework implementing this approach to complement emerging code-generation paradigms. The approach is distinguished by the use of N-dimensional Taylor-series extrapolants constrained by boundary conditions imposed at some suitably distributed set of surface points. The extrapolation process is encapsulated in modified derivative stencils applied in the vicinity of the boundary, using hyperspherical support regions. This method ensures boundary representation is consistent with the FD discretization. Furthermore, high-dimensional and vector boundary conditions can be applied without approximation prior to discretization. A consistent methodology can thus be applied across free and rigid surfaces with first- and second-order acoustic wave equation formulations. Application to both equations is demonstrated, and numerical examples based on analytic and real-world topography implementing free and rigid surfaces in 2D and 3D are presented. Numerical examples and convergence tests demonstrate the accuracy of boundary treatments devised by the prescribed approach, their suitability to practical applications, and the feasibility of automatically generating treatments to suit each case.

MMFG: Multimodal-based Mutual Feature Gating 3D Object Detection

To address the problem that image and point cloud features are fused in a coarse fusion way and cannot achieve deep fusion, this paper proposes a multimodal 3D object detection architecture based on a mutual feature gating mechanism. First, since the feature aggregation approach based on the set abstraction layer cannot obtain fine-grained features, a point-based self-attention mechanism module is designed. This module is added to the extraction branch of point cloud features to achieve fine-grained feature aggregation while maintaining accurate location information. Second, a new gating mechanism is designed for the deep fusion of image and point cloud. Deep fusion is achieved by mutual feature weighting between the image and the point cloud. The newly fused features are then fed into a feature refinement network to extract classification confidence and 3D target bounding boxes. Finally, a multi-scale detection architecture is proposed to obtain a more complete object shape. The location-based encoding feature algorithm is also designed to focus the interest points in the region of interest adaptively. The whole architecture shows outstanding performance on the KITTI3D and nuSenece datasets, especially at the difficult level. It shows that the framework solves the problem of low detection rates in LiDAR mode due to the low number of surface points obtained from distant objects.

Contour Extraction of UAV Point Cloud Based on Neighborhood Geometric Features of Multi-Level Growth Plane

The extraction of UAV building point cloud contour points is the basis for the expression of a three-dimensional lightweight building outline. Previous unmanned aerial vehicle (UAV) building point cloud contour extraction methods have mainly focused on the expression of the roof contour, but did not extract the wall contour. In view of this, an algorithm based on the geometric features of the neighborhood points of the region-growing clustering fusion surface is proposed to extract the boundary points of the UAV building point cloud. Firstly, the region growth plane is fused to obtain a more accurate segmentation plane. Then, the neighboring points are projected onto the neighborhood plane and a vector between the object point and neighborhood point is constructed. Finally, the azimuth of each vector is calculated, and the boundary points of each segmented plane are extracted according to the difference in adjacent azimuths. Experiment results show that the best boundary points can be extracted when the number of adjacent points is 24 and the difference in adjacent azimuths is 120. The proposed method is superior to other methods in the contour extraction of UAV buildings point clouds. Moreover, it can extract not only the building roof contour points, but also the wall contour points, including the window contour points.

Study on the Characteristics and Evolution Laws of Seepage Damage in Red Mud Tailings Dams

Seepage damage is a significant factor leading to red mud tailings dam failures. Laboratory tests on seepage damage were conducted to investigate the damage characteristics and distribution laws of red mud tailings dams, including soil pressure, infiltration line, pore water pressure, dam displacement, and crack evolution. The findings revealed the seepage damage mechanisms of red mud slopes, offering insights for the safe operation and seepage damage prevention of red mud tailings dams. The results showed that the higher the water level is in the red mud tailings dam, the higher position the infiltration line is when it reaches the slope face. At the highest infiltration line point of the slope surface, the increase of pore water pressure is the highest and the change of horizontal soil pressure is the highest. Consequently, increased pore water pressure leads to decreased effective stress and shear strength, increasing the susceptibility to damage. Cracks resulting from seepage damage predominantly form below the infiltration line; the higher the infiltration lines is on the slope surface, the higher the position of the main crack formations is. The displacement of the dam body primarily occurs due to the continuous expansion of major cracks; the higher the infiltration lines are on the slope surface, the larger the displacement of the dam body is.

Machine Learning Molecular Dynamics Shows Anomalous Entropic Effect on Catalysis through Surface Pre-melting of Nanoclusters.

Due to the superior catalytic activity and efficient utilization of noble metals, nanocatalysts are extensively used in the modern industrial production of chemicals. The surface structures of these materials are significantly influenced by reactive adsorbates, leading to dynamic behavior under experimental conditions. The dynamic nature poses significant challenges in studying the structure-activity relations of catalysts. Herein, we unveil an anomalous entropic effect on catalysis via surface pre-melting of nanoclusters through machine learning accelerated molecular dynamics and free energy calculation. We find that due to the pre-melting of shell atoms, there exists a non-linear variation in the catalytic activity of the nanoclusters with temperature. Consequently, two notable changes in catalyst activity occur at the respective temperatures of melting for the shell and core atoms. We further study the nanoclusters with surface point defects, i.e. vacancy and ad-atom, and observe significant decrease in the surface melting temperatures of the nanoclusters, enabling the reaction to take place under more favorable and milder conditions. These findings not only provide novel insights into dynamic catalysis of nanoclusters but also offer new understanding of the role of point defects in catalytic processes.

Machine Learning Molecular Dynamics Shows Anomalous Entropic Effect on Catalysis through Surface Pre‐melting of Nanoclusters

AbstractDue to the superior catalytic activity and efficient utilization of noble metals, nanocatalysts are extensively used in the modern industrial production of chemicals. The surface structures of these materials are significantly influenced by reactive adsorbates, leading to dynamic behavior under experimental conditions. The dynamic nature poses significant challenges in studying the structure–activity relations of catalysts. Herein, we unveil an anomalous entropic effect on catalysis via surface pre‐melting of nanoclusters through machine learning accelerated molecular dynamics and free energy calculation. We find that due to the pre‐melting of shell atoms, there exists a non‐linear variation in the catalytic activity of the nanoclusters with temperature. Consequently, two notable changes in catalyst activity occur at the respective temperatures of melting for the shell and core atoms. We further study the nanoclusters with surface point defects, i.e. vacancy and ad‐atom, and observe significant decrease in the surface melting temperatures of the nanoclusters, enabling the reaction to take place under more favorable and milder conditions. These findings not only provide novel insights into dynamic catalysis of nanoclusters but also offer new understanding of the role of point defects in catalytic processes.

Non-Newtonian thermal elastohydrodynamic mixed lubrication in elliptical contact for nutation-driven double circular-arc spiral bevel gear pair

The new type of nutation reducer with double circular-arc bevel gears as the core is characterized by advantages such as high load-bearing capacity, large transmission ratio, and simple structure. The existing research focuses on the machining methods of gears and the analysis of tooth contact. This article first time establishes an elastohydrodynamic mixed lubrication model for nutation double circular-arc bevel gear. The model's accuracy is verified by comparing it with existing test results. On this basis, combined with the finite element method and tooth contact analysis, the meshing characteristics, tooth contact characteristics, and lubrication characteristics of the double circular-arc bevel gear tooth surface are analyzed. The results show that the entrainment velocity at the contact point of the tooth surface during transmission is much higher than the sliding velocity. Consequently, the meshing motion is close to pure rolling. In constant-speed transmission, the oil film thickness at the engaging-in and exit points near the edge of the tooth surface is smaller than that at other meshing points on the tooth surface. This phenomenon can easily lead to mixed lubrication and lubrication failure, as well as tooth surface wear. During deceleration, i.e., when the speed drops to 50 r/min, the oil film thickness approaches zero, indicating that the gear has entered a mixed lubrication state. The proposed model can be used for lubrication analysis and improving transmission accuracy of nutation double circular-arc bevel gears.

Integrating ecosystem integrity and ES supply–demand process to explore ecological connectivity in Wuhan metropolitan area, China

Protecting ecosystem integrity and maintaining ecosystem service (ES) supply–demand process through ecological connectivity (EC) is crucial for achieving sustainable development of metropolitan area. Currently, EC research focuses more on the combination of protecting the ES supply and maintaining ecosystem integrity, and less on effectively meeting human needs. To bridge this gap, we integrated the theory of ES supply–demand process, connectivity research methods to form a demand-oriented EC research framework, and conducted empirical research on the Wuhan metropolitan area (WHA). Firstly, we evaluated the supply–demand matches status of the key four ESs; And then, from the aspects of flow quantity, flow direction, flow boundary, and allocation principles, the spatial process of ESs flow were decomposed, and the ecological sources that meet demand while supporting the ESs supply–demand process were identified; Finally, based on the comprehensive resistance surface, connected corridors and important strategic points that are crucial for ecological protection were extracted, and an EC network was constructed. The results were the following:(1) the supply and demand of the key ESs in the study area showed a significant spatial mismatch, especially for water conservation (WC) and carbon sequestration (CS); (2)55 ecological sources, 84 ecological corridors and 25 strategic points were identified, and a stable structurally EC network was constructed; (3) The ecological sources were also crucial for ESs supply while meeting human demand and supporting the spatial process of ESs supply–demand. This research is beneficial for deepening the understanding of the supply–demand status of ESs, and providing a support for ecological lands protection planning.

Deep-FG-DSM: Fine-Grained DSM for High-resolution UAV Imagery Based on Deep Implicit Occupancy Networks

Abstract. Nowadays, DSM (Digital Surface Model) is one of most important products that has been widely applied in digital city or smart city. Over the last decades, the common way is to follow the conventional photogrammetric pipeline for generating DSM, which is often not efficient and yields noise with small geometric details lost. Inspired by the development of deep implicit occupancy network, in this paper, we presented a learning-based method for obtaining fine-grained DSM, i.e., fine Deep-FG-DSM. In particular, high-resolution UAV imagery together with the corresponding original point cloud are employed to improve DSMs preserving higher details, two heads that embed the features of images and 3D points are deployed, and a MLP (Muti-layer Perceptron) is appended to decode these embedding into continuous occupancy probabilities for predicting the existence (or not) of surface point. Our experimental results demonstrate the robustness of our model against both sparse and noisy point clouds. While generating DSMs, it retains high-frequency details from high-resolution UAV images while maintaining relatively high accuracy. For point cloud obtained after simplification with average sampling resolution of d=5m, the MAE (Mean Absolute Residual Error) is 2.15m.

Towards Modelling Mechanical Shaking Using Potential Energy Surfaces: A Toy Model Analysis

In this work, we formalize the effect of mechanical shaking by using various forms of an externally exerted force, which may be constant or may be position-dependent, and we examine the changes in the potential energy surfaces that quantify the chemical reaction. We use a simple toy model to model the potential energy surfaces of a chemical reaction, and we study the effect of a constant or position-dependent externally exerted force for various forms of the force. As we demonstrate, the effect of the force can be quite dramatic on the potential energy surfaces, which acquire new stationary points and new Newton trajectories that are distinct from the original ones that were obtained in the absence of mechanochemical effects. We also introduce a new approach to mechanochemical interactions, using a dynamical systems approach for the Newton trajectories. As we show, the dynamical system attractor properties of the trajectories in the phase space are identical to the stationary points of the potential energy surfaces, but the phase space contains much more information regarding the possible evolution of the chemical reaction—information that is quantified by the existence of unstable or saddle fixed points in the phase space. We also discuss how an experimental method for a suitable symmetric liquid solution substance might formalize the effect of shaking via various forms of external force, even in the form of an extended coordinate-dependent force matrix. This approach may experimentally quantify the Epstein effect of shaking in chemical solutions via mechanochemistry methods.

Direct reconstruction of the temperature field of lithium-ion battery based on mapping characteristics

Lithium-ion (Li-ion) batteries are susceptible to the working temperature so that the monitoring is essential to the battery internal temperature. The electrochemical-thermal coupling model completely reflects the battery internal behavior. However, the high complexity and multi-field coupling make it difficult to be applied to reconstruct the battery temperature field based on state observer. Based on mapping characteristics, this work proposes a temperature field direct reconstruction scheme. And the electrochemical-thermal coupling model plays a large role in this scheme. Mapping characteristic vectors of the transient temperature field are obtained from the step response model of the Li-ion battery temperature field to charge and discharge current. Furthermore, the quantitative connection of the mapping characteristic vectors between the surface points of and the internal nodes of the battery is characterized by a relevance degree matrix. And a direct reconstruction model of the internal transient temperature field is established. The internal temperature field is reconstructed directly online and in real time via exploiting the temperature measurements on battery surface. In numerical experiments, the three-dimensional temperature field of the Li-ion battery charge and discharge processes is reconstructed. The effects of charge and discharge rates, model mismatch, and measurement errors on the reconstruction results are investigated. During the charge and discharge processes, the maximum values of the transient average error of the reconstructed temperature field are about 0.4 K and 0.3 K, respectively. And the transient average error is generally below 0.8 K when the standard deviation of measurement errors is 0.5 K.

The architectural application of shells whose boundaries subtend a constant solid angle

Surface geometry plays a central role in the design of bridges, vaults and shells, using various techniques for generating a geometry which aims to balance structural, spatial, aesthetic and construction requirements.In this paper we propose the use of surfaces defined such that given closed curves subtend a constant solid angle at all points on the surface and form its boundary. Constant solid angle surfaces enable one to control the boundary slope and hence achieve an approximately constant span-to-height ratio as the span varies, making them structurally viable for shell structures. In addition, when the entire surface boundary is in the same plane, the slope of the surface around the boundary is constant and thus follows a principal curvature direction. Such surfaces are suitable for surface grids where planar quadrilaterals meet the surface boundaries. They can also be used as the Airy stress function in the form finding of shells having forces concentrated at the corners.Our technique employs the Gauss-Bonnet theorem to calculate the solid angle of a point in space and Newton's method to move the point onto the constant solid angle surface. We use the Biot-Savart law to find the gradient of the solid angle. The technique can be applied in parallel to each surface point without an initial mesh, opening up for future studies and other applications when boundary curves are known but the initial topology is unknown.We show the geometrical properties, possibilities and limitations of surfaces of constant solid angle using examples in three dimensions.

Orbital-Optimized Versus Time-Dependent Density Functional Calculations of Intramolecular Charge Transfer Excited States.

The performance of time-independent, orbital-optimized calculations of excited states is assessed with respect to charge transfer excitations in organic molecules in comparison to the linear-response time-dependent density functional theory (TD-DFT) approach. A direct optimization method to converge on saddle points of the electronic energy surface is used to carry out calculations with the local density approximation (LDA) and the generalized gradient approximation (GGA) functionals PBE and BLYP for a set of 27 excitations in 15 molecules. The time-independent approach is fully variational and provides a relaxed excited state electron density from which the extent of charge transfer is quantified. The TD-DFT calculations are generally found to provide larger charge transfer distances compared to the orbital-optimized calculations, even when including orbital relaxation effects with the Z-vector method. While the error on the excitation energy relative to theoretical best estimates is found to increase with the extent of charge transfer up to ca. -2 eV for TD-DFT, no correlation is observed for the orbital-optimized approach. The orbital-optimized calculations with the LDA and the GGA functionals provide a mean absolute error of ∼0.7 eV, outperforming TD-DFT with both local and global hybrid functionals for excitations with a long-range charge transfer character. Orbital-optimized calculations with the global hybrid functional B3LYP and the range-separated hybrid functional CAM-B3LYP on a selection of states with short- and long-range charge transfer indicate that inclusion of exact exchange has a small effect on the charge transfer distance, while it significantly improves the excitation energy, with the best-performing functional CAM-B3LYP providing an absolute error typically around 0.15 eV.

Ionization layer with collision-free atoms at the edge of partially to fully ionized plasmas

When a hot arc spot has just formed on the cathode surface, e.g. in the course of arc ignition on a cold cathode, a significant part of the current still flows in the glow-discharge mode to the cold surface outside the spot. The near-cathode voltage continues to be high at all points of the cathode surface. The mean free path for collisions between the atoms and the ions within the plasma ball near the spot is comparable to, or exceeds, the thickness of the ionization layer, which is a part of the near-cathode non-equilibrium layer where the ion current to the cathode is generated. The evaluation of the ion current to the cathode surface under such conditions is revisited. A fluid description of the ion motion in the ionization layer is combined with a kinetic description of the atom motion. The resulting problem admits a simple analytical solution. Formulas for the evaluation of the ion current to the cathode for a wide range of conditions are derived and the possibilities of using these formulas to improve the accuracy of existing methods for modeling high-pressure arc discharges in relation to glow-to-arc transitions are discussed.

Investigation of the optimal cutting depth in small incision lenticule extraction based on a collagen fibril crimping constitutive model of the cornea

To investigate the optimal cutting depth (Cap) in small incision lenticule extraction from the perspective of corneal biomechanics, a three-dimensional finite element model of the cornea was established using a stromal sub-regional material model to simulate small incision lenticule extraction. The displacement difference PΔ at the central point of the posterior corneal surface before and after lenticule extraction, as well as the von Mises stress at four points of different thicknesses in the center of the cornea, were analyzed using the finite element model considering the hyperelastic property and the difference in stiffness between the anterior and posterior of the cornea. The numerical curves of PΔ-Cap and von Mises Stress-Cap relations at different diopters show that the displacement difference PΔ has a smallest value at the same diopter. In this case, the von Mises stress at four points with different thicknesses in the center of the cornea was also minimal. Which means that the optimal cutting depth exsisting in the cornea. Moreover, PΔ-Cap curves for different depth of stromal stiffness boundaries show that the optimal cap thickness would change with the depth of the stromal stiffness boundary. These results are of guiding significance for accurately formulating small incision lenticule extraction surgery plans and contribute to the advancement of research on the biomechanical properties of the cornea.

Microchannel modification in membraneless microfluidic fuel cell to control the concentration boundary layer

The concentration boundary layer thickness is one of the most important parameters used to characterize the performance of membraneless microfluidic fuel cells. Low concentration boundary layer thickness leads to a high electrochemical reaction over the electrodes, improving the performance of the fuel cell. For this purpose, this paper proposes a modified microchannel which directs the fuel and the oxidant to the electrode surfaces. According to the results obtained, the amount of concentration on the cathode electrode surface and therefore the amount of current density increased with the modified microchannel. At the same point of the cathode electrode surface, the oxidant concentration in the conventional microchannel was 0.055 mol/m3, while it was 0.19 mol/m3 in the modified microchannel. Since the concentration overpotential reduced, the limiting current density was 0.372 mA/cm2 in the conventional microchannel and 0.741 mA/cm2 in the modified microchannel. Thus, the limiting current density value increased by 91.5 %.

NeRF-NQA: No-Reference Quality Assessment for Scenes Generated by NeRF and Neural View Synthesis Methods.

Neural View Synthesis (NVS) has demonstrated efficacy in generating high-fidelity dense viewpoint videos using a image set with sparse views. However, existing quality assessment methods like PSNR, SSIM, and LPIPS are not tailored for the scenes with dense viewpoints synthesized by NVS and NeRF variants, thus, they often fall short in capturing the perceptual quality, including spatial and angular aspects of NVS-synthesized scenes. Furthermore, the lack of dense ground truth views makes the full reference quality assessment on NVS-synthesized scenes challenging. For instance, datasets such as LLFF provide only sparse images, insufficient for complete full-reference assessments. To address the issues above, we propose NeRF-NQA, the first no-reference quality assessment method for densely-observed scenes synthesized from the NVS and NeRF variants. NeRF-NQA employs a joint quality assessment strategy, integrating both viewwise and pointwise approaches, to evaluate the quality of NVS-generated scenes. The viewwise approach assesses the spatial quality of each individual synthesized view and the overall inter-views consistency, while the pointwise approach focuses on the angular qualities of scene surface points and their compound inter-point quality. Extensive evaluations are conducted to compare NeRF-NQA with 23 mainstream visual quality assessment methods (from fields of image, video, and light-field assessment). The results demonstrate NeRF-NQA outperforms the existing assessment methods significantly and it shows substantial superiority on assessing NVS-synthesized scenes without references. An implementation of this paper are available at https://github.com/VincentQQu/NeRF-NQA.