Gas-solid friction can induce interfacial charge transfer, and the harvested triboelectric charges can, in principle, power distributed sensor networks on aircraft surfaces. However, the charging behavior of gas-solid triboelectrification under varying flight conditions remains insufficiently investigated. In this study, a theoretical estimation model for gas-solid triboelectric charging on moving surfaces is developed, and its key influencing factors are analyzed. Based on the Maxwell-Boltzmann velocity distribution, the model demonstrates that, under constant ambient temperature, the generated charge is jointly governed by the collision frequency between gas molecules and the solid surface and by the effective frictional area. The collision frequency, in turn, depends on the object's velocity and the ambient pressure. A fluid-solid coupled simulation conducted in ANSYS Workbench provides preliminary verification of the model's validity. Additionally, first-principles calculations confirmed that aluminum tends to lose electrons, exhibiting a positive charge during gas-solid triboelectric charging. Furthermore, a non-contact experimental system employing double through-type Faraday cups is designed to measure the charge-transfer efficiency predicted by the Maxwell-Boltzmann-based estimation under controlled variations in motion velocity, effective friction area, and ambient pressure. The experimental results confirm the correctness of the proposed estimation model. This work provides a theoretical foundation for analyzing the feasibility of powering distributed sensors on aircraft skins through gas-solid triboelectric charging.

The necessary condition for using plastic forming to regulate microstructure is a sufficient understanding of the material's hot deformation behavior. This work systematically studied the mechanical response and micro- structural evolution of Mg-8Gd-5Y-1.5Zn-0.5Zr(-0.3Sn) alloys during hot compression, and established corresponding mathematical models, which provide data sources and theoretical support for subsequent plastic forming simulations and process parameter optimization. Sn microalloying induces the formation of numerous submicron sized (31 particles during hot compression, which is related to the ease of bonding between Sn and rare earth (RE) elements. The (31 particle swarm promotes recrystallization by activating the particle-stimulated nucleation (PSN) mechanism, so that the flow stress decreases to steady state with a greater magnitude after reaching its peak. Sn microalloying allows Mg-Gd-Y-Zn-Zr alloys with poor formability to achieve more effective grain refinement at relatively low strains.

Face-centered cubic (FCC) multi-principal element alloys (MPEAs) usually have excellent plasticity but insufficient strength. Previous research has found that it is more effective by tailoring the content of L12 phase to improve its strength without much loss of plasticity. In this work, we developed an ultrafine nano-MPEA material through direct casting and ageing treatment without tedious thermomechanical processing such as rolling. By utilizing the spinodal decomposition process, extremely dense and uniformly distributed spherical L12 particles with a size of about 100 nm were formed in situ in the FCC matrix, achieving significant improvement in alloy strength while ensuring alloy’s ductility. Impressively, the volume fraction of L12 phase is about 65%, while the FCC matrix accounts for 35%. Compared with the as-cast specimens, the yield strength and ultimate tensile strength of aged FeCoNi3Cu0.5Al0.8 can reach to 631 MPa and 900 MPa, which are increased by 65.7% and 22.3%, respectively. Moreover, its elongation can still maintain about 28%. The reasons for the excellent comprehensive performance are the coherent interface between L12 phase and FCC matrix, as well as the synergistic effects of dislocation Cutting Through mechanism and Orowan bypass mechanism. This work provides a paradigm for the microstructure design and performance optimization of strong and ductile structural materials.

The influence of solvent activation on the permeability of nanofiltration (NF) membranes is substantial. However, the mechanism of solvent activation remains relatively understudied. In this paper, both molecular dynamics (MD) and density functional theory (DFT) were applied to provide evidence to show the process through which N, N-dimethylformamide (DMF) solubilizes oligomeric polyamide (PA) structures, as substantiated through experimental verification. Analyzing the degree of swelling, dissolution Gibbs free energies, pore sizes, free volumes, and solvent accessible surface areas (SASA) of four PA structures, this study found that in a DMF solvent environment, each amide bond increases the free energy of dissolution per unit surface by 0.3 kJ/(mol & sdot; & Aring;). The surface area after cross-linking is approximately 1/8 of that without cross-linking, leading to a high activation of the PA layer by DMF. This study reveals some mechanisms of solvent activation in nanofiltration membranes and informs the search for new activation solvents.

Vision-centric 3D environment understanding is both vital and challenging for autonomous driving systems. Recently, object-free methods have attracted considerable attention. Such methods perceive the world by predicting the semantics of discrete voxel grids but fail to construct continuous and accurate obstacle surfaces. To this end, in this paper, we propose SurroundSDF to implicitly predict the signed distance field (SDF) and semantic field for the continuous perception from surround images. Specifically, we introduce a query-based approach and utilize SDF constrained by the Eikonal formulation to accurately describe the surfaces of obstacles. Furthermore, considering the absence of precise SDF ground truth, we propose a novel weakly supervised paradigm for SDF, referred to as the Sandwich Eikonal formulation, which emphasizes applying correct and dense constraints on both sides of the surface, thereby enhancing the perceptual accuracy of the surface. Experiments suggest that our method achieves SOTA for both occupancy prediction and 3D scene reconstruction tasks on the nuScenes dataset.

Neural Scene Flow Prior (NSFP) and Fast Neural Scene Flow (FNSF) have shown remarkable adaptability in the context of large out-of-distribution autonomous driving. Despite their success, the underlying reasons for their astonishing generalization capabilities remain unclear. Our research addresses this gap by examining the generalization capabilities of NSFP through the lens of uniform stability, revealing that its performance is inversely proportional to the number of input point clouds. This finding sheds light on NSFP's effectiveness in handling large-scale point cloud scene flow estimation tasks. Motivated by such theoretical insights, we further explore the improvement of scene flow estimation by leveraging historical point clouds across multiple frames, which inherently increases the number of point clouds. Consequently, we propose a simple and effective method for multi-frame point cloud scene flow estimation, along with a theoretical evaluation of its generalization abilities. Our analysis confirms that the proposed method maintains a limited generalization error, suggesting that adding multiple frames to the scene flow optimization process does not detract from its generalizability. Extensive experimental results on large-scale autonomous driving Waymo Open and Argoverse lidar datasets demonstrate that the proposed method achieves state-of-the-art performance.

L12-Al3X (X = Li, Sc, and Zr) precipitates are the main strengthened phases of high-strength aluminum alloys and are critical for aerospace structural materials. Point defects and substitutional ternary elements change the mechanical properties of Al3X. In this paper, the effect of point defects, including vacancy, antisite, and substitutional element addition defects on the elastic modulus of the off-stoichiometric Al3X (X = Li, Sc, and Zr) phase were investigated by using first-principle calculations. The formation enthalpies of the defective Al3X alloy and isolated point defects in Al3X were calculated, and the results showed that the defects have an effect on the structure and elasticity of the off-stoichiometric Al3X phases. The lattice distortion, elastic constants, and elastic moduli were further investigated. It was found that the point defects increased the Young’s modulus for Al3Zr, and the doping of Er improved the Young’s modulus for off-stoichiometric Al3Li and Al3Sc. Adjusting the position of vacancies can improve the elastic modulus. In addition, the doping of substitutional elements (especially Sc, Ti, Zr, Hf, Ta, Mn, Ir, and Cf) can greatly increase the Young’s modulus of off-stoichiometric Al3Li.

The third generation of Al-Li alloys (e.g. AA2060) have been widely used in aerospace engineering. The formation of porosity during casting is a major obstacle for improving its mechanical properties due to the 10 times the equilibrium hydrogen concentration in the liquid than the traditional aluminum alloys. In this study, the effects of two fundamental parameters are investigated: cooling rate and pressure on the formation of porosity. The centrifugal casting process was used to apply external pressure during solidification and quantify the 3D porosity morphology as a function of the external parameters using X-ray computed tomography (X-CT). Subsequently, a Cellular Automata (CA) model was used to predict porosity as a function of pressure and thermal boundary conditions. It was found that the externally applied compressive pressure contributes to porosity closure within certain limits. In addition, the cooling rate not only refines the grain size but also minimizes porosity defects by increasing their number density, and the experimental results validated the predictions by the CA model. By increasing the solidification pressure and cooling rate, a high-performance AA2060 Al-Li alloy with a tensile strength of 490 MPa and an elongation of 6.1% was obtained.

Aluminum-lithium alloys have wide applications in aerospace industries in the 21st century but their manufacturing is extremely difficult due to the 10 times more equilibrium hydrogen concentration in the liquid than the solubility limit in traditional Al alloys (0.036 mL/100gSTP). The reduction of solubility from liquid to solid by 95% leads to hydrogen porosity being hard to control. In this work, a three-dimensional multicomponent cellular automaton (CA) model is coupled with CALPHAD calculations to simulate the nucleation and growth of hydrogen porosity and its interaction with surrounding dendritic structures during the solidification of Al-Cu-Li alloys. By quantifying the effects of hydrogen concentration, cooling rate, and Li content, it was found the solidification conditions can effectively reduce porosity size. To validate the model, X-ray computed tomography (XCT) has been used to obtain not only the size but also the morphology of porosity as a function of cooling conditions. It was found that porosity grows elongated and tortuous shape at slow cooling rates between columnar dendrites, filling up the empty spaces of secondary arms, while it tends to be dispersed spherical shape when its surrounding grains are refined to equiaxed structures at high cooling rates.

When Al alloy is recycled, it will continue to enrich Fe, Si impurities and form coarse plate-like β-Fe upon solidification, which is very detrimental to the conductivity and mechanical properties of the final components. In this study, a multi-object optimization method was developed to achieve the combination of high-strength and high-conductivity of Al-Mg-Si alloys by optimizing homogenization and hot deformation process. The homogenized elongation reached 14%, which is 50.5% higher than the as-cast state at 9.3% simply by increasing the relative α-Fe fraction fα (α/(α + β) %) from 0.56 to 0.81. By inventing two new thermo-mechanical processing paths: route 1 and 2, a superior combination of strength and conductivity has been achieved. The strength and conductivity of route 1 process are 292.04 MPa and 51.10%IACS, respectively, with fα at 85%; for route 2 process, they are 299.26 MPa and 50.66%IACS, respectively, with fα at 96%. That means the transformation from β-Fe to α-Fe is almost complete. Therefore, the quantitative characterization of β-Fe and α-Fe fractions enables the design of Fe tolerable recycled Al-Mg-Si alloys at high strength, high conductivity, and high ductility.

Although the effects of pores and carbides on the high temperature fatigue performance of nickel-based single crystal superalloys have been studied for decades, few studies have statistically compared their damage effects and identified the most detrimental factors. X-ray computed tomography has been used to collect the microstructure variations while the fatigue damage happens at high temperature. Combining image registration and deep learning algorithm, both carbides and pores have been extracted and quantified by a new damage factor. It shows that pores are more harmful than carbides during crack initiation, and carbides are more significant than pores during the crack propagation stage at elevated temperatures. Furthermore, by developing a multiple linear regression model, the damage effects of pore size, morphology, spacing, and distance to the sample surface on the crack initiation and propagation stage were differentiated. It is found that pore spacing is the most important factor for crack initiation.

It is well known that the addition of Sc and Ag to Al alloys can promote the nucleation of alpha-Al grains and theta' -Al2Cu precipitates. In this study, the effects of Sc and Ag on grain size and precipitates, as well as the contribution of grain refinement and precipitation strengthening to yield strength of Al-Cu alloys were quantitatively characterized. The addition of Sc refines the grain size and theta' precipitate, resulting in the yield strength, ultimate tensile strength and elongation of Al-Cu alloy increasing to 314.2 MPa, 410.6 MPa and 6.31%, respectively. The addition of Ag increases the number density and volume fraction of Omega precipitate, resulting in the contribution of O precipitate to the yield strength increasing to 204.8 MPa, and the yield strength, ultimate tensile strength and elongation of Al-Cu alloy reaching 430.0 MPa, 449.9 MPa and 3.60%, respectively. The elastic modulus of AlCuMnMgTiAg alloy is increased to 73 GPa.

The continuous accumulation of Fe impurities in recycled aluminum is a major obstacle for its wide application in fatigue sensitive parts due to the formation of Fe intermetallics and micropores. In this paper, we study the effects of rare earth elements and superheat temperature on the crystal structure of Fe-rich intermetallic and morphology of microporosity using micron-resolution phase-contrast lens magnification based X-ray computed tomography (XCT). Using image registration and U-net convolutional neural networks (CNN) algorithm, we first segmented the morphology of Fe-rich intermetallic and quantified their distribution as a function of Ce additions and superheating temperature. It was found that the addition of Ce elements promoted the microstructure refinement and restricted the growth of beta-Fe intermetallics effectively. Lowering the superheat temperature from 780 degrees C to 680 degrees C does not show a linear relationship between porosity level and the reduction of the intermetallic volume fraction. In fact, we have found the size of eutectic Si and Fe-rich intermetallic is the smallest at 730 degrees C and the tensile strength at as-cast condition with a superheating at 730 degrees C and Ce addition can give the greatest elongation at 4.26%, and yield and tensile strengths at 175.3and 249.5 MPa, respectively.

For aluminum alloys, grain refinement is the most efficient method to improve both strength and ductility. However, this rule may not apply for the recycled Al due to the large amount of intermetallics. In this paper, both secondary dendritic arm spacing (SDAS) and intermetallics have been quantified as a function of grain refinements including traditional AlTiB and most recent Y refiners. Using U-net CNN machine learning algorithm, both Fe-rich intermetallics and eutectic Si have been successfully segmented from optical and SEM/EDS images. Different from traditional refiners, (AlTiB + Y) not only strengthens the refining ability but also reduces the percentage of harmful needle-like Fe-rich intermetallics and transforms flaky Si particles into fibrous morphology. Harvested from the comprehensive grain refiners, SDAS was reduced by 38.9%, the average equivalent diameter of eutectic Si was reduced by 37.9%, and Fe-rich intermetallics content was significantly reduced. In particular, the microstructure improved by AlTiB+ 0.05Y (0.6 wt% AlTiB + 0.3 wt%Y) resulted in an increase in elongation of the refined alloy to 3.9 +/- 0.4%, which is 63.3% higher compared to the base alloy, while the UTS remained at the original 260 MPa. This paper offers a promising refining technology for the recycling and casting of Al-Si alloys.

It is well known that the microstructure distribution in recycled Al-Si alloys has a large impact on the final mechanical properties. In this study, the microstructure, including Fe-rich intermetallics and microporosity, was quantitatively adjusted using multi-scale characterization with microalloying rare earth elements and traditional grain refiners as the objects of study. It was found that the addition of Al-Ti-B to W319 recycled aluminum alloy reduces the microstructure size and Fe-rich intermetallics, while the addition of La facilitates the transformation of harmful β-Fe into less harmful particles and the densification of coarse eutectic Si, promoting the refining effects on the microstructure additionally. Therefore, the RE and Al-Ti-B master alloy could be a potential new grain refining agent, especially for Al-cast alloys when the ductility is critical for designing. The improvement in elongation far exceeds the original level, up to 69.6%, while maintaining the same level of strength or even better. At the same time, the excessive addition of La may lead to the depletion of Cu and Ti elements during heat treatment, degrading ductility and strength.

The mechanical properties of Al-Si-Mg alloys are largely related to eutectic Si, intermetallics and pores. In this study, the effects of the Sc addition on the eutectic Si, Sc containing precipitates and pores were investigated, and the mechanical properties of the alloy were improved by adding Sc. The Sc addition leads to the formation of Al3M (M = Sc, Zr, Ti, V) and reduction of pore size, increasing the yield strength (YTS), ultimate tensile strength (UTS) and elongation (EL) of the alloy after the T6 heat treatment. The formation of thermally stable Al3M (M = Sc, Zr, Ti, V) after the T6 heat treatment improves the elevated temperature strength.

Recent advances in high-throughput fabrication under non-equilibrium conditions have enabled the simultaneous preparation of many multicomponent Mg alloys with continuous concentration gradients and nano-grain sizes by magnetron co-sputtering. Rapid screening of ultra-strong Mg alloys (55.84~97.89 wt% Mg) has been done as a function of Gd, Y, and Zr solutes. A positive correlation between grain size and solution chemistry with mechanical properties such as hardness and Young’s modulus, has been established by performing nanoindentation tests on the entire chemistry spectra of over 100 samples. Surprisingly, ultra-strong Mg alloys (Hardness: 5.28 GPa, Young’s modulus: 181.20 GPa) have been identified in not only the richest solute-containing alloys but also the low-alloyed alloys. The yield and ultimate tensile strengths reached 1.0 GPa and 1.4 GPa, respectively. The fundamental mechanism of the Hall–Petch relationship and solution strengthening at such conditions was revealed by using DFT calculations and TOF–SIMS, enabling subsequent discoveries of a high-strength Mg alloy materials library.

In the existing research on Chinese natural language processing text feature extraction has always been a core problem in this field, and the advantages and disadvantages of text feature extraction performance are important factors that directly affect the performance of text processing technology. This research offers a dual-channel feature extraction method based on neural networks in order to handle this challenge of text feature extraction from the perspectives of local information and context global information of text sentences. The text's local features are extracted using GCN, while the text's overall semantic features are extracted using BiLSTM, and the full connected neural network is used to fuse the local eigenvector with the global eigenvector to obtain the final features of the text. To confirm the efficiency of the suggested feature extraction technique, the suggested approach is used to classify news material in this research. The outcomes of the experiments demonstrate that the performance of the suggested method outperforms that of the GCN and BiLSTM feature extraction methods used independently. When compared to other feature extraction approaches, this method's experimental results are still better.

近些年航空航天对轻质高强抗疲劳铝合金的需求日益迫切,铸造缺陷的预测与抑制技术受到广泛重视.铝合金凝固过程中产生的氢气孔缺陷是疲劳失效的主要裂纹源,其预测与控制技术是高品质铝合金制备加工的核心工艺技术.在国外,元胞自动机(CA)等具有物理意义且计算效率较高的微观组织结构模型已成功地应用于先进铸造工艺的设计,如飞机发动机单晶叶片、飞机发动机涡轮盘,以及飞机蒙皮高强轻质铝合金工艺窗口优化.总结了计算模型应用于铝合金凝固过程中缺陷的预测及最新研究进展,提出了未来缺陷预测模型的发展方向.

During the direct chill (DC) casting process, primary cooling from the mold and bottom block, and secondary cooling from the waterjets produce a concave solid shell. The depth of this liquid pocket and mushy zone not only depends on the solidification range of the alloy but also the boundary conditions such as cooling rates. Al-Li alloys solidify in a long solidification range increasing the susceptibility of porosity nucleation in the semi-solid region. In this study, the effects of cooling rate on the porosity formation were quantified for the large ingot casting using X-ray computed tomography (XCT). By characterizing pore size distributions at four different cooling conditions, the correlation between the mechanical properties at both room and high temperatures and the microstructure features was identified. The constitutive equations were constructed. It is found that increasing the cooling rate reduces the grain size, increases the number density of micropores, and minimizes the number of large pores, thereby improving the mechanical performance. Therefore, long mushy zones and deep liquid pockets in Al-Li alloys can be effectively controlled by controlling the boundary conditions of the DC casting solidification process, thereby obtaining castings with excellent mechanical properties.