The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is m...The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.展开更多
The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-lear...The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-learning(DL)-driven CV in four key areas of materials science:microstructure-based performance prediction,microstructure information generation,microstructure defect detection,and crystal structure-based property prediction.The CV has significantly reduced the cost of traditional experimental methods used in material performance prediction.Moreover,recent progress made in generating microstructure images and detecting microstructural defects using CV has led to increased efficiency and reliability in material performance assessments.The DL-driven CV models can accelerate the design of new materials with optimized performance by integrating predictions based on both crystal and microstructural data,thereby allowing for the discovery and innovation of next-generation materials.Finally,the review provides insights into the rapid interdisciplinary developments in the field of materials science and future prospects.展开更多
Undesired ice accumulation on infrastructure and transportation systems leads to catastrophic events and significant economic losses.Although various anti-icing surfaces with photothermal effects can initially prevent...Undesired ice accumulation on infrastructure and transportation systems leads to catastrophic events and significant economic losses.Although various anti-icing surfaces with photothermal effects can initially prevent icing,any thawy droplets remaining on the horizontal surface can quickly re-freezing once the light diminishes.To address these challenges,we have developed a self-draining slippery surface(SDSS)that enables the thawy droplets to self-remove on the horizontal surface,thereby facilitating real-time anti-icing with the aid of sunlight(100 m W cm^(-2)).This is achieved by sandwiching a thin pyroelectric layer between slippery surface and photothermal film.Due to the synergy between the photothermal and pyroelectric layers,the SDSS not only maintains a high surface temperature of 19.8±2.2℃at the low temperature(-20.0±1.0℃),but also generates amount of charge through thermoelectric coupling.Thus,as cold droplets dropped on the SDSS,electrostatic force pushes the droplets off the charged surface because of the charge transfer mechanism.Even if the surface freezes overnight,the ice can melt and drain off the SDSS within 10 min of exposure to sunlight at-20.0±1.0℃,leaving a clean surface.This work provides a new perspective on the anti-icing system in the real-world environments.展开更多
基金supported by the Science and Technology Research Project of Henan Province(242102241055)the Industry-University-Research Collaborative Innovation Base on Automobile Lightweight of“Science and Technology Innovation in Central Plains”(2024KCZY315)the Opening Fund of State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment(GZ2024A03-ZZU).
文摘The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.
基金financially supported by the National Science Fund for Distinguished Young Scholars,China(No.52025041)the National Natural Science Foundation of China(Nos.52450003,U2341267,and 52174294)+1 种基金the National Postdoctoral Program for Innovative Talents,China(No.BX20240437)the Fundamental Research Funds for the Central Universities,China(Nos.FRF-IDRY-23-037 and FRF-TP-20-02C2)。
文摘The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-learning(DL)-driven CV in four key areas of materials science:microstructure-based performance prediction,microstructure information generation,microstructure defect detection,and crystal structure-based property prediction.The CV has significantly reduced the cost of traditional experimental methods used in material performance prediction.Moreover,recent progress made in generating microstructure images and detecting microstructural defects using CV has led to increased efficiency and reliability in material performance assessments.The DL-driven CV models can accelerate the design of new materials with optimized performance by integrating predictions based on both crystal and microstructural data,thereby allowing for the discovery and innovation of next-generation materials.Finally,the review provides insights into the rapid interdisciplinary developments in the field of materials science and future prospects.
基金supported by the National Natural Science Foundation of China(52273101,51922018,and 21875011)the Fundamental Research Funds for the Central Universities(KG21015201 and KG21020801)China Postdoctoral Science Foundation(2025M77422)。
文摘Undesired ice accumulation on infrastructure and transportation systems leads to catastrophic events and significant economic losses.Although various anti-icing surfaces with photothermal effects can initially prevent icing,any thawy droplets remaining on the horizontal surface can quickly re-freezing once the light diminishes.To address these challenges,we have developed a self-draining slippery surface(SDSS)that enables the thawy droplets to self-remove on the horizontal surface,thereby facilitating real-time anti-icing with the aid of sunlight(100 m W cm^(-2)).This is achieved by sandwiching a thin pyroelectric layer between slippery surface and photothermal film.Due to the synergy between the photothermal and pyroelectric layers,the SDSS not only maintains a high surface temperature of 19.8±2.2℃at the low temperature(-20.0±1.0℃),but also generates amount of charge through thermoelectric coupling.Thus,as cold droplets dropped on the SDSS,electrostatic force pushes the droplets off the charged surface because of the charge transfer mechanism.Even if the surface freezes overnight,the ice can melt and drain off the SDSS within 10 min of exposure to sunlight at-20.0±1.0℃,leaving a clean surface.This work provides a new perspective on the anti-icing system in the real-world environments.
