针对现有桥梁护栏存在的防护性能不足、大规模改造需求、复杂施工条件等挑战,提出了“强-弱结合、刚-柔协同”的功能梯度型超高性能混凝土-发泡聚苯乙烯(Ultra high performance concrete-Expandable polystyrene,UHPC-EPS)夹芯护栏体系...针对现有桥梁护栏存在的防护性能不足、大规模改造需求、复杂施工条件等挑战,提出了“强-弱结合、刚-柔协同”的功能梯度型超高性能混凝土-发泡聚苯乙烯(Ultra high performance concrete-Expandable polystyrene,UHPC-EPS)夹芯护栏体系,通过LS-Dyna数值仿真技术,模拟了该护栏在小型客车、大型客车、大型货车碰撞场景下的轨迹偏移规律与损伤演化过程,并与现浇混凝土护栏开展了大型货车冲击工况下的力学性能对比研究。结果表明:功能梯度型UHPC-EPS夹芯护栏兼具优异的车辆轨迹导向能力和运行稳定性调控性能,同时具有强抗穿透特性与便捷的后期修复优势;在大型货车冲击工况下,UHPC-EPS夹芯护栏的峰值撞击力较现浇混凝土护栏降低了约12%,峰值剪力降低了约35%;配套研发的标准化施工工艺采用工厂预制、现场装配、一次成型的快速部署,具有显著的工程适配性与产业化推广价值。展开更多
All-solid-state batteries(ASSBs)represent a next-generation energy storage technology,offering enhanced safety,higher energy density,and improved cycling stability compared to conventional liquid-electrolyte-based lit...All-solid-state batteries(ASSBs)represent a next-generation energy storage technology,offering enhanced safety,higher energy density,and improved cycling stability compared to conventional liquid-electrolyte-based lithium-ion batteries.Understanding and optimizing the complex chemistries and interfaces that underpin ASSB performance present significant challenges from both experimental and modeling perspectives.In particular,atomistic simulations face difficulties in capturing the complex structure,disorder,and dynamic evolution of materials and interfaces under practically relevant conditions.While established methods such as density functional theory and classical force fields have provided valuable insights,some questions remain difficult to address,particularly those involving large system sizes or long timescales.Recently,machine learning interatomic potentials(MLIPs)have emerged as a transformative tool,enabling atomistic simulations at length and time scales that were previously challenging to access with conventional approaches.By delivering near first-principles accuracy with much greater efficiency,MLIPs open new avenues for large-scale,long-timescale,and high-throughput simulations of solid-state battery materials.In this review,we present a comparative overview of density functional theory,classical force fields,and MLIPs,highlighting their respective strengths and limitations in ASSB research.We then discuss how MLIPs enable simulations that reach longer timescales,larger system sizes,and support high-throughput calculations,providing unique insights into ion transport and interfacial evolution in ASSBs.Finally,we conclude with a summary and outlook on current challenges and future opportunities for expanding MLIP capabilities and accelerating their impact in solid-state battery research.展开更多
文摘针对现有桥梁护栏存在的防护性能不足、大规模改造需求、复杂施工条件等挑战,提出了“强-弱结合、刚-柔协同”的功能梯度型超高性能混凝土-发泡聚苯乙烯(Ultra high performance concrete-Expandable polystyrene,UHPC-EPS)夹芯护栏体系,通过LS-Dyna数值仿真技术,模拟了该护栏在小型客车、大型客车、大型货车碰撞场景下的轨迹偏移规律与损伤演化过程,并与现浇混凝土护栏开展了大型货车冲击工况下的力学性能对比研究。结果表明:功能梯度型UHPC-EPS夹芯护栏兼具优异的车辆轨迹导向能力和运行稳定性调控性能,同时具有强抗穿透特性与便捷的后期修复优势;在大型货车冲击工况下,UHPC-EPS夹芯护栏的峰值撞击力较现浇混凝土护栏降低了约12%,峰值剪力降低了约35%;配套研发的标准化施工工艺采用工厂预制、现场装配、一次成型的快速部署,具有显著的工程适配性与产业化推广价值。
文摘All-solid-state batteries(ASSBs)represent a next-generation energy storage technology,offering enhanced safety,higher energy density,and improved cycling stability compared to conventional liquid-electrolyte-based lithium-ion batteries.Understanding and optimizing the complex chemistries and interfaces that underpin ASSB performance present significant challenges from both experimental and modeling perspectives.In particular,atomistic simulations face difficulties in capturing the complex structure,disorder,and dynamic evolution of materials and interfaces under practically relevant conditions.While established methods such as density functional theory and classical force fields have provided valuable insights,some questions remain difficult to address,particularly those involving large system sizes or long timescales.Recently,machine learning interatomic potentials(MLIPs)have emerged as a transformative tool,enabling atomistic simulations at length and time scales that were previously challenging to access with conventional approaches.By delivering near first-principles accuracy with much greater efficiency,MLIPs open new avenues for large-scale,long-timescale,and high-throughput simulations of solid-state battery materials.In this review,we present a comparative overview of density functional theory,classical force fields,and MLIPs,highlighting their respective strengths and limitations in ASSB research.We then discuss how MLIPs enable simulations that reach longer timescales,larger system sizes,and support high-throughput calculations,providing unique insights into ion transport and interfacial evolution in ASSBs.Finally,we conclude with a summary and outlook on current challenges and future opportunities for expanding MLIP capabilities and accelerating their impact in solid-state battery research.
