为贯彻落实《教育强国建设规划纲要(2024—2035年)》中关于“拔尖创新人才培养”的战略要求,针对大学一年级力学与热学实验课程中验证性实验占比高、学生创新能力不足的痛点,本文以桥梁振动实验为例,系统探讨了基于PBL(Problem Based Le...为贯彻落实《教育强国建设规划纲要(2024—2035年)》中关于“拔尖创新人才培养”的战略要求,针对大学一年级力学与热学实验课程中验证性实验占比高、学生创新能力不足的痛点,本文以桥梁振动实验为例,系统探讨了基于PBL(Problem Based Learning)教学模式的数字化实验建设与实践成效。研究并构建“问题导入—分组讨论—实验验证—展示汇报—评价反思—拓展探索”六环节闭环教学流程。学生在实验改进、创新设计等拓展研究中的主动性与解决复杂问题的能力显著增强。研究证明,PBL模式能够有效促进力学与热学实验由“验证”向“探究”转型,为高校物理实验课程培养拔尖创新人才提供了可复制、可推广的范例。展开更多
In our recently published paper,[1]a typesetting error occurred during the production process.Figure 1 in the published version was incomplete.The processing of molecular dynamics(MD)simulation data into graph-structu...In our recently published paper,[1]a typesetting error occurred during the production process.Figure 1 in the published version was incomplete.The processing of molecular dynamics(MD)simulation data into graph-structured representations in the left bottom panel of thefigure was inadvertently omitted.展开更多
An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximatio...An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximation,forming a spectral-integrated neural network(SINN)scheme tailored for problems characterized by long-time evolution.Temporal derivatives are treated through a spectral integration strategy based on orthogonal polynomial expansions,which significantly alleviates stability constraints associated with conventional time-marching schemes.A fully connected neural network is employed to approximate the temperature-related variables,while governing equa-tions and boundary conditions are enforced through a physics-informed loss formulation.Numerical investigations demonstrate that the proposed method maintains high accuracy even when large time steps are adopted,where standard numerical solvers often suffer from instability or excessive computational cost.Moreover,the framework exhibits strong robustness for ultrathin configurations with extreme aspect ratios,achieving relative errors on the order of 10−5 or lower.These results indicate that the SINN framework provides a reliable and efficient alternative for transient thermal analysis of thin-walled structures under challenging computational conditions.展开更多
Pursuing significant thermal rectification effect with minimal temperature differences is critical for thermal rectifiers.While asymmetric structures enable spectral matching,they inherently limit thermal rectificatio...Pursuing significant thermal rectification effect with minimal temperature differences is critical for thermal rectifiers.While asymmetric structures enable spectral matching,they inherently limit thermal rectification performance.To address this issue,we developed a thermal rectification structure comprising a current-biased graphene-coated silicon carbide(SiC)substrate paired with another graphene-coated SiC substrate separated by a nanoscale vacuum gap.A current-biased graphene sheet generates nonreciprocal effect that actively modulates radiative energy transfer.Our theoretical framework demonstrates that the current-biased graphene achieves a high thermal diode efficiency even under a modest temperature difference.Remarkably,the thermal diode efficiency exceeds 0.8 at a temperature difference of just 100 K(between 300 K and 400 K).These findings highlight the synergistic enhancement from graphene coatings and current biasing,providing a viable strategy for nanoscale thermal management applications.展开更多
文摘为贯彻落实《教育强国建设规划纲要(2024—2035年)》中关于“拔尖创新人才培养”的战略要求,针对大学一年级力学与热学实验课程中验证性实验占比高、学生创新能力不足的痛点,本文以桥梁振动实验为例,系统探讨了基于PBL(Problem Based Learning)教学模式的数字化实验建设与实践成效。研究并构建“问题导入—分组讨论—实验验证—展示汇报—评价反思—拓展探索”六环节闭环教学流程。学生在实验改进、创新设计等拓展研究中的主动性与解决复杂问题的能力显著增强。研究证明,PBL模式能够有效促进力学与热学实验由“验证”向“探究”转型,为高校物理实验课程培养拔尖创新人才提供了可复制、可推广的范例。
文摘In our recently published paper,[1]a typesetting error occurred during the production process.Figure 1 in the published version was incomplete.The processing of molecular dynamics(MD)simulation data into graph-structured representations in the left bottom panel of thefigure was inadvertently omitted.
基金supported by the National Natural Science Foundation of China(Nos.12422207 and 12372199).
文摘An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximation,forming a spectral-integrated neural network(SINN)scheme tailored for problems characterized by long-time evolution.Temporal derivatives are treated through a spectral integration strategy based on orthogonal polynomial expansions,which significantly alleviates stability constraints associated with conventional time-marching schemes.A fully connected neural network is employed to approximate the temperature-related variables,while governing equa-tions and boundary conditions are enforced through a physics-informed loss formulation.Numerical investigations demonstrate that the proposed method maintains high accuracy even when large time steps are adopted,where standard numerical solvers often suffer from instability or excessive computational cost.Moreover,the framework exhibits strong robustness for ultrathin configurations with extreme aspect ratios,achieving relative errors on the order of 10−5 or lower.These results indicate that the SINN framework provides a reliable and efficient alternative for transient thermal analysis of thin-walled structures under challenging computational conditions.
基金Project supported by the National Natural Science Foundation of China(Grant No.12364008)the Ph.D.Research Startup Foundation of Yan’an University(Grant No.YDBK2019-54)the Yan’an High-level Talent Special Project(Grant No.2019263166)。
文摘Pursuing significant thermal rectification effect with minimal temperature differences is critical for thermal rectifiers.While asymmetric structures enable spectral matching,they inherently limit thermal rectification performance.To address this issue,we developed a thermal rectification structure comprising a current-biased graphene-coated silicon carbide(SiC)substrate paired with another graphene-coated SiC substrate separated by a nanoscale vacuum gap.A current-biased graphene sheet generates nonreciprocal effect that actively modulates radiative energy transfer.Our theoretical framework demonstrates that the current-biased graphene achieves a high thermal diode efficiency even under a modest temperature difference.Remarkably,the thermal diode efficiency exceeds 0.8 at a temperature difference of just 100 K(between 300 K and 400 K).These findings highlight the synergistic enhancement from graphene coatings and current biasing,providing a viable strategy for nanoscale thermal management applications.
