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
采用DSC-TG和XRD分析方法对铁矾渣热分解过程进行了研究。结果表明:铁矾渣的热分解包括脱水、脱氨、氧化和晶型转变等复杂步骤;经750℃焙烧,铁矾渣呈红棕色,最终产物主要为ZnFe_2O_4和Fe_2O_3;基于Kissinger法和Flynn-Wall-Ozawa法得到...采用DSC-TG和XRD分析方法对铁矾渣热分解过程进行了研究。结果表明:铁矾渣的热分解包括脱水、脱氨、氧化和晶型转变等复杂步骤;经750℃焙烧,铁矾渣呈红棕色,最终产物主要为ZnFe_2O_4和Fe_2O_3;基于Kissinger法和Flynn-Wall-Ozawa法得到铁矾渣在350~450℃和630~720℃温度区间热分解反应的活化能分别约为260和230 k J/mol,频率因子分别为3.07×10^(19)和1.29×10^(12)。展开更多
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.
文摘采用DSC-TG和XRD分析方法对铁矾渣热分解过程进行了研究。结果表明:铁矾渣的热分解包括脱水、脱氨、氧化和晶型转变等复杂步骤;经750℃焙烧,铁矾渣呈红棕色,最终产物主要为ZnFe_2O_4和Fe_2O_3;基于Kissinger法和Flynn-Wall-Ozawa法得到铁矾渣在350~450℃和630~720℃温度区间热分解反应的活化能分别约为260和230 k J/mol,频率因子分别为3.07×10^(19)和1.29×10^(12)。
