In the realm of high-speed railway bridge engineering,managing the intricacies of the track-bridge system model(TBSM)during seismic events remains a formidable challenge.This study pioneers an innovative approach by p...In the realm of high-speed railway bridge engineering,managing the intricacies of the track-bridge system model(TBSM)during seismic events remains a formidable challenge.This study pioneers an innovative approach by presenting a simplified bridge model(SBM)optimized for both computational efficiency and precise representation,a seminal contribution to the engineering design landscape.Central to this innovation is a novel model-updating methodology that synergistically melds artificial neural networks with an augmented particle swarm optimization.The neural networks adeptly map update parameters to seismic responses,while enhancements to the particle swarm algorithm’s inertial and learning weights lead to superior SBM parameter updates.Verification via a 4-span high-speed railway bridge revealed that the optimized SBM and TBSM exhibit a highly consistent structural natural period and seismic response,with errors controlled within 7%.Additionally,the computational efficiency improved by over 100%.Leveraging the peak displacement and shear force residuals from the seismic TBSM and SBM as optimization objectives,SBM parameters are adeptly revised.Furthermore,the incorporation of elastoplastic springs at the beam ends of the simplified model effectively captures the additional mass,stiffness,and constraint effects exerted by the track system on the bridge structure.展开更多
The main purpose of this research is the second-order modeling of flow and turbulent heat flux in nonpremixed methane-air combustion.A turbulent stream of non-premixed combustion in a stoichiometric condition,is numer...The main purpose of this research is the second-order modeling of flow and turbulent heat flux in nonpremixed methane-air combustion.A turbulent stream of non-premixed combustion in a stoichiometric condition,is numerically analyzed through the Reynolds averaged Navier-Stokes(RANS) equations.For modeling radiation and combustion,the discrete ordinates(DO) and eddy dissipation concept model have been applied.The Reynolds stress transport model(RSM) also was used for turbulence modeling.For THF in the energy equation,the GGDH model and high order algebraic model of HOGGDH with simple eddy diffusivity model have been applied.Comparing the numerical results of the SED model(with the turbulent Prandtl 0.85) and the second-order heat flux models with available experimental data follows that applying the second-order models significantly led to the modification of predicting temperature distribution and species mass fraction distribution in the combustion chamber.Calculation of turbulent Prandtl number in the combustion chamber shows that the assumption of Pr_(t) of 0.85 is far from reality and Pr_(t) in different areas varies from 0.4 to 1.2.展开更多
为研究大跨度悬索桥在随机车流作用下加劲梁纵向运动及纵向累计位移行程简化计算方法,基于移动荷载作用下加劲梁纵向运动特征,将悬挂加劲梁体系等效为单自度(single-degree-of-freedom,SDOF)振动体系,推导了基于SDOF振动体系的移动荷载...为研究大跨度悬索桥在随机车流作用下加劲梁纵向运动及纵向累计位移行程简化计算方法,基于移动荷载作用下加劲梁纵向运动特征,将悬挂加劲梁体系等效为单自度(single-degree-of-freedom,SDOF)振动体系,推导了基于SDOF振动体系的移动荷载作用下悬索桥加劲梁纵向振动方程和随机车流作用下加劲梁纵向振动方程,提出了一种快速计算随机车流作用下加劲梁纵向振动响应的方法。以某单跨悬索桥为实例,基于实测车流数据,采用蒙特卡罗抽样方法生成随机车流样本,将其等效为SDOF体系下随机荷载时程,进行SDOF体系振动方程求解得到纵向响应位移时程,并与基于ANSYS的全桥模型瞬态分析结果进行对比。结果表明:随机车流作用下,加劲梁发生纵向运动并形成巨大累计位移行程,累计位移包括静态位移和动态位移,后者对累计位移贡献更大;与有限元瞬态动力分析相比,基于简化SDOF体系获得的位移响应结果中除累计位移差别稍大(约13%~19%)外,其幅值和均方根值(root mean square,RMS)均差别很小(小于5%),简化振动模型能反映随机车流下加劲梁纵向运动特征规律,所提计算方法可极大地简化随机车流作用下加劲梁纵向运动分析,可用于结构设计阶段随机车流作用下加劲梁纵向运动评估及振动控制参数优化。展开更多
Tax is very important to the whole country, so a scientific tax predictive model is needed. This paper introduces the theory of the cloud model. On this basis, it presents a cloud neural network, and analyzes the main...Tax is very important to the whole country, so a scientific tax predictive model is needed. This paper introduces the theory of the cloud model. On this basis, it presents a cloud neural network, and analyzes the main factors which influence the tax revenue. Then if proposes a tax predictive model based on the cloud neural network. The model combines the strongpoints of the cloud model and the neural network. The experiment and simulation results show the effectiveness of the algorithm in this paper.展开更多
基金Project(2022YFC3004304)supported by the National Key Research and Development Program of ChinaProjects(52078487,U1934207,52178180)supported by the National Natural Science Foundation of China+2 种基金Project(2022TJ-Y10)supported by the Hunan Province Science and Technology Talent Lifting Project,ChinaProject(2023QYJC006)supported by the Frontier Cross Research Project of Central South University,ChinaProject(SKL-IoTSC(UM)-2024-2026/ORP/GA08/2023)supported by the Science and Technology Development Fund and the State Key Laboratory of Internet of Things for Smart City(University of Macao),China。
文摘In the realm of high-speed railway bridge engineering,managing the intricacies of the track-bridge system model(TBSM)during seismic events remains a formidable challenge.This study pioneers an innovative approach by presenting a simplified bridge model(SBM)optimized for both computational efficiency and precise representation,a seminal contribution to the engineering design landscape.Central to this innovation is a novel model-updating methodology that synergistically melds artificial neural networks with an augmented particle swarm optimization.The neural networks adeptly map update parameters to seismic responses,while enhancements to the particle swarm algorithm’s inertial and learning weights lead to superior SBM parameter updates.Verification via a 4-span high-speed railway bridge revealed that the optimized SBM and TBSM exhibit a highly consistent structural natural period and seismic response,with errors controlled within 7%.Additionally,the computational efficiency improved by over 100%.Leveraging the peak displacement and shear force residuals from the seismic TBSM and SBM as optimization objectives,SBM parameters are adeptly revised.Furthermore,the incorporation of elastoplastic springs at the beam ends of the simplified model effectively captures the additional mass,stiffness,and constraint effects exerted by the track system on the bridge structure.
文摘The main purpose of this research is the second-order modeling of flow and turbulent heat flux in nonpremixed methane-air combustion.A turbulent stream of non-premixed combustion in a stoichiometric condition,is numerically analyzed through the Reynolds averaged Navier-Stokes(RANS) equations.For modeling radiation and combustion,the discrete ordinates(DO) and eddy dissipation concept model have been applied.The Reynolds stress transport model(RSM) also was used for turbulence modeling.For THF in the energy equation,the GGDH model and high order algebraic model of HOGGDH with simple eddy diffusivity model have been applied.Comparing the numerical results of the SED model(with the turbulent Prandtl 0.85) and the second-order heat flux models with available experimental data follows that applying the second-order models significantly led to the modification of predicting temperature distribution and species mass fraction distribution in the combustion chamber.Calculation of turbulent Prandtl number in the combustion chamber shows that the assumption of Pr_(t) of 0.85 is far from reality and Pr_(t) in different areas varies from 0.4 to 1.2.
文摘为研究大跨度悬索桥在随机车流作用下加劲梁纵向运动及纵向累计位移行程简化计算方法,基于移动荷载作用下加劲梁纵向运动特征,将悬挂加劲梁体系等效为单自度(single-degree-of-freedom,SDOF)振动体系,推导了基于SDOF振动体系的移动荷载作用下悬索桥加劲梁纵向振动方程和随机车流作用下加劲梁纵向振动方程,提出了一种快速计算随机车流作用下加劲梁纵向振动响应的方法。以某单跨悬索桥为实例,基于实测车流数据,采用蒙特卡罗抽样方法生成随机车流样本,将其等效为SDOF体系下随机荷载时程,进行SDOF体系振动方程求解得到纵向响应位移时程,并与基于ANSYS的全桥模型瞬态分析结果进行对比。结果表明:随机车流作用下,加劲梁发生纵向运动并形成巨大累计位移行程,累计位移包括静态位移和动态位移,后者对累计位移贡献更大;与有限元瞬态动力分析相比,基于简化SDOF体系获得的位移响应结果中除累计位移差别稍大(约13%~19%)外,其幅值和均方根值(root mean square,RMS)均差别很小(小于5%),简化振动模型能反映随机车流下加劲梁纵向运动特征规律,所提计算方法可极大地简化随机车流作用下加劲梁纵向运动分析,可用于结构设计阶段随机车流作用下加劲梁纵向运动评估及振动控制参数优化。
文摘Tax is very important to the whole country, so a scientific tax predictive model is needed. This paper introduces the theory of the cloud model. On this basis, it presents a cloud neural network, and analyzes the main factors which influence the tax revenue. Then if proposes a tax predictive model based on the cloud neural network. The model combines the strongpoints of the cloud model and the neural network. The experiment and simulation results show the effectiveness of the algorithm in this paper.
