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
A mathematical mechanism model was proposed for the description and analysis of the heat-stirring-acid leaching process.The model is proved to be effective by experiment.Afterwards,the leaching problem was formulated ...A mathematical mechanism model was proposed for the description and analysis of the heat-stirring-acid leaching process.The model is proved to be effective by experiment.Afterwards,the leaching problem was formulated as a constrained multi-objective optimization problem based on the mechanism model.A two-stage guide multi-objective particle swarm optimization(TSG-MOPSO) algorithm was proposed to solve this optimization problem,which can accelerate the convergence and guarantee the diversity of pareto-optimal front set as well.Computational experiment was conducted to compare the solution by the proposed algorithm with SIGMA-MOPSO by solving the model and with the manual solution in practice.The results indicate that the proposed algorithm shows better performance than SIGMA-MOPSO,and can improve the current manual solutions significantly.The improvements of production time and economic benefit compared with manual solutions are 10.5% and 7.3%,respectively.展开更多
To improve the operational efficiency of global optimization in engineering, Kriging model was established to simplify the mathematical model for calculations. Ducted coaxial-rotors aircraft was taken as an example an...To improve the operational efficiency of global optimization in engineering, Kriging model was established to simplify the mathematical model for calculations. Ducted coaxial-rotors aircraft was taken as an example and Fluent software was applied to the virtual prototype simulations. Through simulation sample points, the total lift of the ducted coaxial-rotors aircraft was obtained. The Kriging model was then constructed, and the function was fitted. Improved particle swarm optimization(PSO) was also utilized for the global optimization of the Kriging model of the ducted coaxial-rotors aircraft for the determination of optimized global coordinates. Finally, the optimized results were simulated by Fluent. The results show that the Kriging model and the improved PSO algorithm significantly improve the lift performance of ducted coaxial-rotors aircraft and computer operational efficiency.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.
基金Project(2006AA060201) supported by the National High Technology Research and Development Program of China
文摘A mathematical mechanism model was proposed for the description and analysis of the heat-stirring-acid leaching process.The model is proved to be effective by experiment.Afterwards,the leaching problem was formulated as a constrained multi-objective optimization problem based on the mechanism model.A two-stage guide multi-objective particle swarm optimization(TSG-MOPSO) algorithm was proposed to solve this optimization problem,which can accelerate the convergence and guarantee the diversity of pareto-optimal front set as well.Computational experiment was conducted to compare the solution by the proposed algorithm with SIGMA-MOPSO by solving the model and with the manual solution in practice.The results indicate that the proposed algorithm shows better performance than SIGMA-MOPSO,and can improve the current manual solutions significantly.The improvements of production time and economic benefit compared with manual solutions are 10.5% and 7.3%,respectively.
基金Project(2013AA063903)supported by High-tech Research and Development Program of China
文摘To improve the operational efficiency of global optimization in engineering, Kriging model was established to simplify the mathematical model for calculations. Ducted coaxial-rotors aircraft was taken as an example and Fluent software was applied to the virtual prototype simulations. Through simulation sample points, the total lift of the ducted coaxial-rotors aircraft was obtained. The Kriging model was then constructed, and the function was fitted. Improved particle swarm optimization(PSO) was also utilized for the global optimization of the Kriging model of the ducted coaxial-rotors aircraft for the determination of optimized global coordinates. Finally, the optimized results were simulated by Fluent. The results show that the Kriging model and the improved PSO algorithm significantly improve the lift performance of ducted coaxial-rotors aircraft and computer operational efficiency.
