The performance of a material is directly affected by its microstructural development during the solidification phase. Discrete cellular automaton (CA) models are widelyused in materials science to simulate and predic...The performance of a material is directly affected by its microstructural development during the solidification phase. Discrete cellular automaton (CA) models are widelyused in materials science to simulate and predict microstructural growth. This review comprehensively explains the developments and applications of CA in solidification structure simulation, including the theoretical underpinnings, computational procedures, software development, and recent advances. Summarizes the potential and limitations of cellular automata in understanding microstructure evolution during solidification, explores the evolution of microstructures during solidification, and adds to our existing knowledge of cellular automaton theory. Finally, the research trend in simulating the evolution of the solidification microstructure using cellular automaton theory is explored.展开更多
[Objective]Urban floods are occurring more frequently because of global climate change and urbanization.Accordingly,urban rainstorm and flood forecasting has become a priority in urban hydrology research.However,two-d...[Objective]Urban floods are occurring more frequently because of global climate change and urbanization.Accordingly,urban rainstorm and flood forecasting has become a priority in urban hydrology research.However,two-dimensional hydrodynamic models execute calculations slowly,hindering the rapid simulation and forecasting of urban floods.To overcome this limitation and accelerate the speed and improve the accuracy of urban flood simulations and forecasting,numerical simulations and deep learning were combined to develop a more effective urban flood forecasting method.[Methods]Specifically,a cellular automata model was used to simulate the urban flood process and address the need to include a large number of datasets in the deep learning process.Meanwhile,to shorten the time required for urban flood forecasting,a convolutional neural network model was used to establish the mapping relationship between rainfall and inundation depth.[Results]The results show that the relative error of forecasting the maximum inundation depth in flood-prone locations is less than 10%,and the Nash efficiency coefficient of forecasting inundation depth series in flood-prone locations is greater than 0.75.[Conclusion]The result demonstrated that the proposed method could execute highly accurate simulations and quickly produce forecasts,illustrating its superiority as an urban flood forecasting technique.展开更多
文摘The performance of a material is directly affected by its microstructural development during the solidification phase. Discrete cellular automaton (CA) models are widelyused in materials science to simulate and predict microstructural growth. This review comprehensively explains the developments and applications of CA in solidification structure simulation, including the theoretical underpinnings, computational procedures, software development, and recent advances. Summarizes the potential and limitations of cellular automata in understanding microstructure evolution during solidification, explores the evolution of microstructures during solidification, and adds to our existing knowledge of cellular automaton theory. Finally, the research trend in simulating the evolution of the solidification microstructure using cellular automaton theory is explored.
文摘[Objective]Urban floods are occurring more frequently because of global climate change and urbanization.Accordingly,urban rainstorm and flood forecasting has become a priority in urban hydrology research.However,two-dimensional hydrodynamic models execute calculations slowly,hindering the rapid simulation and forecasting of urban floods.To overcome this limitation and accelerate the speed and improve the accuracy of urban flood simulations and forecasting,numerical simulations and deep learning were combined to develop a more effective urban flood forecasting method.[Methods]Specifically,a cellular automata model was used to simulate the urban flood process and address the need to include a large number of datasets in the deep learning process.Meanwhile,to shorten the time required for urban flood forecasting,a convolutional neural network model was used to establish the mapping relationship between rainfall and inundation depth.[Results]The results show that the relative error of forecasting the maximum inundation depth in flood-prone locations is less than 10%,and the Nash efficiency coefficient of forecasting inundation depth series in flood-prone locations is greater than 0.75.[Conclusion]The result demonstrated that the proposed method could execute highly accurate simulations and quickly produce forecasts,illustrating its superiority as an urban flood forecasting technique.
