High-resolution spatiotemporal simulations effectively capture the complexities of atmospheric plume sion disper-in complex terrain.However,their high computational cost makes them impractical for applications requiri...High-resolution spatiotemporal simulations effectively capture the complexities of atmospheric plume sion disper-in complex terrain.However,their high computational cost makes them impractical for applications requiring rapid responses or iterative processes,such as optimization,uncertainty quantification,or inverse modeling.To address this challenge,this work introduces the Dual-Stage Temporal Three-dimensional UNet Super-resolution(DST3D-UNet-SR)model,a highly efficient deep learning model for plume dispersion predictions.DST3D-UNet-SR is composed of two sequential modules:the temporal module(TM),which predicts the transient evolution of a plume in complex terrain from low-resolution temporal data,and the spatial refinement module(SRM),which subsequently enhances the spatial resolution of the TM predictions.We train DST3D-UNet-SR using a comprehensive dataset derived from high-resolution large eddy simulations(LES)of plume transport.We propose the DST3D-UNet-SR model to significantly accelerate LES of three-dimensional(3D)plume dispersion by three orders of magnitude.Additionally,the model demonstrates the ability to dynamically adapt to evolving conditions through the incorporation of new observational data,substantially improving prediction accuracy in high-concentration regions near the source.展开更多
文摘High-resolution spatiotemporal simulations effectively capture the complexities of atmospheric plume sion disper-in complex terrain.However,their high computational cost makes them impractical for applications requiring rapid responses or iterative processes,such as optimization,uncertainty quantification,or inverse modeling.To address this challenge,this work introduces the Dual-Stage Temporal Three-dimensional UNet Super-resolution(DST3D-UNet-SR)model,a highly efficient deep learning model for plume dispersion predictions.DST3D-UNet-SR is composed of two sequential modules:the temporal module(TM),which predicts the transient evolution of a plume in complex terrain from low-resolution temporal data,and the spatial refinement module(SRM),which subsequently enhances the spatial resolution of the TM predictions.We train DST3D-UNet-SR using a comprehensive dataset derived from high-resolution large eddy simulations(LES)of plume transport.We propose the DST3D-UNet-SR model to significantly accelerate LES of three-dimensional(3D)plume dispersion by three orders of magnitude.Additionally,the model demonstrates the ability to dynamically adapt to evolving conditions through the incorporation of new observational data,substantially improving prediction accuracy in high-concentration regions near the source.
