In this paper,we consider the optimization problem of identifying the pollution sources of convection-diffusion-reaction equations in a groundwater process.The optimization model is subject to a convection-diffusion-r...In this paper,we consider the optimization problem of identifying the pollution sources of convection-diffusion-reaction equations in a groundwater process.The optimization model is subject to a convection-diffusion-reaction equation with pumping point and pollution point sources.We develop a linked optimization and simulation approach combining with the Differential Evolution(DE)optimization algorithm to identify the pumping and injection rates from the data at the observation points.Numerical experiments are taken with injections of constant rates and timedependent variable rates at source points.The problem with one pumping point and two pollution source points is also studied.Numerical results show that the proposed method is efficient.The developed optimized identification approach can be extended to high-dimensional and more complex problems.展开更多
基金supported by Natural Sciences and Engineering Research Council of Canada,and by the Doctor Research Foundation for Advanced Talents(No.2018BS026)of Henan University of Technology.
文摘In this paper,we consider the optimization problem of identifying the pollution sources of convection-diffusion-reaction equations in a groundwater process.The optimization model is subject to a convection-diffusion-reaction equation with pumping point and pollution point sources.We develop a linked optimization and simulation approach combining with the Differential Evolution(DE)optimization algorithm to identify the pumping and injection rates from the data at the observation points.Numerical experiments are taken with injections of constant rates and timedependent variable rates at source points.The problem with one pumping point and two pollution source points is also studied.Numerical results show that the proposed method is efficient.The developed optimized identification approach can be extended to high-dimensional and more complex problems.