In this article,we derive the a posteriori error estimators for a class of steadystate Poisson-Nernst-Planck equations.Using the gradient recovery operator,the upper and lower bounds of the a posteriori error estimato...In this article,we derive the a posteriori error estimators for a class of steadystate Poisson-Nernst-Planck equations.Using the gradient recovery operator,the upper and lower bounds of the a posteriori error estimators are established both for the electrostatic potential and concentrations.It is shown by theory and numerical experiments that the error estimators are reliable and the associated adaptive computation is efficient for the steady-state PNP systems.展开更多
基金supported by the China NSF(NSFC Nos.11971414,and 11571293).Y.Yang was supported by the China NSF(NSFC Nos.11561016,11701119 and 11771105)Guangxi Natural Science Foundation(Nos.2017GXNSFFA198012 and 2017GXNSFBA198056)the Hunan Key Laboratory for Computation and Simulation in Science and Engineering,Xiangtan University,Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation open fund.R.G.Shen was supported by Postgraduate Scientific Research and Innovation Fund of the Hunan Provincial Education Department(No.CX2017B268).
文摘In this article,we derive the a posteriori error estimators for a class of steadystate Poisson-Nernst-Planck equations.Using the gradient recovery operator,the upper and lower bounds of the a posteriori error estimators are established both for the electrostatic potential and concentrations.It is shown by theory and numerical experiments that the error estimators are reliable and the associated adaptive computation is efficient for the steady-state PNP systems.
