We study continuous data assimilation(CDA)applied to projection and penalty methods for the Navier-Stokes(NS)equations.Penalty and projection methods are more efficient than consistent Ns discretizations,however are l...We study continuous data assimilation(CDA)applied to projection and penalty methods for the Navier-Stokes(NS)equations.Penalty and projection methods are more efficient than consistent Ns discretizations,however are less accurate due to modeling error(penalty)and splitting error(projection).We show analytically and numerically that with measurement data and properly chosen parameters,CDA can effectively remove these splitting and modeling errors and provide long time optimally accurate solutions.展开更多
This paper is dedicated to the expansion of the framework of general interpolant observables introduced by Azouani,Olson,and Titi for continuous data assimilation of nonlinear partial differential equations.The main f...This paper is dedicated to the expansion of the framework of general interpolant observables introduced by Azouani,Olson,and Titi for continuous data assimilation of nonlinear partial differential equations.The main feature of this expanded framework is its mesh-free aspect,which allows the observational data itself to dictate the subdivision of the domain via partition of unity in the spirit of the so-called Partition of Unity Method by Babuska and Melenk.As an application of this framework,we consider a nudging-based scheme for data assimilation applied to the context of the two-dimensional Navier-Stokes equations as a paradigmatic example and establish convergence to the reference solution in all higher-order Sobolev topologies in a periodic,mean-free setting.The convergence analysis also makes use of absorbing ball bounds in higherorder Sobolev norms,for which explicit bounds appear to be available in the literature only up to H^(2);such bounds are additionally proved for all integer levels of Sobolev regularity above H^(2).展开更多
文摘We study continuous data assimilation(CDA)applied to projection and penalty methods for the Navier-Stokes(NS)equations.Penalty and projection methods are more efficient than consistent Ns discretizations,however are less accurate due to modeling error(penalty)and splitting error(projection).We show analytically and numerically that with measurement data and properly chosen parameters,CDA can effectively remove these splitting and modeling errors and provide long time optimally accurate solutions.
基金partially supported by the award PSC-CUNY64335-0052,jointly funded by The Professional Staff Congress and The City University of New York。
文摘This paper is dedicated to the expansion of the framework of general interpolant observables introduced by Azouani,Olson,and Titi for continuous data assimilation of nonlinear partial differential equations.The main feature of this expanded framework is its mesh-free aspect,which allows the observational data itself to dictate the subdivision of the domain via partition of unity in the spirit of the so-called Partition of Unity Method by Babuska and Melenk.As an application of this framework,we consider a nudging-based scheme for data assimilation applied to the context of the two-dimensional Navier-Stokes equations as a paradigmatic example and establish convergence to the reference solution in all higher-order Sobolev topologies in a periodic,mean-free setting.The convergence analysis also makes use of absorbing ball bounds in higherorder Sobolev norms,for which explicit bounds appear to be available in the literature only up to H^(2);such bounds are additionally proved for all integer levels of Sobolev regularity above H^(2).
