dc.contributor.advisor | Lie, Knut-Andreas | |
dc.contributor.advisor | Møyner, Olav | |
dc.contributor.author | Mona-Lena Norheim | |
dc.date.accessioned | 2019-10-17T14:00:24Z | |
dc.date.available | 2019-10-17T14:00:24Z | |
dc.date.issued | 2019 | |
dc.identifier.uri | http://hdl.handle.net/11250/2622852 | |
dc.description.abstract | I denne masteroppgaven ser vi på alternative iterative løsere til MATLABs innebygde direkte løser \texttt{mldivide} for Poisson-type problemer. Løserene er testet på to forskjellige modeller som er vanlig å bruke for optimeringstester: Olympus og SPE10. Det viser seg at de iterative løserene generelt er bedre enn \texttt{mldivide}, for store systemer. Det er klart at bruk av algebraisk multigrid (AMG) som prekondisjoner forbedrer konvergensen drastisk. Blandt de testede iterative løserene var det Krylov-løsere BiCGstab og BiCGstab($l$) kombinert med glatteren ILU(0) og forgrovningen "smoothed aggregation" som gjorde det best. | |
dc.description.abstract | In this thesis we investigate alternative iterative solvers to MATLAB's in-built direct solver \texttt{mldivide} for Poisson-type problems. The solvers are tested on two models used for the purpose of benchmark studies for field development optimization: Olympus and SPE10. It is found that the iterative solvers, in general, perform better than \texttt{mldivide} for large, computationaly heavy systems. It is evident that the use of algebraic multigrid (AMG) as a preconditioner improves convergence dramatically. Among the tested iterative solvers it is Krylov solvers BiCGstab and BiCGstab($l$) combined with the smoother ILU(0) and the coarsening strategy smoothed aggregation that performed overall best. | |
dc.language | eng | |
dc.publisher | NTNU | |
dc.title | Investigating iterative solvers of Poisson-type equations discretized by the Two-Point Flux-Approximation scheme | |
dc.type | Master thesis | |