English version
Tore Flåtten

Tore Flåtten

Vitenskapelige publikasjoner

Popescu, Mihaela; Flåtten, Tore Halsne (2021). A study of blockage effects at the wind turbine and wind farm scales. 19 s. Energies. Vol. 14.
https://doi.org/10.3390/en14196124

Bore, Sigbjørn Løland; Flåtten, Tore Halsne (2020). High-resolution large time-step schemes for inviscid fluid flow. Applied Mathematical Modelling. Vol. 81.
https://doi.org/10.1016/j.apm.2019.12.006

Linga, Gaute; Flåtten, Tore Halsne (2019). A Hierarchy of Non-Equilibrium Two-Phase Flow Models. ESAIM: Proceedings and Surveys. Vol. 66.
https://doi.org/10.1051/proc/201966006

Prebeg, Marin; Flåtten, Tore Halsne ; Müller, Bernhard (2018). Large time step HLL and HLLC schemes. Modélisation mathématique et analyse numérique. Vol. 52.
https://doi.org/10.1051/m2an/2017051

Flåtten, Tore Halsne ; Pelanti, Marica; Shyue, Keh-Ming (2018). A Numerical Model for Three-Phase Liquid–Vapor–Gas Flows with Relaxation Processes. Springer Proceedings in Mathematics & Statistics. Vol. 237.
https://doi.org/10.1007/978-3-319-91548-7_32

Prebeg, Marin; Flåtten, Tore ; Müller, Bernhard (2017). Large Time Step Roe scheme for a common 1D two-fluid model. Applied Mathematical Modelling. Vol. 44.
https://doi.org/10.1016/j.apm.2016.12.010

Morin, Alexandre; Flåtten, Tore (2016). A two-fluid four-equation model with instantaneous thermodynamical equilibrium. Modélisation mathématique et analyse numérique. Vol. 50.
https://doi.org/10.1051/m2an/2015074

Lindqvist, Sofia Margareta; Aursand, Peder; Flåtten, Tore ; Solberg, Anders Aase (2016). Large time step TVD schemes for hyperbolic conservation laws. SIAM Journal on Numerical Analysis. Vol. 54.
https://doi.org/10.1137/15M104935X

Aarsnes, Ulf Jakob Flø; Flåtten, Tore ; Aamo, Ole Morten (2016). Review of two-phase flow models for control and estimation. Annual Reviews in Control. Vol. 42.
https://doi.org/10.1016/j.arcontrol.2016.06.001

Reigstad, Gunhild Allard; Flåtten, Tore (2015). Numerical Investigation of Network Models for Isothermal Junction flow. Lecture Notes in Computational Science and Engineering. Vol. 103.
https://doi.org/10.1007/978-3-319-10705-9_66





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