Replication Data for: Uncertainty propagation through a point model for steady-state two-phase pipe flowdoi:10.18710/OWKABRDataverseNO2020-01-302Strand, Andreas, 2020, "Replication Data for: Uncertainty propagation through a point model for steady-state two-phase pipe flow", https://doi.org/10.18710/OWKABR, DataverseNO, V2Replication Data for: Uncertainty propagation through a point model for steady-state two-phase pipe flowdoi:10.18710/OWKABRStrand, AndreasSmith, Ivar EskerudUnander, Tor ErlingSteinsland, IngelinHellevik, Leif RuneNTNU – Norwegian University of Science and TechnologySINTEF IndustryEquinorPython267620DataverseNONTNU – Norwegian University of Science and TechnologyStrand, AndreasStrand, Andreas2020-01-20EngineeringMathematical SciencesPhysicstwo-phase flowunit celluncertainty quantificationsensitivity analysismonte carlopolynomial chaosCode and data for performing uncertainty quantification and sensitivity analysis of a multiphase flow model.
The software computes the uncertainty in model predictions in the presence of uncertain input variables. The analysis also determines which variables the predictions are sensitive two.
Both Monte Carlo simulations and polynomial chaos expansions are implemented.Source codeUncertainty propagation through a point model for steady-state two-phase pipe flow.
Andreas Strand, Ivar E. Smith, Tor E. Unander, Ingelin Steinsland and Leif R. Hellevik. Algorithms 2020, 13, 53.10.3390/a13030053Uncertainty propagation through a point model for steady-state two-phase pipe flow.
Andreas Strand, Ivar E. Smith, Tor E. Unander, Ingelin Steinsland and Leif R. Hellevik. Algorithms 2020, 13, 53.00_ReadMe.txttext/plainmc.pytext/plainmc_functions.pytext/plainmc_input.pytext/plainpc.pytext/plainpc_functions.pytext/plainpc_input.pytext/plainpc_setup.pytext/plainplot_functions.pytext/plainpointmodel.pytext/plainrequirements.txttext/plain