Replication Data for: Uncertainty propagation through a point model for steady-state two-phase pipe flowdoi:10.18710/OWKABRSocial Sciences and Digital Humanities Archive – SODHA [test instance]2020-02-201Strand, Andreas, 2020, "Replication Data for: Uncertainty propagation through a point model for steady-state two-phase pipe flow", https://doi.org/10.18710/OWKABR, Social Sciences and Digital Humanities Archive – SODHA [test instance], V1Replication 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 RuneNorwegian University of Science and TechnologySINTEF IndustryEquinorPython267620Social Sciences and Digital Humanities Archive – SODHA [test instance]NTNU Open Research DataStrand, 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 codeThis dataset is made available under a Creative Commons CC0 license with the following additional/modified terms and conditions: CC0 WaiverSubmitted for review. Uncertainty 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.00_ReadMe.txttext/plainmc_functions.pytext/plainmc_input.pytext/plainmc.pytext/plainpc_functions.pytext/plainpc_input.pytext/plainpc.pytext/plainpc_setup.pytext/plainplot_functions.pytext/plainpointmodel.pytext/plain