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Part 1: Document Description
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Citation |
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Title: |
Replication Data for: Closure Law Model Uncertainty Quantification |
Identification Number: |
doi:10.18710/3OJHDN |
Distributor: |
DataverseNO |
Date of Distribution: |
2021-12-05 |
Version: |
1 |
Bibliographic Citation: |
Strand, Andreas; Kjølaas, Jørn; Bergstrøm, Trond H.; Steinsland, Ingelin; Hellevik, Leif R., 2021, "Replication Data for: Closure Law Model Uncertainty Quantification", https://doi.org/10.18710/3OJHDN, DataverseNO, V1 |
Citation |
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Title: |
Replication Data for: Closure Law Model Uncertainty Quantification |
Identification Number: |
doi:10.18710/3OJHDN |
Authoring Entity: |
Strand, Andreas (NTNU – Norwegian University of Science and Technology) |
Kjølaas, Jørn (SINTEF) |
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Bergstrøm, Trond H. (SINTEF) |
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Steinsland, Ingelin (NTNU – Norwegian University of Science and Technology) |
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Hellevik, Leif R. (NTNU – Norwegian University of Science and Technology) |
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Other identifications and acknowledgements: |
SINTEF Multiphase Flow Laboratory |
Other identifications and acknowledgements: |
IFE Well Flow Loop |
Producer: |
NTNU – Norwegian University of Science and Technology |
Software used in Production: |
LedaFlow |
Software used in Production: |
Python |
Grant Number: |
267620 |
Distributor: |
DataverseNO |
Distributor: |
NTNU – Norwegian University of Science and Technology |
Access Authority: |
Strand, Andreas |
Depositor: |
Strand, Andreas |
Date of Deposit: |
2021-11-29 |
Date of Distribution: |
2021-11-12 |
Holdings Information: |
https://doi.org/10.18710/3OJHDN |
Study Scope |
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Keywords: |
Engineering, Mathematical Sciences, Physics, multiphase flow, ledaflow, closure laws, uncertainty propagation, prediction |
Abstract: |
The prediction uncertainty in simulators for industrial processes is due to uncertainties in the input variables and uncertainties in specification of the models, in particular the closure laws. In this work, the uncertainty in each closure law was modeled as a random variable and the parameters of its distribution were optimized to correctly quantify the uncertainty in predictions. We have developed two methods for optimization, based on the integrated quadratic distance and the energy score. The proposed methods were applied to the commercial multiphase flow simulator LedaFlow with the liquid volume fraction and pressure gradient as output variables. Two datasets were analyzed. Both describe two-phase gas-liquid flow, but are otherwise fundamentally different. One is gas-dominated stratified/annular flow and the other is liquid-dominated slug flow. |
Time Period: |
2018-01-01-2018-12-31 |
Date of Collection: |
2018-01-01-2018-12-31 |
Kind of Data: |
Program source code |
Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Title: |
Closure law model uncertainty quantification. Andreas Strand, Jørn Kjølaas, Trond H. Bergstrøm, Ingelin Steinsland and Leif R. Hellevik. 2022 |
Identification Number: |
10.1615/Int.J.UncertaintyQuantification.2021037714 |
Bibliographic Citation: |
Closure law model uncertainty quantification. Andreas Strand, Jørn Kjølaas, Trond H. Bergstrøm, Ingelin Steinsland and Leif R. Hellevik. 2022 |
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00_ReadMe.txt |
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experiment.py |
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text/x-python |
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gas_dominated.py |
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text/x-python |
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liquid_dominated.py |
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text/x-python |
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main.py |
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text/x-python |
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plot_functions.py |
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text/x-python |
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requirements.txt |
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text/plain |
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scoring.py |
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text/x-python |