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27_Table-8.Comparison between experimental data and simulation results, chiller configuration, brazed plate gas.JPG
27_Table-8.Comparison between experimental data and simulation results, chiller configuration, brazed plate gas.JPG
Dec 9, 2021 - Replication data for: Dynamic Modelling and Validation of an Air-to-Water Reversible R744 Heat Pump for High Energy Demand Buildings
JPEG Image - 55.3 KB - MD5: 507527bda0416d6637461baa2d34240d
28_Table-9.Comparison between experimental data and simulation results, chiller configuration, brazed plate gas cooler included.JPG
28_Table-9.Comparison between experimental data and simulation results, chiller configuration, brazed plate gas cooler included.JPG
Dec 9, 2021 - Replication data for: Dynamic Modelling and Validation of an Air-to-Water Reversible R744 Heat Pump for High Energy Demand Buildings
JPEG Image - 59.6 KB - MD5: 9bd0fd23b997302d59776fd87d5b9a4b
29_Table-10.Water set-point temperature and return temperature.JPG
29_Table-10.Water set-point temperature and return temperature.JPG
Dec 9, 2021 - Replication data for: Dynamic Modelling and Validation of an Air-to-Water Reversible R744 Heat Pump for High Energy Demand Buildings
JPEG Image - 20.0 KB - MD5: fb1a55d2c305ba39d883bbadd511a944
30_Table-11.Mean value of high pressure, evaporation pressure, water mass flow rate at the gas-cooler and gas-cooler heat flow rate and COP during the simulation time.JPG
30_Table-11.Mean value of high pressure, evaporation pressure, water mass flow rate at the gas-cooler and gas-cooler heat flow rate and COP during the simulation time.JPG
Dec 9, 2021 - Replication data for: Dynamic Modelling and Validation of an Air-to-Water Reversible R744 Heat Pump for High Energy Demand Buildings
JPEG Image - 57.9 KB - MD5: 678aede56c1902ee5ce2d27ae2d37da6
Replication Data for: Closure Law Model Uncertainty Quantification
Dec 5, 2021
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
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 an...
00_ReadMe.txt
Dec 5, 2021 - Replication Data for: Closure Law Model Uncertainty Quantification
Plain Text - 828 B - MD5: fc098393ea1a0256a9d530355cb495fc
experiment.py
Dec 5, 2021 - Replication Data for: Closure Law Model Uncertainty Quantification
Python Source Code - 30.1 KB - MD5: 60d99f5b466ead54f16e330c1c51857b
gas_dominated.py
Dec 5, 2021 - Replication Data for: Closure Law Model Uncertainty Quantification
Python Source Code - 2.7 KB - MD5: 60ae3241fa833a7ffac649a68d5bc32d
liquid_dominated.py
Dec 5, 2021 - Replication Data for: Closure Law Model Uncertainty Quantification
Python Source Code - 3.0 KB - MD5: 877f2f0b9aaea09431332b1f2cf49607
main.py
Dec 5, 2021 - Replication Data for: Closure Law Model Uncertainty Quantification
Python Source Code - 1.5 KB - MD5: 2d2d3d40662efa4e48ef20028f012311
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