Data
Predicting-Critical-Heat-Flux

Predicting-Critical-Heat-Flux

active ARFF Attribution 4.0 International (CC BY 4.0) Visibility: public Uploaded 24-03-2022 by Dustin Carrion
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Context This dataset was prepared for the journal article entitled "On the prediction of critical heat flux using a physics-informed machine learning-aided framework" (doi: 10.1016/j.applthermaleng.2019.114540). The dataset contains processed and compiled records of experimental critical heat flux and boundary conditions used for the work presented in the article. Acknowledgements Zhao, Xingang (2020), Data for: On the prediction of critical heat flux using a physics-informed machine learning-aided framework, Mendeley Data, V1, doi: 10.17632/5p5h37tyv7.1

9 features

id (ignore)numeric1865 unique values
0 missing
authorstring10 unique values
0 missing
geometrystring3 unique values
0 missing
pressure_[MPa]numeric114 unique values
0 missing
mass_flux_[kg/m2-s]numeric578 unique values
0 missing
x_e_out_[-]numeric1360 unique values
0 missing
D_e_[mm]numeric36 unique values
0 missing
D_h_[mm]numeric41 unique values
0 missing
length_[mm]numeric54 unique values
0 missing
chf_exp_[MW/m2]numeric109 unique values
0 missing

19 properties

1865
Number of instances (rows) of the dataset.
9
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
7
Number of numeric attributes.
0
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
77.78
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.

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