Results of implementation of Feed Forward Neural Networks for modeling of heat transfer coefficient during flow condensation for low and high values of saturation temperature

Description

This database present results of implementation of Feed Forward Neural Networks for modeling of heat transfer coefficient during flow condensation for low and high values of saturation temperature. Databse contain one table and 7 figures.

Dataset file

Database feed forward neural network.pdf

174.0 kB, S3 ETag e1da0b98ffba5f3c393dc3e521cca170-1, downloads: 1

The file hash is calculated from the formula
hexmd5(md5(part1)+md5(part2)+...)-{parts_count} where a single part of the file is 512 MB in size.

Example script for calculation:
https://github.com/antespi/s3md5

File details

License:: open in new tab

CC BY

Attribution
File embargo:: 2025-01-10

Details

Year of publication:

2024

Verification date:

2024-03-20

Creation date:

2023

Dataset language:

English

Fields of science:

mechanical engineering (Engineering and Technology)
environmental engineering, mining and energy (Engineering and Technology)

DOI:

10.52202/069564-0036

Funding:

Heat transfer and pressure drop during condensation in minichannels of new refrigeration fluids at the parameters close to thermodynamic critical point

Verified by:

Gdańsk University of Technology

Keywords

References

publication Application of Feed Forward Neural Networks for Modeling of Heat Transfer Coefficient During Flow Condensation for Low and High Values of Saturation Temperatur

Cite as

Authors

seen 24 times

Search