Share

Recommended by the IIR / IIR document

Machine learning based pressure drop estimation of evaporating R134a flow in micro-fin tubes: Investigation of the optimal dimensionless feature set.

Author(s) : ARDAM K., NAJAFI B., LUCCHINI A., RINALDI F., COLOMBO L. P. M.

Type of article: IJR article

Summary

The present study is focused on proposing, implementing, and optimizing machine learning based pipelines for estimating the pressure drop in evaporating R134a flow passing through micro-fin horizontal tubes. Accordingly, an experimental activity is first conducted, in which the pressure drop of the flow at various operating conditions is measured. Physical models that are available in the literature are then implemented and the corresponding accuracy, while being applied to the obtained dataset, is determined. Machine learning based pipelines, with dimensionless parameters provided as features and two-phase flow multipliers as targets, are then developed. In the next step, a feature selection procedure is performed and an optimization process is then conducted to find the algorithms and the corresponding hyper-parameters, using which results in the highest possible accuracy. The optimal pipeline is demonstrated to be the one in which the liquid only two-phase multiplier is chosen as the target and is provided with only 5 dimensionless parameters as selected input features. Employing the latter pipeline leads to a mean absolute relative deviation (MARD) of 6.27 % on the validation set and 6.41 % on the test set, which is notably lower than the one achieved using the most promising physical model (MARD of 18.74 % on the validation set and 18.08 % on the test set). Furthermore, as the dataset and the obtained optimal pipeline will be made publicly accessible, the proposed methodology also offers higher ease of use and reproducibility.

Available documents

Format PDF

Pages: 20-32

Available

  • Public price

    20 €

  • Member price*

    Free

* Best rate depending on membership category (see the detailed benefits of individual and corporate memberships).

Details

  • Original title: Machine learning based pressure drop estimation of evaporating R134a flow in micro-fin tubes: Investigation of the optimal dimensionless feature set.
  • Record ID : 30029107
  • Languages: English
  • Subject: Technology
  • Source: International Journal of Refrigeration - Revue Internationale du Froid - vol. 131
  • Publication date: 2021/11
  • DOI: http://dx.doi.org/10.1016/j.ijrefrig.2021.07.018
  • Document available for consultation in the library of the IIR headquarters only.

Links


See other articles in this issue (95)
See the source

Indexing