期刊
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
卷 163, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijheatmasstransfer.2020.120450
关键词
Boiling; Condensation; GBM; Machine learning; BPHE; Pressure drop
资金
- Italian Ministry of University
This paper presents a Gradient Boosting Machines (GBM) model for predicting refrigerant two-phase frictional pressure gradient inside Brazed Plate Heat Exchangers (BPHE) based on an extensive database that includes 1624 boiling data-points, 925 condensation data-points, 16 different plate geometries, and 16 different refrigerants (including 4 natural refrigerants and 6 other low-GWP refrigerants). The model accounts for the effect of plate geometry, operating conditions and refrigerant properties. The model is able to reproduce the whole database with a Mean Absolute Percentage Error (MAPE) of 6.6%. The GBM model exhibits a better predictive performance than the state-of-the-art analytical-computational procedures for two-phase pressure drop inside BPHE available in the open literature. The characteristic parameters of the GBM model are thoroughly reported in the paper. (C) 2020 Elsevier Ltd. All rights reserved.
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