Heat Transfer Estimation in Flow Boiling of R134a within Microfin Tubes: Development of Explainable Machine Learning-Based Pipelines

Shayan Milani, Keivan Ardam, Farzad Dadras Javan, Behzad Najafi (), Andrea Lucchini, Igor Matteo Carraretto and Luigi Pietro Maria Colombo
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Shayan Milani: Dipartimento di Energia, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy
Keivan Ardam: Dipartimento di Energia, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy
Farzad Dadras Javan: Dipartimento di Energia, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy
Behzad Najafi: Dipartimento di Energia, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy
Andrea Lucchini: Dipartimento di Energia, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy
Igor Matteo Carraretto: Dipartimento di Energia, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy
Luigi Pietro Maria Colombo: Dipartimento di Energia, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy

Energies, 2024, vol. 17, issue 16, 1-24

Abstract: The present study is focused on identifying the most suitable sequence of machine learning-based models and the most promising set of input variables aiming at the estimation of heat transfer in evaporating R134a flows in microfin tubes. Utilizing the available experimental data, dimensionless features representing the evaporation phenomena are first generated and are provided to a machine learning-based model. Feature selection and algorithm optimization procedures are then performed. It is shown that the implemented feature selection method determines only six dimensionless parameters ( S u l : liquid Suratman number, B o : boiling number, F r g : gas Froude number, R e l : liquid Reynolds number, B d : Bond number, and e / D : fin height to tube’s inner diameter ratio) as the most effective input features, which reduces the model’s complexity and facilitates the interpretation of governing physical phenomena. Furthermore, the proposed optimized sequence of machine learning algorithms (providing a mean absolute relative difference (MARD) of 8.84% on the test set) outperforms the most accurate available empirical model (with an MARD of 19.7% on the test set) by a large margin, demonstrating the efficacy of the proposed methodology.

Keywords: machine learning; heat transfer estimation; evaporating flows; R134a; feature selection; relative feature importance (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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