Improving pressure drop predictions for R134a evaporation in corrugated vertical tubes using a machine learning technique trained with the Levenberg–Marquardt method

Çolak A. B., Bacak A., Karakoyun Y., Koca A., DALKILIÇ A. S.

Journal of Thermal Analysis and Calorimetry, 2024 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Publication Date: 2024
  • Doi Number: 10.1007/s10973-024-13082-y
  • Journal Name: Journal of Thermal Analysis and Calorimetry
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Chemical Abstracts Core, Chimica, Communication Abstracts, Compendex, Index Islamicus, INSPEC, Metadex, Civil Engineering Abstracts
  • Keywords: Evaporation, Levenberg–Marquardt, Machine learning, Pressure drop
  • Van Yüzüncü Yıl University Affiliated: Yes


The present investigation utilized a machine learning structure to ascertain the pressure drop in vertically positioned, corrugated copper tubes during the evaporation process of R134a. The evaporator was a counter-flow heat exchanger, in which R134a flowed in the inner corrugated tube and hot water flowed in the smooth annulus. Different evaporation mass fluxes (195–406 kg m-2 s-1) and heat fluxes (10.16–66.61 kW m-2) were used with artificial neural networks at different corrugation depths. A multilayer perceptron artificial neural network model with 13 neurons in the hidden layer was proposed. Tan-Sig and Purelin transfer functions were used in the network model developed with the Levenberg–Marquardt training algorithm. The dataset, which consisted of 252 data points, related to the evaporation process, was divided into training (70%), validation (15%), and testing (15%) groups in an arbitrary manner. The artificial neural network model has been demonstrated to effectively forecast the pressure drop that occurs during evaporation. The mean squared error was computed for the ΔP values observed during the evaporation processes, yielding a value of 1.96E-03. The artificial neural network exhibited a high correlation coefficient value of 0.94479. The estimation fluctuations exhibited a range of ± 10%, whereas the experimental and anticipated ΔP data demonstrated a divergence of ± 10.3%.