THERMAL SCIENCE

International Scientific Journal

Authors of this Paper

External Links

Jinjuan Sun

,

Zhiheng Ma

,

Jianhui Tian

PREDICTION OF FROSTING PROCESS ON COLD WALL SURFACE BASED ON ANN WITH BACK PROPAGATION ALGORITHM

ABSTRACT
The ANN with back propagation algorithm is a multi-layer feed-forward neural network, which is suitable to study unsteady frost formation with multiple factors. The back propagation ANN algorithm is used to study frost layer growth on cold flat surface, where four feature variables including temperature of cold flat surface, the velocity, relative humidity, and temperature of air are adopted. The frost growth experiment generates the database, which is good for training frost growth due to its fast speed and high precision based on Levenberg-Marquardt learning rule. The establishment of neural network model in this paper can quickly and accurately predict the frost layer height on cold flat surface of different control variables, which is helpful for the implementation of defrosting.
KEYWORDS
Artificial Neural Network, Back Propagation Algorithm, Frosting Process, Frosting Characteristics, Frost Height
PAPER SUBMITTED: 2022-11-26
PAPER REVISED: 2022-12-17
PAPER ACCEPTED: 2023-02-25
PUBLISHED ONLINE: 2023-03-11
DOI REFERENCE: https://doi.org/10.2298/TSCI221126055S
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2023, VOLUME 27, ISSUE Issue 5, PAGES [4135 - 4144]
REFERENCES
  1. Liang, P., et al., Experimental Study of Cyclic Frosting and Defrosting on Micro-Channel Heat Exchangers with Different Coatings, Energy Build, 226 (2020), 110382
  2. Lai, T., et al., Experimental Study on the Frosting Characteristics of Round Tube in Confined Circular Flow Path at Low Temperature, Appl. Therm. Eng., 171 (2020), 1, 115075
  3. Pu, J., et al., A Semi-Experimental Method for Evaluating Frosting Performance of Air Source Heat Pumps, Renew. Energ., 173 (2021), 1, pp. 913-925
  4. Gall, R. L., et al., Modelling of Frost Growth and Densification, Int. J. Heat Mass Tran., 40 (1997), 13, pp. 3177-3187
  5. Gong, J., et al., A Numerical Investigation of Frost Growth on Cold Surfaces Based on the Lattice Boltzmann Method, Energies, 11 (2018), 8, pp. 1-13
  6. Baghoolizadeh, M., et al., Multi-Objective Optimization of Venetian Blinds in Office Buildings to Reduce Electricity Consumption and Improve Visual and Thermal Comfort by NSGA-II, Energy Build, 278 (2023), Jan., pp. 112639-112661
  7. Skrypnik, A. N., et al., Artificial Neural Networks Application on Friction Factor and Heat Transfer Coefficients Prediction in Tubes with Inner Helical-Finning, Appl. Therm. Eng., 206 (2022), 118049
  8. Wang, H., et al., Inverse Estimation of Hot-Wall Heat Flux Using Non-linear Artificial Neural Networks, Measurement, 181 (2021), 109648
  9. Zhang, W., et al., Application of Machine Learning, Deep Learning and Optimizationalgorithms in Geoengineering and Geoscience: Comprehensive Reviewand Future Challenge, Gondwana Res., 109 (2022), Sept., pp. 1-17
  10. Phoon, K., Zhang, W., Future of Machine Learning in Geotechnics, Georisk: Assessment and Management of Risk for Engineered, Systems and Geohazards, 17 (2023), 1, pp. 7-22
  11. Hemmat, E. M., et al., Increasing the Accuracy of Estimating the Dynamic Viscosity of Hybrid Nanolubricants Containing MWCNT-MgO Nanoparticles by Optimizing Using an Artificial Neural Network, Arab. J. Chem., 16 (2023), 2
  12. Tian, S., et al., Using Perceptron Feed-Forward Artificial Neural Network (ANN) for Predicting the Thermal Conductivity of Graphene Oxide-Al2O3/Water-Ethylene Glycol Hybrid Nanofluid, Case Studies in Therm. Eng., 26 (2021), Aug., 101055
  13. He, W., et al., Using of Artificial Neural Networks (ANN) to Predict the Thermal Conductivity of Zinc Oxide-Silver (50-50%)/Water Hybrid Newtonian nanofluid, Int. Commun. Heat Mass Transf., 116 (2020), July, 104645
  14. Ruhani, B., et al., Statistical Modelling and Investigation of Thermal Characteristics of a New Nanofluid Containing Cerium Oxide Powder, Heliyon, 8 (2022), 11, pp. 11373-11379
  15. Esfe, M. H., et al., Determining the Optimal Structure for Accurate Estimation of the Dynamic Viscosity of Oil-based Hybrid Nanofluid Containing MgO and MWCNT Nanoparticles Using Multilayer Perceptron Neural Networks with Levenberg-Marquardt Algorithm, Powder Technology, 415 (2023), 118085
  16. Kalogirou, S. A., Applications of Artificial Neural-networks for Energy Systems, Appl. Energ., 67 (2000), 1-2, pp. 17-35
  17. Temeyer, B. R., et al., Using Artificial Neural Network to Predict Parameters for Frost Deposition on Lowa Bridgeways, Proceedings, Mid-Continent Transportation Research Symposium, Ames, Ia., USA, 2003
  18. Tahavvor, A. R., Yaghoubi, M., Analysis of Natural-convection from a Column of Cold Horizontal Cylinders Using Artificial Neural Network, Appl. Math. Model., 36 (2012), 7, pp. 3176-3188
  19. Tahavvor, A. R., Modelling of Frost Crystal Growth over a Flat Plate Using Artificial Neural Networks and Fractal Geometries, Heat Mass Transfer, 53 (2016), 3, pp. 1-11
  20. Zhang, W., et al., Prediction of Undrained Shear Strength Using Extreme Gradient Boosting and Random Forest Based on Bayesian Optimization, Geosci. Front., 12 (2021), 1, pp. 469-477
  21. McDonald, J. E., Homogeneous Nucleation of Vapor Condensation, Am. J. Phys., 30 (1962), 12, pp. 870-877
PDF VERSION [DOWNLOAD]
Prediction of frosting process on cold wall surface based on ANN with back propagation algorithm

2025 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence