THERMAL SCIENCE

International Scientific Journal

Thermal Science - Online First

Authors of this Paper

External Links

Jinjuan Sun

,

Zhiheng Ma

,

Jianhui Tian

online first only

Prediction of frosting process on cold wall surface based on artificial neural network with back propagation algorithm

ABSTRACT
The artificial neural network 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 artificial neural network 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
REFERENCES
  1. Liang, P., Ran, L., Hai, H, et al., Experimental Study of Cyclic Frosting and Defrosting on Microchannel Heat Exchangers with Different Coatings, Energy Build, 226(2020), pp. 110382 DOI No. 10.1016/j.enbuild.2020.110382
  2. Lai, T., Ding, P., Dong, X., 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, pp. 115075 DOI No. 10.1016/j.applthermaleng.2020.115075
  3. Pu, J., Shen, C., Zhang, C., et al., A Semi-experimental Method for Evaluating Frosting Performance of Air Source Heat Pumps, Renew. Energ., 173(2021), 1, pp. 913-925 DOI No. 10.1016/J.RENENE.2021.04.029
  4. Gall, R.L., Grillot, J.M., Jallut, C., Modeling of Frost Growth and Densification, Int. J. Heat Mass Tran., 40(1997), 13, pp. 3177-3187 DOI No. 10.1016/S0017-9310(96)00359-6
  5. Gong, J., Hou J., Sun, 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 DOI No. 10.3390/en11082077
  6. Baghoolizadeh, M., Rostamzadeh-Renani, M., Rostamzadeh-Renani, R., 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), pp. 112639-112661 DOI No. 10.1016/j.enbuild.2022.112639
  7. Skrypnik, A. N., Shchelchkov, A.V., Gortyshov, Y.F., 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), pp. 118049 DOI No. 10.1016/j.applthermaleng.2022.118049
  8. Wang, H., Zhu, T., Zhu, X., et al., Inverse Estimation of Hot-wall Heat Flux Using Nonlinear Artificial Neural Networks, Measurement, 181(2021), pp. 109648 DOI No. 10.1016/j.measurement.2021.109648
  9. Zhang, W., Gu, X., Tang, L., et al., Application of Machine Learning, Deep Learning and Optimization algorithms in Geoengineering and Geoscience: Comprehensive Reviewand Future Challenge, Gondwana Res., 109(2022), pp. 1-17 DOI No. 10.1016/j.gr.2022.03.015
  10. Phoon, K., Zhang, W., Future of Machine Learning in Geotechnics, Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 16(2022), pp. 1-17 DOI No. 10.1080/17499518.2022.2087884
  11. Hemmat, E.M., Esfandeh, S., Amoozadkhalili, F., et al., Increasing the Accuracy of Estimating the Dynamic Viscosity of Hybrid Nano-lubricants Containing MWCNT-MgO Nanoparticles by Optimizing Using an Artificial Neural Network, Arab J Chem, 16(2023), 2 DOI No. 10.1016/j.arabjc.2022.104405
  12. Tian, S., Arshad, N.L., Toghraie, D., 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), pp. 101055-101065 DOI No. 10.1016/j.csite.2021.101055
  13. He, W., Ruhani, B., Toghraie, D., et al., Using of Artificial Neural Networks (ANNs) to Predict the Thermal Conductivity of Zinc Oxide-Silver (50%-50%)/Water Hybrid Newtonian nanofluid, Int. Commun. Heat Mass Transf., 116(2020), pp. 104645-104653 DOI No. 10.1016/j.icheatmasstransfer.2020.104645
  14. Ruhani, B., Andani, M.T., Abed, A.M., et al., Statistical Modeling and Investigation of Thermal Characteristics of a New Nanofluid Containing Cerium Oxide Powder, Heliyon, 8(2022), pp. 11373-11379 DOI No. 10.1016/j.heliyon.2022.e11373
  15. Esfe, M.H., Amoozadkhalili, F.A., Toghraie, D., Determining the Optimal Structure for Accurate Estimation of the Dynamic Viscosity of Oil-based Hybrid Nanofluid Containing MgO and MWCNTs Nanoparticles Using Multilayer Perceptron Neural Networks with Levenberg-Marquardt Algorithm, Powder Technology., 415(2023), pp. 118085 DOI No. 10.1016/j.powtec.2022.118085
  16. Kalogirou, S.A., Applications of Artificial Neural-networks for Energy Systems, Appl. Energ., 67(2000), 1-2, pp. 17-35 DOI No. 10.1016/S0306-2619(00)00005-2
  17. Temeyer, B.R., Gallus, W.A., Jungbluth, K.A., et al., Using Artificial Neural Network to Predict Parameters for Frost Deposition on Lowa Bridgeways, Proceedings of the Mid-Continent Transportation Research Symposium, Iowa, 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 DOI No. 10.1016/j.apm.2011.10.003
  19. Tahavvor, A.R., Modeling of Frost Crystal Growth over a Flat Plate Using Artificial Neural Networks and Fractal Geometries, Heat Mass Transfer, 53(2016), 3, pp. 1-11 DOI No. 10.1007/s00231-016-1848-8
  20. Zhang, W., Wu, C., Zhong, H., et al., Prediction of Undrained Shear Strength Using Extreme Gradient Boosting and Random Forest Based on Bayesian Optimization, Geosci. Front., 12(2021), pp. 469-477 DOI No. 10.1016/j.gsf.2020.03.007
  21. McDonald, J.E., Homogeneous Nucleation of Vapor Condensation, Am. J. Phys., 30(1962), 12, pp. 870-877 DOI No. 10.1119/1.1941841
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Prediction of frosting process on cold wall surface based on artificial neural network with back propagation algorithm