Applying genetic programming in estimation of frost layer thickness on horizontal and vertical plates at ultra-low temperature

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@Article{HOSSEINI:2021:IJR,

• author = "S. H. Hosseini and M. A. Moradkhani and M. Valizadeh and G. Ahmadi",
• title = "Applying genetic programming in estimation of frost layer thickness on horizontal and vertical plates at ultra-low temperature",
• journal = "International Journal of Refrigeration",
• volume = "125",
• pages = "113--121",
• year = "2021",
• ISSN = "0140-7007",
• DOI = "doi:10.1016/j.ijrefrig.2020.12.035",
• URL = "https://www.sciencedirect.com/science/article/pii/S0140700720305296",
• keywords = "genetic algorithms, genetic programming, Frost layer thickness, Smart model, Cryogenic condition, Horizontal and vertical plates, Epaisseur de la couche de givre, Modele intelligent, Condition cryogenique, Plaques verticales et horizontales",
• abstract = "In this study, the intelligent method of genetic programming (GP) was used for developing predictive models for estimating the frost layer thickness under natural and forced convection on ultra-low temperature surfaces. The affecting dimensionless parameters were used as GP input variables, and realistic empirical correlations were developed for estimating the frost thickness under different conditions. The coefficient of determination of 0.9731, 0.9812, and 0.9906, and average absolute relative error of 6.52percent, 11.65percent, and 2.87percent, were obtained by the developed models for natural convection on vertical plates, natural convection on horizontal plates, and forced convection on horizontal plates, respectively. The physical trends of the developed models were evaluated by comparing the model predictions with the experimental data for different operating conditions, and reasonable agreements were obtained. The same experimental database was also compared to some existing correlations for ordinary-low temperature surfaces, but they failed to provide reasonable estimates for the data",

}

Genetic Programming entries for Seyyed Hossein Hosseini Mohammad Amin Moradkhani Mohammadreza Valizadeh Goodarz Ahmadi

Citations