Robust predictive models for estimating frost deposition on horizontal and parallel surfaces
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Zendehboudi and Li developed a black-box frosting model based on multilayer perceptron trained by Artificial Neural Network, trained by experimental data from 444 frosting cases, and it can predict average frost thickness and density on vertical and horizontal parallel plates.
“Zendehboudi and Li [137] developed a black-box frosting model based on an intelligent method: multilayer perceptron trained by Artificial Neural Network. The model was trained by exper imental data from 444 frosting cases and can be used to predict average frost thickness and density on vertical and horizontal parallel plates.”
From Frosting mechanism and behaviors on surfaces with simple geometries: A state-of-the-art literature review (2022) · cites this paper as [137]
Cites
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A one-dimensional model for frost formation on a cold flat surface
Lee, K.-S., Kim, W.-S., & Lee, T.-H. (1997) · International Journal of Heat and Mass Transfer · doi:10.1016/s0017-9310(97)00074-4 · Cited by 296 · see citing works
A study of frost growth and densification on flat surfaces
Hermes, C. J. L., Piucco, R. O., Barbosa, J. R., & Melo, C. (2009) · Experimental Thermal and Fluid Science · doi:10.1016/j.expthermflusci.2008.10.006 · Cited by 257 · see citing works
Frost formation with varying environmental parameters
Jones, B. W., & Parker, J. D. (1975) · Journal of Heat Transfer · doi:10.1115/1.3450350 · Cited by 171 · see citing works
Dimensionless correlations of frost properties on a cold plate
Yang, D.-K., & Lee, K.-S. (2004) · International Journal of Refrigeration · doi:10.1016/s0140-7007(03)00118-x · Cited by 134 · see citing works
State-of-the-art review of frost deposition on flat surfaces
Léoni, A., Mondot, M., Durier, F., Revellin, R., & Haberschill, P. (2016) · International Journal of Refrigeration · doi:10.1016/j.ijrefrig.2016.04.004 · Cited by 103 · see citing works
A generalized simple model for predicting frost growth on cold flat plate
Wang, W., Guo, Q. C., Lu, W. P., Feng, Y. C., & Na, W. (2012) · International Journal of Refrigeration · doi:10.1016/j.ijrefrig.2011.10.011 · Cited by 89 · see citing works
A semi-empirical correlation for the frost density
Hermes, C. J. L., Loyola, F. R., & Nascimento, V. S. (2014) · International Journal of Refrigeration · doi:10.1016/j.ijrefrig.2014.02.008 · Cited by 69 · see citing works
Moallem, E., Hong, T., Cremaschi, L., & Fisher, D. E. (2013) · International Journal of Refrigeration · doi:10.1016/j.ijrefrig.2012.09.009 · Cited by 57 · see citing works
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Zhang, L., Song, M., Deng, S., Shen, J., & Dang, C. (2022) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2022.118984 · Cited by 83 · see citing works
- Authors
- Zendehboudi, A., & Li, X.
- Affiliations
- Tsinghua University (CN)
- Corresponding author
- Li, Xianting
- ORCID
- Zendehboudi, Alireza
- Venue
- International Journal of Refrigeration
- Publisher
- Elsevier BV
- Published
- 2017-08
- Volume
- 80
- Pages
- 225-237
- ISSN
- 0140-7007 (ISSN-L)
- OpenAlex
- W2614985478
- Cited by
- 49
- References
- 9
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 4 · 2023: 4 · 2022: 11 · 2021: 6 · 2020: 6 · 2019: 5 · 2018: 3
- FWCI
- 2.98
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Refrigeration (publishedVersion)
- Funders
- National Natural Science Foundation of China (51521005)
- Topics
- Physical Sciences › Engineering › Mechanical Engineering › Refrigeration and Air Conditioning Technologies
- Physical Sciences › Engineering › Aerospace Engineering › Icing and De-icing Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- frost formation, frost density, frost thickness, horizontal surface, multilayer perceptron, artificial neural networks, applicability domain
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