2022
DOI: 10.1016/j.applthermaleng.2021.118009
|View full text |Cite
|
Sign up to set email alerts
|

Modeling of water-PCM solar thermal storage system for domestic hot water application using Artificial neural networks

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
6
1

Relationship

0
7

Authors

Journals

citations
Cited by 32 publications
(3 citation statements)
references
References 25 publications
0
1
0
Order By: Relevance
“…However, the hourly heat dissipation of the Kang obtained by Xing Chaojie showed that the heat dissipation of the Kang still had large volatility, the Kang's combustion heating could only last for 2 h, and the heat storage capacity of the Kang itself was not enough to maintain the stability of the indoor thermal environment [11]. At present, many studies choose to use new materials, such as PCM [26][27][28][29][30][31][32] or CaO [33][34][35][36], as thermal storage materials, but additional investment costs are required by the new materials. The large thermal mass of the earthen envelope makes it the most suitable thermal storage equipment in cave dwellings.…”
Section: Introductionmentioning
confidence: 99%
“…However, the hourly heat dissipation of the Kang obtained by Xing Chaojie showed that the heat dissipation of the Kang still had large volatility, the Kang's combustion heating could only last for 2 h, and the heat storage capacity of the Kang itself was not enough to maintain the stability of the indoor thermal environment [11]. At present, many studies choose to use new materials, such as PCM [26][27][28][29][30][31][32] or CaO [33][34][35][36], as thermal storage materials, but additional investment costs are required by the new materials. The large thermal mass of the earthen envelope makes it the most suitable thermal storage equipment in cave dwellings.…”
Section: Introductionmentioning
confidence: 99%
“…The methods listed above, except the methods using fuzzy sets [73,74] and the artificial neural network methods [75][76][77], are modified solutions of the J. Stefan problem [78]. The problem involves determining the position of a variable relative to time and a coordinate system of a discrete boundary grid between a liquid and solid substance.…”
mentioning
confidence: 99%
“…Such calculations are often performed using MAT-LAB [63], Adina [59] Energy Plus [86], or Ansys fluent [88] software. A different approach is presented for using the fuzzy inference method and artificial neural networks [73][74][75][76][77]. In this case, it is enough to have a pool of empirical results from experiments to obtain a result at an acceptable error level.…”
mentioning
confidence: 99%