2013
DOI: 10.1007/s00231-013-1282-0
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Estimation and optimization of thermal performance of evacuated tube solar collector system

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Cited by 30 publications
(6 citation statements)
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“…(6) pump power of water heater system is negligible; (7) heat loss between the collector and water tank is negligible;…”
Section: Numerical Model and Model Validation Of Solar Water Heating mentioning
confidence: 99%
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“…(6) pump power of water heater system is negligible; (7) heat loss between the collector and water tank is negligible;…”
Section: Numerical Model and Model Validation Of Solar Water Heating mentioning
confidence: 99%
“…Heat transfer coefficient between inner glass surface and water flow can be calculated by (6) [11] and (7) [12]. Heat transfer coefficient is calculated by (6) when Re = 2300 ∼ 10 6 and is calculated by (7) when Re ≤ 2300. Here Re is the Reynolds number; is the diameter of inner glass tube; is the length of inner glass tube; pr is the water Prandtl number for water temperature; pr is the water Prandtl number for inner glass tube temperature; consider The simulation of mean water tank temperature based on the numerical model described in Section 4 is also carried out under the same condition as the experiment and the simulation results are given along with the experimental data.…”
Section: Heat Transfer Coefficientmentioning
confidence: 99%
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“…Dikmen et al [17] worked on the modeling of an evacuated tube through the application of ANFIS and ANN algorithms. The results gathered in their study were reliable and were in good agreement with the results collected from experimental studies done by different researches.…”
Section: Introductionmentioning
confidence: 99%
“…Caner et al [25] and Benli [26] applied ANN model for investigation on thermal performance calculation of two types of solar air collectors. Dikmen et al [27] structured artificial neural networks and adaptive neuro fuzzy inference system (ANFIS) models to predict the performance of evacuated tube solar collectors. Kalogirou et al [28] implemented ANN tool for performance prediction of large solar systems.…”
Section: Introductionmentioning
confidence: 99%