Thermal characteristics of evacuated tube solar collectors with coil inside: An experimental study and evolutionary algorithms
Introduction
“Nano” and “Energy” are two nationwide compelling keywords in the modern world. On the other hand, renewable energy has become of great significance due to considerable plunge in fossil fuels. Solar energy, as one of the most well-known renewable energy resources, can be substituted for many thermodynamic industrial applications, such as solar collectors and batteries [1,2]. Although there exist some limitations in terms of stability, and cost of using nanofluids, it is extensively applied to a large number of heating and cooling solar systems, such as combined cooling, heating and power (CCHP) stations, water heating systems, heat pumps, desalination systems, in order to enhance the efficiency level of solar systems [3,4].
Nowadays, the use of nanofluids, such as aluminum oxide (Al2O3) and copper oxide (Cu2O) in thermal systems has become a reliable approach so as to enhance the thermo-physical characteristics of the working fluid inside the thermodynamic systems [5,6]. Furthermore, Sadeghi et al. [7] improved the thermal efficiency level of an evacuate tube solar collectors (ETSC) by means of Cu2O/distilled water nanofluid and parabolic concentrator up to 11%. MA Sharafeldin et al. [8] used WO3/water nanofluid to enhance efficiency of an ETSC experimentally. The results demonstrated that the useful heat gain of the collector rised up to 23%, and the efficiency of the proposed ETSC was reported as 72.83%. H. Kaya et al. [9] applied ZnO/Etylene glycol-pure water nanofluid at different volume concentrations to a U-Tube solar collector. It was reported that this type of nanofluid can increase the thermal efficiency of the solar collector up to 5.2% compared to using the base fluid as the working fluid. I.M. Mahbubul et al. [10] increased the thermal efficiency of an ETSC up to 10% using carbon nanotube nanofluid for 0.2 vol fraction of nanoparticles (VF). Jowzi et al. [11] enhanced the efficiency level of an ETSC of about 12% by means of a bypass tube connecting the bottom of the thermal storage tank to the bottom of the evacuated tube in order to eradicate the stagnant zone at the bottom of the evacuated tube. Additionally, Sadeghi et al. [12] carried out experimental studies to investigate the influence of argan and air gases between cover and absorber coil in a tubular solar collector at various rates of mass flow. The results indicated that the optimum mass flow rate obtained 3.5 kg/h and argon gas presented higher level of efficiency due to less Prandtl number. McEnaney et al. [13] modelled a new form of tubular receiver using aerogels between the two glasses. They concluded that the modified type of solar receiver performs better than the evacuated tubes. In 2014, Mahian et al. [14] mathematically analyzed the effect of volume fraction of nanoparticles in the base fluid, and the shape of nano materials on second law efficiency of thermal solar systems. Goudarzi et al. [15] studied the effects of nanofluids pH on the performance of solar collectors. It was found that changing pH of nanofluids can tangibly influence thermo-physical properties of the fluid, and they could report the 52% efficiency.
According to the optimum design of solar systems, obtaining pieces of information pertained to thermal properties is necessary. This is highly recommended to evaluate efficiency level of various kinds of solar systems by means of thermal characteristics. Basically, it can be noted that there are a few mathematical expressions to assess efficiency level of solar systems. In the case of experimental studies, empirical equations may produce inaccurate predictions due to lack of facilities and limited ranges of variables. In contrast to regression based equations, another type of predictive tools, known AI approaches, have been employed in order to assess thermal performance of solar systems. AI techniques were applied to appraise the performance of various solar systems. According to application of AI technique, artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), least square-support vector machine (LS-SVM), M5 model tree (MT), and multivariate adaptive regression splines (MARS) were applied to assess operation of solar systems. Results of previous studies proved that applications of AI models have provided more accurate predictions in comparison with regression based equations. Longo et al. [16] predicted the thermal conductivity of the group of oxide-water nanofluids using artificial neural networks technique. They found that the proposed ANN model could forecast precise the amount of thermal conductivity of the nanofluids regarding the average size of the nanoparticles.
In this study, the use of AI techniques is considered to perceive mechanism of ETSCs. In this way, a laboratory analysis is conducted on the thermal characteristics of a constructed ETSC with parabolic concentrator and additionally, the use of copper oxide/distilled water nanofluid for different mass flow rates is considered. Afterwards, energy efficiency and inlet-outlet water temperature difference for the ETSC are formulated by using the three well-known AI methods, namely gene-expression programming (GEP), MARS, and M5MT. Furthermore, the accuracy level of formulations is compared with the experimental observations. Ultimately, the physical meaning of experimental observations in terms of the effect of parabolic concentrator on the diffuse irradiance is examined.
Section snippets
The preparation procedure
In this case, in order for preparation of the Cu2O/DW nanofluid with 0.04 and 0.08 vol concentrations, the two-step technique is more efficient compared with the one-step one in dwindling sedimentation, and in rising the dispersal behavior [17,18]. In the present procedure, polyvidone (PVP–K90) has been considered to the prepared solution used as the surfactant, and the nanoparticles were scattered by the magnetic stirrer. In the next step, the nanofluid was set under an Ultra-Sonicator device,
Experimentation procedure description
A scheme of the adopted experimental procedures with a concise explanation of each part is indicated in Fig. 7. The experimentations were carried out in Kermanshah at longitude 34.3 oE and latitude 46.7 oN in August in 2018. The nanofluids were prepared for different volume concentrations of 0.4 and 0.08 VF. As illustrated in Fig. 8, the ETSC setup contains three WGETSC evacuated tubes, seven storage tanks with various volumes of 10, 20, 25, 30, 40, 50, and 60 l, a curved mirror as the
First law efficiency
The first law efficiency of the proposed collector could be obtained via Eqs. (1), (2) [20].where is the multiplication of collector effective transmittance-absorptance. The specific heat of the nanofluid at different volume fractions was obtained from Ref. [21]: is the nanofluid density derived from Ref. [21]:
In addition, Table 2 lists the thermodynamic characteristics of the
An overview of MARS algorithm
Multivariate adaptive regression splines (MARS) technique formulated as a non-parametric mathematical model is able to transform the governing equations among Input-output systems. In MARS model, pattern recognition is performed by means of setting a number of basis functions (BFs) and their corresponded weighted coefficients [26,27]. Mathematically, the MARS model is defined as,in which is indicative of knot. BFs are occasionally
Accuracy of the measurement devices
Since, there are many errors while conducting the experiments, such as data mining, the uncertainty investigation of all parameters should be carried out to verify the applicability of the experimentations. The uncertainty equations related to validation of thermal characteristics (energy and exergy analyses) of the constructed ETSC are represented as follows:
In Eq. (19),
Results and implementations
The experiments have been conducted for different fractions of nanoparticles, but the results are presented just for 0.04 and 0.08 VF of the nanofluid for summarizing the findings, and extracting the trend. The results have been divided into two parts; experimental and numerical investigations. In this section, first of all the effects of nanofluid on the thermal characteristics of the fluid have been studied, then the performance of proposed AI techniques are compared and eventually the
Conclusion
This paper presented some findings about effect of using Cu2O/DW nanofluid on improving the thermodynamic efficiency of the ETSCs. The experimentations were carried out for three flow rates of the fluid passing through the coil. Moreover, three powerful AI-based formulations have been utilized to assess thermal performance of the ETSC system. The predictive AI models were fed by three input variables as, tank volume, nanofluid concentration, and mass flow rate of the fluid. Additionally, two
Author contribution
I declare that all the authors had a significant scientific contribution to the paper, and all the contents of this paper have been shared with all authors. The roles of all authors are listed as follows:
• Gholamabbas Sadeghi: constructing the ETSC, preparation of the nanofluid, conducting the experiments, data mining, writing the entire paper.
• Dr. Mohammad Najafzadeh: conducting robust three AI techniques, extracting the AI-based results.
• Prof. Mehran Ameri: verification of the results,
Declaration of competing interest
I declare no conflict of interest, and my agreement for submission of this manuscript and I claim that this work is novel and has not been submitted elsewhere.
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