Optimization and parametric analysis of a nanofluid based photovoltaic thermal system: 3D numerical model with experimental validation

Hosseinzadeh, Mohammad; Salari, Ali; Sardarabadi, Mohammad; Passandideh-Fard, Mohammad

doi:10.1016/j.enconman.2018.01.006

Cited by 162 publications

(57 citation statements)

References 51 publications

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“…Many factors affect the thermophysical properties of nanofluids that are presented in Fig. 2 [219]. Kolade et al [220] investigated the effect of the particle fraction on the effective thermal conductivity of Al2O3-water and MWCNT-water nanofluids and reported 6% and 10% enhancement at 2% and 0.2% fractions, respectively.…”

Section: Thermophysical Properties Of Nanofluidsmentioning

confidence: 99%

Thermal conductivity, viscosity and heat transfer process in nanofluids: A critical review

Noorzadeh

Moghanlou

Vajdi

et al. 2020

jcc

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Section: Thermophysical Properties Of Nanofluidsmentioning

confidence: 99%

Thermal conductivity, viscosity and heat transfer process in nanofluids: A critical review

Noorzadeh

Moghanlou

Vajdi

et al. 2020

jcc

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“…A host of several investigations in the literature about utilizing nanofluids in PVT systems agreed that there is a valuable improvement in the performance of nanofluid cooled PVT systems compared to other conventional cooled PVT systems. Hosseinzadeh et al [12] used ZnO/ deionized water nanofluid with 0.2 wt% concentration in their study work at a flow rate of 30 kg/h. The study indicated that reducing the coolant inlet temperature from 40°C to 20°C enhances the thermal efficiency of ZnO/water-cooled PVT system by 61.21%.…”

Section: Introductionmentioning

confidence: 99%

Experimental study about utilization of MWCNTs and graphene nanoplatelets water-based nanofluids in flat non-concentrating PVT systems

2021

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Although the increment the performance of PVT systems by using the nanofluids as working fluids have gained the attention of researchers during the last two decades, there is still, a lack in the literature study associated to this application. This study contributes to the investigations and researches of applying the nanofluids to increase the performance of PVT collectors. A flat non-concentrating PVT collector has been designed, constructed and outdoor tested in Karabuk University, Turkey. The considered working fluids in this study are MWCNTs (multiwall carbon nanotubes), and graphene nanoplatelets dispersed in water as a base fluid with a concentration of 0.5 wt%. The experiments were run with a volume flow rate of 0.5 L/min for the aforementioned nanofluids and distilled water (as a reference fluid). The study results have shown and revealed that the MWCNT-water nanofluid presented a better performance in terms of electrical energetic efficiency compared to graphene nanoplateletswater nanofluid and distilled water, while graphene nanoplateletswater nanofluid revealed the highest thermal energetic efficiency. Moreover adding thermal unit to PV module enhanced the total energetic efficiency by 53.4% for distilled water, 57.2% for MWCNTwater, and 63.1% for graphene-water.

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“…Besides, the heated fluid can be actually used in various applications depending on the form of heat energy (water or air). Many studies throughout the world have been done so far to study the performance of photovoltaic thermal (PVT) collectors by all means of analytical solutions, experimental measurements, and numerical simulations . The aim of analytical solutions is to simplify the process of performance estimation, while experimental studies provide a more realistic approach in view of the environmental and operating conditions.…”

Section: Introductionmentioning

confidence: 99%

Machine learning predictive models for optimal design of building‐integrated photovoltaic‐thermal collectors

et al. 2020

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This research article aims to examine the feasibility of several machine learning techniques to forecast the exergetic performance of a building-integrated photovoltaic-thermal (BIPVT) collector. In this regard, it uses multiple linear regression, multilayer perceptron, radial basis function regressor, sequential minimal optimization improved support vector machine, lazy.IBK, random forest (RF), and random tree approaches. Moreover, it implements the performance evaluation criteria (PEC) to evaluate the system's performance from the perspective of exergy. The use of these approaches serves the identification process to realize the relationship between the input-output parameters of the BIPVT system. The novelty of this work is that it utilizes and compares multiple learning algorithms to predict the PEC of BIPVT through design parameters. Hence, the research considers the parameter (PEC) as the essential output of the BIPVT collector, while the input parameters are the length, width, and depth of the duct, located under the PV modules, as well as the air mass flow rate. The results of the research for the statistical indexes of mean absolute error, root mean square error, relative absolute error (%), and root relative squared error (%) show values of (0.2967, 0.3885, 1.8754, and 1.5237) and (0.4957, 0.8153, 2.9586, and 2.8289), respectively, for the training and testing datasets. While R 2 ranges (0.9997-0.9999) for those datasets. Therefore, to estimate the exergy performance of the BIPVT collector, the RF model is superior to other proposed models. K E Y W O R D SBIPVT, exergetic performance, machine learning, performance evaluation criteria, predictive model

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Optimization and parametric analysis of a nanofluid based photovoltaic thermal system: 3D numerical model with experimental validation

Cited by 162 publications

References 51 publications

Thermal conductivity, viscosity and heat transfer process in nanofluids: A critical review

Thermal conductivity, viscosity and heat transfer process in nanofluids: A critical review

Experimental study about utilization of MWCNTs and graphene nanoplatelets water-based nanofluids in flat non-concentrating PVT systems

Machine learning predictive models for optimal design of building‐integrated photovoltaic‐thermal collectors

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