Innovative hybrid solar panels combining photovoltaic cells along with an efficient heat exchanger with attached fins to the parallel plates and water‐Al2O3 nanofluid as a working fluid is presented in this work. Twenty‐seven fins at the upper wall and 27 fins at the lower wall in labyrinth arrangement are used in simulations with fin lengths of 0, ¼, ½, and ¾ of the flow path height. Moreover, nanosolid particles dispersed in the base fluid range as 0 ≤ ϕ ≤ 0.2. In addition, Reynolds number Re at the inlet was varied such that 10 < Re < 80. Numerical finite element analysis using COMSOL software is utilized to investigate flow and thermal characteristics as well the overall efficiency of the hybrid system. Results show that as the Reynolds number, the length of the fin, and the volume fraction of the nanosolid particles increase, the overall efficiency increases. Moreover, increasing nanoparticle volume fraction and the fin length was found to increase the friction coefficient.
This paper proposes computational models to investigate the effects of dust and ambient temperature on the performance of a photovoltaic system built at the Hashemite University, Jordan. The system is connected on-grid with an azimuth angle of 0° and a tilt angle of 26°. The models have been developed employing optimized architectures of artificial neural network (ANN) and extreme learning machine (ELM) models to estimate conversion efficiency based on experimental data. The methodology of building the models is demonstrated and validated for its accuracy using different metrics. The effect of each parameter was found to be in agreement with the well-known relationship between each parameter and the predicted efficiency. It is found that the optimized ELM model predicts conversion efficiency with the best accuracy, yielding an R2 of 91.4%. Moreover, a recommendation for cleaning frequency of every two weeks is proposed. Finally, different scenarios of electricity tariffs with their sensitivity analyses are illustrated.
The paper proposes the validation of the latest System Advisor Model (SAM) vs. the experimental data for concentrated solar power energy facilities. Both parabolic trough, and solar tower, are considered, with and without thermal energy storage. The 250 MW parabolic trough facilities of Genesis, Mojave, and Solana, and the 110 MW solar tower facility of Crescent Dunes, all in the United States South-West, are modeled. The computed monthly average capacity factors for the average weather year are compared with the experimental data measured since the start of the operation of the facilities. While much higher sampling frequencies are needed for proper validation, as monthly averaging dramatically filters out differences between experiments and simulations, computational results are relatively close to measured values for the parabolic trough, and very far from for solar tower systems. The thermal energy storage is also introducing additional inaccuracies. It is concluded that the code needs further development, especially for the solar field and receiver of the solar tower modules, and the thermal energy storage. Validation of models and sub-models vs. high-frequency data collected on existing facilities, for both energy production, power plant parameters, and weather conditions, is a necessary step before using the code for designing novel facilities. National Electricity Market data tell us that even installed capacity of 18.1 GW sometimes is not enough to deliver 0.2 GW to the grid in cases, for example, of low wind after sunset), the coupling of concentrating solar power with molten salt thermal energy storage is extremely attractive, as it may produce fully dispatchable energy.While the concentrating solar power solar tower coupled with thermal energy storage has so far performed badly in the real world, the concentrating solar power parabolic trough with thermal energy storage is already delivering good performances, , even if still less than the predictions. Solar photovoltaic works with annual average ε about 0.27-0.29, but with much larger than the capacity factor high-frequency standard deviations, for coefficients of variability in excess of unity . While in the virtual reality of model computations concentrating solar power with thermal energy storage may achieve much larger and more uniform ε, with average ε well in excess of 0.5, also addressing the issues of lack of production during night times, and drastically reduced production with clouds, with dramatically reduced standard deviations, the best real-world experience has so far delivered an annual average ε of about 0.36 (Solana). By contrast with photovoltaic, concentrating solar power, especially the solar tower, suffers from cloud coverage, and this may considerably affect the accuracy of the models.As concluded in , the concentrating solar power parabolic trough has the potential, once a satisfactory design will be industrialized, to deliver the same or better than photovoltaic costs of electricity with...
Massive improvements in the thermophysical properties of nanofluids over conventional fluids have led to the rapid evolution of using multiwalled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) in the field of heat transfer. In this study, the heat transfer and entropy generation abilities of MWCNTs/GNPs hybrid nanofluids were explored. Experiments on forced convective flow through a brass microtube with 300 µm inner diameter and 0.27 m in length were performed under uniform heat flux. MWCNTs/GNPs hybrid nanofluids were developed by adding 0.035 wt.% GNPs to MWCNTs water-based nanofluids with mass fractions of 0.075–0.125 wt.%. The range of the Reynolds number in this experiment was maintained at Re = 200–500. Results showed that the conventional approach for predicting the heat transfer coefficient was applicable for microtubes. The heat transfer coefficient increased markedly with the use of MWCNTs and MWCNTs/GNPs nanofluids, with increased pressure dropping by 12.4%. Results further showed a reduction by 37.5% in the total entropy generation rate in microtubes for hybrid nanofluids. Overall, MWCNTs/GNPs hybrid nanofluids can be used as alternative fluids in cooling systems for thermal applications.
Background: Efficient production and reliable availability of electricity requires comprehensive understanding of load demand trends to plan and match production with consumption. Although electricity demand depends on a combination of cultural and economic conditions, weather conditions remain as the major driver. With increased capabilities of accurate predictions of weather, the importance of investigating and quantifying its impact on electricity demand becomes obvious. The electrical system in Jordan has been facing several challenges including the failure to respond to increased demands induced by extreme temperatures. This paper covers a clear gap in literature through presenting a detailed investigation of the electricity consumption trends and in identifying the susceptibility of these trends to weather. Methods: This study relies on the statistical processing and analysis, through modeling of hourly electricity demands in Jordan in the period of 10 years between 2007 and 2016. Actual weather data was used employing the degree-day approach. The monthly, daily, and hourly seasonal variation indices were determined. Optimally formulated piecewise functions were used to track the thermal comfort zone and rate of increase in electricity demand for temperatures beyond it for each year. Moreover, the elasticity of polynomial functions was adopted to identify saturation points to thermally map the electricity consumption. Results: The developed models successfully described the relationship between the daily electricity demand and the mean daily ambient temperature. The average comfort zone width was 4°C and the average mean base temperature was 17.9°C. The sensitivity of electricity demand to both high and low temperatures has increased on average, with 11% and 16.4% to hot and cold weather, respectively. Finally, the electricity demand in cooling was found to saturate at 32.9°C, whereas it saturates for heating at 4.7°C. Conclusions: The electricity demand in Jordan observes seasonal trends in a consistent and predictable manner. An optimally formulated piecewise function successfully tracked the thermal comfort zone and the rate of increase in electricity demand for temperatures beyond it for each year of the study period. Finally, saturation heating and cooling temperatures were acquired from the elasticity of the daily electricity demands modeled against daily HDD and CDD.
In this study, a new cavity form filled under a constant magnetic field by Ag/MgO/H2O nanofluids and porous media consistent with natural convection and total entropy is examined. The nanofluid flow is considered to be laminar and incompressible, while the advection inertia effect in the porous layer is taken into account by adopting the Darcy–Forchheimer model. The problem is explained in the dimensionless form of the governing equations and solved by the finite element method. The results of the values of Darcy (Da), Hartmann (Ha) and Rayleigh (Ra) numbers, porosity (εp), and the properties of solid volume fraction (ϕ) and flow fields were studied. The findings show that with each improvement in the Ha number, the heat transfer rate becomes more limited, and thus the magnetic field can be used as an outstanding heat transfer controller.
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