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Recent technological advances have made it possible to produce particles with nanometer dimensions that are uniformly and steadily suspended in traditional solar liquids and have enhanced the impact of thermo-physical parameters. In this research, a three-dimensional flat plate solar collector was built using a thin flat plate and a single working fluid pipe. The physical model was solved computationally under conditions of conjugated laminar forced convection in the range 500 ≤ Re ≤ 1900 and a heat flux of 1000W/m². Distilled water (DW) and different types of hybrid nanofluids (namely, 0.1%-Al2O3@Cu/DW, 0.1%-MWCNTs@Fe3O4/DW, 0.3%-MWCNTs@Fe3O4/DW, 0.5%-Ag@MgO/DW, 1%-Ag@MgO/DW, 1%-S1 and 1%-S2, where MWCNTs are multi-wall carbon nanotubes, S1 means 2CuO--1Cu and S2 means 1CuO--2Cu nanocomposites) were evaluated via a set of parameters. The numerical results revealed that, by increasing the working fluid velocity (the Reynolds number), the average heat transfer coefficient, pressure loss, heat gain and solar collector efficiency were increased. Meanwhile, outlet fluid temperature and flat plate surface temperature were decreased. At Re = 1900, 1%-S2 and 1%-S1 presented higher thermal performance enhancement by 44.28% and 36.72% relative to DW. Moreover, low thermal performance enhancement of 7.59% and 7.44% were reported by 0.1%-Al2O3@Cu/DW and 0.3%-MWCNTs@Fe3O4/DW, respectively. [ABSTRACT FROM AUTHOR]

The flat-plate solar collector (FPSC) three-dimensional (3D) model was used to numerically evaluate the energy and economic estimates. A laminar flow with 500 ≤ Re ≤ 1900, an inlet temperature of 293 K, and a solar flux of 1000W/m² were assumed the operating conditions. Two mono nanofluids, CuO-DW and Cu-DW, were tested with different shapes (Spherical, Cylindrical, Platelets, and Blades) and different volume fractions. Additionally, hybrid nanocomposites from CuO@Cu/DW with different shapes (Spherical, Cylindrical, Platelets and Blades), different mixing ratios (60%+40%, 50%+50% and 40%+60%) and different volume fractions (1 volume%, 2 volume%, 3 volume% and 4 volume%) were compared with mono nanofluids. At 1 volume% and Re = 1900, CuO-Platelets demonstrated the highest pressure drop (33.312 Pa). CuO-Platelets achieved the higher thermal enhancement with (8.761%) at 1 vol.% and Re = 1900. CuO-Platelets reduced the size of the solar collector by 25.60%. Meanwhile, CuO@Cu-Spherical (40:60) needed a larger collector size with 16.69% at 4 vol.% and Re = 1900. CuO-Platelets with 967.61, CuO -- Cylindrical with 976.76, Cu Platelets with 983.84, and Cu-Cylindrical with 992.92 presented the lowest total cost. Meanwhile, the total cost of CuO -- Cu -- Platelets with 60:40, 50:50, and 40:60 was 994.82, 996.18, and 997.70, respectively. [ABSTRACT FROM AUTHOR]

Ensuring accurate estimation of evaporation is weighty for effective planning and judicious management of available water resources for agricultural practices. Thus, this work enhances the potential of support vector regression (SVR) optimized with a novel nature-inspired algorithm, namely, Slap Swarm Algorithm (SVR-SSA) against Whale Optimization Algorithm (SVR-WOA), Multi-Verse Optimizer (SVR-MVO), Spotted Hyena Optimizer (SVR-SHO), Particle Swarm Optimization (SVR-PSO), and Penman model (PM). Daily EP (pan-evaporation) was estimated in two different agro-climatic zones (ACZ) in northern India. The optimal combination of input parameters was extracted by applying the Gamma test (GT). The outcomes of the hybrid of SVR and PM models were equated with recorded daily EP observations based on goodness-of-fit measures along with graphical scrutiny. The results of the appraisal showed that the novel hybrid SVR-SSA-5 model performed superior (MAE = 0.697, 1.556, 0.858 mm/day; RMSE = 1.116, 2.114, 1.202 mm/day; IOS = 0.250, 0.350, 0.303; NSE = 0.0.861, 0.750, 0.834; PCC = 0.929, 0.868, 0.918; IOA = 0.960, 0.925, 0.956) than other models in testing phase at Hisar, Bathinda, and Ludhiana stations, respectively. In conclusion, the hybrid SVR-SSA model was identified as more suitable, robust, and reliable than the other models for daily EP estimation in two different ACZ. [ABSTRACT FROM AUTHOR]

This study compares several advanced machine learning models to obtain the most accurate method for predicting the aeration efficiency (E20) through the Parshall flume. The required dataset is obtained from the laboratory tests using different flumes fabricated in National Institute Technology Kurukshetra, India. Besides, the potential of K Nearest Neighbor (KNN), Random Forest Regression (RFR), and Decision Tree Regression (DTR) models are evaluated to predict the aeration efficiency. In this way, several input combinations (e.g. M1-M15) are provided using the laboratory parameters (e.g. W/L, S/L, Fr, and Re). Different predictive models are obtained based on those input combinations and machine learning models proposed in the present study. The predictive models are assessed based on several performance metrics and visual indicators. Results show that the KNN-M11 model ( R M S E t e s t i n g = 0.002 , R t e s t i n g 2 = 0.929), which includes W/L, S/L, and Fr as predictive variables outperforms the other predictive models. Furthermore, an enhancement is observed in KNN model estimation accuracy compared to the previously developed empirical models. In general, the predictive model dominated in the present study provides adequate performance in predicting the aeration efficiency in the Parshall flume. [ABSTRACT FROM AUTHOR]

Sediment transport in the ejector is highly stochastic and non-linear in nature, and its accurate estimation is a complex and challenging mission. This study attempts to investigate the sediment removal estimation of sediment ejector using newly developed hybrid data-intelligence models. The proposed models are based on the hybridization of adaptive neuro-fuzzy inference systems (ANFIS) with different metaheuristic algorithms, namely, particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), and ant colony optimization (ACO). The proposed models are constructed with various related input variables such as sediment concentration, flow depth, velocity, sediment size, Froude number, extraction ratio, number of tunnels and sub-tunnels, and flow depth at upstream of the sediment ejector. The estimation capacity of the developed hybrid models is assessed using several statistical evaluation indices. The modeling results obtained for the studied ejector sediment removal estimation demonstrated an optimistic finding. Among the developed hybrid models, ANFIS-PSO model exhibited the best predictability potential with maximum correlation coefficient values CC Train = 0.915 and CCTest = 0.916. [ABSTRACT FROM AUTHOR]