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Abstract Experimental study on the effects of solid volume fraction and temperature on thermal conductivity of DWCNT(inner diameter of 3 nm)-ZnO(diameter of 10-30 nm)/water-ethylene glycol (60:40) nanofluids have been performed using KD2-Pro thermal analyzer in details. The experiments are carried out at solid concentration up to 1% and temperature ranging from 25 to 50 °C. Based on experimental results, using non-linear regression on results of experiments, a correlation as a function of temperature and solid volume fraction has been proposed. Measured data show that the relative thermal conductivity enhances with increasing concentration of nanoparticles. The increasing temperatures also increase the thermal conductivity of nanofluids, although its effect on the thermal conductivity compared to the effect of volume fraction is lower.

Continuous diamond film growth on SiO2 was achieved by an effective pretreatment. Carbon implantation and diamond powder abrasion were used to enhance diamond nucleation on SiO2. The possibility of using two gas mixtures (CH4–CO2 and CH4–H2) in order to achieve the growth of continuous diamond films on SiO2 was examined. The diamond film growth rate obtained using the CH4–CO2 gas mixture was 0.6 µm/h which is roughly three times higher than that of the CH4–H2 gas mixture. CH4–H2 gas mixtures had a lower etching reaction rate than that of CH4–CO2 gas mixtures. The higher etching rate of the CH4–CO2 gas mixture was considered to be due to a higher concentration of active carbon radicals or carbon-containing species in the plasma. Scanning electron microscopy (SEM) and optical emission spectroscopy (OES) indicated that active carbon radicals and carbon-containing species are the major cause of pits arising at the surface. Using CH4–H2 gas mixtures at a low microwave power gave the optimum reaction conditions for depositing diamond films on SiO2 layers.

This study provides an automatic method for detecting finger interruptions in electroluminescence (EL) images of multicrystalline solar cells. The proposed method is a supervised classification method. We obtain regions of interest (ROI) by separating theELimage to several regions. The fingers within eachROIare candidates for defect detection. We horizontally scan eachROIregion and extract features from each finger pixel. In the training stage, we record a set of features which are extracted from interrupted fingers and noninterrupted fingers. These features are represented as points in a spectral embedding space produced by spectral clustering method. These points will be classified into two clusters: interrupted fingers and noninterrupted fingers. In the classification stage, we firstly detect the position of fingers in anELimage and obtain features from each finger. The set of features in each finger combined with known features in the training stage will be represented as points in the spectral embedding space and then will be classified to the cluster with nearer cluster centroid of known features. Experimental results show that the proposed method can effectively detect finger interruptions on a set ofELimages of various solar cells.

Reservoirs in Taiwan often provide hydroelectric power, irrigation water, municipal water, and flood control for the whole year. Taiwan has the climatic characteristics of concentrated rainy seasons, instantaneous heavy rains due to typhoons and rainy seasons. In addition, steep rivers in mountainous areas flow fast and furiously. Under such circumstances, reservoirs have to face sudden heavy rainfall and surges in water levels within a short period of time, which often causes the water level to continue to rise to the full level even though hydroelectric units are operating at full capacity, and as reservoirs can only drain the flood water, this results in the waste of hydropower resources. In recent years, the impact of climate change has caused extreme weather events to occur more frequently, increasing the need for flood control, and the reservoir operation has faced severe challenges in order to fulfil its multipurpose requirements. Therefore, in order to avoid the waste of hydropower resources and improve the effectiveness of the reservoir operation, this paper proposes a real-time 48-h ahead water level forecasting system, based on fuzzy neural networks with multi-stage architecture. The proposed multi-stage architecture provides reservoir inflow estimation, 48-h ahead reservoir inflow forecasting, and 48-h ahead water level forecasting. The proposed method has been implemented at the Techi hydropower plant in Taiwan. Experimental results show that the proposed method can effectively increase energy efficiency and allow the reservoir water resources to be fully utilized. In addition, the proposed method can improve the effectiveness of the hydropower plant, especially when rain is heavy.

The recrystallization behavior, grain growth kinetics, and corresponding hardness variation of homogenized and 80% cold-rolled FeCoNiCrPd, FeCoNiCrMn, and their quaternary/ternary FCC-structured high/medium entropy alloys (H/MEAs) annealed under different conditions were investigated. Experimental results indicate that the grain size and hardness of these H/MEAs follow the Hall–Petch equation, with the Hall–Petch coefficient KH value being mainly dominated by the alloy’s stacking fault energy and shear modulus. The FeCoNiCrPd alloy exhibits the highest hardness of the H/MEAs at the same grain size due to the largest Young’s modulus difference between Cr and Pd. The grain growth exponent n, kinetic constant k, and activation energy for grain growth QG of all H/MEAs are calculated. The k can be expressed by the Arrhenius equation with QG, which is attributed to the diffusion rate. The results demonstrate that the QG values of these H/MEAs are much higher than those of conventional alloys; most notable is FeCoNiCrPd HEA, which has an unusually lattice distortion effect that hinders grain growth.

Abstract The impact of a twisted turbulator in a parabolic solar collector on the improvement of thermal–hydraulic performance (THP), as well as energy and exergy Efficiency of MgO-Cu/water hybrid nanofluid (HNF) is numerically evaluated. The main goal of this study is to use a twisted turbulator with pitch ratios (γ) of 1, 1.5, 2, and 2.5 for Reynolds numbers (Re) of 8000 to 32,000 and volume fractions (ϕ) of 1, 2, and 3% of MgO and Cu nanoparticles. Numerical simulation findings are reported in the form of graphs of average Nusselt number (Nuave), pressure drop (Δp), THP, energy Efficiency ( η c ) , and exergy Efficiency ( η ex ) . It can be concluded that Nuave and Δp depends on ϕ and Re and augments linearly with their values. Moreover, Nuave and Δp increases significantly by increasing the pitch ratio of the turbulator. The results revealed that the maximum values of augmentation of η c and η ex are respectively 23.79% and 21.15% by raising Re from 8000 to 32000. In a SC with a turbulator with γ = 2 and ϕ = 3%, η c is raised by 24.16% by increasing Re from 8000 to 32000; while, in the case of γ = 2.5 and ϕ = 3%, η c is enhanced by 18.2%.

Abstract Optimal capacitor placement in distribution systems solved by the hybrid method of CODEQ (called HCODEQ method) is proposed in this work. The concepts of chaotic search, opposition-based learning, and quantum mechanics are used in the CODEQ method to overcome the drawback of parameters selection in the differential evolution (DE). However, a larger population size must be used in the CODEQ method. That is a drawback for all evolutionary algorithms (EAs). To overcome this drawback, acceleration operation and migrating operation are embedded into the CODEQ method, i.e. HCODEQ method. The use of these two operations can increase the convergence speed without decreasing the diversity among individuals. One benchmark function and various-scale capacitor placement systems are used to compare the performance of the proposed method, CODEQ method, DE, simulated annealing (SA), and ant system (AS). Numerical results show that the performance of the HCODEQ method is better than the other methods.