Experimental Study of Subcooled Boiling Heat Transfer of Axial and Swirling Flows inside Mini Annular Gaps

An experimental study of the subcooled boiling heat transfer of axial and swirling upward flows inside vertical mini annular gaps was conducted using deionized water. The subcooled boiling heat transfer coefficients and the boiling curves of the flow inside mini annular gaps with different gap sizes have been investigated. The experimental results both for the single phase heat transfer and subcooled boiling heat transfer inside mini annular gaps showed very good agreement with correlations in the literature. The results showed that the subcooled boiling heat transfer coefficient for a given heat flux increases as the size of the annular gap is decreased. The maximum wall superheat is also influenced negligibly by mass flux. Furthermore, the effects of swirl flow by using spring insets inside the mini annuli on the single phase and subcooled boiling heat transfer have been studied. The results showed that the single phase and subcooled boiling heat transfer coefficients are increased by having swirl flow inside mini annuli using spring inserts. The obtained results also showed that the heat transfer enhancement by having swirl flow inside the annuli using spring inserts decreases as the applied heat flux is increased in the subcooled boiling heat transfer region

Micro Droplets Merging by Electrowetting: Lattice Boltzmann Study

Abstract In this paper, the Free Energy Based Lattice Boltzmann (FEB-LB) method which has been recently extended by the author for modeling and simulation of Electrowetting (EW) phenomenon is applied to another application of EW, i.e., droplets merging. The obtained results ware compared against experimental data and the results show good accuracy of the numerical simulation

Study on antibacterial and antioxidant activity of Oak gall (Quercus infectoria ( extracts from Iran

Abstract The antioxidant and antibacterial are group from food additive that use on food as preservative. The objective of this study was to determine antioxidant and antibacterial activity of Quercus infectoria galls the using different in vitro methodologies. The extracts of aquatic, ethanolic and methanolic, at a concentration from 300, 600 and 1200 µg/ml, showed a significant antibacterial effect expressed as minimum inhibitory concentration (MIC) against Gram-positive bacteria. In particular, staphylococcus arouse (MIC=300 µg/ml) and Bacillus cereus (MIC=600 µg/ml) were the most inhibited. The antioxidant activity were determined by the 2,2-diphenylpicrylhydrazyl (DPPH)assay and a β-carotene bleaching assay, and compared with that of butylatedhydroxyl toluene (BHT).The data were expressed as the mean ± the standard deviation and they were statistically analyzed by SPSS software using ANOVA (P<0.05). The results showed that among all the solvent extracts, water extract of Quercus infectoria galls had high antioxidant activities as measured by DPPH scavenging (30/15±0.83 µg /ml) and β-carotene linolic acid (89/4±1.11/ml). These parameters for BHT were 5±0.25 and 7.4±0.3 µg/ml respectively

Multi-objective configuration of an intelligent parking lot and combined hydrogen, heat and power (IPL-CHHP) based microgrid

Recently, financial issues have been considered as the main aspects of microgrid (MG) evaluation in the literature. In this study, the optimal configuration of the MG has been calculated by presenting a reliability-constrained optimization model. In this optimization approach, the MG units are considered in full available state and random outage state through the planning horizon. To model a proposed MG in details, its uncertainties are formulated in the main function. A combination of Latin hypercube sampling (LHS) algorithm and K-means clustering algorithm is applied to generate all uncertainties. The proposed model simultaneously optimizes two objectives, namely, economic costs and emission performance. Time of use (TOU) based demand response (DR) program has been employed for optimal management of the demand side. At first, the bi-objective function is converted to a sequence of single-objective constrained problems by employing ε-constraints. All Pareto front solutions are obtained by utilizing GAMS for solving the developed mixed-integer linear programming (MILP) model. To make a trade-off among solutions, the max-min fuzzy decision-making method has been used. Due to the positive effect of the DR program on the configuring problem, the total emission and economic costs of MG have been reduced up to 4.01% and 1.72%, respectively.</p

Determination of heavy metals in apricot (Prunus armeniaca) and almond (Prunus amygdalus) oils

Background and purpose: Determination of heavy metals in oils is necessary to establish quality standards on a country level. This study aimed to determine of heavy metal contents (Cr, Ni, As, Cd, Hg, Pb, Sb, Sn, Sr, Al) in 12 seed oil samples in Iran by inductively coupled plasma-optical emission spectrometry (ICP-OES). Materials and Methods: The concentrations of heavy metal were determined by wet acid digestion methods with nitric acid (65%) and 4 ml peroxide hydrogenate on same samples using ICP-OES. Results: Results showed that the average of most important toxic metals detected in apricot oil samples was as follows 721.72 &mu;g/kg for Al 15 &mu;g/kg for Cd, 18 &mu;g/kg for Pb, 14 &mu;g/kg for As and <1 &mu;g/kg for Hg. Furthermore, The average of heavy metals detected in almond oil samples were as follows 1019.73 &mu;g/kg for Al, 10 &mu;g/kg for Cd, 21 &mu;g/kg for Pb and 11 &mu;g/kg for As and <1 &mu;g/kg for Hg. Also in the studied samples, Al was the highest concentrations among all metals. Conclusion: Most of the samples of oils were found to be contaminated with notable amounts of toxic metals which could be a threat to oil quality and human health

Physicochemical Characteristics of Citrus Seed Oils from Kerman, Iran

Recently, there has been a great deal of attention on usage, byproducts, and wastes of the food industry. There have been many studies on the properties of citrus seeds and extracted oil from citrus grown in Kerman, Iran. The rate of oil content of citrus seeds varies between 33.4% and 41.9%. Linoleic acid (33.2% to 36.3%) is the key fatty acid found in citrus seeds oil and oleic (24.8% to 29.3%) and palmitic acids (23.5% to 29.4%) are the next main fatty acids, respectively. There are also other acids found at trivial rates such as stearic, palmitoleic, and linolenic. With variation between 0.54 meg/kg and 0.77 mgq/kg in peroxide values of citrus seed oils, acidity value of the oil varies between 0.44% and 0.72%. The results of the study showed that citrus seeds under study (orange and sour lemon grown in Kerman province) and the extracted oil have the potential of being used as the source of edible oil

Attack detection and localization in smart grid with image-based deep learning

Smart grid's objective is to enable electricity and information to flow two-way while providing effective, robust, computerized, and decentralized energy delivery. This necessitates the use of state estimation-based techniques and real-time analysis to ensure that effective controls are deployed properly. However, the reliance on communication technologies makes such systems susceptible to sophisticated data integrity attacks imposing serious threats to the overall reliability of smart grid. To detect such attacks, advanced and efficient anomaly detection solutions are needed. In this paper, a two-stage deep learning-based framework is carefully designed by embedding power system's characteristics enabling precise attack detection and localization. First, we encode temporal correlations of the multivariate power system time-series measurements as 2D images using image-based representation approaches such as Gramian Angular Field (GAF) and Recurrence Plot (RP) to obtain the latent data characteristics. These images are then utilized to build a highly reliable and resilient deep Convolutional Neural Network (CNN)-based multi-label classifier capable of learning both low and high level characteristics in the images to detect and discover the exact attack locations without leveraging any prior statistical assumptions. The proposed method is evaluated on the IEEE 57-bus system using real-world load data. Also, a comparative study is carried out. Numerical results indicate that the proposed multi-class cyber-intrusion detection framework outperforms the current conventional and deep learning-based attack detection methods