Implementation of Linear Stability Theory on Hollow Cone-shaped Liquid Sheet

Surface instability of a swirling liquid sheet emanating from a centrifugal injector in presence of external and internal gas flows is studied in this paper. A three-dimensional flow for the liquid sheet and two-dimensional flows for external and internal gas flows are considered. The set of equations involved in this analysis differs from the earlier analyzes. In previous studies, a cylindrical liquid sheet has been considered to implement the linear theory but in this study, the linear stability theory is implemented on a cone-shaped liquid sheet for different cone angles. Actually more over than axial and tangential movements, the radial movements of liquid sheet and gas flows are considered in the present model. Due to complexity of the derived governing equations, semi-analytical and numerical methods were applied to solve them. The case study is oxidizer injector of rocket engines. Implementation of linear stability theory on a hollow cone-shaped liquid sheet better can predict instability phenomenon than the general linear stability analysis for this type of liquid sheets. The results show very close agreement with available experimental data

Application of maximum entropy principle for estimation of droplet-size distribution using internal flow analysis of a swirl injector

The maximum entropy principle is one of the first methods, which have been used to predict droplet size and velocity distributions of liquid sprays. Due to some drawbacks in this model, the predicted results do not match well with the experimental data. This paper presents a different approach for improving the maximum entropy principle model. It is suggested to improve the available energy source in the maximum entropy principle model equation by numerical solution of flow inside the injector based on the computational fluid dynamics technique. This will enhance the calculation accuracy of the turbulent kinetic energy of the output spray. Application of this procedure enhances the model predictions. The liquid sheet properties resulted from the analysis are also applied for calculation of the momentum source in the maximum entropy principle model. The proposed model is applied to predict the droplet size distribution of a hollow-cone spray formed by a swirl injector. The results show a better agreement with the available experimental data than the results of prior models

Persistent sample circulation microextraction combined with graphite furnace atomic absorption spectroscopy for trace determination of heavy metals in fish species marketed in Kermanshah, Iran, and human health risk assessment

BACKGROUND Persistent sample circulation microextraction (PSCME) combined with graphite furnace atomic absorption spectrometry (GFAAS) was developed as a high pre‐concentration technique for the determination of heavy metals in fish species. In this method, a few microliters of organic solvent (40.0 µL carbon tetrachloride) was transferred to the bottom of a conical sample cup. Then 10.0 mL of aqueous solution was transformed to fine droplets while passing through the organic solvent. At this stage, metal–ligand hydrophobic complex was extracted into the organic solvent. After extraction, 20 µL of extraction solvent was injected into the graphite tube using an auto‐sampler. RESULTS Under optimal conditions, enrichment factors and enhancement factor were in the range of 180–240 and 155–214, respectively. The calibration curves were linear in the range of 0.03–200 µg kg–1 and the limits of detection (LODs) were in the range of 0.01–0.05 µg kg–1. Repeatability (intra‐day) and reproducibility (inter‐day) for 0.50 µg L–1 Hg and 0.10 µg L–1 Cd and Pb were in the range of 3.1–4.2% (n = 7) and 4.3–6.1% (n = 7), respectively. CONCLUSION Potential human health risk assessment was conducted by calculating estimated weekly intake (EWI) of the metals from eating fish and comparison of these values with provisional tolerable weekly intake (PTWI) values. EWI data for the studied metals through fish consumption were lower than the PTWI values. © 2017 Society of Chemical Industr

Data on using macro invertebrates to investigate the biological integrity of permanent streams located in a semi-arid region

The aquatic ecosystems are continuously endangered due to variety of hazardous chemicals containing different toxic agents which can be emitted from anthropogenic sources. Besides the increasing of human population, various kinds of contaminants enter into the surface water resources. The aim of the present study was to investigate the abundance and diversity of macro invertebrates in two permanent streams located in the northern part of Tehran. The biological integrity of the streams was determined by manual sampling approach at five points. The distances between the sampling points were at least 2 km. The bio indicator organisms, organic pollution, and dissolved oxygen were measured. The different types of benthic invertebrates such as riffle beetle, midge and caddish fly larvae, dragon fly, may fly and stone fly nymph, riffle beetle adult, pyralid caterpillar, leech, and pouch snail were identified. It can be concluded that, the identified benthic macro invertebrates can be served as appropriate biological indicator in the studied area. Keywords: Biological integrity, Tehran, Macro invertebrate

Optimization of a methodology for simultaneous determination of twelve chlorophenols in environmental water samples using: In situ derivatization and continuous sample drop flow microextraction combined with gas chromatography-electron-capture detection

Continuous sample drop flow microextraction (CSDFME) combined with gas chromatography-electron-capture detection (GC-ECD) has been developed as a high preconcentration technique for the determination of chlorophenols (CPs) in environmental water samples. In this method, a few microliters of organic solvent (11.0 μL chlorobenzene) is transferred to the bottom of a conical test tube. Then 10.0 mL of aqueous solution transforms into fine droplets while passing through the organic solvent. At this stage, CPs are extracted into the organic solvent. Under the optimum conditions, enrichment factors and extraction recoveries are in the range of 630-1770 and 31.5-88.5, respectively. The calibration graphs are linear in the range of 0.01-300 μg L-1 and limits of detection (LODs) are in the range of 0.005-0.50 μg L-1. The relative standard deviations (RSDs, for 100 μg L-1 of MCPs, 50 μg L-1 of DCPs, 2.00 μg L-1 of TCPs, 1.00 μg L-1 of TeCPs and PCP in water) with and without using an internal standard are in the range of 0.9-5.5% (n = 7) and 1.2-6.4% (n = 7), respectively. The relative recoveries of well, tap and river water samples which have been spiked with different levels of CPs are in the range of 90.6-104.2%

MWCNT-Fe3O4 as a superior adsorbent for microcystins LR removal: Investigation on the magnetic adsorption separation, artificial neural network modeling, and genetic algorithm optimization

Magnetic multi-wall carbon nanotube (MMWCNT) was prepared by simple protocol and its structural features were characterized using SEM, TEM, and XRD analysis. The association between removal (%) and variables such as pH (3 − 11), adsorbent amounts (0.005, 0.1, 0.25, 0.5, 0.75, and 1 g/L), reaction time (5–180 min), and concentration of microcystins-LR (10, 25, 50, 75, and 125 μg/L) was investigated and optimized. The results of the isotherm study indicated that Langmuir offered high determination coefficients (R2 = 0.993, 0.996, and 0.998, for the three different working temperatures of 20 °C, 35 °C, and 50 °C respectively) and was the optimum isotherm to anticipate adsorption of MC-LR (microcystins-LR) by magnetic MWCNT adsorbent. The kinetic study revealed that the adsorption kinetics of MC-LR could be better defined using the pseudo-second-order model. A three-layer model of an artificial neural network was applied to forecast the MC-LR removal efficiency by magnetic MWCNTs over 66 runs. To forecast the MC-LR removal efficiency, the minimum mean squared error of 0.0011 and determination coefficient (R2) of 0.9813 were obtained. The use of the artificial neural network model achieved a good level of compatibility between the acquired and anticipated data