In this study, multi-objective optimization of nanofluid aluminum oxide in a mixture of water and ethylene glycol (40:60) is studied. In order to reduce viscosity and increase thermal conductivity of nanofluids, NSGA-II algorithm is used to alter the temperature and volume fraction of nanoparticles. Neural network modeling of experimental data is used to obtain the values of viscosity and thermal conductivity on temperature and volume fraction of nanoparticles. In order to evaluate the optimization objective functions, neural network optimization is connected to NSGA-II algorithm and at any time assessment of the fitness function, the neural network model is called. Finally, Pareto Front and the corresponding optimum points are provided and

The 3-parameter Weibull distribution is used as a model for failure since this distribution is proper when the failure rate somewhat high in starting operation and these rates will be decreased with increasing time .

In practical side a comparison was made between (Shrinkage and Maximum likelihood) Estimators for parameter and reliability function using simulation , we conclude that the Shrinkage estimators for parameters are better than maximum likelihood estimators but the maximum likelihood estimator for reliability function is the better using statistical measures (MAPE)and (MSE) and for different sample sizes.

Note:- n_s : small sample ; n_m=median sample

In this work, two different laser dye solutions were used to host highly-pure silicon nitride nanoparticles as scattering centers to fabricate random gain media. The laser dye was dissolved in three different solvents (ethanol, methanol and acetone) and the final results were obtained for methanol only. The silicon nitride nanoparticles were synthesized by dc reactive magnetron sputtering technique with average particle size of 35 nm. The random gain medium was made as a solid rod with high spectral efficiency and low production cost. Optical emission with narrow linewidth was detected at 532-534 nm as 9 mg of silicon nitride nanoparticles were added to the 10 -5 M dye solution. The FWHM of 0.3 and 3.52 nm was determined for Rhodamine B and

The manganese doped zinc sulfide nanoparticles were synthesized by simple aqueous chemical reaction of manganese chloride, zinc acetate and thioacitamide in aqueous solution. Thioglycolic acid is used as capping agent for controlling the nanoparticle size. The main advantage of the ZnS:Mn nanoparticles of diameter ~ 2.73 nm is that the sample is prepared by using non-toxic precursors in a cost effective and eco-friendly way. The structural, morphological and chemical composition of the nanoparticles have been investigated by X-ray diffraction (XRD), Scanning Electron Microscopy (SEM) with energy dispersion spectroscopy (EDS) and Fourier transform infrared (FTIR) spectroscopy. The nanosize of the prepared nanoparticles was elucidated by Scan

Abstract: In the present work, the heat transfer of Nano Aluminum Oxide -NAO- has been studied practically to define the appropriate insulation conditions.  This study focuses on finding of the amount of heat transfer through a glass substrate that is coated with nanoalumina doped on PMMA matrix. The optical and thermal properties were systematically investigated. The density of heat flow rate, was calculated in the range values (240-260) W/m2 while the optimum values confine between (250-260) W/m2 at temp. (25-35)C^o. The results showed that the thermal insulation of the sample was significantly enhanced at temp. (30-50)C^o. The simulated net heat transfer through window decreased linearly with incr