An experimental and numerical investigation of the effect of using two types of nanofluids with suspending of (Al₂O₃ and CuO) nanoparticles in deionized water with a volume fraction of (0.1% vol.), in addition to use three types of fin plate configurations of (smooth, perforated, and dimple plate) to study the heat transfer enhancement characteristics of commercial fin plate heat sink for cooling computer processing unit. All experimental tests under simulated conditions by using heat flux heater element with input power range of (5, 16, 35, 70, and 100 W). The experimental parameters calculated are such as water and nanofluid as coolant with Reynolds number of (7000, 8000, 9400 and 11300); the air

In this work, the effect of different particle size on the nonlinear optical properties of silver nanoparticles in de-ionized water was studied. The experimental observation of the far field diffraction patterns by CCD camera in two and three dimensions. The maximum change of nonlinear refractive index and the relative phase shift were calculated. The self-defocusing technique was used with a continuous-wave radiation from DPSS Blue laser .The wavelength is 473 nm with an output power of 270 mW. All the Ag colloids samples containing the sizes 15, 30, 50, and 70 nm of silver nanoparticles used in the study were chemically prepared. It was found that the nonlinear refractive index is a particle size dependent and of the order of 10-7 cm2/

      An experimental and computational study is conducted to analyze the thermal performance of heat sinks and to pick up more profound information in this imperative field in the electronic cooling. One important approach to improve the heat transfer on the air-side of the heat exchanger is to adjust the fin geometry. Experiments are conducted to explore the impact of the changing of diverse operational and geometrical parameters on the heat sink thermal
performance. The working fluid used is air. Operational parameters includes: air Reynolds number (from 23597 to 3848.9) and heat flux (from 3954 to 38357 W/m
2 ). Conformational parameters includes: change the direction of air flow and the area of conduct

In this paper, nanofluid of TiO₂/water of concentrations of 0.002% and 0.004% volume was used. This nanofluid was flowing through heat exchanger of shell and concentric double tubes with counter current flow to the hot oil. The thermal conductivity of nanofluid is enhanced with increasing concentrations of the TiO₂, this increment was by 19% and 16.5% for 0.004% and 0.002% volume respectively relative to the base fluid (water). Also the heat transfer coefficient of the nanofluid is increased as Reynold's number and nanofluid concentrations increased too. The heat transfer coefficient is increased by 66% and 49% for 0.004% and 0.002% volume respectively relative to the base fluid. This study showed that the friction

A Stereomicroscopic Evaluation of Four Endodontic Sealers Penetration into Artificial Lateral Canals Using Gutta-Percha Single Cone Obturation Technique, Omar Jihad Banawi*, Raghad

One of the most important challenges facing project management at present time is to ensure project accomplishment in spite of the specific restrictions like the specific time the financial resources specialized to do the project ; which require an accurate consideration for time and cost . the modern village project (residential building aspect) is one of the great project that ministry of agriculture is trying to do Wasit  governorate it is chosen as the work in this project is dilatory for that is being studied in term of some modern mathematical and scientific methods like critical path method (CPM)which is one of the project management and scheduling methods to know the time needed to accomplish residential building pro

The objective of this study is to apply Artificial Neural Network for heat transfer analysis of shell-and-tube heat exchangers widely used in power plants and refineries. Practical data was obtained by using industrial heat exchanger operating in power generation department of Dura refinery. The commonly used Back Propagation (BP) algorithm was used to train and test networks by divided the data to three samples (training, validation and testing data) to give more approach data with actual case. Inputs of the neural network include inlet water temperature, inlet air temperature and mass flow rate of air. Two outputs (exit water temperature to cooling tower and exit air temperature to second stage of air compressor) were taken in ANN.