Identification of the Key Variables on Thermal Conductivity of CuO Nanofluid by a Fractional Factorial Design Approach
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Seven important parameters (temperature, concentration, average primary particle size, pH of nanofluid, density of nanoparticle, elapsed time, and sonication time) which are responsible for the change of the thermal conductivity of nanofluids were experimentally investigated on CuO/water nanofluid and were statistically surveyed by a factorial design method. In order to investigate the main effects and their interactions on the thermal conductivity ratio, a fractional factorial design (FFD) with three other experiments at the center of the design for analysis of variance were applied. Also, the factorial model was statistically validated by analysis of variance (ANOVA). The predicted responses was compared with the experimental ones. Generally, the predicted values were in reasonable agreement with the experimental data, further confirming the high predictability of the models.Keywords:
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The effect of factors in full and fractional factorial designs is being studied ubiquitously in all fields of science and engineering. At times, researchers would want to gather additional information than the fractional factorial design provided, there is no restriction to conducting more experimental runs. In this study, we propose a reduced fractional factorial design consisting of all significant factors. This paper illustrates the effectiveness of factors through real data application and simulation by comparing the full factorial, reduced factorial, and fractional factorial designs. The actual weightage of the main/interaction effects in these three designs was found by identifying and quantifying the Bayes factors through the simulation datasets. It is observed that the reduced factorial design produces better results when there are no constraints to select or add factors to the model.
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The present research reports nanofluid effective thermal conductivity enhancements (ETCE) using an accurate transient short hot wire method system. Preparation of nanofluids was carried out through a two-step method with highly powered pulses similar to that for nanoparticle dispersion in base fluids. Parameters affecting nanofluid heat conductivity such as concentration, sizes, and material of nanoparticleş type of base fluid, temperature, ultrasonic mixing time, and elapsed time after preparation were studied. In the present study, nanoparticles of Al, Al2O3, CuO, SnO2, TiO2, and SiO2 with base fluids of water and ethylene glycol were used. Parameters like concentration, size, temperature, and the type of base fluid showed more noticeable effect on the effective thermal conductivity than the others, and mixing time had the least effect. The results showed that any increase in concentration and temperature, and also any decrease in size of nanoparticles and time elapsed after nanofluid preparation, leads to the ETCE of the nanofluid. However, the effects of nanoparticle material, base fluid, and mixing time on thermal conductivity of the nanofluid showed varying trends. Last, a number of mathematical models for prediction of thermal conductivity of nanofluids were applied.
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Graphical presentations have been used by many statisticians and engineers to view the properties of particular two- and three-factor factorial and fractional-factorial designs, and to communicate the nature of a planned experiment to production workers and management. The geometric characteristics of the graphical representation are related to the statistical properties of the design. These geometric characteristics can be examined for irregular designs, when defining relations do not exist. This paper examines design-plots for factorial and fractional-factorial designs involving as many as ten factors; and illustrates that even these higher-order design-plots can be used to display the results of an experiment and to provide useful insight on the nature of the response surface.
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A design of experiment (DOE) is used in many industrial sectors in the development and optimization of manufacturing processes. DOE is a more effective way to determine the impact of two or more factors on a response. In this paper, a Two-Level Fractional Factorial design was employed to develop the important factors that significantly affect new promising OT Rubber recycling machine using a DOEs analysis. Central composite design (CCD) and response surface methodology (RSM) were used to optimize the isolating screen size. The CCD considered five factors with two-level full factorial design. CCD experiments using RSM was proved to be an optimal tool for optimization.
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