PREDICTION OF STABILITY AND THERMAL CONDUCTIVITY OF SnO2NANOFLUID VIA STATISTICAL METHOD AND AN ARTIFICIAL NEURAL NETWORK
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Central composite rotatable design (CCRD) and artificial neural networks (ANN) have been applied to optimize the performance of nanofluid systems. In this regard, the performance was evaluated by measuring the stability and thermal conductivity ratio based on the critical independent variables such as temperature, particle volume fraction and the pH of the solution. A total of 20 experiments were accomplished for the construction of second-order polynomial equations for both target outputs. All the influential factors, their mutual effects and their quadratic terms were statistically validated by analysis of variance (ANOVA). According to the results, the predicted values were in reasonable agreement with the experimental data as more than 96% and 95% of the variation could be predicted by the respective models for zeta potential and thermal conductivity ratio. Also, ANN proved to be a very promising method in comparison with CCD for the purpose of process simulation due to the complexity involved in generalization of the nanofluid system.Keywords:
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A numerical model for predicting the effective thermal conductivity of fused silica fiber/aerogel composites by simultaneously considering the effects of the fiber volume fraction and fiber diameter is presented. The predicted effective thermal conductivity of the fiber/aerogel composites agreed well with the existing measured and predicted results. The effects of the volume fraction (0−25%) and diameter (0.3−10 µm) of fibers on the effective thermal conductivity of aerogel composites were investigated under a large range of temperatures (300−1300 K). The results indicated that the minimum effective thermal conductivity of the fiber/aerogel composites by simultaneously considering the optimized fiber volume fraction and diameter was significantly lower than when individually considering the optimized fiber volume fraction and diameter values. For instance, the minimum effective thermal conductivity by simultaneous optimization was 0.0262 W/m−1 K−1 at 1000 K, which was much lower than 0.0327 W/m−1 K−1 by individually optimizing the fiber volume fraction at a diameter of 8 µm and 0.0532 W/m−1 K−1 by individually optimizing the fiber diameter at a volume fraction of 3%. Moreover, the quantitative relations between the minimum effective thermal conductivity of the fiber/aerogel composites and the temperatures are presented, with the aim of identifying the optimal thermal insulation for applications in aeronautics and astronautics, construction, and other industrial fields.
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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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By using copper oxide nanofluid fabricated by the self-made Submerged Arc Nanofluid Synthesis System (SANSS), this paper measures the thermal conductivity under different volume fractions and different temperatures by thermal properties analyzer, and analyzes the correlation among the thermal conductivity, volume fraction, and temperature of nanofluid. The CuO nanoparticles used in the experiment are needle-like, with a mean particle size of about 30 nm. They can be stably suspended in deionized water for a long time. The experimental results show that under the condition that the temperature is 40 degrees C, when the volume fraction of nanofluid increases from 0.2% to 0.8%, the thermal conductivity increment of the prepared nanofluid towards deionized water can be increased from 14.7% to 38.2%. Under the condition that the volume fraction is 0.8%, as the temperature of nanofluid rises from 5 degrees C to 40 degrees C, the thermal conductivity increment of the prepared nanofluid towards deionized water increases from 5.9% to 38.2%. Besides, the effects of temperature change are greater than the effects of volume fraction on the thermal conductivity of nanofluid. Therefore, when the self-made copper oxide nanofluid is applied to the heat exchange device under medium and high temperature, an optimal radiation effect can be acquired.
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为了调查提高热和氨水吸收的集体转移的 nanoparticles 的机制，处理，二进制 nanofluids 的几种类型被与 polyacrylic 混合 Al2O3 nanoparticles 准备酸(泛美航空公司)，有聚乙烯乙二醇(木钉 1000 )的 TiO2 ，和锡，原文如此 hydroxyapatite (像笨蛋)与到氨水答案的木钉 10000 分别地。热传导性被使用 KD2 测量职业人员热性质分析器。二进制 nanofluids 的分散稳定性上的表面活化剂和氨的影响被轻吸收性比率索引方法调查。结果证明 nanoparticles，温度以及分散稳定性的类型，内容和尺寸是影响 nanofluids 的热传导性的关键参数。为给定的 nanoparticle 材料和基础液体，到氨水液体的 nanofluid 的热传导性比率作为 nanoparticle 内容和温度增加被增加，并且 nanoparticle 的直径被减少。而且，热传导性比率由改进 nanofluids 的稳定性显著地增加，它被增加表面活化剂或在液体执行合适的氨内容完成。
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