Aqueous nanofluids containing paraffin-filled MWCNTs for improving effective specific heat and extinction coefficient.

This paper presents measurements of the effective specific heat and the extinction coefficient for aqueous nanofluids dispersed with paraffin-filled Multi-Walled Carbon NanoTubes (MWCNTs). The MWCNTs were filled with paraffin wax by capillary action. Centrifugal decanting was used to modify the traditional two-step method so as to produce a nanofluid dispersion that was more stable than that produced by the traditional method. The stability of each suspension was quantitatively evaluated with a laser scattering method over 7 days. A differential scanning calorimetry (DSC) and the three-slap method were used to measure the effective specific heat and the extinction coefficient of the nanofluids, respectively. The measured effective specific heat of the water-based paraffin-filled MWCNTs nanofluid, with a volume fraction of 1%, was up to 5.1% larger than that for the water-based MWCNT nanofluids without paraffin wax. The nanofluid extinction coefficient was shown to increase linearly with the volume fraction for data within the independent scattering regime, which occurred when the nanoparticle-distance/wavelength ratio (c/λ) was less than 2.

An adaptive Cauchy differential evolution algorithm for global numerical optimization.

Adaptation of control parameters, such as scaling factor (F), crossover rate (CR), and population size (NP), appropriately is one of the major problems of Differential Evolution (DE) literature. Well-designed adaptive or self-adaptive parameter control method can highly improve the performance of DE. Although there are many suggestions for adapting the control parameters, it is still a challenging task to properly adapt the control parameters for problem. In this paper, we present an adaptive parameter control DE algorithm. In the proposed algorithm, each individual has its own control parameters. The control parameters of each individual are adapted based on the average parameter value of successfully evolved individuals' parameter values by using the Cauchy distribution. Through this, the control parameters of each individual are assigned either near the average parameter value or far from that of the average parameter value which might be better parameter value for next generation. The experimental results show that the proposed algorithm is more robust than the standard DE algorithm and several state-of-the-art adaptive DE algorithms in solving various unimodal and multimodal problems.